# Impact of HPC and Automated CFD Simulation Processes on Virtual Product Development--A Case Study
## 2 Materials and Methods
### Implementation
Design iteration 0 represents the status quo at the beginning of the season. workflow. It is also worth considering that the overall performance gain would have been far less after one iteration in this particular year. The graphs also show setbacks throughout the development process which can lead to even better results or are easier to cope with. This opens the opportunity to explore designs that are even more experimental. Figure 6 shows the drag and downforce KPIs of the final racecar designs for the last three seasons. Compared to the previous season 18/19, downforce increased in 19/20 by 214% and drag was reduced by 17%. Since optimizations on the basis of full-scale simulations on the entire racecar were first enabled with HPC in season 19/20, setbacks of initial designs in previous seasons could hardly be improved. For example, the racecar of the season 18/19 was worse than the design in 17/18 due to missing computational power and the remaining time budget. HPC has also changed the way team members work. Table 2 shows the estimated time spent on the individual phases of the development workflow of a new design before and after HPC introduction. The reduction in waiting times, the elimination of the manual modification of design variables or the generation of reports allows a stronger focus on value-adding activities such as model development or testing. However, as a precaution, we would like to emphasize that this is merely a shift in expenditure. The time required to generate valid meshes, to verify and validate models, etc. is only being moved from the day-to-day operations to the implementation and deployment phase of the HPC optimization approach. However, this shifting leads to shorter development cycles and can increase the internal capability of an organization's R&D unit. Despite the high initial effort for such an approach, HPC and the ability to derive and implement adjustments or optimizations quickly can still be seen as a major competitive advantage, both on the product side and economically. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Process Step** & **Without HPC** & **With HPC** \\ \hline Initial meshing & 25\% & 0\% \\ Model preprocessing and verification & 35\% & 10\% \\ Computational phase 1 & 25\% & 85\% \\ Variation of design variables & 5\% & 0\% \\ Model postprocessing & 10\% & 5\% \\ \hline \hline \end{tabular}

* Not including model verification.

\end{table} 
Table 2: Estimated rel. temporal expenditure for design iterations w/o and w/ HPC. Figure 6: KPIs “drag” and “downforce” of final designs from the last three seasons. Without the possibility of more than one design iteration per season, the team was not able to mitigate setbacks such as those in the 18/19 season. ## 4 Discussion

Due to HPC, the formula student team increased the major KPI downforce up to 115% while also decreasing drag force by about 20% within one project lifetime or a formula season. However, there is still a lot to consider towards reasonable handling of HPC-based optimization procedures. The degree of automation and the costs of hardware and software licenses (if one is using proprietary software) must be weighed up accurately. For the iterative development of a single and parameterizable product, the approach shown is suitable since the initial implementation effort can be quickly amortized. However, if one conducts simulation not on a regular basis or uses simulation software in a more general manner on different products, such an implementation might be disproportionate. The most significant restriction on HPC-based product development is probably the automation of mesh generation. Since meshing can be considered as a critical, time-consuming and complex step, which can heavily affect the quality of results, the possible degree of automation is limited and considered a major bottleneck [22].