Browse Topic: Finite element analysis
This study explores the application of reverse engineering (RE) and digital twin (DT) technology in the design and optimization of advanced powertrain systems. Traditional approaches to powertrain development often rely on legacy designs with limited adaptability to modern efficiency and emission standards. In this work, we present a methodology combining 3D scanning, computational modeling, and machine learning to reconstruct, analyze, and enhance internal combustion engines (ICEs) and electric vehicle (EV) drivetrains. By digitizing physical components through RE, we generate high-fidelity DT models that enable virtual testing, performance prediction, and iterative improvement without costly physical prototyping. Key innovations include a novel mesh refinement technique for scanned geometries and a hybrid simulation framework integrating finite element analysis (FEA) and multi-body dynamics (MBD). Our case study demonstrates a 12% increase in thermal efficiency for a retrofitted ICE
The rapid advancement of electric vehicle (EV) technology has created a demand for reliable and Thermal - efficient electronic components for power electronics and control systems on printed circuit boards (PCBs). The research looks at the overall simulation and study of a PCB for Electric Vehicles, including how it handles heat, stress, and reliability in real working conditions like considering casing (Heat Sink) in which PCB is held, into the simulation. We have used numerical based methods (reliability), Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD) methods to simulate heat performance looking at steady-state and changing load profiles common in EV powertrains. We ran structural and thermal simulations to check the PCB's toughness against heat expansion and shaking loads often seen in cars. We also did a reliability check looking at heat cycling life for PCB components, and possible ways it could break to guess long-term toughness. The results show critical
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