Computational Mechanical Engineering for Automotive Design: Digital Twin Development, Generative Structural Optimization, and CFD-Based Vehicle Performance Evaluation
Author(s): Saahil
Publication #: 2603004
Date of Publication: 08.01.2026
Country: India
Pages: 1-18
Published In: Volume 12 Issue 1 January-2026
DOI: https://doi.org/10.62970/IJIRCT.v12.i1.2603004
Abstract
This article enunciates a conceptual architecture for AI-driven vehicle design anchored in digital twin ontology, constraint-satisfying generative synthesis, multi-fidelity CFD optimization, and virtual prototyping governance as a unified cyber-physical decision ecology. It specifies how vehicle twins evolve from exploratory representations to evidence-bearing instruments through bidirectional synchronization, calibration under identifiability constraints, and uncertainty-calibrated decision thresholds. It reframes generative design as feasibility-first synthesis within multi-disciplinary design optimization and Pareto-governed trade spaces, where geometry representations, manufacturability predicates, and semantic assembly constraints delimit admissible design manifolds. It conceptualizes CFD optimization as sequential resource allocation under computational scarcity, integrating surrogate inference, trust-region multi-fidelity regimes, and robustness logic to prevent brittle optima and distribution-shift collapse. It positions virtual prototyping as the institutional backbone that binds configuration control, traceability semantics, verification-validation-calibration discipline, and auditable sign-off into a digital thread suitable for globally distributed, multi-supplier programs. The article contributes by proposing a five-axis appraisal grammar that operationalizes credibility, interoperability, and accountability, thereby translating technical capability into governance-ready engineering practice for academics, policymakers, and technologists.
Keywords: Digital Twin, AI-Driven Design, Generative Design, Computational Fluid Dynamics, Multi-Fidelity Optimization, Surrogate Modeling, Virtual Prototyping, Topology Optimization, Cyber-Physical Systems, Robust Optimization.
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