Research and Publications - Department of Chemical & Biological Engineering

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Browsing Research and Publications - Department of Chemical & Biological Engineering by Author "Argonne National Laboratory"

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    Lightweight Chassis Design of Hybrid Trucks Considering Multiple Road Conditions and Constraints
    (MDPI, 2020) De, Shuvodeep; Singh, Karanpreet; Seo, Junhyeon; Kapania, Rakesh K.; Ostergaard, Erik; Angelini, Nicholas; Aguero, Raymond; University of Alabama Tuscaloosa; United States Department of Energy (DOE); Argonne National Laboratory; University of Colorado System; University of Colorado Boulder; Columbia University; University of Chicago; Fermi National Accelerator Laboratory; State University System of Florida; University of Florida; University of Hawaii System; University of Hawaii Manoa; Indiana University System; Indiana University Bloomington; Lancaster University; Los Alamos National Laboratory; Louisiana State University System; Louisiana State University; University of London; King's College London; Massachusetts Institute of Technology (MIT); Universidad Nacional Autonoma de Mexico; University of Michigan System; University of Michigan; University of Texas System; University of Texas Arlington; Virginia Polytechnic Institute & State University
    The paper describes a fully automated process to generate a shell-based finite element model of a large hybrid truck chassis to perform mass optimization considering multiple load cases and multiple constraints. A truck chassis consists of different parts that could be optimized using shape and size optimization. The cross members are represented by beams, and other components of the truck (batteries, engine, fuel tanks, etc.) are represented by appropriate point masses and are attached to the rail using multiple point constraints to create a mathematical model. Medium-fidelity finite element models are developed for front and rear suspensions and they are attached to the chassis using multiple point constraints, hence creating the finite element model of the complete truck. In the optimization problem, a set of five load conditions, each of which corresponds to a road event, is considered, and constraints are imposed on maximum allowable von Mises stress and the first vertical bending frequency. The structure is optimized by implementing the particle swarm optimization algorithm using parallel processing. A mass reduction of about 13.25% with respect to the baseline model is achieved.