Notice of Pre-AIA or AIA Status
Claims 1-20 are currently presented for Examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on 03/30/2026 has been entered and considered by the examiner. By the
amendment, claims 2 and 20 are amended and the claim objection is withdrawn. In view of the amendment made, the previous 101 rejection and the prior rejection is still maintained. See office action.
Applicant 101 arguments
Applicant disagrees that the pending claims are directed to patent-ineligible subject matter because the claims do not recite an abstract idea under Step 2A Prong One. The Examiner characterizes the "execute an optimization routine to sequentially activate varying subsets of active flow control actuators" step as a mental process. However, this limitation requires the physical activation of hardware actuators distributed within a flow field. Sequentially activating physical actuators in a real flow field cannot practically be performed in the human mind or with pencil and paper.
Similarly, the limitation "calculate a cost function of each of the subsets of sequentially activated active flow control actuators based on respective measurements of the one or more parameters by the one or more sensors within the flow field" is not merely a mathematical calculation performed in the abstract. Rather, the calculating step requires obtaining actual physical measurements from sensors in a flow field responsive to the physical actuation of actuators. The cost function is calculated based on real-world sensor data gathered from a physical system, not abstract or hypothetical data. The limitation "cause operation of the plurality of active flow control actuators based on the optimal subset of active flow control actuators" likewise requires physical operation of hardware and cannot be performed mentally.
When the claims are evaluated as a whole and as an ordered combination, as required by the 2019 Patent Eligibility Guidance, the claimed steps describe a closed-loop physical control system: actuators are physically activated, sensors physically measure flow parameters, cost functions are calculated from those physical measurements, and actuators are physically operated based on the results. This ordered combination of steps is not practically performable in the human mind. Additionally, the Examiner's characterization of the "cause operation of the plurality of active flow control actuators" step as post-solution activity under MPEP § 2106.05(g) in page 8 of the Office Action is incorrect. The "cause operation" step is not related to the optimization; rather, the "cause operation" step is the very purpose of the claimed system. The optimization routine sequentially activates actuators, calculates cost functions based on sensor measurements, and determines an optimal subset specifically so that the actuators can be operated based on that optimal subset. Without the "cause operation" step, the optimization would have no practical effect on the flow field. The "cause operation" step closes the loop between the optimization and the physical system, applying the optimization results to physically control the actuators in the flow field. This is not a case where an abstract result is merely outputted or displayed after computation. The optimization result directly dictates how physical actuators are operated in a flow field, which is integral to the functioning of the claimed system.
Examiner response
Applicant arguments are not persuasive. The claim recites concepts including: executing optimization, calculating a cost function, determining an optimal subset, evaluating subset and selecting optimal results based on comparison. These limitations recite mathematical concepts and mental process since it involves evaluation, comparison, optimization and selected operation that can be performed conceptually or mathematically. See MPEP 2106.05(a)(2) The additional recitation of generic components including active flow control actuators and sensors do not remove the abstract nature of the claimed optimization and evaluation concepts. Applicant argues “calculate a cost function of each of the subsets of sequentially activated active flow control actuators …" limitation cannot be performed mentally because it uses a real-world sensor data. However, the source of data used on a mathematical calculation does not alter the abstract nature of the calculation itself. Here, the claim broadly recites calculating a cost function using a cost function using measured parameters without reciting any specific technological improvement in how the measurement are obtained or processed.
The limitation “causing operation of the plurality of active flow control actuators based on the optimal subset” does not render the claims patent-eligible. The claims remain directed to an abstract idea and lack an inventive concept, because the added language constitutes insignificant post-solution activity, result-oriented functional claiming, and generic application of an optimization result. The “cause limitation” limitation is recited at a high level of generality and merely applies the result of the abstract optimization process to generic actuator operation. The claims do not recite: a specific control technique, a specific actuator-control protocol or a specific transformation of the flow field. Rather, the claims merely recite operating actuators “based on” the optimization result. Such generic application of the abstract amounts to insignificant extra-solution activity and does not integrate the abstract idea into a practical application. (See MPEP 2106.05(g))
Applicant further argues that the claims describe a “closed-loop physical control system”. However, the mere inclusion of generic sensors, actuators and feedback does not render the claims patent eligible where the claims are directed primarily to abstract data analysis and optimization concepts implemented using generic computer components. Thus, 101 rejection is still maintained.
Applicant arguments
Even assuming, arguendo, that certain claim limitations recite abstract ideas, the claims as a whole integrate any such exception into a practical application by improving the technology of active flow control systems.
The Examiner in page 4 of the Office Action argues that "a claim improvement must be recited in the claim itself, not merely argued." This misstates the applicable standard. Under MPEP § 2106.04(d)(1), the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Moreover, the claim must include the components or steps of the invention that provide the improvement described in the specification. However, the claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel"). See MPEP § 2106.05(a).
Here, the specification clearly identifies a technical problem: "one of the many challenges that prevents wide usage of this technique in engineering applications is related to the difficulty in identifying the most effective locations and patterns for actuator placement as well as their operating conditions." Specification, paragraph [0004]. The specification also describes the technical solution and quantified improvement: "The novel optimization process found an actuator arrangement that is markedly different from those obtained through 'experience- informed' actuator arrangements. This non-intuitive, but optimal solution reduced the vortex circulation, which is a surrogate for drag reduction, by 10.6%, which was three times the maximum circulation reduction through traditional actuator arrangements." Specification, paragraph [0051]. That is, the specification provides a specific percentage improvement and a direct comparison to prior approaches, supported by experimental data. The claims reflect the improvement disclosed in the specification. Claim 1 recites: (i) a plurality of active flow control actuators spatially distributed within a flow field, each configured to be individually actuated; (ii) sensors configured to measure parameters within the flow field; (iii) executing an optimization routine to sequentially activate varying subsets of actuators; (iv) calculating a cost function based on sensor measurements; (v) determining an optimal subset based on the cost functions; and (vi) causing operation of the plurality of active flow control actuators based on the optimal subset. Per MPEP § 2106.05(a), the claim must include the components or steps of the invention that provide the improvement, but the claim itself does not need to explicitly recite the improvement. The components and steps as recited in claim 1 are the components and steps that enable the claimed system to experimentally explore multiple actuator configurations and converge to an optimal actuator arrangement, as described in the specification. Further, the claims are analogous to claim 2 of the USPTO Subject Matter Eligibility Example 45 (Controller for Injection Mold), which was found eligible. In that example, a controller recited abstract ideas (mathematical calculations using the Arrhenius equation), but the claim was eligible because it included a limitation requiring the controller to send control signals to the injection molding apparatus once the polyurethane reached a target percentage, instructing the apparatus to open the mold and eject the molded polyurethane. The USPTO found that this limitation "does not merely link the judicial exceptions to a technical field, but instead adds a meaningful limitation in that it employs the information provided by the judicial exceptions to control the operation of the injection molding apparatus." Here, similar to claim 2 of Example 45, claim 1 requires "causing operation of the plurality of active flow control actuators based on the optimal subset of active flow control actuators," which employs the optimization results to control physical actuators in a flow field.
Examiner response
Applicant’s arguments are not persuasive. Although the specification discusses improved actuator arrangement and improved vortex circulation reduction, the claims do not recite a specific improvement in actuator operation. Instead, the claims broadly recite optimization and selection of actuator subsets using cost functions. The claim therefore results the desired results of optimization rather than a specific technological mechanism for achieving the improvement.
Although MPEP 2106.05(a) permits consideration of the specification, the claim must recite a specific technological implementation sufficient to integrate the abstract idea into a practical application. Here the claim broadly recites optimization concepts without reciting a specific technological mechanism that achieves the alleged aerodynamic improvements discussed in the specification. Accordingly, consideration of the specification does not alter the eligibility analysis.
Unlike the Example 45, the instant claim does not recite a specific control operation tied to a particular machine or physical transformation. In Example 45, the claims specifically controlled model operation based on a particular curing state. Here, the claims merely recite generic actuator operation “based on” an optimization and the instant claims do not improve actuator hardware, do not recite a specific control law or feedback loop, do not specify how actuation is performed in a non-conventional way and it is merely state the result that actuators are operated “based on” an optimization. Applicant asserts that the claims” improve operation of the actuator system”. However, a claim improvement must be recited in the claim itself, not merely argued. The claims: do not define new actuator structures, do not define new control signals, do not define timing, synchronization, stability, or efficiency improvements and do not specify any non-conventional actuator behavior. Applicant simply state what is optimized, not how the system is technologically improved.
