Discretization-independent surrogate modeling over complex geometries using hypernetworks and implicit representations

Duvall, James; Duraisamy, Karthik; Pan, Shaowu

doi:10.48550/arxiv.2109.07018

Cited by 2 publications

(1 citation statement)

References 45 publications

(74 reference statements)

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“…This does not mean that ML cannot be successful in turbulence modeling, as it is still in its early stages and many possibilities have not been explored yet. Despite its limitations, such as its black-box nature and limited extrapolation capabilities, ML might have the a) Electronic mail: abkar@mpe.au.dk potential to overcome these challenges through the use of various types of neural networks [23][24][25][26] . It is important to note that the above-mentioned observations and criticisms about ML are not applicable to all ML techniques and are more commonly made about ML in general rather than specific ML methods.…”

Section: Introductionmentioning

confidence: 99%

Log-law recovery through reinforcement-learning wall model for large-eddy simulation

Vadrot¹,

Yang²,

Bae³

et al. 2023

Preprint

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This paper focuses on the use of reinforcement learning (RL) as a machine-learning (ML) modeling tool for near-wall turbulence. RL has demonstrated its effectiveness in solving high-dimensional problems, especially in domains such as games. Despite its potential, RL is still not widely used for turbulence modeling and is primarily used for flow control and optimization purposes. A new RL wall model (WM) called VYBA23 is developed in this work, which uses agents dispersed in the flow near the wall. The model is trained on a single Reynolds number (Re τ = 10 4 ) and does not rely on high-fidelity data, as the back-propagation process is based on a reward rather than output error. The states of the RLWM, which are the representation of the environment by the agents, are normalized to remove dependence on the Reynolds number. The model is tested and compared to another RLWM (BK22) and to an equilibrium wall model, in a half-channel flow at eleven different Reynolds numbers (Re τ ∈ [180; 10 10 ]). The effects of varying agents' parameters such as actions range, time-step, and spacing are also studied. The results are promising, showing little effect on the average flow field but some effect on wall-shear stress fluctuations and velocity fluctuations. This work offers positive prospects for developing RLWMs that can recover physical laws, and for extending this type of ML models to more complex flows in the future.

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Section: Introductionmentioning

confidence: 99%

Log-law recovery through reinforcement-learning wall model for large-eddy simulation

Vadrot¹,

Yang²,

Bae³

et al. 2023

Preprint

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Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows

Zanardi,

Venturi,

Panesi

2023

Sci Rep

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This work proposes a new machine learning (ML)-based paradigm aiming to enhance the computational efficiency of non-equilibrium reacting flow simulations while ensuring compliance with the underlying physics. The framework combines dimensionality reduction and neural operators through a hierarchical and adaptive deep learning strategy to learn the solution of multi-scale coarse-grained governing equations for chemical kinetics. The proposed surrogate’s architecture is structured as a tree, with leaf nodes representing separate neural operator blocks where physics is embedded in the form of multiple soft and hard constraints. The hierarchical attribute has two advantages: (i) It allows the simplification of the training phase via transfer learning, starting from the slowest temporal scales; (ii) It accelerates the prediction step by enabling adaptivity as the surrogate’s evaluation is limited to the necessary leaf nodes based on the local degree of non-equilibrium of the gas. The model is applied to the study of chemical kinetics relevant for application to hypersonic flight, and it is tested here on pure oxygen gas mixtures. In 0-$$\textrm{D}$$ D scenarios, the proposed ML framework can adaptively predict the dynamics of almost thirty species with a maximum relative error of 4.5% for a wide range of initial conditions. Furthermore, when employed in 1-$$\textrm{D}$$ D shock simulations, the approach shows accuracy ranging from 1% to 4.5% and a speedup of one order of magnitude compared to conventional implicit schemes employed in an operator-splitting integration framework. Given the results presented in the paper, this work lays the foundation for constructing an efficient ML-based surrogate coupled with reactive Navier-Stokes solvers for accurately characterizing non-equilibrium phenomena in multi-dimensional computational fluid dynamics simulations.

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Discretization-independent surrogate modeling over complex geometries using hypernetworks and implicit representations

Cited by 2 publications

References 45 publications

Log-law recovery through reinforcement-learning wall model for large-eddy simulation

Log-law recovery through reinforcement-learning wall model for large-eddy simulation

Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows

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