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Title: Advanced Fluid Reduced Order Models for Compressible Flow.

Abstract

This report summarizes fiscal year (FY) 2017 progress towards developing and implementing within the SPARC in-house finite volume flow solver advanced fluid reduced order models (ROMs) for compressible captive-carriage flow problems of interest to Sandia National Laboratories for the design and qualification of nuclear weapons components. The proposed projection-based model order reduction (MOR) approach, known as the Proper Orthogonal Decomposition (POD)/Least- Squares Petrov-Galerkin (LSPG) method, can substantially reduce the CPU-time requirement for these simulations, thereby enabling advanced analyses such as uncertainty quantification and de- sign optimization. Following a description of the project objectives and FY17 targets, we overview briefly the POD/LSPG approach to model reduction implemented within SPARC . We then study the viability of these ROMs for long-time predictive simulations in the context of a two-dimensional viscous laminar cavity problem, and describe some FY17 enhancements to the proposed model reduction methodology that led to ROMs with improved predictive capabilities. Also described in this report are some FY17 efforts pursued in parallel to the primary objective of determining whether the ROMs in SPARC are viable for the targeted application. These include the implemen- tation and verification of some higher-order finite volume discretization methods within SPARC (towards using the code tomore » study the viability of ROMs on three-dimensional cavity problems) and a novel structure-preserving constrained POD/LSPG formulation that can improve the accuracy of projection-based reduced order models. We conclude the report by summarizing the key takeaways from our FY17 findings, and providing some perspectives for future work.« less

Authors:
 [1];  [2];  [1];  [2];  [3];  [1];  [4]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  3. Stanford Univ., CA (United States)
  4. Univ. of Illinois, Urbana-Champaign, IL (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1395816
Report Number(s):
SAND-2017-10335
657261
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEFENSE

Citation Formats

Tezaur, Irina Kalashnikova, Fike, Jeffrey A., Carlberg, Kevin Thomas, Barone, Matthew F., Maddix, Danielle, Mussoni, Erin E., and Balajewicz, Maciej. Advanced Fluid Reduced Order Models for Compressible Flow.. United States: N. p., 2017. Web. doi:10.2172/1395816.
Tezaur, Irina Kalashnikova, Fike, Jeffrey A., Carlberg, Kevin Thomas, Barone, Matthew F., Maddix, Danielle, Mussoni, Erin E., & Balajewicz, Maciej. Advanced Fluid Reduced Order Models for Compressible Flow.. United States. doi:10.2172/1395816.
Tezaur, Irina Kalashnikova, Fike, Jeffrey A., Carlberg, Kevin Thomas, Barone, Matthew F., Maddix, Danielle, Mussoni, Erin E., and Balajewicz, Maciej. Fri . "Advanced Fluid Reduced Order Models for Compressible Flow.". United States. doi:10.2172/1395816. https://www.osti.gov/servlets/purl/1395816.
@article{osti_1395816,
title = {Advanced Fluid Reduced Order Models for Compressible Flow.},
author = {Tezaur, Irina Kalashnikova and Fike, Jeffrey A. and Carlberg, Kevin Thomas and Barone, Matthew F. and Maddix, Danielle and Mussoni, Erin E. and Balajewicz, Maciej},
abstractNote = {This report summarizes fiscal year (FY) 2017 progress towards developing and implementing within the SPARC in-house finite volume flow solver advanced fluid reduced order models (ROMs) for compressible captive-carriage flow problems of interest to Sandia National Laboratories for the design and qualification of nuclear weapons components. The proposed projection-based model order reduction (MOR) approach, known as the Proper Orthogonal Decomposition (POD)/Least- Squares Petrov-Galerkin (LSPG) method, can substantially reduce the CPU-time requirement for these simulations, thereby enabling advanced analyses such as uncertainty quantification and de- sign optimization. Following a description of the project objectives and FY17 targets, we overview briefly the POD/LSPG approach to model reduction implemented within SPARC . We then study the viability of these ROMs for long-time predictive simulations in the context of a two-dimensional viscous laminar cavity problem, and describe some FY17 enhancements to the proposed model reduction methodology that led to ROMs with improved predictive capabilities. Also described in this report are some FY17 efforts pursued in parallel to the primary objective of determining whether the ROMs in SPARC are viable for the targeted application. These include the implemen- tation and verification of some higher-order finite volume discretization methods within SPARC (towards using the code to study the viability of ROMs on three-dimensional cavity problems) and a novel structure-preserving constrained POD/LSPG formulation that can improve the accuracy of projection-based reduced order models. We conclude the report by summarizing the key takeaways from our FY17 findings, and providing some perspectives for future work.},
doi = {10.2172/1395816},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {9}
}

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