Applicant argues claim 2 is eligible because it recites positioning actuators or applying operating conditions. Claim 2 merely: adds additional parameters (position, operating conditions), that are inputs to the same optimization, and are still selected via abstract comparison and evaluation. This is still mathematical optimization of variables. As MPEP §2106.04(a)(2)(I): “Optimizing a result using mathematical techniques is a mathematical concept.” The recited “position” and “operating conditions” merely constitute variables or parameters of the optimization problem. The additional recitation that positions or operating conditions “vary across each subset” merely describes the abstract optimization process itself and does not constitute a technological improvement. Therefore, claims 1, 2 and 19 remain ineligible under 35 U.S.C §101, AND §101 rejection is still maintained.
Applicant 103 arguments
Independent claims 1 and 19 recite, inter alia: "execute an optimization routine to sequentially activate varying subsets of active flow control actuators"; "calculate a cost function of each of the subsets of sequentially activated active flow control actuators based on respective measurements of the one or more parameters by the one or more sensors within the flow field"; "determine an optimal subset of active flow control actuators based on the respective cost functions of each of the subsets of sequentially activated active flow control actuators"; and "cause operation of the plurality of active flow control actuators based on the optimal subset of active flow control actuators." Neither Potami nor Benard, alone or in combination, teaches or suggests these limitations. Potami is directed to "the vibration control problem for flexible structures" using "four pairs of collocated piezoceramic patches." See Potami, Abstract. Potami's switching occurs during real-time control operation of an already-optimized system, not for experimentally determining which actuator configurations are optimal. Potami explains that "[i]t is therefore required to determine an activation strategy that identifies the efficient actuators to be turned on during the time domain of interest. The second step of this work is therefore to provide the hybrid system with a switching strategy to define in real time the actuators activation sequence." See Potami, page 5. This is fundamentally different from the claimed optimization routine that sequentially activates varying subsets, calculates a cost function for each subset, and determines an optimal subset by comparing the respective cost functions.
Examiner response
Applicant argues that Potami merely performs real-time switching of actuators in an already optimized system, not for experimentally determining which actuators configuration are optimal. Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. However, Applicant’s arguments improperly import narrowing limitation into the claims. Since the claim 1 do not recite exhaustive experimental exploration, excludes optimization during operation and never requires offline search. Rather, claim 1 broadly recites sequentially activating varying subsets of a plurality of actuators and calculating cost functions for each subset to determine an optimal subset. Under the broadest reasonable sense, the claims encompass selecting efficient subsets of actuators according to the evaluated performance during system operation. Potami expressly teaches selecting and activating subsets of actuators according to the evaluated performances measures and switching criteria. Potami teaches:
“Activating a subset of the available and optimally placed actuators and sensors in a flexible structure…” (see abstract)
Potami further teaches:
“…determine an activation strategy that identifies the efficient actuators to be turned on during the time domain of interest. The second step of this work is therefore to provide the hybrid system with a switching strategy to define in real time the actuators activation sequence.” (see section 1.1.2)
Potami additionally teaches evaluating actuators subsets using evaluated and performance metrics. (see section 4.2 Cost-to-go and algorithm 4.4) Also see page Potami – “For example, one may look for the sequence that maximizes the results, increases stability or minimizes cost functions.”
Thus, Potami teaches activating varying subsets of a plurality of actuators and calculating cost functions for each subset to determine an optimal subset. Claims merely require determining an “optimal subset” selected from a plurality of actuators. Potami expressly teaches selecting efficient actuators subsets from an available of actuators.
Applicant further argues that Potami allegedly concerns only “switching” rather than “optimization routine”. However, claim 1 itself do not recite any specialized optimization methodology beyond evaluating subsets using cost functions and selecting an optimal subset. Potami switching strategy is itself based on evaluated cost/performance criteria and therefore reasonably satisfied the broadly claimed optimization limitations. Thus, 103 rejection is still maintained.
Applicant 103 arguments
Benard does not cure the deficiencies of Potami. Benard teaches "[a]n autonomous multi- variable optimization by genetic algorithm is implemented in an experiment for optimizing simultaneously the voltage amplitude, the burst frequency and the duty-cycle of the high voltage signal producing the surface plasma discharge." See Benard, Abstract. Benard optimizes operating parameters of a single actuator configuration, not varying subsets of multiple actuators. Benard does not teach sequentially activating varying subsets of a plurality of actuators and calculating cost functions for each subset to determine an optimal subset.
Examiner response
Applicant further argues that Bernard merely optimizes operation parameters of a “single actuator configuration”. This argument is not persuasive. Bernard clearly teaches: see Abatract-“An autonomous multi-variable optimization by genetic algorithm is implemented in an experiment for optimizing simultaneously the voltage amplitude, the burst frequency and the duty cycle of the high-voltage signal producing the surface plasma discharge.” Also see section Conclusion-Beyond simply using the robustness of an autonomous experimental optimization method, and its feasibility for wind tunnel experiments in context of flow control optimization, it is expected that autonomous seeking of optimal control conditions including multi-input and multi-objective capabilities would be of great interest for the flow control community. Indeed, in complex control scenario, for instance when several actuators are considered individually”.
Thus, Bernard optimizes operating parameters of a multiple actuator’s configurations. It would have obvious to POSITA to apply Bernard’s known algorithm to Potami’s actuator subset activation framework in order to improve actuator selection and system performance because both Potami and Bernard are directed to optimization of active control system suing actuator activation strategies and performance-based evaluation. Thus, 103 rejection is still maintained.
Claim Rejections - 35 USC §101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These claims are directed to an abstract idea without significantly more.
(Step 1) Is the claims to a process, machine, manufacture, or composition of matter?
Claims: 1-18 is directed to system or machine that falls on one of statutory category.
Claims: 19-20 are directed to method or process that falls on one of statutory category.
Claim 1 and 19 recites
A system or method for optimizing placement and operating conditions of active flow control actuators, comprising;
execute an optimization routine to sequentially activate varying subsets of active flow control actuators of the plurality of active flow control actuators; (Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas. The process of designing, implementing, and understanding the algorithm/optimization routine, as well as the reasoning behind choosing specific actuator subsets, falls under the category of mental process.)
calculate a cost function of each of the subsets of sequentially activated active flow control actuators based on respective measurements of the one or more parameters by the one or more sensors within the flow field; Under the broadest reasonable interpretation, these limitations involve mathematical calculation in view of instant specification [0057]. If a claim, under its broadest reasonable interpretation, covers a mathematical calculation, then it falls within the “Mathematical Concepts” grouping of abstract ideas.)
determine an optimal subset of active flow control actuators based on the respective cost functions of each of the subsets of sequentially activated active flow control actuators.; (Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. In particular, claim 1 and 19 recites the additional elements of one or more sensors configured to measure one or more parameters within the flow field which is recited at a high level of generality (i.e., as a general means of obtaining data using generic sensor), and fall under the insignificant pre-solution activity. (See MPEP 2106.05(g)). The additional elements of a plurality of active flow control actuators spatially distributed within a flow field, each of the plurality of active flow control actuators configured to be individually actuated is more than generally linking the use of a judicial exception to a particular technological environment or field of use as discussed in MPEP § 2106.05(h). The additional element of “cause operation of the plurality of active flow control actuators based on the optimal subset of active flow control actuators” is merely apply the result of the abstract optimization and constitutes post-solution activity -see insignificant extra solution activity as discussed MPEP 2106.05(g). The additional element of the system comprising: a non-transitory computer readable medium programmed with instructions that, when executed by a processor of a computer, cause the computer to in claim 1 and a computerized method in claim 19 is merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
In view of Step 2B, the claim as a whole does not amount to significantly more than the recited exception,
i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. In particular, claim 1 and 19 recites the additional elements of one or more sensors configured to measure one or more parameters within the flow field which is recited at a high level of generality (i.e., as a general means of obtaining data using generic sensor), and fall under the insignificant pre-solution activity. (See MPEP 2106.05(g)) and recognized it as generic computer functions that is well‐understood, routine, and conventional functions See MPEP 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); The additional elements of a plurality of active flow control actuators spatially distributed within a flow field, each of the plurality of active flow control actuators configured to be individually actuated is more than generally linking the use of a judicial exception to a particular technological environment or field of use as discussed in MPEP § 2106.05(h). The additional element of “cause operation of the plurality of active flow control actuators based on the optimal subset of active flow control actuators” is merely apply the result of the abstract optimization and constitutes post-solution activity -see insignificant extra solution activity as discussed MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) (see also, Potami; Bénard) The additional element of the system comprising: a non-transitory computer readable medium programmed with instructions that, when executed by a processor of a computer, cause the computer to in claim 1 and a computerized method in claim 19 is merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim does not include specific technological steps, use particular tools or data processing techniques that are novel and non-obvious that’s more likely to be considered a concrete application. Thus, claims 1 and 19 are not patent eligible.
