Explicit stochastic ODE solution methods applied to high-temperature combustion
Mosbach S. and
Kraft M.
Additional contact information
Mosbach S.: Department of Chemical Engineering, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, UK; Email: , mk306@cam.ac.uk sm453@cam.ac.uk
Kraft M.: Department of Chemical Engineering, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, UK; Email: , mk306@cam.ac.uk sm453@cam.ac.uk
Monte Carlo Methods and Applications, 2006, vol. 12, issue 1, 19-45
Abstract:
New stochastic algorithms for the numerical solution of systems of ordinary differential equations (ODEs) are proposed. Furthermore, a correspondence principle is established between these algorithms, which are based on the theory of Markov jump processes, and deterministic schemes. For one of the proposed stochastic algorithms, a detailed numerical study of some of its properties is carried out using examples from high-temperature homogeneous gas-phase combustion. One deterministic method yielded by our correspondence principle is used to shed light on various aspects of the considered stochastic algorithm. In addition, we use the widespread stiff ODE-solver package DASSL for comparison. Advantages of our methods include among others their exceptional simplicity of implementation and negligible start-up costs. For a large system at moderate accuracy requirements, the proposed stochastic algorithms exhibit computational efficiency in the same order of magnitude as implicit solvers, assuming multiple runs. In view of the stiffness of the considered systems and the explicit nature of our algorithms, this is rather surprising. Limitations of our methods concerning the choice of system, initial conditions, and accuracy requirements are also addressed.
Keywords: Numerical ODE solution; explicit methods; stochastic methods; combustion; ignition (search for similar items in EconPapers)
Date: 2006
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
https://doi.org/10.1515/156939606776886670 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bpj:mcmeap:v:12:y:2006:i:1:p:19-45:n:5
Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/mcma/html
DOI: 10.1515/156939606776886670
Access Statistics for this article
Monte Carlo Methods and Applications is currently edited by Karl K. Sabelfeld
More articles in Monte Carlo Methods and Applications from De Gruyter
Bibliographic data for series maintained by Peter Golla ().