Leveraging Large Language Models for CAD Automation and Multi-Objective Optimization of Automotive Heat Exchangers

The design of automotive heat exchangers is a critical challenge, requiring the careful balance of thermal performance, aerodynamic efficiency, structural integrity, and manufacturability. Traditional design processes rely heavily on manual CAD modeling and iterative simulations, which are both time-intensive and computationally demanding. Recent advances in artificial intelligence, particularly in Large Language Models (LLMs) and Transformer-based architectures, open new possibilities for automating these tasks. This work introduces a novel framework that integrates LLMs with CAD kernels to generate parametric heat exchanger geometries directly from natural language descriptions. The models are automatically exported into standard formats for computational fluid dynamics simulations and seamlessly incorporated within a multi-objective optimization pipeline. By coupling natural language-driven CAD automation with advanced optimization techniques, the framework enables rapid exploration of design alternatives, reduces human intervention, and accelerates the identification of Pareto-optimal automotive heat exchanger configurations. Case studies demonstrate the framework’s potential to streamline early-stage design, highlighting its advantages in efficiency, adaptability, and integration with existing engineering workflows. This study marks a step toward the next generation of AI-assisted engineering design, where LLM-powered automation complements traditional simulation and optimization methods.