Claim 2 and 20 further recites wherein causing operation of the plurality of active flow control actuators comprises at least one of: positioning or applying one or more operating conditions of one or more active flow control actuators, wherein at least one of a position or the one or more operating conditions of the one or more active flow control actuators is configured to vary across each subset of sequentially actuated active flow control actuators, such that the determined optimal subset of active flow control actuators comprises a combination of optimal placement and optimal operating conditions. Claim recites "determined optimal subset" and "optimal placement." Determining "optimal" without defined, rigid algorithm steps constitute a judgment or evaluation. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim.
Claim 3 further recites wherein the one or more operating conditions comprise at least one of amplitude, phase, or frequency for each individual active flow control actuator. It is more than generally linking the use of a judicial exception to a field of use as discussed in MPEP § 2106.05(h) within active flow control systems. It's a key aspect of actuator design and implementation. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim.
Claim 4 further recites wherein each subset of sequentially actuated active flow control actuators comprises one or more active flow control actuators, each of the one or more active flow control actuators comprising a predefined position and one or more predefined operating conditions. It is more than generally linking the use of a judicial exception to a particular technological environment or field of use as discussed in MPEP § 2106.05(h). Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim.
Claim 5 further recites wherein the varying subsets of active flow control actuators are selected at random by the optimization routine. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas. It involves using algorithms and mathematical models to identify the best combination of actuators based on predefined criteria and constraints. While it can be thought of as a mathematical process, the underlying idea and the interpretation of the results can involve some degree of mental abstraction. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim.
Claim 6 further recites wherein the varying subsets of active flow control actuators are selected in a predetermined manner by the optimization routine. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas. It involves using algorithms and mathematical models to identify the best combination of actuators based on predefined criteria and constraints. While it can be thought of as a mathematical process, the underlying idea and the interpretation of the results can involve some degree of mental abstraction. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim.
Claim 7 further recites wherein each active flow control actuator of the plurality of active flow control actuators is a jexel comprising one or more microjets. It is more than generally linking the use of a judicial exception to a particular technological environment or field of use as discussed in MPEP § 2106.05(h). within aerodynamics and related fields. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim.
Claim 8 further recites wherein the one or more parameters within the flow field comprises integral variables or proxies to the integral variables within the flow field. It is more than generally linking the use of a judicial exception to a particular technological environment or field of use as discussed in MPEP § 2106.05(h) within aerodynamics and related fields. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim.
Claim 9 further recites wherein the integral variables comprise at least one of drag, lift, noise, or energy consumption. It is more than generally linking the use of a judicial exception to a particular technological environment or field of use as discussed in MPEP § 2106.05(h) within aerodynamics and related fields. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim.
Claim 10 further recites wherein the optimization routine comprises a genetic algorithm that, when executed: is initialized with a randomly chosen or a manually defined set of actuator configurations; sequentially activates a first generation of varying subsets of active flow control actuators of the plurality of active flow control actuators; calculates the cost function of each of the subsets of sequentially activated active flow control actuators based on the respective measurements of the one or more parameters by the one or more sensors within the flow field; and iteratively generates subsequent generations of varying subsets of active flow control actuators of the plurality of active flow control actuators based on the cost functions of each of the subsets of sequentially activated active flow control actuators of a previous generation to thereby determine the optimal subset of active flow control actuators. The described process of a genetic algorithm sequentially activating varying subsets of flow control actuators represents a combination of both mental and mathematical concepts within the framework of an abstract idea. The algorithm's approach involves simulating evolutionary processes, which relies on the mental concepts of selection, variation, and adaptation. Simultaneously, the algorithm's mathematical implementation involves defining fitness functions, evaluating solutions, and iteratively optimizing parameters, representing mathematical concepts. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim.
Claim 11 further recites wherein the genetic algorithm, when executed, iteratively generates subsequent generations of varying subsets of active flow control actuators of the plurality of active flow control actuators by: selecting an elite group of subsets from a current generation of the varying subsets of active flow control actuators based on the respective cost functions; mutating each subset of the elite group of subsets through a set of operations over one or more operating conditions of the plurality of active flow control actuators; and generating a subsequent generation of varying subsets of active flow control actuators based on randomly selected mutated subsets The described process of a genetic algorithm sequentially activating varying subsets of flow control actuators represents a combination of both mental and mathematical concepts within the framework of an abstract idea. The algorithm's approach involves simulating evolutionary processes, which relies on the mental concepts of selection, variation, and adaptation. Simultaneously, the algorithm's mathematical implementation involves defining fitness functions, evaluating solutions, and iteratively optimizing parameters, representing mathematical concepts. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim.
Claim 12 further recites wherein mutating each subset of the elite group of subsets comprises executing operations selected from a list consisting of: change active flow control actuators count, change active flow control actuators addresses, change active flow control actuators frequency, change active flow control actuators phases, change active flow control actuators duty cycles, and change back-pressure. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim.
Claim 13 further recites wherein generating the subsequent generation of varying subsets of active flow control actuators based on randomly selected mutated subsets comprises: randomly selecting a pair of mutated subsets; copying a genome of a first mutated subset of the pair of mutated subsets; and swapping a randomly selected portion of the copied genome with a randomly selected portion of a genome of a second mutated subset of the pair of mutated subsets. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim.
Claim 14 further recites wherein the optimization routine further comprises executing a clean-up operation on the optimal subset of active flow control actuators by iteratively deactivating an active flow control actuator of the optimal subset of active flow control actuators to assess contribution of the deactivated active flow control actuator. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim.
Claim 15 further recites wherein the flow field is representative of an aerodynamic body. It is more than generally linking the use of a judicial exception to a particular technological environment or field of use as discussed in MPEP § 2106.05(h) within aerodynamics and related fields. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim.
Claim 16 further recites wherein the aerodynamic body comprises an aerial vehicle, a land vehicle, an aquatic vehicle, or their components. It is more than generally linking the use of a judicial exception to a particular technological environment or field of use as discussed in MPEP § 2106.05(h) within aerodynamics and related fields. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim.
Claim 17 further recites wherein the flow field is representative of a turbomachine. It is more than generally linking the use of a judicial exception to a particular technological environment or field of use as discussed in MPEP § 2106.05(h) within aerodynamics and related fields. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim.
Claim 18 further recites wherein the turbomachine comprises a compressor, fan, or turbine. It is more than generally linking the use of a judicial exception to a particular technological environment or field of use as discussed in MPEP § 2106.05(h) within aerodynamics and related fields. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
8. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
9. Claims 1-6, 8-16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Potami, Raffaele. Optimal sensor/actuator placement and switching schemes for control of flexible structures. Diss. Worcester Polytechnic Institute, 2008) in view of Benard, N., et al. "Turbulent separated shear flow control by surface plasma actuator: experimental optimization by genetic algorithm approach." Experiments in Fluids 57.2 (2016): 22.)
