Active Manifold and Model Order Reduction to Accelerate Multidisciplinary Analysis and Optimization - presented by Prof. Charbel Farhat

Active Manifold and Model Order Reduction to Accelerate Multidisciplinary Analysis and Optimization

Prof. Charbel Farhat

Prof. Charbel Farhat
Slide at 45:05
Sampling the Parameter Domain for Training: Greedy Procedure Generic procedure for training and building a global ROB V Initial point Sampled points step 0 : randomly sample one point and construct initial global ROB at this point step j : j = 2, 3, ... Nsmp randomly pick Nc candidate points using, for example, LHS at each candidate point, assess the accuracy of the global PROM associated with the current global ROB using a residual-based error indicator sample the parameter point where the global PROM has largest error, compute snapshots at this point, and update the global ROB lessens the curse of dimensionality but alone does not mitigate it
Share slide
Summary (AI generated)

The main focus of this paper is to apply the concept to solve an MDAO problem. The governing PDEs are separated into linear and nonlinear equations. The applications will demonstrate the multidisciplinary nature of the problem. The RL represents the residual for linear PDEs and RNL for heavily nonlinear CFD-based turbulent flow. The solutions are Q, Z, and μ is the parameter vector. An MDAO approach will be used to reformulate everything in terms of a function of μ. The constraints, such as Box Constraints, are scalar and not necessarily PDEs. The idea is to create a database of local linear ROMs for flutter.