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San Diego, CA, United States

Reaction Design is a San Diego-based developer of combustion simulation software used by engineers to design cleaner burning and fuel-efficient combustors and engines, found in everything from automobiles to turbines for power generation and aircraft propulsion to large diesel engines that use pistons the size of rooms to propel ships locomotives. The technology is also used to model spray vaporization in electronic materials processing applications and predict mixing reactions in chemical plants. ANSYS, a leader in engineering simulation software, acquired Reaction Design in January 2014. Wikipedia.


Westbrook C.K.,Lawrence Livermore National Laboratory | Naik C.V.,Reaction Design | Herbinet O.,University of Lorraine | Pitz W.,Lawrence Livermore National Laboratory | And 3 more authors.
Combustion and Flame | Year: 2011

A detailed chemical kinetic reaction mechanism is developed for the five major components of soy biodiesel and rapeseed biodiesel fuels. These components, methyl stearate, methyl oleate, methyl linoleate, methyl linolenate, and methyl palmitate, are large methyl ester molecules, some with carbon. carbon double bonds, and kinetic mechanisms for them as a family of fuels have not previously been available. Of particular importance in these mechanisms are models for alkylperoxy radical isomerization reactions in which a C. C double bond is embedded in the transition state ring. The resulting kinetic model is validated through comparisons between predicted results and a relatively small experimental literature. The model is also used in simulations of biodiesel oxidation in jet-stirred reactor and intermediate shock tube ignition and oxidation conditions to demonstrate the capabilities and limitations of these mechanisms. Differences in combustion properties between the two biodiesel fuels, derived from soy and rapeseed oils, are traced to the differences in the relative amounts of the same five methyl ester components. © 2010 The Combustion Institute. Source


Shi Y.,University of Wisconsin - Madison | Liang L.,Reaction Design | Ge H.-W.,University of Wisconsin - Madison | Reitz R.D.,University of Wisconsin - Madison
Combustion Theory and Modelling | Year: 2010

Acceleration of the chemistry solver for engine combustion is of much interest due to the fact that in practical engine simulations extensive computational time is spent solving the fuel oxidation and emission formation chemistry. A dynamic adaptive chemistry (DAC) scheme based on a directed relation graph error propagation (DRGEP) method has been applied to study homogeneous charge compression ignition (HCCI) engine combustion with detailed chemistry (over 500 species) previously using an R-valuebased breadth-first search (RBFS) algorithm, which significantly reduced computational times (by as much as 30-fold). The present paper extends the use of this on-the-fly kinetic mechanism reduction scheme to model combustion in direct-injection (DI) engines. It was found that the DAC scheme becomes less efficient when applied to DI engine simulations using a kinetic mechanism of relatively small size and the accuracy of the original DAC scheme decreases for conventional non-premixed combustion engine. The present study also focuses on determination of search-initiating species, involvement of the NOx chemistry, selection of a proper error tolerance, as well as treatment of the interaction of chemical heat release and the fuel spray. Both the DAC schemes were integrated into the ERC KIVA-3v2 code, and simulations were conducted to compare the two schemes. In general, the present DAC scheme has better efficiency and similar accuracy compared to the previous DAC scheme. The efficiency depends on the size of the chemical kinetics mechanism used and the engine operating conditions. For cases using a small n-heptane kinetic mechanism of 34 species, 30% of the computational time is saved, and 50% for a larger n-heptane kinetic mechanism of 61 species. The paper also demonstrates that by combining the present DAC scheme with an adaptive multi-grid chemistry (AMC) solver, it is feasible to simulate a direct-injection engine using a detailed n-heptane mechanism with 543 species with practical computer time. © 2010 Taylor & Francis. Source


Naik C.V.,Reaction Design | Westbrook C.K.,Lawrence Livermore National Laboratory | Herbinet O.,University of Lorraine | Pitz W.J.,Lawrence Livermore National Laboratory | Mehl M.,Lawrence Livermore National Laboratory
Proceedings of the Combustion Institute | Year: 2011

New chemical kinetic reaction mechanisms are developed for two of the five major components of biodiesel fuel, methyl stearate and methyl oleate. The mechanisms are produced using existing reaction classes and rules for reaction rates, with additional reaction classes to describe other reactions unique to methyl ester species. Mechanism capabilities were examined by computing fuel/air autoignition delay times and comparing the results with more conventional hydrocarbon fuels for which experimental results are available. Additional comparisons were carried out with measured results taken from jet-stirred reactor experiments for rapeseed oil methyl ester fuels. In both sets of computational tests, methyl oleate was found to be slightly less reactive than methyl stearate, and an explanation of this observation is made showing that the double bond in methyl oleate inhibits certain low temperature chain branching reaction pathways important in methyl stearate. The resulting detailed chemical kinetic reaction mechanism includes more approximately 3500 chemical species and more than 17,000 chemical reactions. © 2010 Published by Elsevier Inc. on behalf of The Combustion Institute. All rights reserved. Source


Patent
Reaction Design | Date: 2012-04-13

Method and apparatus for reducing chemical reaction mechanisms are disclosed. A method comprises obtaining data for one or more chemical species in a chemical reaction model from a database, the database including properties of the one or more chemical species; grouping the chemical species in a chemical reaction model into one or more isomer groups according to molecular properties of the chemical species; assigning a representative isomer to at least one isomer group; replacing, in one or more chemical reaction equations of the chemical reaction model, one or more groups of chemical species with a corresponding representative isomer; and executing the chemical reaction model by an apparatus to determine results.


Grant
Agency: National Science Foundation | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 499.24K | Year: 2004

This Small Business Innovation Research (SBIR) Phase II project proposes to create a robust software system for performing uncertainty analysis of process simulations for manufacturing. For simulations that are large or that contain many parameters, even the best Monte Carlo, or importance-based sampling methods for uncertainty analysis can be prohibitively expensive. Consequently, systematic uncertainty analyses are rarely implemented for complex systems. This proposal presents a plan to produce a commercially viable package of a new method for quantifying simulation uncertainty, based on polynomial chaos expansions. The method can determine the probability density functions of black-box model responses and can identify quantitatively which of the parameters contribute most to uncertainties in responses for multivariate inputs and outputs. The unique sampling approach enabled by the use of polynomial chaos expansions allows more accurate resolution of probability distribution functions at a very small fraction of the cost to achieve similar results with more traditional uncertainty-analysis methods. While illustrative examples from the chemical manufacturing industries will be used to demonstrate the software functionality, the methodology has broad application to such fields as circuit design, risk management, allocation of experimental resources, chemical plant design and operation of production systems. Due to the ability to handle arbitrary or black-box simulations, the methods can be applied as easily to economic market analysis, or global climate modeling, as to chemical process design.

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