Regarding claim 1
Potami teaches a system for optimizing placement , (see page 99-In this comprehensive study the problem of vibration control for flexible structure was investigated. First, the issue of designing a network of actuators and sensors was considered. In order to accomplish such task the desired positions for sensors and actuators had been identified.) the system comprising:
a plurality of , (see abstract- The set up utilized four pairs of collocated piezoceramic patches that serve to provide sensing and actuating capabilities. Extensive numerical simulations were performed for both the placement strategies and the switching policies proposed, in order to predict the behavior of the flexible plate and provide the optimal actuator and sensor locations that were to be affixed on the flexible structure.)
each of the plurality of ; (see section 4.6.2- Only one actuator at time is switched on to provide the control moment.)
one or more sensors configured to measure one or more parameters within the flow field; (see page 28-The term y(t) represents the system output or in other words the available information gathered from the sensors. The state estimator utilizes the sensors information as input and generates as output the estimated full state information x(t).) and a non-transitory computer readable medium programmed with instructions that, when executed by a processor of a computer, cause the computer to (see abstract- Extensive numerical simulations were performed for both the placement strategies and the switching policies proposed, in order to predict the behavior of the flexible plate and provide the optimal actuator and sensor locations that were to be affixed on the flexible structure. See also Matlab/Simulink optimization)
execute an optimization routine to sequentially activate varying subsets of ; (see abstract- Activating a subset of the available and optimally placed actuators and sensors in a flexible structure provides enhanced performance with reduced energy consumption. Such approach of switching on and off different actuating devices, depending on their local-in-time authority, results in a hybrid system. see section 1.1-The second step of this work is therefore to provide the hybrid system with a switching strategy to define in real time the actuators activation sequence. The most general definition of a hybrid system is a dynamic system that exhibits both continuous and discrete dynamic behavior. This is the case of a system provided with a network of sensors and actuators that can be alternately activated. The control system described above exhibits a continuous dynamics behavior when a subset of sensors and actuators are used. If at a certain discrete time the elements of such subset are modified, the system dynamics will also exhibit a discrete behavior. In several cases, the switching sequence may not be determined by a human supervisor. For example, this could be due to high switching frequency or excessive computations required at each switch. In this situation an automated controller is required to define the sequence of devices to be activated. Several options can be considered for the activation policies. For example, one may look for the sequence that maximizes the results, increases stability or minimizes cost functions. Hybrid system switching presents two main challenges. The first one is to identify the sequence of subsystems to be activated. The second challenge is to define the time sequence or switching times. The goal here is to provide the controller with a set of decision rules enabling its ability to autonomously determine the subsystems activation sequence. Switching sequences can be defined for both sensing and controlling subsystems, but due to the higher impact of the actuators efficiency on the overall system performance, only the second class of subsystems is considered. Several switching strategies have been proposed. In this work three different approaches to implement an autonomous switching controller are presented. The first switching policy is based on plant’s global properties. It defines the activation sequence minimizing a cost function for the system. The goal with the second policy is to choose the activation sequence that minimizes the plant’s kinetic energy. See Algorithm 5-switching ON only the optimal actuator per interval)
Examiner note: Automatic controllers can "define the activation sequence minimizing a cost function" or "minimizes the plant’s kinetic energy", which constitutes an optimization routine.
calculate a cost function of each of the subsets of sequentially activated ; (see section 4.2-The goal with the first switching policy is to increase optimality for the overall switched system. To achieve this optimality, we define a “cost to go” function and we look for the path σ that minimizes this cost function. The “cost to go” index is
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where [t0, tf ] is the total time interval considered. See also page 28- The state estimator utilizes the sensors information as input and generates as output the estimated full state information x(t). see page 101- Switched strategies could be used, for example, in active flow control, allowing a significant reduction for power consumption and an overall improvement of system efficiency. See also algorithm 5-6)
determine an optimal subset of ; (see section 4.2-The goal with the first switching policy is to increase optimality for the overall switched system. To achieve this optimality, we define a “cost to go” function and we look for the path σ that minimizes this cost function. The “cost to go” index is
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where [t0, tf ] is the total time interval considered.
cause operation of the plurality of (see algorithm 5 and see section 4.6.1- 4.6.2- In this section we present the experimental results obtained by defining the switching strategy according with the ”Cost to go” rule. The control moment is applied to the structure by only one of the actuators at time according with the activation sequence defined on real time. Only one actuator at time is switched on to provide the control moment.)
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Potami does not teach operating conditions of active flow control actuators and the active flow control actuators with a flow field and the active flow control actuators with a flow field.
In the related field of invention, Benard teaches operating conditions of active flow control actuators and the active flow control actuators with a flow field and the active flow control actuators with a flow field. (See abstract-The potential benefits of active flow control are no more debated. Among many others applications, flow control provides an effective mean for manipulating turbulent separated flows. Here, a nonthermal surface plasma discharge (dielectric barrier discharge) is installed at the step corner of a backward-facing step (U0 = 15 m/s, Reh = 30,000, Reθ = 1650). Wall pressure sensors are used to estimate the reattaching location downstream of the step (objective function #1) and also to measure the wall pressure fluctuation coefficients (objective function #2). An autonomous multi-variable optimization by genetic algorithm is implemented in an experiment for optimizing simultaneously the voltage amplitude, the burst frequency and the duty cycle of the high-voltage signal producing the surface plasma discharge. The single-objective optimization problems concern alternatively the minimization of the objective function #1 and the maximization of the objective function #2. The present paper demonstrates that when coupled with the plasma actuator and the wall pressure sensors, the genetic algorithm can find the optimum forcing conditions in only a few generations. At the end of the iterative search process, the minimum reattaching position is achieved by forcing the flow at the shear layer mode where a large spreading rate is obtained by increasing the periodicity of the vortex street and by enhancing the vortex pairing process. The objective function #2 is maximized for an actuation at half the shear layer mode. In this specific forcing mode, time-resolved PIV shows that the vortex pairing is reduced and that the strong fluctuations of the wall pressure coefficients result from the periodic passages of flow structures whose size corresponds to the height of the step model.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimal actuator placement and switching schemes for control of flexible structures as disclosed by Potami to include operating conditions of active flow control actuators and the active flow control actuators with a flow field and the active flow control actuators with a flow field as taught by Benard in the system of Potami in order to improve and predictable flow control performance by optimizing how actuators are selected and driven. (see abstract, Benard)
Regarding claim 19
Potami teaches a computerized method. (See page 84- Simulink® package was used to create the virtual model for the switched controller while dSPACE® ACE1103 Kit was used to interface the hardware (plate and actuators/sensors) and computer. The computer used for implementing real-time control)
The rest of the limitation of claim 19 is similar to the claim 1 and thus are rejected for the similar reason of claim 1.
Regarding claim 2
Potami in view of Bernard teaches the system of claim 1. Potami further teaches wherein causing operation of the plurality of active flow control actuators comprises at least one of: positioning or (see abstract- Activating a subset of the available and optimally placed actuators and sensors in a flexible structure provides enhanced performance with reduced energy consumption. Such approach of switching on and off different actuating devices, depending on their local-in-time authority, results in a hybrid system. see section 1.1-The second step of this work is therefore to provide the hybrid system with a switching strategy to define in real time the actuators activation sequence. The most general definition of a hybrid system is a dynamic system that exhibits both continuous and discrete dynamic behavior. This is the case of a system provided with a network of sensors and actuators that can be alternately activated. The control system described above exhibits a continuous dynamics behavior when a subset of sensors and actuators are used. If at a certain discrete time the elements of such subset are modified, the system dynamics will also exhibit a discrete behavior. In several cases, the switching sequence may not be determined by a human supervisor. For example, this could be due to high switching frequency or excessive computations required at each switch. In this situation an automated controller is required to define the sequence of devices to be activated. Several options can be considered for the activation policies. For example, one may look for the sequence that maximizes the results, increases stability or minimizes cost functions. Hybrid system switching presents two main challenges. The first one is to identify the sequence of subsystems to be activated. The second challenge is to define the time sequence or switching times. The goal here is to provide the controller with a set of decision rules enabling its ability to autonomously determine the subsystems activation sequence. Switching sequences can be defined for both sensing and controlling subsystems, but due to the higher impact of the actuators efficiency on the overall system performance, only the second class of subsystems is considered. Several switching strategies have been proposed. In this work three different approaches to implement an autonomous switching controller are presented. The first switching policy is based on plant’s global properties. It defines the activation sequence minimizing a cost function for the system. The goal with the second policy is to choose the activation sequence that minimizes the plant’s kinetic energy. See Algorithm 5-switching ON only the optimal actuator per interval. see section 4.6.2- Only one actuator at time is switched on to provide the control moment. See pahe 82-83- The moving disturbance is simulated using only one actuator at time and switching between the remaining according with a predetermined activation sequence)
Potami does not teach to apply one or more operating conditions of one or more active flow control actuators and optimal operating conditions.
