Session: Research Posters

Paper Number: 166153

Simulation-Driven Design Optimization for Additive Manufacturing in Formula Student Racecars 

In the highly competitive world of Formula Student racing, every gram of weight saved directly enhances vehicle performance, driving engineering teams to optimize their designs within tight development cycles. Advanced computational methods enable them to identify optimal solutions across various scenarios, load cases, and configurations. Given the increasing complexity of these designs and their short production runs—often lasting only a single race season—additive manufacturing (AM) emerges as an ideal production method. This study highlights the transformative role of simulation-driven design in motorsports, demonstrating how computationally efficient optimization techniques can push the boundaries of lightweight design while ensuring structural integrity. By integrating novel optimization strategies with AM-specific design considerations, this work presents an end-to-end workflow that enhances both manufacturability and reliability. 

This study presents real-world case studies of Formula Student components optimized using a novel GPU-accelerated Level-Set method alongside conventional CPU-based topology optimization. The research highlights the advantages and limitations of each method in terms of available optimization objectives, constraints, and computational efficiency. Furthermore, it integrates a complete simulation-driven design workflow tailored for metal additive manufacturing, ensuring a seamless transition from conceptual design to production. As a case study,end-to-end design optimization workflow of a bellcrank – from structural optimization to validation, additive manufacturing process simulation to manufacturing, was performed.

This research provides valuable insights into the comparative performance of GPU-accelerated Level-Set methods and traditional CPU-based topology optimizatio, the integration of Design for Additive Manufacturing (DfAM) principles to enhance manufacturability and minimize print failures, and the role of process simulation and validation in achieving high-accuracy, first-time-right prints. 

By incorporating process-aware optimization and predictive validation, this methodology reduces costly trial-and-error iterations and enhances the reliability of AM-fabricated components.  

The GPU-based Level-Set topology optimization significantly reduces computation times compared to the CPU-based solver, enabling rapid design iteration in the early stages of development. The transition from optimized geometry to CAD solids using subdivision modeling facilitates easier downstream processing and structural validation. Furthermore, transient thermal-structural analysis has proven effective in predicting and mitigating distortion-related failures before manufacturing, leading to improved part accuracy and reduced material waste. This study provides a comprehensive blueprint for simulation-driven design optimization tailored for metal AM, enabling Formula Student teams and other high-performance engineering applications to achieve first-time-right prints. By integrating optimization, validation, and process simulation, engineers can make more informed decisions regarding design constraints, manufacturability, and overall performance. It also underscores the importance of simulation-driven design in advancing lightweight engineering and manufacturability for motorsports and beyond. The integration of novel computational methods with AM process considerations provides a robust framework for developing high-performance components efficiently. This work demonstrates how a fully digital workflow can enhance design accuracy, minimize production risks, and accelerate the adoption of AM in motorsports and other high-performance applications. 

Presenting Author: Alaa Olleak Ansys, Inc.

Presenting Author Biography: Alaa Olleak is a Senior Application Engineer at Ansys, specializing in Manufacturing Simulations. Before joining Ansys, he was a postdoctoral associate at the University of Pittsburgh and earned his Ph.D. from Rutgers University. Alaa has led and co-authored multiple research papers in additive and subtractive manufacturing.

Authors:

Janos Plocher Ansys, Inc.
Sebastian Stahn Ansys,Inc.
Jonas Pagel Technische Universität Berlin
Markus Hofmann Ostbayerische Technische Hochschule Amberg-Weiden
Luis Atzenhofer Ostbayerische Technische Hochschule Amberg-Weiden
Alaa Olleak Ansys, Inc.
Jimmy He Ansys, Inc.
Ali Najafi Ansys, Inc.
Hesam Moghaddam Anys,Inc.