Optimal renormalization of multiscale systems
[1];
[2];
[3];
[4]
- Department of Mathematics and Computer Science, University of Puget Sound, Tacoma, WA 98416,
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195,
- Department of Mathematics and Computer Science, University of Puget Sound, Tacoma, WA 98416,, Computing &, Analytics Division, Pacific Northwest National Laboratory, Richland, WA 99354,
- Department of Applied Mathematics, University of Washington, Seattle, WA 98195,, Advanced Computing, Mathematics and Data Division, Pacific Northwest National Laboratory, Richland, WA 99354
Significance Many systems involve more variables than can be reasonably simulated. Even when only some of these variables are of interest, they usually depend strongly on the other variables. Reduced order models of the relevant variables, which behave as those variables would in a full simulation, are of great interest. Many such models involve a “memory” term that is difficult to compute and can lead to instability if not properly approximated. We have developed a time-dependent renormalization approach to stabilize such models. We validate the approach on the inviscid Burgers equation. We use it to obtain a perturbative renormalization of the three-dimensional Euler equations of incompressible fluid flow including all the complex effects present in the dynamics.
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1819994
- Journal Information:
- Proceedings of the National Academy of Sciences of the United States of America, Journal Name: Proceedings of the National Academy of Sciences of the United States of America Vol. 118 Journal Issue: 37; ISSN 0027-8424
- Publisher:
- Proceedings of the National Academy of SciencesCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
A computational investigation of the finite-time blow-up of the 3D incompressible Euler equations based on the Voigt regularization
Computationally Efficient Multiscale Neural Networks Applied to Fluid Flow in Complex 3D Porous Media