An Efficient Bi-Level Surrogate Approach for Optimizing Shock Control Bumps under Uncertainty

Sabater, Christian; Goertz, Stefan

doi:10.2514/6.2019-2214

Cited by 6 publications

(9 citation statements)

References 34 publications

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“…As shown in Figure 2, the unstructured mesh of the baseline configuration, the RAE2822 airfoil, has 29,000 grid nodes, and is quasi two-dimensional. This test case has been successfully used in the past in similar aerodynamic shape optimization problems [3,10]. A mesh deformation tool developed by DLR using linear elasticity theory [11] is used to change the geometry at any given design vector.…”

Section: Numerical Solvermentioning

confidence: 99%

“…3 Comparison of the gradients of the drag obtained with finite differences and the adjoint Gaussian Processes models, GPs (also known as Kriging) have been traditionally used in aerodynamic shape optimization as surrogate models for global optimization [15]. However, these have been recently used as non-intrusive approach to perform uncertainty quantification due to its good capability to globally represent the stochastic space [10,16].…”

Section: Surrogate Based Uncertainty Quantificationmentioning

confidence: 99%

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Gradient-Based Aerodynamic Robust Optimization Using the Adjoint Method and Gaussian Processes

Sabater¹,

Görtz²

2020

Computational Methods in Applied Sciences

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The use of robust design in aerodynamic shape optimization is increasing in popularity in order to come up with configurations less sensitive to operational conditions. However, the addition of uncertainties increases the computational cost as both design and stochastic spaces must be explored. The objective of this work is the development of an efficient framework for gradient-based robust design by using an adjoint formulation and a non-intrusive surrogate-based uncertainty quantification method. At each optimization iteration, the statistic of both the quantity of interest and its gradients are efficiently obtained through Gaussian Processes models. The framework is applied to the aerodynamic shape optimization of a 2D airfoil. With the presented approach it is possible to reduce both the mean and standard deviation of the drag compared to the deterministic optimum configuration. The robust solution is obtained at a reduced run time that is independent of the number of design parameters. This is a preprint of the book Chapter: Sabater, C. and Görtz, S. "Gradient-Based Aerodynamic Robust Optimization using the Adjoint Method and Gaussian Processes". In: Gaspar-Cunha A. et. al. Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences. Springer.

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Section: Numerical Solvermentioning

confidence: 99%

Section: Surrogate Based Uncertainty Quantificationmentioning

confidence: 99%

Gradient-Based Aerodynamic Robust Optimization Using the Adjoint Method and Gaussian Processes

Sabater¹,

Görtz²

2020

Computational Methods in Applied Sciences

Self Cite

View full text Add to dashboard Cite

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“…Another possibility is to determine the design such that the probability distribution function (pdf) of the QoI matches a prescribed target pdf [6]. Alternatively, the minimization of a prescribed quantile of the QoI is a flexible approach often chosen in engineering problems [7,8,9], because it ensures a best minimal performance with controlled probability (design requirements).…”

Section: Introductionmentioning

confidence: 99%

“…Traditional approaches for robust design use surrogate models to approximate the random QoI Y (•, θ). These surrogate models usually consist of Polynomial Chaos (PC) Expansions [11,12,13] or Gaussian Processes (GP) [5,8,14]. Their construction requires the evaluation of the deterministic full order model at suitable training points.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian approach for quantile optimization problems with high-dimensional uncertainty sources

Sabater

Maître

Congedo

et al. 2021

Computer Methods in Applied Mechanics and Engineering

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…Multi-stage optimization has been rarely treated with surrogate models. In Sabater et al [8], a two-stage optimization is used for uncertainty quantification on the lower level. Uncertainty quantification is replaced by a surrogate model prediction and global optimization follows the maximization of EI scheme.…”

Section: Introductionmentioning

confidence: 99%

Surrogate-Assisted Optimization for Multi-stage Optimal Scheduling of Virtual Power Plants

Gobert

Gmys

Toubeau

et al. 2019

2019 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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This paper presents a comparison between two surrogate-assisted optimization methods dealing with two-stage stochastic programming. The Efficient Global Optimization (EGO) framework is challenging a method coupling Genetic Algorithm (GA) and offline-learnt kriging model for the lower stage optimization. The objective is to prove the good behavior of bayesian optimization (and in particular EGO) applied to a real-world two-stage problem with strong dependencies between the stages. The problem consists in determining the optimal strategy of an electricity market player participating in reserve (first stage) as well as day-ahead energy and real-time markets (second stage). The decisions optimized at the first stage induce constraints on the second stage so that both stages can not be dissociated. One additional difficulty is the stochastic aspect due to uncertainties of several parameters (e.g. renewable energybased generation) that requires more computational power to be handled. Surrogate models are introduced to deal with that additional computational burden. Experiments show that the EGO-based approach gives better results than GA with offline kriging model using smaller budget.

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An Efficient Bi-Level Surrogate Approach for Optimizing Shock Control Bumps under Uncertainty

Cited by 6 publications

References 34 publications

Gradient-Based Aerodynamic Robust Optimization Using the Adjoint Method and Gaussian Processes

Gradient-Based Aerodynamic Robust Optimization Using the Adjoint Method and Gaussian Processes

A Bayesian approach for quantile optimization problems with high-dimensional uncertainty sources

Surrogate-Assisted Optimization for Multi-stage Optimal Scheduling of Virtual Power Plants

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