Coaxial-Injector Surrogate Modeling Based on Reynolds-Averaged Navier–Stokes Simulations Using Deep Learning

Krügener, Moritz; Usandivaras, José Felix Zapata; Bauerheim, Michaël; Urbano, Annafederica

doi:10.2514/1.b38696

Cited by 11 publications

(12 citation statements)

References 40 publications

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“…(1 × 10 -5 ) (1 × 10 typically encountered in such non-convex optimisation problems. The NSGA II algorithm has proven to be efficient in predicting Pareto fronts in fluid mechanics, and has already been coupled to neural networks, as proposed by Krügener et al (2022) to optimise the combustion chamber of a rocket engine. For each generation of the algorithm, a mating pool is formed by selecting individuals from the previous generation population.…”

Section: Liftmentioning

confidence: 99%

“…Nevertheless, for low-dimensional spaces where dense sampling can be achieved, standard training strategies can be sufficient. For instance, Krügener et al (2022) trained an MLP from Reynolds-averaged Navier-Stokes simulations to optimise the geometry of the combustion chamber of a rocket engine on a 10-dimensional space, showing an accurate Pareto front for this configuration. Similarly, Baqué et al (2018) trained a geodesic convolutional neural network (GCNN) to minimise the drag of 3-D objects such as a sphere and a car.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Krügener et al. (2022) trained an MLP from Reynolds-averaged Navier–Stokes simulations to optimise the geometry of the combustion chamber of a rocket engine on a 10-dimensional space, showing an accurate Pareto front for this configuration. Similarly, Baqué et al.…”

Section: Introductionmentioning

confidence: 99%

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Discovering optimal flapping wing kinematics using active deep learning

Corban,

Bauerheim,

Jardin

2023

J. Fluid Mech.

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This paper focuses on the discovery of optimal flapping wing kinematics using a deep learning surrogate model for unsteady aerodynamics and multi-objective optimisation. First, a surrogate model of the unsteady forces experienced by a 3-D flapping wing is built, based on deep neural networks. The model is trained on a dataset of randomly generated kinematics simulated using direct numerical simulation (DNS). Once trained, the neural networks can quickly predict the unsteady lift and torques experienced by the wing, using sparse information on the kinematics. This fast surrogate model allows multi-objective optimisation to be performed. The resulting Pareto front consists of new kinematics that may be very different from the kinematics of the initial dataset. A few arbitrarily chosen kinematics on the Pareto front are thus simulated using DNS and used to enhance the database. The new dataset is used to train again the networks, and this active deep learning/optimisation framework is performed until convergence, obtained after only two iterations. Overall, this method reduced the cost of optimisation by 83 %. Results reveal two distinct families of motions. Kinematics promoting high efficiency are characterised by large stroke amplitudes and relatively low angles of attack, as observed for fruit flies, honeybees or hawkmoths. For those, lift production is driven by quasi-steady effects and the formation of a stable leading edge vortex. Kinematics promoting high lift are characterised by small stroke amplitudes and high angles of attack, reminiscent of mosquitoes. Lift production is driven by the rapid generation of vorticity at the trailing edge.

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Section: Liftmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discovering optimal flapping wing kinematics using active deep learning

Corban,

Bauerheim,

Jardin

2023

J. Fluid Mech.

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“…The GP models and the Bayesian Optimization (BO) that could be performed with them are implemented in the Surrogate Modeling Toolbox (SMT) v2.0 1 [27]. Our model-ing software is free and open-source and has been used regularly in the aircraft industry, for example with a deep learning model [125,137,138,243] or with a deep gaussian process [110,150].…”

Section: Introductionmentioning

confidence: 99%

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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“…The latter modeling toolbox is developed by ONERA, ISAE-Supaero, ICA (CNRS), NASA Glenn and the University of Michigan [26]. Our modeling software is free and open-source and has been used regularly in the aircraft industry, for example with a deep learning model [27][28][29][30] or with a deep gaussian process [31,32].…”

Section: Introductionmentioning

confidence: 99%

A mixed-categorical correlation kernel for Gaussian process

Saves¹,

Diouane²,

Bartoli³

et al. 2022

Preprint

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Recently, there has been a growing interest for mixed-categorical meta-models based on Gaussian process (GP) surrogates. In this setting, several existing approaches use different strategies either by using continuous kernels (e.g., continuous relaxation and Gower distance based GP) or by using a direct estimation of the correlation matrix. In this paper, we present a kernel-based approach that extends continuous exponential kernels to handle mixedcategorical variables. The proposed kernel leads to a new GP surrogate that generalizes both the continuous relaxation and the Gower distance based GP models. We demonstrate, on both analytical and engineering problems, that our proposed GP model gives a higher likelihood and a smaller residual error than the other kernel-based state-of-the-art models. Our method is available in the open-source software SMT.

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Coaxial-Injector Surrogate Modeling Based on Reynolds-Averaged Navier–Stokes Simulations Using Deep Learning

Cited by 11 publications

References 40 publications

Discovering optimal flapping wing kinematics using active deep learning

Discovering optimal flapping wing kinematics using active deep learning

High-dimensional multidisciplinary design optimization for aircraft eco-design

A mixed-categorical correlation kernel for Gaussian process

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