Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 76 tok/s
Gemini 2.5 Pro 59 tok/s Pro
GPT-5 Medium 24 tok/s Pro
GPT-5 High 23 tok/s Pro
GPT-4o 95 tok/s Pro
Kimi K2 207 tok/s Pro
GPT OSS 120B 449 tok/s Pro
Claude Sonnet 4 37 tok/s Pro
2000 character limit reached

Direct Flux Gradient Approximation to Close Moment Model for Kinetic Equations (2102.07641v1)

Published 3 Feb 2021 in physics.comp-ph

Abstract: To close the moment model deduced from kinetic equations, the canonical approach is to provide an approximation to the flux function not able to be depicted by the moments in the reduced model. In this paper, we propose a brand new closure approach with remarkable advantages than the canonical approach. Instead of approximating the flux function, the new approach close the moment model by approximating the flux gradient. Precisely, we approximate the space derivative of the distribution function by an ansatz which is a weighted polynomial, and the derivative of the closing flux is computed by taking the moments of the ansatz. Consequently, the method provides us an improved framework to derive globally hyperbolic moment models, which preserve all those conservative variables in the low order moments. It is shown that the linearized system at the weight function, which is often the local equilibrium, of the moment model deduced by our new approach is automatically coincided with the system deduced from the classical perturbation theory, which can not be satisfied by previous hyperbolic regularization framework. Taking the Boltzmann equation as example, the linearlization of the moment model gives the correct Navier-Stokes-Fourier law same as that the Chapman-Enskog expansion gives. Most existing globally hyperbolic moment models are re-produced by our new approach, and several new models are proposed based on this framework.

Summary

We haven't generated a summary for this paper yet.

Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.