However, Benard further teaches to apply one or more operating conditions of one or more active flow control actuators and optimal operating conditions. (see abstract-The potential benefits of active flow control are no more debated. Among many others applications, flow control provides an effective mean for manipulating turbulent separated flows. Here, a nonthermal surface plasma discharge (dielectric barrier discharge) is installed at the step corner of a backward-facing step (U0 = 15 m/s, Reh = 30,000, Reθ = 1650). Wall pressure sensors are used to estimate the reattaching location downstream of the step (objective function #1) and also to measure the wall pressure fluctuation coefficients (objective function #2). An autonomous multi-variable optimization by genetic algorithm is implemented in an experiment for optimizing simultaneously the voltage amplitude, the burst frequency and the duty cycle of the high-voltage signal producing the surface plasma discharge. The single-objective optimization problems concern alternatively the minimization of the objective function #1 and the maximization of the objective function #2. The present paper demonstrates that when coupled with the plasma actuator and the wall pressure sensors, the genetic algorithm can find the optimum forcing conditions in only a few generations. At the end of the iterative search process, the minimum reattaching position is achieved by forcing the flow at the shear layer mode where a large spreading rate is obtained by increasing the periodicity of the vortex street and by enhancing the vortex pairing process. The objective function #2 is maximized for an actuation at half the shear layer mode. In this specific forcing mode, time-resolved PIV shows that the vortex pairing is reduced and that the strong fluctuations of the wall pressure coefficients result from the periodic passages of flow structures whose size corresponds to the height of the step model. see page 9- The convergence of the iterative process is clearly shown in Fig. 5. For instance, the design variable corresponding to the voltage amplitude reaches its final value in only a few generations (see generation 7 in Fig. 5), but other parameters such as the burst frequency need more time to converge. The mutation of the individuals slows the convergence rate but also insures that the best solution is found even in context of several local minima in the static response of the flow.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimal actuator placement and switching schemes for control of flexible structures as disclosed by Potami to include to apply one or more operating conditions of one or more active flow control actuators and optimal operating conditions as taught by Benard in the system of Potami in order to improve and predictable flow control performance by optimizing how actuators are selected and driven. (see abstract, Benard)
Regarding claim 20
Claim 20 is rejected for the same reason as claim 2 since they are exactly same.
Regarding claim 3
Potami in view of Bernard teaches the system of claim 1 and 2. Potami does not teach wherein the one or more operating conditions comprise at least one of amplitude, phase, or frequency for each individual active flow control actuator.
However, Benard further teaches wherein the one or more operating conditions comprise at least one of amplitude, phase, or frequency for each individual active flow control actuator. (See abstract-The potential benefits of active flow control are no more debated. Among many others applications, flow control provides an effective mean for manipulating turbulent separated flows. Here, a nonthermal surface plasma discharge (dielectric barrier discharge) is installed at the step corner of a backward-facing step (U0 = 15 m/s, Reh = 30,000, Reθ = 1650). Wall pressure sensors are used to estimate the reattaching location downstream of the step (objective function #1) and also to measure the wall pressure fluctuation coefficients (objective function #2). An autonomous multi-variable optimization by genetic algorithm is implemented in an experiment for optimizing simultaneously the voltage amplitude, the burst frequency and the duty cycle of the high-voltage signal producing the surface plasma discharge.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimal actuator placement and switching schemes for control of flexible structures as disclosed by Potami to include wherein the one or more operating conditions comprise at least one of amplitude, phase, or frequency for each individual active flow control actuator as taught by Benard in the system of Potami in order to improve and predictable flow control performance by optimizing how actuators are selected and driven. (see abstract, Benard)
Regarding claim 4
Potami in view of Bernard teaches the system of claim 1. Potami further teaches wherein each subset of sequentially actuated active flow control actuators comprises one or more active flow control actuators, each of the one or more active flow control actuators comprising a predefined position. (see abstract- Activating a subset of the available and optimally placed actuators and sensors in a flexible structure provides enhanced performance with reduced energy consumption. Such approach of switching on and off different actuating devices, depending on their local-in-time authority, results in a hybrid system. see section 1.1-The second step of this work is therefore to provide the hybrid system with a switching strategy to define in real time the actuators activation sequence. The most general definition of a hybrid system is a dynamic system that exhibits both continuous and discrete dynamic behavior. This is the case of a system provided with a network of sensors and actuators that can be alternately activated. The control system described above exhibits a continuous dynamics behavior when a subset of sensors and actuators are used. If at a certain discrete time the elements of such subset are modified, the system dynamics will also exhibit a discrete behavior. In several cases, the switching sequence may not be determined by a human supervisor. For example, this could be due to high switching frequency or excessive computations required at each switch. In this situation an automated controller is required to define the sequence of devices to be activated. Several options can be considered for the activation policies. For example, one may look for the sequence that maximizes the results, increases stability or minimizes cost functions. Hybrid system switching presents two main challenges. The first one is to identify the sequence of subsystems to be activated. The second challenge is to define the time sequence or switching times. The goal here is to provide the controller with a set of decision rules enabling its ability to autonomously determine the subsystems activation sequence. Switching sequences can be defined for both sensing and controlling subsystems, but due to the higher impact of the actuators efficiency on the overall system performance, only the second class of subsystems is considered. Several switching strategies have been proposed. In this work three different approaches to implement an autonomous switching controller are presented. The first switching policy is based on plant’s global properties. It defines the activation sequence minimizing a cost function for the system. The goal with the second policy is to choose the activation sequence that minimizes the plant’s kinetic energy. See Algorithm 5-switching ON only the optimal actuator per interval. see section 4.6.2- Only one actuator at time is switched on to provide the control moment. See pahe 82-83- The moving disturbance is simulated using only one actuator at time and switching between the remaining according with a predetermined activation sequence. See page 25- To initialize the problem, locations are assigned corresponding to the maximum modal shape deformation given by (4.12). The overall structure’s properties are now affected by the contribution to the stiffness and mass from the piezo, the wires and the control action. The originally found optimal locations may be not anymore optimal and they need to be recalculated.)
Potami does not teach each of the one or more active flow control actuators comprising one or more predefined operating conditions.
However, Benard further teaches each of the one or more active flow control actuators comprising one or more predefined operating conditions. (See abstract-The potential benefits of active flow control are no more debated. Among many others applications, flow control provides an effective mean for manipulating turbulent separated flows. Here, a nonthermal surface plasma discharge (dielectric barrier discharge) is installed at the step corner of a backward-facing step (U0 = 15 m/s, Reh = 30,000, Reθ = 1650). Wall pressure sensors are used to estimate the reattaching location downstream of the step (objective function #1) and also to measure the wall pressure fluctuation coefficients (objective function #2). An autonomous multi-variable optimization by genetic algorithm is implemented in an experiment for optimizing simultaneously the voltage amplitude, the burst frequency and the duty cycle of the high-voltage signal producing the surface plasma discharge. see page 6- The procedure starts with a randomly generated population (also called generation), including a predefined number of individuals. Here, each generation is composed of ten individuals, one individual being composed of the three design variables introduced previously in the paper. The set of parameters of each individual is applied to the surface plasma discharge, and then the objective function (or fitness function) is evaluated.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimal actuator placement and switching schemes for control of flexible structures as disclosed by Potami to include each of the one or more active flow control actuators comprising one or more predefined operating conditions as taught by Benard in the system of Potami in order to improve and predictable flow control performance by optimizing how actuators are selected and driven. (see abstract, Benard)
Regarding claim 5
Potami in view of Bernard teaches the system of claim 1. Potami further teaches wherein the varying subsets of active flow control actuators . (see abstract- Activating a subset of the available and optimally placed actuators and sensors in a flexible structure provides enhanced performance with reduced energy consumption. Such approach of switching on and off different actuating devices, depending on their local-in-time authority, results in a hybrid system. see section 1.1-The second step of this work is therefore to provide the hybrid system with a switching strategy to define in real time the actuators activation sequence. The most general definition of a hybrid system is a dynamic system that exhibits both continuous and discrete dynamic behavior. This is the case of a system provided with a network of sensors and actuators that can be alternately activated. The control system described above exhibits a continuous dynamics behavior when a subset of sensors and actuators are used. If at a certain discrete time the elements of such subset are modified, the system dynamics will also exhibit a discrete behavior. In several cases, the switching sequence may not be determined by a human supervisor. For example, this could be due to high switching frequency or excessive computations required at each switch. In this situation an automated controller is required to define the sequence of devices to be activated. Several options can be considered for the activation policies. For example, one may look for the sequence that maximizes the results, increases stability or minimizes cost functions. Hybrid system switching presents two main challenges. The first one is to identify the sequence of subsystems to be activated. The second challenge is to define the time sequence or switching times. The goal here is to provide the controller with a set of decision rules enabling its ability to autonomously determine the subsystems activation sequence. Switching sequences can be defined for both sensing and controlling subsystems, but due to the higher impact of the actuators efficiency on the overall system performance, only the second class of subsystems is considered. Several switching strategies have been proposed. In this work three different approaches to implement an autonomous switching controller are presented. The first switching policy is based on plant’s global properties. It defines the activation sequence minimizing a cost function for the system. The goal with the second policy is to choose the activation sequence that minimizes the plant’s kinetic energy. See Algorithm 5-switching ON only the optimal actuator per interval. see section 4.6.2- Only one actuator at time is switched on to provide the control moment. See page 82-83- The moving disturbance is simulated using only one actuator at time and switching between the remaining according with a predetermined activation sequence.)
