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The present work discusses the potential benefits of using computational fluid dynamics (CFD) simulation and artificial intelligence (AI) in the design and optimization of hydrogen internal combustion engines (H2ICEs). A Machine Learning (ML) model is developed and applied to the CFD simulation data to identify optimal injection system parameters on the Sandia H2ICE Engine to improve the mixing. This approach can aid in developing predictive ML models to guide the design of future H2ICEs. For the current engine configuration, it is observed that hydrogen (H2) gas injection contributes mixing of H2 with air. If the injector parameters are optimized, mixture preparation is better and eventually combustion. A base CFD model is validated from the Sandia H2ICE engine data against Particle Image Velocimetry (PIV) data for velocity and Planar Laser Induced Fluorescence (PLIF) data for H2 mass fraction.

Gerotor pump is a positive displacement pump unit which is widely used for lubrication in on-road and off-road engine applications. This paper is focused on Gerotor pump design competency established at ARAI comprising of design of inner and outer rotors, suction & delivery ports, optimizing inlet and outlet diameters & its position, development of methodology to calculate oil flow rate, volumetric efficiency, mechanical efficiency & slippage. The finalization of design is followed by CFD of Gerotor pump to optimize the pressure and flow pulsation. A trochoidal profile is used to design the inner and outer rotors and its conjugate profile are realized by a set of equations using a method based on the theory of gearing. Suction and delivery port is analytically designed based on the same design parameters of the trochoidal profile.