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Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy, 2005.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Additional Creators
Prairie View A & M University, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
This is an annual technical report for the work done over the last year (period ending 9/30/2005) on the project titled ''Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks''. The aim of the project is to develop an efficient chemistry model for combustion simulations. The reduced chemistry model will be developed mathematically without the need of having extensive knowledge of the chemistry involved. To aid in the development of the model, Neural Networks (NN) will be used via a new network topology know as Non-linear Principal Components Analysis (NPCA). We report on the development of a novel procedure to speed up the training of NPCA. The same procedure termed Lâ‚‚Boost can be used to increase the order of approximation of the Generalized Regression Neural Network (GRNN). It is pointed out that GRNN is a basic procedure for the emerging mesh free CFD. Also reported is an efficient simple approach of computing the derivatives of GRNN function approximation using complex variables or the Complex Step Method (CSM). The results presented demonstrate the significance of the methods developed and will be useful in many areas of applied science and engineering.
Report Numbers
E 1.99:875887
Subject(s)
Other Subject(s)
Note
Published through SciTech Connect.
12/01/2005.
Nelson Butuk.
Type of Report and Period Covered Note
annual;
Funding Information
FG26-03NT41913
View MARC record | catkey: 14393028