OptEx: An integrated framework for experimental design and combustion kinetic model optimization

Zijun Zhou, Keli Lin, Yiru Wang, Jiaxing Wang, Chung K. Law, Bin Yang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Computational fluid dynamics (CFD) simulation in the design of combustion devices imposes increased demand on combustion kinetic models with acceptable uncertainties. Model optimization is often utilized to constrain the model parameters with experimental data to reduce the prediction uncertainties. Since it is unaffordable to conduct experiments under all the concerned conditions, experimental design approaches are proposed to find the most valuable experiments to be conducted. An integrated computational framework, OptEx (Optimal Experiments), is proposed to facilitate applications of experimental design, data clustering, and model optimization with optimal experimental data. Specifically, this framework integrates the functions of dimension reduction, global sensitivity analysis, forward uncertainty quantification, model-analysis-based experimental design, and model optimization. The share of data and surrogate models between different modules significantly improves the computational efficiencies of model analysis, experimental design and model optimization. Two case studies of a methanol system are utilized to demonstrate the functionalities of OptEx. First, experimental designs are performed based on sensitivity entropy and surrogate model similarity analysis to find out informative while independent experiments. Second, experimental data clustering with OptEx is demonstrated by grouping massive experiments and selecting optimal experiments for each group. For both cases, a small size of optimal experimental datasets are utilized for model optimization, yielding an optimized model with smaller uncertainty bounds. Meanwhile, the input parameter uncertainties are significantly reduced and parameter correlations are identified via the joint probability distributions of optimized parameters.

Original languageEnglish (US)
Article number112298
JournalCombustion and Flame
Volume245
DOIs
StatePublished - Nov 2022

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • General Chemical Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology
  • General Physics and Astronomy

Keywords

  • Combustion kinetic model
  • Data clustering
  • Experimental design
  • Integrated framework
  • Model optimization

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