## 6 Conclusions
We would like to thank [PERSON], Institute for Aircraft Design at the University of Stuttgart, Germany, for the support provided with the portfolio analysis in Appendix B and the team at the SHW Automotive GmbH for their support during the test bench measurements. Author [PERSON] was employed by the company SHW Automotive GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} BEV & Battery electric vehicle \\ CFD & Computational fluid dynamics \\ FEM & Finite element method \\ GHZ & Gumbel-Hersey number \\ digital DNA & in analogy to the notion of DNA in biology, a digital and machine-executable design representation, which, due its knowledge content and generation power, is the decisive part of the future assets in an engineering company \\ \end{tabular} \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Level** & **Error in \%** & **Model** & \(\mathbf{c_{1}}\) & \(\mathbf{c_{2}}\) & \(\mathbf{c_{3}}\) \\ \hline
1 & 92.748 & \(y=Re\) & - & - & - \\ \hline
2 & 92.418 & \(y=\sin(Re)\) & - & - & - \\
2 & 93.466 & \(y=\tan(Re)\) & - & - & - \\
2 & 100.000 & \(y=\cos(Re)\) & - & - & - \\ \hline
3 & 36.928 & \(y=c_{1}+Re\) & 0.1395 & - & - \\
3 & 50.476 & \(y=Re^{c_{1}}\) & 0.4809 & - & - \\
3 & 91.458 & \(y=c_{1}Re\) & 0.75 & - & - \\
3 & 95.767 & \(y=Re^{2}\) & - & - & - \\ \hline
4 & 25.356 & \(y=c_{1}\cos(Re)\) & 0.1832 & - & - \\
4 & 26.645 & \(y=c_{1}+\cos(Re)\) & \(-0.8129\) & - & - \\
4 & 27.711 & \(y=e^{c_{1}+Re}\) & -1.76 & - & - \\
4 & 27.713 & \(y=c_{1}e^{Re}\) & 0.1714 & - & - \\ \hline
5 & 24.033 & \(y=c_{1}Re^{c_{2}}\) & 0.232 & 0.0579 & - \\
5 & 25.390 & \(y=c_{1}(c_{2}+Re)\) & \(-0.0485\) & \(-3.80\) & - \\
5 & 25.390 & \(y=c_{1}+c_{2}Re\) 0.1843 & \(-0.0485\) & - & \\
5 & 25.770 & \(y=c_{1}+Re^{Pr}\) & 0.1823 & - & - \\ \hline
6 & 13.418 & \(y=c_{1}Re^{c_{2}}Pr\) & 0.0003988 & 0.6909 & - \\
6 & 13.466 & \(y=c_{2}Re^{c_{1}}Pr\) & 0.6901 & 0.0003987 & - \\
6 & 16.386 & \(y=c_{1}RePr\ln(Re)\) & \(-3.536\times 10^{-4}\) & - & - \\
6 & 17.738 & \(y=c_{1}+c_{2}RePr\) & 0.1206 & \(-4.578\times 10^{-4}\) & - \\ \hline
7 & 13.184 & \(y=c_{1}Pr\tan(Re^{c_{2}})\) & 0.0003831 & 0.6823 & - \\
7 & 13.332 & \(y=c_{1}Pr\tan(Re)^{c_{2}}\) & 0.0003947 & 0.6881 & - \\
7 & 13.344 & \(y=c_{1}ReRe^{c_{2}}Pr\) & \(3.977\times 10^{-4}\) & \(-0.3081\) & - \\ \hline
8 & 13.203 & \(\left[y=c_{1}Re^{c_{2}}Pr^{c_{3}}\right]\) & 0.000457 & 0.6787 & 0.9785 \\
8 & 13.206 & \(y=c_{1}Re^{c_{2}}Pr\tan(Re)\) & \(-3.915\times 10^{-4}\) & \(-0.3122\) & - \\
8 & 13.290 & \(y=c_{1}Re^{c_{2}}Pr\tan(Re)\) & \(-3.922\times 10^{-4}\) & \(-0.3133\) & - \\
8 & 13.343 & \(y=c_{1}Re^{c_{2}+Re}Pr\) & \(5.477\times 10^{-4}\) & 0.7469 & - \\ \hline
9 & 12.803 & \(y=c_{1}Pr(c_{3}+Re+Re^{c_{2}})\) & \(2.170\times 10^{-4}\) & 0.5571 & \(-0.0091\) \\
9 & 12.923 & \(y=c_{1}Pr^{c_{3}}\tan(Re)^{c_{2}}\) & 0.0005202 & 0.6673 & 0.9591 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Portfolio analysis based on a symbolic regression to determine possible correlations of the Nusselt number shown with 4 examples for each complexity level 1–9 with the chosen correlation highlighted (in red box).