Polynomial Ridge Flowfield Estimation

Abstract

Computational fluid dynamics plays a key role in the design process across many industries. Recently, there has been increasing interest in data-driven methods, in order to exploit the large volume of data generated by such computations. This paper introduces the idea of using spatially correlated polynomial ridge functions for rapid flowfield estimation. Dimension reducing ridge functions are obtained for numerous points within training flowfields. The functions can then be used to predict flow variables for new, previously unseen, flowfields. Their dimension reducing nature alleviates the problems associated with visualising high dimensional datasets, enabling improved understanding of design spaces and potentially providing valuable physical insights. The proposed framework is computationally efficient; consisting of either readily parallelisable tasks, or linear algebra operations. To further reduce the computational cost, ridge functions need only be computed at only a small number of subsampled locations. The flow physics encoded within covariance matrices obtained from the training flowfields can then be used to predict flow quantities, conditional upon those predicted by the ridge functions at the sampled points. To demonstrate the efficacy of the framework, the incompressible flow around an ensemble of aerofoils is used as a test case. On unseen aerofoils the ridge functions' predictive accuracy is found to be reasonably competitive with a state-of-the-art convolutional neural network (CNN). The local ridge functions can also be reused to obtain surrogate models for integral quantities such a loss coefficient, which is advantageous in situations where long-term storage of the CFD data is problematic. Finally, use of the ridge framework with varying boundary conditions is demonstrated on a three dimensional transonic wing flow.

arXiv comment24 pages, 18 figures

References in corpus (16)

• Adam: A Method for Stochastic Optimization
• Array Programming with NumPy
• A Unified Approach to Interpreting Model Predictions
• Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
• Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows
• Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks
• Data-Driven Sparse Sensor Placement for Reconstruction
• The Unreasonable Effectiveness of Deep Learning in Artificial Intelligence
• Accelerating Eulerian Fluid Simulation With Convolutional Networks
• Dying ReLU and Initialization: Theory and Numerical Examples
• Collapse of Deep and Narrow Neural Nets
• Shallow Neural Networks for Fluid Flow Reconstruction with Limited Sensors
• Contour regression: A general approach to dimension reduction
• Data-driven polynomial ridge approximation using variable projection
• A near-stationary subspace for ridge approximation
• A Lipschitz Matrix for Parameter Reduction in Computational Science