Potami does not teach wherein the varying subsets of active flow control actuators are selected at random.
However, Benard further teaches wherein the varying subsets of active flow control actuators are selected at random. (see page 6-The iterative process for optimizing the forcing conditions by a genetic algorithm approach is shown in Fig. 4. The procedure starts with a randomly generated population (also called generation), including a predefined number of individuals. Here, each generation is composed of ten individuals, one individual being composed of the three design variables introduced previously in the paper. The set of parameters of each individual is applied to the surface plasma discharge and then the objective function (or fitness function) is evaluated.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimal actuator placement and switching schemes for control of flexible structures as disclosed by Potami to include wherein the varying subsets of active flow control actuators are selected at random as taught by Benard in the system of Potami in order to improve and predictable flow control performance by optimizing how actuators are selected and driven. (see abstract, Benard)
Regarding claim 6
Potami in view of Bernard teaches the system of claim 1. Potami further teaches wherein the varying subsets of active flow control actuators are selected in a predetermined manner by the optimization routine. (See abstract- Activating a subset of the available and optimally placed actuators and sensors in a flexible structure provides enhanced performance with reduced energy consumption. Such approach of switching on and off different actuating devices, depending on their local-in-time authority, results in a hybrid system. see Algorithm 5-find the optimal device)
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Regarding claim 8
Potami in view of Bernard teaches the system of claim 1. Potami further teaches wherein the one or more parameters within the flow field comprises integral variables or proxies to the integral variables within the flow field. (see section 4.2 and page 59)
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Regarding claim 9
Potami in view of Bernard teaches the system of claim 1 and 8. Potami further teaches wherein the integral variables comprise at least one of drag, lift, noise, or energy consumption. (See abstract-The vibration control problem for flexible structures is examined within the context of overall controller performance and power reduction. First, the issue of optimal sensor and actuator placement is considered along with its associated control robustness aspects. Then the option of alternately activating subsets of the available devices is investigated. Such option is considered in order to better address the effects of spatiotemporally varying disturbances acting on a flexible structure while reducing the overall energy consumption.)
Regarding claim 10
Potami in view of Bernard teaches the system of claim 1. Potami further teaches sequentially activates (see abstract- Activating a subset of the available and optimally placed actuators and sensors in a flexible structure provides enhanced performance with reduced energy consumption. Such approach of switching on and off different actuating devices, depending on their local-in-time authority, results in a hybrid system. see page 55- An actuation strategy optimal for certain stages may result in unstable behavior for different stages. In such cases it is therefore required to alternatively switch between the available control subsystems characterized by different actuation strategies.)
calculates the cost function of each of the subsets of sequentially activated active flow control actuators based on the respective measurements of the one or more parameters by the one or more sensors within the flow field; (see page 59 and section 4.2)
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Potami does not teach wherein the optimization routine comprises a genetic algorithm that, when executed: is initialized with a randomly chosen or a manually defined set of actuator configurations, generation based subsets and iteratively generates subsequent generations of varying subsets of active flow control actuators of the plurality of active flow control actuators based on the cost functions of each of the subsets of sequentially activated active flow control actuators of a previous generation to thereby determine the optimal subset of active flow control actuators.
However, Benard further teaches wherein the optimization routine comprises a genetic algorithm that, (see abstract and fig 4-The present paper demonstrates that when coupled with the plasma actuator and the wall pressure sensors, the genetic algorithm can find the optimum forcing conditions in only a few generations) when executed:
is initialized with a randomly chosen or a manually defined set of actuator configurations; (see page 6 and fig 4- The iterative process for optimizing the forcing conditions by a genetic algorithm approach is shown in Fig. 4. The procedure starts with a randomly generated population (also called generation), including a predefined number of individuals. Here, each generation is composed of ten individuals, one individual being composed of the three design variables introduced previously in the paper. The set of parameters of each individual is applied to the surface plasma discharge, and then the objective function (or fitness function) is evaluated.
generation based subsets. (see page 6 and fig 4-The iterative process for optimizing the forcing conditions by a genetic algorithm approach is shown in Fig. 4. The procedure starts with a randomly generated population (also called generation), including a predefined number of individuals. Here, each generation is composed of ten individuals, one individual being composed of the three design variables introduced previously in the paper. The set of parameters of each individual is applied to the surface plasma discharge, and then the objective function (or fitness function) is evaluated. The evaluation of one individual takes 5 s due to the unsteady aspects of the BFS flow. Furthermore, in order to discard transient regimes the pressure acquisition starts 5 s after the plasma is turned on. To recover a natural flow regime, a period of 5 s is considered after the plasma is turned off. Then, the whole acquisition process takes 15 s per individual plus a few hundred of microseconds to read and write the input and output ASCII files.
iteratively generates subsequent generations of varying subsets of active flow control actuators of the plurality of active flow control actuators based on the cost functions of each of the subsets of sequentially activated active flow control actuators of a previous generation to thereby determine the optimal subset of active flow control actuators. (see page 6 and fig 4- The set of parameters of each individual is applied to the surface plasma discharge, and then the objective function (or fitness function) is evaluated. The evaluation of one individual takes 5 s due to the unsteady aspects of the BFS flow. Furthermore, in order to discard transient regimes the pressure acquisition starts 5 s after the plasma is turned on. To recover a natural flow regime, a period of 5 s is considered after the plasma is turned off. Then, the whole acquisition process takes 15 s per individual plus a few hundred of microseconds to read and write the input and output ASCII files. At the end of the evaluation process, all individuals can be ranked according to the value of their fitness function. See fig 5-6 and page 9- The convergence of the iterative process is clearly shown in Fig. 5. For instance, the design variable corresponding to the voltage amplitude reaches its final value in only a few generations (see generation 7 in Fig. 5), but other parameters such as the burst frequency need more time to converge. The mutation of the individuals slows the convergence rate but also ensures that the best solution is found even in context of several local minima in the static response of the flow. The convergence of the best solution of each generation is shown in Fig. 6. This plot shows how fast the convergence of the optimization is. After 60 evaluations (i.e., six generations), the minimized reattachment length is 4.55 h meaning that the recirculation bubble has been reduced by 20 %. As shown in Fig. 7 where the distribution of the design variables of the 120 individuals are plotted, many of the individuals have design variable values around the optimal forcing condition due to the fast convergence rate of the method.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimal actuator placement and switching schemes for control of flexible structures as disclosed by Potami to include wherein the optimization routine comprises a genetic algorithm that, when executed: is initialized with a randomly chosen or a manually defined set of actuator configurations, generation based subsets and iteratively generates subsequent generations of varying subsets of active flow control actuators of the plurality of active flow control actuators based on the cost functions of each of the subsets of sequentially activated active flow control actuators of a previous generation to thereby determine the optimal subset of active flow control actuators as taught by Benard in the system of Potami in order to efficiently explore combinations of actuator selection and operating conditions with predictable results. (see abstract and fig 4, Benard)
Regarding claim 11
Potami in view of Bernard teaches the system of claim 1 and 10. Potami does not teach wherein the genetic algorithm, when executed, iteratively generates subsequent generations of varying subsets of active flow control actuators of the plurality of active flow control actuators by: selecting an elite group of subsets from a current generation of the varying subsets of active flow control actuators based on the respective cost functions; mutating each subset of the elite group of subsets through a set of operations over one or more operating conditions of the plurality of active flow control actuators; and generating a subsequent generation of varying subsets of active flow control actuators based on randomly selected mutated subsets.
