Investigation and improvement of reaction mechanisms using sensitivity analysis and optimization

Alison S. Tomlin, T. Turányi

Research output: Chapter in Book/Report/Conference proceedingChapter

11 Citations (Scopus)

Abstract

The Chapter will describe a range of mathematical tools for model sensitivity and uncertainty analysis which may assist in the evaluation of large combustion mechanisms. The aim of such methods is to determine key model input parameters that drive the uncertainty in predicted model outputs. Approaches based on linear sensitivity, linear uncertainty and global uncertainty analysis will be described as well as examples of their application to chemical kinetic modelling in combustion. Improving the robustness of model predictions depends on reducing the extent of uncertainty within the input parameters. This can be achieved via a variety of methods including measurements and theoretical calculations. Optimization techniques which bring together wide sources of data can assist in further constraining the input parameters of a model and therefore reducing the overall model uncertainty. Such methods and their recent application to several combustion mechanisms will be described here.

Original languageEnglish
Title of host publicationGreen Energy and Technology
PublisherSpringer Verlag
Pages411-445
Number of pages35
Edition9781447153061
DOIs
Publication statusPublished - Jan 1 2013

Publication series

NameGreen Energy and Technology
Number9781447153061
ISSN (Print)1865-3529
ISSN (Electronic)1865-3537

Keywords

  • Combustion
  • Covariance
  • Enthalpy
  • H2O2
  • Methane

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Industrial and Manufacturing Engineering
  • Management, Monitoring, Policy and Law

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  • Cite this

    Tomlin, A. S., & Turányi, T. (2013). Investigation and improvement of reaction mechanisms using sensitivity analysis and optimization. In Green Energy and Technology (9781447153061 ed., pp. 411-445). (Green Energy and Technology; No. 9781447153061). Springer Verlag. https://doi.org/10.1007/978-1-4471-5307-8_16