This conference paper was submitted for presentation at the NAFEMS World Congress 2025, held in Salzburg, Austria from May 19–22, 2025.

Abstract

In today'™s highly competitive landscape, engineering organizations face increasing pressure to deliver complex, high-performing products at an accelerated pace, all while managing tighter margins. To maintain an edge, teams must shorten design cycles and enhance product performance and quality simultaneously. However, traditional simulation and optimization tools often fall short of meeting the demands of today'™s fast-moving production environments. Design teams struggle to fully utilize the insights from these tools and seamlessly integrate them into their workflows. Engineering Intelligence (EI) represents a significant advancement, leveraging modern AI techniques to overcome these challenges. EI enables a paradigm shift by offering multi-physics and multi-component generative design, more flexible automation, and the ability to harness historical data to expedite simulations. Adopting EI offers a transformative approach, integrating simulation, CAD, and engineering data into an intelligent, cohesive platform that accelerates the product development lifecycle from concept to market. By drawing on enterprise engineering knowledge and existing processes, EI facilitates near-autonomous design, significantly reducing development time. At the heart of this transformation is a new breed of engineers'”the CAE Data Scientists'”who blend simulation expertise with data science skills. To address the engineering challenge of accelerating multi-hour modal analysis, Neural Concept in collaboration with AVL developed a 3D deep learning surrogate model to predict results within seconds. The model takes 3D bracket geometries (used in engine compartments) as input and predicts the high-fidelity 3D stress field (in MPa) and the first four frequency modes. Using 62 training samples and 11 testing samples, a proprietary 3D deep learning architecture based on geodesic convolutions was trained to replace simulations for new geometries. Key results demonstrate exceptional accuracy, with frequency mode errors below 0.2 Hz (well within the 1 Hz workflow threshold) and stress errors averaging below 2 MPa, with extreme value errors under 3 MPa. An ablation study revealed that as few as 10 training samples would suffice for this level of precision. By leveraging data science tools in engineering and upskilling engineers as CAE data scientists, significant gains in efficiency and design capabilities can be achieved. A key limitation of this approach is the model'™s reliance on the boundaries of its training design space, which constrains its applicability to broader scenarios. Overcoming this challenge requires not only a deeper understanding of the data and problem space but also the deployment of advanced engineering intelligence tools such as generative design and automated re-simulation. AVL is planning to implement these tools at scale, to expand the model'™s robustness and adaptability, enabling it to address a wider range of use cases effectively. Moreover, this case study highlights the transformative role of workforce upskilling in engineering. The integration of AI into CAE workflows requires a new breed of professionals: CAE data scientists capable of bridging engineering expertise with advanced data science techniques. By fostering such skills, we can unlock the full potential of data science for a wider variety of engineering challenges. The project highlighted efficient onboarding, with a young engineer at AVL rapidly building their own engineering intelligence workflows.