Learning Optimal Aerodynamic Designs
Transcript of Learning Optimal Aerodynamic Designs
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Learning Optimal Aerodynamic Designs
The University of Texas at Austin Oden Institute for
Computational Engineering & SciencesOmar Ghattas, Karen Willcox,
Anirban Chaudhuri, Tom O’Leary-Roseberry
University of Michigan MDO Lab, Dept of Aerospace Engineering
Joaquim Martins, Xiaosong Du
ARPA-E DIFFERENTIATE Program
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CFD-based aerodynamic design optimization
● CFD-based design optimization is a powerful technology that has revolutionized the efficient design of aerodynamic systems, including aircraft, ground vehicles, marine vessels, and energy generation via wind, water, and gas turbines
● CFD-based design optimization is challenging due to the need for: ○ Days of computing time to do a single 3D design optimization ○ HPC resources○ Sophisticated algorithms for adjoints, differentiable and robust
shape parameterization and mesh generation, PDE-constrained optimization, robust flow solvers
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Our solution: Deep learning of CFD design optimization
● Use deep neural network to learn aerodynamic design optimization
● Specifically, learn the map from design requirements to optimal aerodynamic design with high accuracy (>95%)
● The neural network is executed at interactive speeds (millisecond)● Fast decision-making for tradespace exploration: Conceptual
design phase greatly accelerated● Supplement expert knowledge: No need to set up and run an
expensive CFD optimization every time you change performance requirements
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Train DNN with MACH-Aero CFD design optimization framework
Developed at University of Michigan MDO Lab (Joaquim Martins, PI)
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MACH-Aero framework
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Example: design optimization of CRM wing benchmark
Wave drag is eliminated; total drag reduced by 8.5%
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AeroLearn: A data-parsimonious deep neural network trained by MACH-Aero
● Train a DNN to learn the map… ○ From design requirements (lift & moment bounds, geometric constraints such as
thickness, environmental parameters such as Mach & Re, across flight envelope) ...○ … To optimal shape
● This results in a neural network with high-dimensional inputs & outputs (100s to 1000s)● Training a predictive network with black-box ML techniques would require 104--106 CFD
optimizations -- completely intractable! ● Our solution: AeroLearn
○ Sensitivity-based projections to find low-dimensional manifolds for inputs & outputs○ Multifidelity neural networks by enriching expensive high-fidelity data with cheaper
low-fidelity data● Result: Neural network that has high generalization accuracy (99.8% for drag, 99% for
geometry) for few training data (100s), executing at interactive speeds (milliseconds)
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AeroLearn vs. CFD-based design optimization runtime:2D airfoil case with 4 inputs and 20 outputs
Mesh MACH-Aero design optimization time AeroLearn design optimization time
medium 1555 seconds 0.0005 seconds
fine 7732 seconds 0.0005 seconds
Design requirement inputs
Optimal design variable outputs
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Generalization error vs. number of training data
99% accuracy in optimal shape, 99.8% accuracy in objective function (drag) with just ~100 training data
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Rapid exploration of design requirement space
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Summary and invitation to partner with us● CFD-based aerodynamic design optimization is a powerful technology, but is
often inaccessible to many who could benefit from it, due to the need for HPC resources, sophisticated algorithms and solvers, and advanced expertise.
● We have developed AeroLearn -- parsimonious deep neural networks that learn aerodynamic design by training on MACH-Aero optimizations
● AeroLearn networks are capable of very high accuracy in predicting the optimal shape and objective function with a limited number of training data
● Once trained, AeroLearn networks can explore the design requirement space to generate optimal designs at interactive (< millisecond) speeds
● We are looking for partners to commercialize this technology● Please stop by our booth and chat with us, or else email:
● Omar Ghattas ([email protected])