A Multi-Objective Robust Aircraft Wing Shape Optimization Approach

Demir, Görkem; Gorguluarslan, Recep M.

doi:10.2514/6.2021-3045

Cited by 1 publication

(1 citation statement)

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“…The Eigenspace Perturbation Method (EPM) (Emory et al, 2013;Iaccarino et al, 2017) is a physics based framework to quantify model-form uncertainty introduced in EVMs. This method has shown reliable uncertainty quantification with low computational cost, and has been applied to a variety of engineering problems including virtual certification of aircraft designs (Mukhopadhaya et al, 2020;Nigam et al, 2021), design of urban structures (Gorlé et al, 2019), aerospace design and analysis (Mishra et al, 2019b;Mishra & Iaccarino, 2017a;Mishra et al, 2019a;Mishra & Iaccarino, 2017b), application to design under uncertainty (DUU) (Demir et al, 2023;Cook et al, 2019;Mishra et al, 2020;Righi, 2023), etc .…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods

Chu¹

2021

Uncertainty Quantification of Stochastic Defects in Materials

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Reliable prediction of turbulent flows is an important necessity across different fields of science and engineering. In Computational Fluid Dynamics (CFD) simulations, the most common type of models are eddy viscosity models that are computationally inexpensive but introduce a high degree of epistemic error. The Eigenspace Perturbation Method (EPM) attempts to quantify this predictive uncertainty via physics based perturbation in the spectral representation of the predictions. While the EPM outlines how to perturb, it does not address how much or even where to perturb. We address this need by introducing machine learning models to predict the details of the perturbation, thus creating a physics inspired machine learning (ML) based perturbation framework. In our choice of ML models, we focus on incorporating physics based inductive biases while retaining computational economy. Specifically we use a Convolutional Neural Network (CNN) to learn the correction between turbulence model predictions and true results. This physics inspired machine learning based perturbation approach is able to modulate the intensity and location of perturbations and leads to improved estimates of turbulence model errors.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Methods

Chu¹

2021

Uncertainty Quantification of Stochastic Defects in Materials

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A Multi-Objective Robust Aircraft Wing Shape Optimization Approach

Cited by 1 publication

References 10 publications

Machine Learning Methods

Machine Learning Methods

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