A collaborative project between Nissan Technical Centre Europe, RBF Morph, and the University of Rome “Tor Vergata” showcases how multi-physics optimization is revolutionizing automotive wheel design, particularly for electric vehicles (EVs). By integrating styling, structural analysis, and aerodynamics within a unified workflow enabled by advanced mesh morphing technology (rbfCAE), designers can optimize wheels for lightweight, strength, and aerodynamic efficiency without compromising aesthetics.
The approach uses finite element analysis (FEA) for structural integrity and computational fluid dynamics (CFD) for aerodynamic performance, combined with two optimization methods: parametric morphing (user-controlled shape adjustments) and biologically inspired growth morphing (adaptive shape changes based on stress distribution). Both methods reduced wheel mass and improved performance metrics, with only slight aerodynamic trade-offs.
This digital, multi-physics platform fosters real-time collaboration between designers and engineers, accelerating development while ensuring robust, visually appealing wheel designs that meet the demands of modern EVs. The work also points to future integration of AI and reduced order models for even faster, smarter design cycles.
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CASE STUDY
This article is based on a collaboration between RBF Morph and AVIO to configure a numerical optimization procedure to improve the Vega E M10 engine’s performance by optimizing the methane circuit of the injector head.
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CASE STUDY
The article focuses on the difference in fatigue behaviour between rubber and metal materials. While metal fatigue is often described by a simple rule: increasing mean strain is detrimental to fatigue life, rubber fatigue is more complex and depends on the material's ability to strain crystallize. The text concludes that while tensile mean stresses are always detrimental in metals, in rubber they may be either beneficial or harmful depending on whether the rubber can strain crystallize.
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