Use of federated object modeling to develop a macro-system model for the U.S. Department of Energy's hydrogen program
Pages 1438 - 1445
Abstract
The U.S. Department of Energy (DOE) is working on technology that could change our transportation fuel from gasoline to hydrogen. To assist in that effort, we are developing a macro-system model (MSM) that will link existing or developmental component models together to analyze crosscutting hydrogen issues. The MSM uses a federated simulation framework that extends the High Level Architecture (HLA). In this initial phase, three existing models have been linked to analyze two primary issues. The first issue we will examine will be the combined price of hydrogen production and delivery and the second will be a comparison of energy requirements and air emissions for multiple hydrogen production / delivery pathways (i.e., hydrogen produced from different feedstocks and transported via different means). Future work will involve linking other models to allow us to better analyze transition issues and making the MSM available to the hydrogen analysis community.
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Information
Published In
December 2006
2429 pages
ISBN:1424405017
- Editors:
- L. Felipe Perrone,
- Barry G. Lawson,
- Jason Liu,
- Frederick P. Wieland,
- General Chair:
- David Nicol,
- Program Chair:
- Richard Fujimoto
Sponsors
- IIE: Institute of Industrial Engineers
- ASA: American Statistical Association
- IEICE ESS: Institute of Electronics, Information and Communication Engineers, Engineering Sciences Society
- IEEE-CS\DATC: The IEEE Computer Society
- SIGSIM: ACM Special Interest Group on Simulation and Modeling
- NIST: National Institute of Standards and Technology
- (SCS): The Society for Modeling and Simulation International
- INFORMS-CS: Institute for Operations Research and the Management Sciences-College on Simulation
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Winter Simulation Conference
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Published: 03 December 2006
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- IIE
- ASA
- IEICE ESS
- IEEE-CS\DATC
- SIGSIM
- NIST
- (SCS)
- INFORMS-CS
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WSC '06 Paper Acceptance Rate 177 of 252 submissions, 70%;
Overall Acceptance Rate 3,413 of 5,075 submissions, 67%
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