Using active subspace-based similarity analysis for design of combustion experiments

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

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Experimental data under a wide range of conditions are essential for the optimization of combustion kinetic models. However, some laboratory measurements under given conditions may not be conducted due to the constraint of existing techniques. It is thus needed to employ the experimental data obtained under alternate conditions to improve the model predictions for the desired conditions. In this work, an active subspace-based similarity analysis is proposed as an experimental design method to find substitutes for experiments (or measurements) that are difficult to conduct. The eigenvalues and eigenvectors of the matrix that contains the gradient information of a model output with respect to inputs (matrix C of the active subspace) are used to calculate the cosine-based similarity of key reactions of two model targets. The method is demonstrated in three combustion systems, i.e., ignition of hydrogen/oxygen mixture, premixed flame of the dimethyl ether (DME), and C2H6/O2 systems in different reactors. The results show that if the similarity coefficient is large, the key reactions for the two model targets are similar, and the measurement of one target can improve the model prediction of the other target. In addition to designing experimental targets or conditions with strong constraint effects beforehand, this method can also be used to classify potential experimental targets/conditions.

Original languageEnglish (US)
Pages (from-to)5177-5186
Number of pages10
JournalProceedings of the Combustion Institute
Volume39
Issue number4
DOIs
StatePublished - Jan 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Chemical Engineering
  • Mechanical Engineering
  • Physical and Theoretical Chemistry

Keywords

  • Active subspace
  • Experimental design
  • Model optimization
  • Similarity analysis
  • Uncertainty quantification

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