However, Benard teaches wherein the genetic algorithm, when executed, iteratively generates subsequent generations of varying subsets of active flow control actuators of the plurality of active flow control actuators by: selecting an elite group of subsets from a current generation of the varying subsets of active flow control actuators based on the respective cost functions;(see page 5-6 and fig 6- The optimization strategy is inspired on the natural evolution of species, formulated by Darwin. It gets the optimal individuals using a combination of selection, crossover and mutation of the fittest individuals, which are the candidate solutions. At the end of the evaluation process, all individuals can be ranked according to the value of their fitness function. At this time, the basic operators which manage the genetic algorithm, namely the selection, the crossover and the mutation, are applied. The selection operator takes care of selecting the best-performing individuals of each population to enable other operators to produce new offspring. It is the most basic operator and usually the first one to be applied. The most usual selection techniques are roulette wheel, ranking selection and elitism, among others. In the present investigation, the best individuals are selected by a ranking tournament. Crossover operator takes two selected individuals, and it combines them to create a new offspring)
mutating each subset of the elite group of subsets through a set of operations over one or more operating conditions of the plurality of active flow control actuators; (see page 6 and fig 4-5- Finally, the mutation operator creates new offspring modifying the information contained in one design variable of a selected individual. Bit inversion and order changing are usual mutation techniques for binary encoding, but here (because of real-value encoding approach) a small quantity is added to the design variable under mutation process. Furthermore, the mutation probability is set as a function of the number of design variable; it is defined as 1/3 because three designs variables are considered. At the end of the operator procedure, a new generation is produced and the iterative process starts. This iterative process finish when the stopping criterion is reached (it could be accuracy, time, number of iterations, etc…). Here, the genetic approach tests a total of 12 generations for a total running time of 45 min. The procedure is fully automatized, and it corresponds to a closed loop approach by autonomous experiment optimization. See page 9- The convergence of the iterative process is clearly shown in Fig. 5. For instance, the design variable corresponding to the voltage amplitude reaches its final value in only a few generations (see generation 7 in Fig. 5), but other parameters such as the burst frequency need more time to converge. The mutation of the individuals slows the convergence rate but also ensures that the best solution is found even in context of several local minima in the static response of the flow.) and
generating a subsequent generation of varying subsets of active flow control actuators based on randomly selected mutated subsets. (see page 6 and fig 4- At this time, the basic operators which manage the genetic algorithm, namely the selection, the crossover and the mutation, are applied. The selection operator takes care of selecting the best-performing individuals of each population to enable other operators to produce new offspring. Crossover operator takes two selected individuals, and it combines them to create a new offspring. Several techniques can be used to determine the point where the genetic information is split to add the genetic information from the second individual. Here, single point crossover, the most standard techniques for crossover, is applied (probability of 0.9). Finally, the mutation operator creates new offspring modifying the information contained in one design variable of a selected individual)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimal actuator placement and switching schemes for control of flexible structures as disclosed by Potami to include wherein the genetic algorithm, when executed, iteratively generates subsequent generations of varying subsets of active flow control actuators of the plurality of active flow control actuators by: selecting an elite group of subsets from a current generation of the varying subsets of active flow control actuators based on the respective cost functions; mutating each subset of the elite group of subsets through a set of operations over one or more operating conditions of the plurality of active flow control actuators; and generating a subsequent generation of varying subsets of active flow control actuators based on randomly selected mutated subsets as taught by Benard in the system of Potami in order to efficiently explore combinations of actuator selection and operating conditions with predictable optimization results. (see abstract and fig 4, Benard)
Regarding claim 12
Potami in view of Bernard teaches the system of claim 1 and 11. Potami does not teach wherein mutating each subset of the elite group of subsets comprises executing operations selected from a list consisting of: change active flow control actuators count, change active flow control actuators addresses, change active flow control actuators frequency, change active flow control actuators phases, change active flow control actuators duty cycles, and change back-pressure.
However, Benard teaches wherein mutating each subset of the elite group of subsets comprises executing operations selected from a list consisting of: change active flow control actuators frequency, change active flow control actuators duty cycles, change active flow control actuators count, change active flow control actuators addresses, change active flow control phases and change back-pressure. (see fig 4-6 and page 6, 9- The iterative process for optimizing the forcing conditions by a genetic algorithm approach is shown in Fig. 4. The procedure starts with a randomly generated population (also called generation), including a predefined number of individuals. Here, each generation is composed of ten individuals, one individual being composed of the three design variables introduced previously in the paper. The set of parameters of each individual is applied to the surface plasma discharge, and then the objective function (or fitness function) is evaluated. The convergence of the iterative process is clearly shown in Fig. 5. For instance, the design variable corresponding to the voltage amplitude reaches its final value in only a few generations (see generation 7 in Fig. 5), but other parameters such as the burst frequency need more time to converge. The mutation of the individuals slows the convergence rate but also ensures that the best solution is found even in context of several local minima in the static response of the flow. At the end of the iterative optimization process, the best individual find by the solver corresponds to periodic forcing with voltage amplitude at its maximal value (i.e., 20 kV here), a duty cycle in the 50–60 % range and superimposed perturbations in a 120–130 Hz range)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimal actuator placement and switching schemes for control of flexible structures as disclosed by Potami to include wherein mutating each subset of the elite group of subsets comprises executing operations selected from a list consisting of: change active flow control actuators count, change active flow control actuators addresses, change active flow control actuators frequency, change active flow control actuators phases, change active flow control actuators duty cycles, and change back-pressure as taught by Benard in the system of Potami in order to efficiently explore combinations of actuator selection and operating conditions with predictable optimization results. (see abstract and fig 4, Benard)
Regarding claim 13
Potami in view of Bernard teaches the system of claim 1 and 11. Potami does not teach wherein generating the subsequent generation of varying subsets of active flow control actuators based on randomly selected mutated subsets comprises: randomly selecting a pair of mutated subsets; copying a genome of a first mutated subset of the pair of mutated subsets; and swapping a randomly selected portion of the copied genome with a randomly selected portion of a genome of a second mutated subset of the pair of mutated subsets.
However, Benard teaches wherein generating the subsequent generation of varying subsets of active flow control actuators based on randomly selected mutated subsets comprises: randomly selecting a pair of mutated subsets; copying a genome of a first mutated subset of the pair of mutated subsets; and swapping a randomly selected portion of the copied genome with a randomly selected portion of a genome of a second mutated subset of the pair of mutated subsets. (see fig 4 and page 6- The iterative process for optimizing the forcing conditions by a genetic algorithm approach is shown in Fig. 4. The procedure starts with a randomly generated population (also called generation), including a predefined number of individuals. Here, each generation is composed of ten individuals, one individual being composed of the three design variables introduced previously in the paper. The set of parameters of each individual is applied to the surface plasma discharge, and then the objective function (or fitness function) is evaluated. The evaluation of one individual takes 5 s due to the unsteady aspects of the BFS flow. Furthermore, in order to discard transient regimes the pressure acquisition starts 5 s after the plasma is turned on. To recover a natural flow regime, a period of 5 s is considered after the plasma is turned off. Then, the whole acquisition process takes 15 s per individual plus a few hundred of microseconds to read and write the input and output ASCII files. At the end of the evaluation process, all individuals can be ranked according to the value of their fitness function. At this time, the basic operators which manage the genetic algorithm, namely the selection, the crossover and the mutation, are applied. The selection operator takes care of selecting the best-performing individuals of each population to enable other operators to produce new offspring. It is the most basic operator and usually the first one to be applied. The most usual selection techniques are roulette wheel, ranking selection and elitism, among others. In the present investigation, the best individuals are selected by a ranking tournament. Crossover operator takes two selected individuals, and it combines them to create a new offspring. Several techniques can be used to determine the point where the genetic information is split to add the genetic information from the second individual. Here, single point crossover, the most standard techniques for crossover, is applied (probability of 0.9). Finally, the mutation operator creates new offspring modifying the information contained in one design variable of a selected individual. Bit inversion and order changing are usual mutation techniques for binary encoding, but here (because of real-value encoding approach) a small quantity is added to the design variable under mutation process. Furthermore, the mutation probability is set as a function of the number of design variable; it is defined as 1/3 because three designs variables are considered. At the end of the operator procedure, a new generation is produced and the iterative process starts. This iterative process finish when the stopping criterion is reached (it could be accuracy, time, number of iterations, etc…). Here, the genetic approach tests a total of 12 generations for a total running time of 45 min. The procedure is fully automatized, and it corresponds to a closed loop approach by autonomous experiment optimization.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimal actuator placement and switching schemes for control of flexible structures as disclosed by Potami to include wherein generating the subsequent generation of varying subsets of active flow control actuators based on randomly selected mutated subsets comprises: randomly selecting a pair of mutated subsets; copying a genome of a first mutated subset of the pair of mutated subsets; and swapping a randomly selected portion of the copied genome with a randomly selected portion of a genome of a second mutated subset of the pair of mutated subsets as taught by Benard in the system of Potami in order to efficiently explore combinations of actuator selection and operating conditions with predictable optimization results. (see abstract and fig 4, Benard)
Regarding claim 14
Potami in view of Bernard teaches the system of claim 1 and 11. Potami further teaches wherein the optimization routine further comprises executing a clean-up operation on the optimal subset of active flow control actuators by iteratively deactivating an active flow control actuator of the optimal subset of active flow control actuators to assess contribution of the deactivated active flow control actuator. (see page 73 and algorithm 5)
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Regarding claim 15
Potami in view of Bernard teaches the system of claim 1. Potami does not teach wherein the flow field is representative of an aerodynamic body.
