查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting originating in Notre Dame, Indiana, by NewsRx journalists, research stated, "We develop neural-network active flow controllers using a deep learning partial differential equation augmentation method (DPM). The end-to-end sensitivities for optimization are computed using adjoints of the governing equations without restriction on the terms that may appear in the objective function." Financial support for this research came from University of Notre Dame Center for Research Computing (CRC). The news reporters obtained a quote from the research from the University of Notre Dame, "In one- dimensional Burgers' examples with analytic (manufactured) control functions, DPM-based control is com- parably effective to standard supervised learning for in-sample solutions and more effective for out-of-sample solutions, i.e., with different analytic control functions. The influence of the optimization time interval and neutral-network width is analyzed, the results of which influence algorithm design and hyperparameter choice, balancing control efficacy with computational cost. We subsequently develop adjoint-based con- trollers for two flow scenarios. First, we compare the drag-reduction performance and optimization cost of adjoint-based controllers and deep reinforcement learning (DRL)-based controllers for two-dimensional, incompressible, confined flow over a cylinder at Re = 100, with control achieved by synthetic body forces along the cylinder boundary. The required model complexity for the DRL-based controller is 4229 times that required for the DPM-based controller. In these tests, the DPM-based controller is 4.85 times more effective and 63.2 times less computationally intensive to train than the DRL-based controller. Second, we test DPM-based control for compressible, unconfined flow over a cylinder and extrapolate the controller to out-of-sample Reynolds numbers. We also train a simplified, steady, offline controller based on the DPM control law. Both online (DPM) and offline (steady) controllers stabilize the vortex shedding with a 99% drag reduction, demonstrating the robustness of the learning approach. For out-of-sample flows (Re = {50, 200, 300, 400}), both the online and offline controllers successfully reduce drag and stabilize vortex shedding, indicating that the DPM-based approach results in a stable model."
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