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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.

A common scenario in engineering design is the evaluation of expensive black-box functions: simulation codes or physical experiments that require long evaluation times and/or significant resources, which results in lengthy and costly design cycles. In the last years, Bayesian optimization has emerged as an efficient alternative to solve expensive black-box function design problems. Bayesian optimization has two main components: a probabilistic surrogate model of the black-box function and an acquisition functions that drives the design process. Successful Bayesian optimization strategies are characterized by accurate surrogate models and well-balanced acquisition functions. The Gaussian process (GP) regression model is arguably the most popular surrogate model in Bayesian optimization due to its flexibility and mathematical tractability. GP regression models are defined by two elements: the mean and covariance functions.

The design of better active materials for lithium-ion batteries (LIBs) is crucial to satisfy the increasing demand of high performance batteries for portable electronics and electric vehicles. Currently, the development of new active materials is driven by physical experimentation and the designer’s intuition and expertise. During the development process, the designer interprets the experimental data to decide the next composition of the active material to be tested. After several trial-and-error iterations of data analysis and testing, promising active materials are discovered but after long development times (months or even years) and the evaluation of a large number of experiments. Bayesian global optimization (BGO) is an appealing alternative for the design of active materials for LIBs. BGO is a gradient-free optimization methodology to solve design problems that involve expensive black-box functions. An example of a black-box function is the prediction of the cycle life of LIBs.

This work presents the implementation of the Efficient Global Optimization (EGO) approach for the design of composite materials under dynamic loading conditions. The optimization algorithm is based on design and analysis of computer experiments (DACE) in which smart sampling and continuous metamodel enhancement drive the design towards a global optimum. An expected improvement function is maximized during each iteration to locate the designs that update the metamodel until convergence. The algorithm solves single and multi-objective optimization problems. In the first case, the penetration of an armor plate is minimized by finding the optimal fiber orientations. Multi-objective formulation is used to minimize the intrusion and impact acceleration of a composite tube. The design variables include the fiber orientations and the size of zones that control the tube collapse.

This work proposes a new method to design crashworthiness structures that made of functionally graded cellular (porous) material. The proposed method consists of three stages: The first stage is to generate a conceptual design using a topology optimization algorithm so that a variable density is distributed within the structure minimizing its compliance. The second stage is to cluster the variable density using a machine-learning algorithm to reduce the dimension of the design space. The third stage is to maximize structural crashworthiness indicators (e.g., internal energy absorption) and minimize mass using a metamodel-based multi-objective genetic algorithm. The final structure is synthesized by optimally selecting cellular material phases from a predefined material library. In this work, the Hashin-Shtrikman bounds are derived for the two-phase cellular material, and the structure performances are compared to the optimized structures derived by our proposed framework.

This work introduces a new design algorithm to optimize progressively folding thin-walled structures and in order to improve automotive crashworthiness. The proposed design algorithm is composed of three stages: conceptual thickness distribution, design parameterization, and multi-objective design optimization. The conceptual thickness distribution stage generates an innovative design using a novel one-iteration compliant mechanism approach that triggers progressive folding even on irregular structures under oblique impact. The design parameterization stage optimally segments the conceptual design into a reduced number of clusters using a machine learning K-means algorithm. Finally, the multi-objective design optimization stage finds non-dominated designs of maximum specific energy absorption and minimum peak crushing force.