However, Benard teaches wherein the flow field is representative of an aerodynamic body. (See abstract- The potential benefits of active flow control are no more debated. Among many others applications, flow control provides an effective mean for manipulating turbulent separated flows. Here, a nonthermal surface plasma discharge (dielectric barrier discharge) is installed at the step corner of a backward-facing step (U₀ = 15 m/s, Reh = 30,000, Reθ = 1650). See introduction- Flow control devices are potential options for simplified aerofoil design and manipulation of turbulent separated flows while maintaining high aerodynamic performances and low noise emission.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimal actuator placement and switching schemes for control of flexible structures as disclosed by Potami to include wherein the flow field is representative of an aerodynamic body as taught by Benard in the system of Potami in order to improve and predictable flow control performance by optimizing how actuators are selected and driven. (see abstract, Benard)
Regarding claim 16
Potami in view of Bernard teaches the system of claim 1 and 15. Potami does not teach wherein the aerodynamic body comprises an aerial vehicle, a land vehicle, an aquatic vehicle, or their components.
However, Benard teaches wherein the aerodynamic body comprises an aerial vehicle, a land vehicle, an aquatic vehicle, or their components. (See abstract- The potential benefits of active flow control are no more debated. Among many others applications, flow control provides an effective mean for manipulating turbulent separated flows. Here, a nonthermal surface plasma discharge (dielectric barrier discharge) is installed at the step corner of a backward-facing step (U₀ = 15 m/s, Reh = 30,000, Reθ = 1650). See introduction- Flow control devices are potential options for simplified aerofoil design and manipulation of turbulent separated flows while maintaining high aerodynamic performances and low noise emission.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimal actuator placement and switching schemes for control of flexible structures as disclosed by Potami to include wherein the aerodynamic body comprises an aerial vehicle, a land vehicle, an aquatic vehicle, or their components as taught by Benard in the system of Potami in order to improve and predictable flow control performance by optimizing how actuators are selected and driven. (see abstract, Benard)
10. Claims 7 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Potami, Raffaele. Optimal sensor/actuator placement and switching schemes for control of flexible structures. Diss. Worcester Polytechnic Institute, 2008) in view of Benard, N., et al. "Turbulent separated shear flow control by surface plasma actuator: experimental optimization by genetic algorithm approach." Experiments in Fluids 57.2 (2016): 22.) and further in view of Kumar et al. (PUB NO: US 20190264636 A1)
Regarding claim 7
Potami in view of Bernard teaches the system of claim 1. The combination of Potami and Benard does not teach wherein each active flow control actuator of the plurality of active flow control actuators is a jexel comprising one or more microjets.
In the related field of invention, Kumar teaches wherein each active flow control actuator of the plurality of active flow control actuators is a jexel comprising one or more microjets. (see para 10-An exemplary engine noise reduction system, can be provided, which can include a noise reduction fluid source, and a microjet(s) placed at an axial location downstream from a nozzle exit of an engine and configured to asymmetrically inject a noise reduction fluid from the noise reduction fluid source into a jet flow of the engine. The engine can be a jet engine. The microjet(s) can include four microjets, which can be about 90 degrees apart in a plane at the axial location. The four microjets can be asymmetric microjets. The microjet(s) can be configured to inject the noise reduction fluid in a direction that is normal with respect to the jet flow.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimal actuator placement and switching schemes for control of flexible structures as disclosed by Potami to include wherein each active flow control actuator of the plurality of active flow control actuators is a jexel comprising one or more microjets as taught by Kumar in the system of Potami and Benard in order to provide significant improvement over other asymmetric noise reduction methods due to the following. It can offer the ability to shape the jet plume and tailor the acoustic filed around it as per user's need. While previous systems can affect the shear layer symmetry, and can thus affect the directionality of the jet, the exemplary system and method does not affect shear layer directly, but can enhance mixing from the inside out, thus having a minimal impact on the directionality or thrust direction. (see para 066, Kumar)
Regarding claim 17
Potami in view of Bernard teaches the system of claim 1. The combination of Potami and Benard does not teach wherein the flow field is representative of a turbomachine.
In the related field of invention, Kumar teaches wherein the flow field is representative of a turbomachine. (See para 59-Such an injection can be implemented in a high-speed jet engine for reducing the turbulent mixing noise being radiated towards the ground, while keeping the noise signature unchanged above the aircraft. see para 80- One of the challenges in modeling compressible flows for high-speed jet noise problems can be in the simultaneous treatment of high gradients, flow discontinuities (e.g., shocks) and fine scale turbulence structures. An exemplary goal can be to successfully and efficiently capture the discontinuities while simultaneously using accurate centered procedures for turbulent flows. This can indicate the need for low dissipation procedures that can efficiently resolve the small-scale turbulent structures in the flow field simultaneously. A diffusive procedure can be utilized for a flow field with sharp gradients and discontinuities, in order to help maintain the stability of the simulation.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimal actuator placement and switching schemes for control of flexible structures as disclosed by Potami to include wherein the flow field is representative of a turbomachine as taught by Kumar in the system of Potami and Benard in order to provide significant improvement over other asymmetric noise reduction methods due to the following. It can offer the ability to shape the jet plume and tailor the acoustic filed around it as per user's need. While previous systems can affect the shear layer symmetry, and can thus affect the directionality of the jet, the exemplary system and method does not affect shear layer directly, but can enhance mixing from the inside out, thus having a minimal impact on the directionality or thrust direction. (see para 066, Kumar)
Regarding claim 18
Potami in view of Bernard and Kumar teaches the system of claim 17. The combination of Potami and Benard does not teach herein the turbomachine comprises a compressor, fan, or turbine.
In the related field of invention, Kumar teaches wherein the turbomachine comprises a compressor, fan, or turbine. (See para 59-Such an injection can be implemented in a high-speed jet engine for reducing the turbulent mixing noise being radiated towards the ground, while keeping the noise signature unchanged above the aircraft. see para 67-The exemplary system according to various exemplary embodiments of the present disclosure can provide operational flexibility absent in permanent design based asymmetric solutions which cannot be turned off when not needed, and can reduce the associated thrust penalty. Considerably less thrust loss can be seen as compared to a full 360-degree noise suppressor. Considerably less fluid injection requirements can also be seen as compared to a full 360-degree fluid injection-based noise suppression procedure. Less load on the compressor or any device that would supply the high-pressure fluid needed for fluid injection can also be seen.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimal actuator placement and switching schemes for control of flexible structures as disclosed by Potami to include wherein the turbomachine comprises a compressor, fan, or turbine as taught by Kumar in the system of Potami and Benard in order to provide significant improvement over other asymmetric noise reduction methods due to the following. It can offer the ability to shape the jet plume and tailor the acoustic filed around it as per user's need. While previous systems can affect the shear layer symmetry, and can thus affect the directionality of the jet, the exemplary system and method does not affect shear layer directly, but can enhance mixing from the inside out, thus having a minimal impact on the directionality or thrust direction. (see para 066, Kumar)
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
GOLLING et al. US 20130035808 A1
Discussing an aircraft with aerofoils including a main wing and a control flap that includes an adjustment flap. The aircraft includes an actuator for the control flap, as well as a sensor device for acquiring the position of the control flap, an arrangement of flow-influencing devices for influencing the fluid that flows over a segment of the main wing, and flow-state sensor devices for measuring the flow state. The aircraft includes a flight control device connected to the sensor device for acquiring the position of the control flap and to the flow-state sensor devices, and connected to the actuator and flow-influencing devices for transmitting actuating commands, and a flight-state sensor device connected to the flight control device for transmitting flight states. The flight control device includes a function that selects the flow-influencing devices that are operated for optimizing local lift coefficients on the aerofoil, depending on the flight state.
Patel et al. US 9541106 B1
Discussing a method for designing or optimizing a control surface for use with plasma actuators for controlling an aircraft, missile, munition or automobile, and more particularly to controlling fluid flow across their surfaces or other surfaces using plasma actuators.
10. All claims 1-20 are rejected.
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/PURSOTTAM GIRI/
Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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