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Title: Formulations for Surrogate-Based Optimization Using Data Fit and Multifidelity Models.

Abstract

Abstract not provided.

Authors:
;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1143351
Report Number(s):
SAND2007-0813C
523919
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the SIAM Conference on Computational Science and Engineering held February 19-23, 2007 in Costa Mesa, CA.
Country of Publication:
United States
Language:
English

Citation Formats

Dunlavy, Daniel, and Eldred, Michael S. Formulations for Surrogate-Based Optimization Using Data Fit and Multifidelity Models.. United States: N. p., 2007. Web.
Dunlavy, Daniel, & Eldred, Michael S. Formulations for Surrogate-Based Optimization Using Data Fit and Multifidelity Models.. United States.
Dunlavy, Daniel, and Eldred, Michael S. Thu . "Formulations for Surrogate-Based Optimization Using Data Fit and Multifidelity Models.". United States. doi:. https://www.osti.gov/servlets/purl/1143351.
@article{osti_1143351,
title = {Formulations for Surrogate-Based Optimization Using Data Fit and Multifidelity Models.},
author = {Dunlavy, Daniel and Eldred, Michael S},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Feb 01 00:00:00 EST 2007},
month = {Thu Feb 01 00:00:00 EST 2007}
}

Conference:
Other availability
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  • Abstract not provided.
  • With increasing demand placed on power generation plants to reduce carbon dioxide (CO2) emissions, processes to separate and capture CO2 for eventual sequestration are highly sought after. Carbon capture processes impart a parasitic load on the power plants; it is estimated that this would increase the cost of electricity from existing pulverized coal plants anywhere from 71-85 percent [1]. The National Energy and Technology Lab (NETL) is working to lower this to below a 30 percent increase. To reach this goal, work is being done not only to accurately simulate these processes, but also to leverage those accurate and detailedmore » simulations to design optimal carbon capture processes. The major challenges include the lack of accurate algebraic models of the processes, computationally costly simulations, and insufficiently robust simulations. The first challenge bars the use of provable derivative-based optimization algorithms. The latter two can either lead to difficult or impossible direct derivative-free optimization. To overcome these difficulties, we take a more indirect method to solving this problem by, first, generating an accurate set of algebraic surrogate models from the simulation then using derivative-based solvers to optimize the surrogate models. We developed a method that uses derivative-based and derivative-free optimization alongside machine learning and statistical techniques to generate the set of low-complexity surrogate models using data sampled from detailed simulations. The models are validated and improved through the use of derivative-free solvers to adaptively sample new simulation points. The resulting surrogate models can then be used in a superstructure-based process synthesis and solved using derivative-based methods to optimize carbon capture processes.« less
  • Abstract not provided.