Reverse Engineering and Digital Twins in Powertrain Design: Enhancing Performance and Sustainability

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 and a 15% weight reduction in an EV motor housing through topology optimization. The proposed approach not only accelerates R&D cycles but also supports circular economy principles by facilitating the remanufacturing of legacy components. This work contributes to the ongoing shift toward sustainable mobility by bridging the gap between legacy engineering and next-generation powertrain innovation.