Disciplinary proper orthogonal decomposition and interpolation for the resolution of parameterized multidisciplinary analysis

Berthelin, Gaspard; Dubreuil, Sylvain; Salaün, Michel; Bartoli, Nathalie; Gogu, Christian

doi:10.1002/nme.6981

Cited by 9 publications

(2 citation statements)

References 53 publications

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“…Variance derivatives for Kriging To perform uncertainty quantification for system analysis purposes, it could be interesting to know more about the variance derivatives of a model [16,38,142]. For that purpose and also to pursue the original publication about derivatives [27], SMT 2.0 extends the derivative support to Kriging variances and kernels.…”

Section: Contributions To Surrogate Modelsmentioning

confidence: 99%

“…However, in MDO, we can optimize every discipline in parallel and not sequentially. This is what has been done in Efficient Global Multidisciplinary Design Optimization (EGMDO) [16,38] and, more generally, we restrict this work to the MultiDisciplinary Feasible (MDF) approach [62] but other monolitic architectures such as Independent Disciplinary Feasible (IDF) need to be investigate [10]. In consequence, our work is limited to one fidelity and everything as been treated as if there was only one discipline to optimize.…”

Section: Limitations and Perspectivesmentioning

confidence: 99%

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High-dimensional multidisciplinary design optimization for aircraft eco-design

SAVES

2024

Preprint

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Nowadays, there is a significant and growing interest in improving the efficiency of vehicle design processes through the development of tools and techniques in the field of MDO. Specifically, in aerostructure design, aerodynamic and structural variables influence each other and have a joint effect on quantities of interest like weight or fuel consumption and, as such, MDO arises as a powerful tool for automatically making interdisciplinary trade-offs. In the aircraft design context, the process generally involves mixed continuous and categorical design variables. For instance, the size of an aircraft’s structural parts can be described using continuous variables, while discrete variables may include either integer variables, like the number of panels, or categorical variables, like cross-sections or material choices. The objective of this Philosophiae Doctor (Ph.D) thesis is to propose an efficient approach for optimizing a multidisciplinary black-box model when the optimization problem is constrained and involves a large number of mixed integer design variables (typically 100 variables). The targeted optimization approach, called EGO, is based on a sequential enrichment of an adaptive surrogate model and, in this context, GP surrogate models are one of the most widely used in engineering problems to approximate time-consuming high fidelity models. EGO is a heuristic BO method that performs well in terms of solution quality. However, like any other global optimization method, EGO suffers from the curse of dimensionality, meaning that its performance is satisfactory on lower dimensional problems, but deteriorates as the dimensionality of the optimization search space increases. For realistic aircraft design problems, the typical size of the design variables can even exceed 100 and, thus, trying to solve directly the problems using EGO is ruled out. The latter is especially true when the problems involve both continuous and categorical variables increasing even more the size of the search space. In this Ph.D thesis, effective parameterization tools are investigated, including techniques like partial least squares regression, to significantly reduce the number of design variables. Additionally, Bayesian optimization is adapted to handle discrete variables and high-dimensional spaces in order to reduce the number of evaluations when optimizing innovative aircraft concepts such as the “DRAGON” hybrid airplane to reduce their climate impact.

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Section: Contributions To Surrogate Modelsmentioning

confidence: 99%

Section: Limitations and Perspectivesmentioning

confidence: 99%

High-dimensional multidisciplinary design optimization for aircraft eco-design

SAVES

2024

Preprint

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Interdisciplinary design optimization of compressor blades combining low- and high-fidelity models

Pretsch

Arsenyev

Czech

et al. 2023

Struct Multidisc Optim

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Multidisciplinary design optimization has great potential to support the turbomachinery development process by improving designs at reduced time and cost. As part of the industrial compressor design process, we seek for a rotor blade geometry that minimizes stresses without impairing the aerodynamic performance. However, the presence of structural mechanics, aerodynamics, and their interdisciplinary coupling poses challenges concerning computational effort and organizational integration. In order to reduce both computation times and the required exchange between disciplinary design teams, we propose an inter- instead of multidisciplinary design optimization approach tailored to the studied optimization problem. This involves a distinction between main and side discipline. The main discipline, structural mechanics, is computed by accurate high-fidelity finite element models. The side discipline, aerodynamics, is represented by efficient low-fidelity models, using Kriging and proper-orthogonal decomposition to approximate constraints and the gas load field as coupling variable. The proposed approach is shown to yield a valid blade design with reasonable computational effort for training the aerodynamic low-fidelity models and significantly reduced optimization times compared to a high-fidelity multidisciplinary design optimization. Especially for expensive side disciplines like aerodynamics, the multi-fidelity interdisciplinary design optimization has the potential to consider the effects of all involved disciplines at little additional cost and organizational complexity, while keeping the focus on the main discipline.

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