Predictive simulations of ammonia spray dynamics and multi-regime combustion: fundamental physics and modeling aspects

  • Lorenzo Angelilli

Student thesis: Doctoral Thesis


Because of its thermochemical qualities, ammonia is an attractive alternative to carbon-based fuels. Indeed, the lack of carbon atoms in its molecular structure and the ease of storage make its widespread use desirable. However, there are a number of technological challenges that must be overcome due to the slow burning rate and its large latent heat. The objective of the dissertation is to model ammonia spray flames because direct liquid fuel injection in a combustion chamber is an essential aspect of the design of practical devices. The topic has been divided into a number of sub-problems, which are examined in each chapter of the thesis, due to the lack of fundamental physical details of the individual processes occurring and modeling considerations that cannot be ignored anymore.To better understand how the large latent heat affects the spray dynamics, a campaign of direct numerical simulations is initially performed at various ambient temperatures. Then, conducting large eddy simulations is preferred to lower the computational cost. The assessment of the dispersion models showed that the available options, however, are unable to reproduce the averaged droplet distribution across the entire domain and an improved model is proposed. Droplet evaporation causes local inhomogeneities in the mixture, which simultaneously induces multiple combustion modes. The Darmstadt Multi-Regime Burner (MRB) was the ideal candidate to investigate the physical aspects in advance. The best option for capturing its flame structure was the physically-derived multi-modal manifold and a regime classification index is formulated and tested on the MRB.Then, a machine learning strategy based on neural networks is suggested to quicken the look-up procedure, and preliminary validation of the methodology revealed that a time reduction of 30% is achieved without affecting the results' accuracy.
Date of AwardJun 2023
Original languageEnglish (US)
Awarding Institution
  • Physical Sciences and Engineering
SupervisorHong G. Im (Supervisor)


  • DNS
  • LES
  • Eulerian-Lagrangian spray
  • UQ
  • CSP
  • Machine Learning
  • Tabulated Chemistry

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