# Impact of HPC and Automated CFD Simulation Processes on Virtual Product Development--A Case Study
## 2 Materials and Methods
### Implementation
Current mesh automation approaches such as conducting mesh generation and manipulation on an HPC's \"super node\" are widely considered unpractical, especially for high-fidelity meshes [23], and lack acceptance. Although the meshing process can benefit from automation, user interaction is still needed for preparing geometry, choosing proper cells or improving the mesh's quality. Even with current defeaturing methods and adaptive mesh refinement, preparing the initial mesh generation remains one of the most time-consuming steps throughout the CFD process and highly depends on specialist knowledge and experience. Therefore, complete automation is neither currently possible nor reasonable, as argued. Besides mesh generation, the CFD process does benefit from automation on HPC systems. The reason for that is that these problems are solved by sophisticated software, which typically has a time-consuming set up phase that rarely differs throughout the development of a specific product. Several studies (e.g., [24]) state that CFD solvers can also benefit from general-purpose computation on GPU (GPGPU) acceleration in principle. However, we could not confirm a significant effect. Although the manufacturer of the GPUs of the \"cuda\" nodes cooperates with the software vendor and advertises possible speedup factors up to 3.7 [25], we could not achieve such a significant speedup by the use of GPGPU acceleration. It turns out that these high speedups are limited to particular cases, physical preferences and machines with a considerably high number of GPGPUs, which might lead to bad cost-benefit ratios. Unfortunately, this fact is not self-evident, and a bit misleading since the official user guide for graphic-accelerated runs only states a comprehensive formula to calculate the maximum mesh size. Consequently, no overall statement on the effect of GPGPU acceleration can be made but GPGPU acceleration is still a promising current trend towards increasing the overall performance of HPC systems and should always be considered. It is clear that CFD simulations are not an end in themselves and require a lot of expertise. However, the latest developments in artificial intelligence (AI) and machine learning can assist the user further in the future. Current research shows that AI can already support the generation of problem-adapted geometries and models [26]. The evaluation of the mesh quality, which is now mostly conducted by the user, could also be automated with the help of AI [27]. The practical applicability of AI-based meta-modeling is also continuously increasing [28], making AI a promising perk for the HPC and simulation-driven product development of the future. Whether with or without AI support, high-performance computing and automation enable organizations to fundamentally speed up and improve their product development processes. This not only results in more design iterations in less time, but also in a solid increase in the product's performance and/or quality, as indicated by our case study. Of course, the general validity of the results is limited, since the subject of the study was a formula student racing team. Therefore, the results need to be verified with real company data. However, considering the usually small data basis in this research field due to their sensitivity and most companies' healthy skepticism about revealing them, thisarticle still proves the positive effects of HPC for product development and indicates that a combination of HPC and automation is beneficial for product development. Therefore, this research can serve as a reference and help to facilitate the acceptance of HPC and automation, especially among SMEs. Conceptualization, C.L. ; methodology, C.L. ; software, P.B. ; validation, C.L., P.B. and T.R. ; formal analysis, C.L. and T.R. ; investigation, C.L., P.B. and T.R. ; resources, S.T. and F.R. ; data curation, C.L. and P.B. ; writing--original draft preparation, C.L. and P.B. ; writing--review and editing, C.L., T.R. and S.T. ; visualization, T.R. and C.L. ; supervision, S.T.