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Modelling fuel injector spray characteristics in jet engines by using vine copulas

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  • Maximilian Coblenz
  • Simon Holz
  • Hans‐Jörg Bauer
  • Oliver Grothe
  • Rainer Koch

Abstract

The emission requirements for jet engines are becoming more stringent and the combustion process determines pollutant emissions. Therefore, we model the distribution of fuel drops generated by a fuel injector in a jet engine, which can be assumed to be a five‐dimensional problem in terms of drop size, x‐position, y‐position, x‐velocity and y‐velocity. The data are generated by numerical simulations of the fuel atomization process for several jet engine operating conditions. In combustion simulations, the variables are usually assumed to be independent at the start of the simulation, which is clearly not so as our data show. The dependence between some of the variables is non‐monotone and asymmetric, which makes the modelling task difficult. Our aim is to provide a realistic parametric model for the dependence structure. For this, we employ vine copulas which provide a flexible way to construct a multivariate distribution function. However, we need to use non‐standard bivariate copulas as building blocks. Using this copula representation enables us to create realistic samples of fuel spray droplets which improve the prediction of the combustion process and the pollutant emissions. Moreover, this approach is significantly faster than solving the set of differential equations describing fuel disintegration.

Suggested Citation

  • Maximilian Coblenz & Simon Holz & Hans‐Jörg Bauer & Oliver Grothe & Rainer Koch, 2020. "Modelling fuel injector spray characteristics in jet engines by using vine copulas," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 863-886, August.
    • Handle: RePEc:bla:jorssc:v:69:y:2020:i:4:p:863-886
    DOI: 10.1111/rssc.12421
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    1. Markus Wicker & Cihan Ates & Max Okraschevski & Simon Holz & Rainer Koch & Hans-Jörg Bauer, 2023. "Modeling Multivariate Spray Characteristics with Gaussian Mixture Models," Energies, MDPI, vol. 16(19), pages 1-15, September.

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