Fuel spray modeling for application in internal combustion engines

• Ano de defesa: 2019
• Autor(a) principal: Ribeiro, Mateus Dias [UNESP]
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Estadual Paulista (Unesp)
• Palavras-chave em Português: Spray; CFD; LES; Internal Combustion Engine; Lagrangian-Eulerian approach; OpenFOAM; ANN; Motores de combustão interna; Fluidodinâmica computacional; Inteligência artificial
• Link de acesso: http://hdl.handle.net/11449/183102

Direct injection spark ignition (DISI) engines aim at reducing specific fuel consumption and achieving the strict emission standards in state of the art internal combustion engines. Therefore, in this work the goal is to develop code for simulations of the internal flow in DISI engines, as well as the phenomenon of fuel spray injection into the combustion chamber using a Lagrangian-Eulerian approach for representing the multiphase flow, and Large-eddy Simulations (LES) for modeling the turbulence of the continuum medium by means of the open-source CFD library OpenFOAM. In order to validate the obtained results and the developed models, experimental data from the Darmstadt optical engine, and the non-reactive “Spray G” gasoline injection case, along with the reactive “Spray A” case from the Engine Combustion Network (ECN) will be employed. Finally, a novel open-source solver will be proposed to simulate the Darmstadt optical engine in motored and fired operation under stratified mixture condition, using data compiled by the Darmstadt Engine Workshop (DEW) for validation. Moreover, a deep learning framework is presented to train an artificial neural network (ANN) with the engine LES data generated in this work, in order to make predictions of the small scale turbulence behavior.