Using Genetic Algorithms with Reduced Order Modeling for Flow Control Optimization
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Date
2023-05
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The Ohio State University
Abstract
Active flow control theory has been extensively developed and applied to mitigate undesirable flow features of aerodynamic systems. However, traditional active control theory is difficult to implement for applications described by complex, nonlinear physics. Genetic algorithms (GA) offer an attractive alternative by mimicking natural selection to converge on an optimal control input for a given objective function. GAs are data driven, i.e., agnostic to the governing equations of the flow and thus do not need to incur simplifications typically adopted with traditional control approaches. In this work, a real-coded GA is first validated using the Schwefel function, an algebraic test function, as a surrogate fitness function. This exercise serves to not only validate the GA, but also guides the choice of mutation, selection, and crossover parameters for rapid convergence to the optimal solution. The GA is then considered for the problem of a supersonic planar impinging jet. A dynamic mode decomposition based reduced order model (DMD-ROM) from a large eddy simulation (LES) database is used to provide an economical fitness function for evaluating the effectiveness of a wide variety of control forcing inputs (amplitude, frequency and phase). The forcing inputs are imparted on the system by notional actuators near the nozzle. When using the GA with the impinging jet system, it was able to identify optimal forcing parameters in significantly fewer total calculations when compared to the brute force approach. In addition, results from this study will show the power GAs can have for finding optimal forcing parameters when they are combined with low-cost fitness functions.
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Keywords
reduced order modeling, genetic algorithms, flow control, dynamic mode decomposition