Simulation of hypersonic flows in near-continuum regime using DSMC method and new extended continuum model

Chen, Jie; Ou, Jihui; Zhao, Lijuan

doi:10.1063/1.5119602

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…In transitional flows, the continuum assumption loses its validity and the local equilibrium hypothesis may not be always fulfilled since restoring equilibrium after a perturbation requires a non-negligible finite relaxation time. Thus, 𝜙 may significantly deviate from the Maxwellian equilibrium form and be instead bimodal [4,5]. In such cases, the hydrodynamic problem is undetermined with respect to the conserved variables, as obtaining an expression for 𝝉 and 𝒒 requires the knowledge of 𝜙, which in turn necessitates the resolution of the Boltzmann equation.…”

Section: 𝜕 𝜌 𝜕𝑡mentioning

confidence: 99%

Physics-constrained deep learning-based model for non-equilibrium flows

Monti,

Singh,

Sirignano

et al. 2024

AIAA SCITECH 2024 Forum

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The construction of accurate and computationally affordable computer models for simulating non-equilibrium fluid flows is a challenging task, particularly when dealing with hypersonic applications. The popular Navier-Stokes (NS) model yields unreliable predictions whenever the continuum assumption fails (approx. for a Knudsen number Kn > 0.1). On the other hand, the large computational cost entailed in solving the more suited Boltzmann equation makes its numerical solution impossible for non-trivial cases. In such cases, even the most efficient Direct-Simulation Monte Carlo (DSMC) solution methods may result prohibitively expensive, in particular if approx. Kn < 10 and if thermochemical nonequilibrium is considered.This paper focuses on improving the computational capabilities for predicting nonequilibrium hypersonic flows in the transitional regime 0.1 < Kn < 10. Namely, we build physics-constrained Deep Learning (DL) augmentation terms that take advantage of the known and resolvable physics e.g., first principles, thermodynamics laws, and conservation laws, and exploit them to improve the accuracy of the NS solution. The aim is to achieve a prediction accuracy comparable to that of the DSMC solution while maintaining a computational cost comparable to solving the standard NS equations.Previous studies employed Deep Neural Networks (DNNs) for enhancing the modeling of incompressible fluid flows, with particular reference to turbulent phenomena, but their potential has not been fully deployed for hypersonic applications, for which literature concerning DL-DNNs augmentation models is still scarce. Recent research by Sirignano et al. [1] proposed an innovative online training strategy that leverages the adjoint method to optimize the parameters of a DL model by solving a constrained PDE. This approach allows consistent mathematical-physical training and was successfully applied to hypersonic problems by Nair et al. [2] for a 1D shock flow in the transitional regime. However, this model does not embed the first principle of physics, making it case-dependent and preventing its extension to general 2D/3D applications.The present work aims to overcome these issues and extend the state-of-the-art by proposing a flexible framework for building physics-constrained neural network closures of general application. To overcome these drawbacks, the pursued approach is based on the premise that physical laws and constraints are inherently embedded in real applications and they can be exploited to guide the development of more robust and accurate closures. By incorporating these constraints into the training process, we aim to develop a model that can capture both the universal physics, which is general, and the physics encoded in the data, which is case-dependent. It is important to note that the main advantage of the approach proposed in this work, compared to other alternative approaches, is its ability to generalize: the structure of the developed model is case-independent and permits the use of the same model on very differ...

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Section: 𝜕 𝜌 𝜕𝑡mentioning

confidence: 99%

Physics-constrained deep learning-based model for non-equilibrium flows

Monti,

Singh,

Sirignano

et al. 2024

AIAA SCITECH 2024 Forum

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Rarefied gas effect in hypersonic shear flows

Chen

Zhou

2021

Acta Mech. Sin.

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Nonlinear transport of rarefied Couette flows from low speed to high speed

Chen

2020

Physics of Fluids

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The nonlinear transport properties and macroscopic flow features of rarefied plane Couette flows from low speed to high speed for a monatomic gas are investigated in detail using the direct simulation Monte Carlo (DSMC) method. The effective viscosity and thermal conductivity are directly computed from the DSMC results according to the linear constitutive relations. The detailed structure of the Knudsen layer (KL) and the functional dependence of the effective transport coefficients on local Knudsen numbers in the whole system are presented and compared with existing theoretical models. The results show that the effective viscosity and thermal conductivity distributions in the KL for different Mach number flows can be recast into the same profile (i.e., isothermal scaling function) in terms of a scaled wall distance η=∫0y1/λ(y)dy, though the local flow is nonisothermal. For all cases, the shear-stress Knudsen number distributions across the channel show a well opposite trend to the effective transport coefficient profiles. The functional dependence between them in the bulk region always coincides with the normal solution that is derived from the Boltzmann model equations for unbounded shear flows, while that in the KL for low-speed cases shows a large difference with the normal solution. As the Mach number increases, the DSMC data in the KL can also agree approximately with the normal solution at a large shear-stress Knudsen number. These results can be very useful for developing phenomenological models to describe a wall-bounded rarefied shear flow, showing a good prospect in both microflow and high-altitude applications.

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Simulation of hypersonic flows in near-continuum regime using DSMC method and new extended continuum model

Cited by 3 publications

References 6 publications

Physics-constrained deep learning-based model for non-equilibrium flows

Physics-constrained deep learning-based model for non-equilibrium flows

Rarefied gas effect in hypersonic shear flows

Nonlinear transport of rarefied Couette flows from low speed to high speed

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