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Title: A new approach to stochastic reduced order modeling

Journal Article · · Computers and Chemical Engineering
 [1]; ORCiD logo [2]
  1. University of Illinois, Chicago, IL (United States)
  2. University of Illinois, Chicago, IL (United States); Vishwamitra Research Institute, Crystal Lake, IL (United States)

Here this short note presents a new method for stochastic reduced order (SROM) model based on BONUS reweighting scheme. An illustrative case study of IGCC power plant compares the new method with the neural network based reduced order model. The new method shows promising results.

Research Organization:
Univ. of Illinois at Urbana-Champaign, IL (United States); University of Illinois, Chicago, IL (United States)
Sponsoring Organization:
USDOE Office of Fossil Energy (FE); USDOE
Grant/Contract Number:
FE0011227
OSTI ID:
1533671
Alternate ID(s):
OSTI ID: 1397362
Journal Information:
Computers and Chemical Engineering, Vol. 93, Issue C; ISSN 0098-1354
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 1 work
Citation information provided by
Web of Science

References (5)

Nonlinear model order reduction based on local reduced-order bases: NONLINEAR MODEL REDUCTION BASED ON LOCAL REDUCED-ORDER BASES
  • Amsallem, David; Zahr, Matthew J.; Farhat, Charbel
  • International Journal for Numerical Methods in Engineering, Vol. 92, Issue 10 https://doi.org/10.1002/nme.4371
journal June 2012
Stochastic reduced order models for random vectors: Application to random eigenvalue problems journal January 2013
Stochastic reduced order models for uncertainty quantification of intergranular corrosion rates journal March 2014
Better Optimization of Nonlinear Uncertain Systems (BONUS): A New Algorithm for Stochastic Programming Using Reweighting through Kernel Density Estimation journal November 2004
Optimization of IGCC processes with reduced order CFD models journal September 2011

Figures / Tables (4)


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