Flow Control Optimization using Genetic Algorithms and DMD Reduced Order Model
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Date
2023-03
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Abstract
The theory of feedback control has been extensively studied and applied to mitigate undesirable flow features of aerodynamic systems. However, feedback control is often 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. The principal benefit of the GA for our purposes is that it is effectively data driven, ie., agnostic to the governing equations of the flow and thus not incurring simplifications typically adopted with other traditional feedback control approaches. In this work, the GA is first validated using a two-dimensional, algebraic test function as a surrogate fitness function; this exercise guides the choice of mutation, selection, and crossover parameters to quickly converge on the optimal solution. The GA is then considered for the problem of a supersonic planar impinging jet to mitigate noise from aeroacoustic resonance modes. The formulation uses a dynamic mode decomposition based reduced order model (DMD-ROM) from a large eddy simulation (LES) database. This allows various combinations of control input forcing variables of notional actuators near the nozzle (amplitude, frequency and phase) to be tested, that would be cost-prohibitive without the ROM. Results on the efficiency of the GA in finding the optimal energy forcing gain relative to a brute force parametric sweep will be discussed. Ongoing work will exhibit how the GA can converge on a control scheme that reduces jet noise with more complex fitness functions.
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Engineering and Technology (The Ohio State University Denman Undergraduate Research Forum)
Keywords
computational fluid dynamics, reduced order model, genetic algorithm, flow control, optimization, jet