Efficient Design of Shell-and-Tube Heat Exchangers Using CAD Automation and Fluid flow Analysis in a Multi-Objective Bayesian Optimization Framework

Shell-and-tube heat exchangers, commonly referred to as radiators, are the most prevalent type of heat exchanger within the automotive industry. A pivotal goal for automotive designers is to increase their thermal effectiveness while mitigating pressure drop effects and minimizing the associated costs of design and operation. Their design is a lengthy and intricate process involving the manual creation and refinement of computer-aided design (CAD) models coupled with iterative multi-physics simulations. Consequently, there is a pressing demand for an integrated tool that can automate these discrete steps, yielding a significant enhancement in overall design efficiency. This work aims to introduce an innovative automation tool to streamline the design process, spanning from CAD model generation to identifying optimal design configurations. The proposed methodology is applied explicitly to the context of shell-and-tube heat exchangers, showcasing the tool's efficacy. The automation of CAD tasks is facilitated through custom Python code, leveraging the CadQuery library to parameterize CAD models and expedite the CAD process. Meshing and Computational Fluid Dynamics (CFD) simulations are seamlessly integrated within a Python environment, utilizing Ansys Fluent. Concurrently, a multi-objective Bayesian optimization is executed using a Gaussian process regression model facilitated by the GPflow library. By significantly reducing the time required for design tasks, this automation tool addresses a critical challenge that has long persisted in the industry. The tool automates the design processes and identifies an optimal design for the Shell and Tube Heat Exchanger. The tool explores the design space for new non-dominant designs. Three new designs are added to the space, with two dominant and one non-dominant design, further improving the pareto front. Similarly, this tool can be applied to multidisciplinary fields to identify the optimal design quickly with less human intervention in the design process.