# Optimisation of Segregation Distances between Electric Cable Bundles Embedded in a Structure
## 6 Conclusions  
In this article, we developed a strategy to evaluate the optimal segregation distance between two electric cable bundles connecting two equipment to ensure their inter-compatibility. We first showed that this problem can be solved as an optimisation under constraints. We then proposed a new algorithm based on Monte Carlo samples with a Kriging surrogate model and the COBYLA optimisation method to find the optimal segregation distance. This approach was then applied on two realistic test-cases of electric cable bundle segregation. With a similar algorithm, one can also evaluate the risk to exceed the susceptibility thresholds for a segregation distance lower than the optimum distance, which will give cable bundle integrators possible justification for derogation. At this point, the process of optimisation was fully validated by brute force Monte Carlo analysis. The next step of this work will consist of comparing the simulated results to experimental results on a bundle installation configuration for which their definition, composition, installation, and electrical characterisation are fully controlled. The method proposed in this paper is a first step in order to make it one day a real industrial tool to help installation and derogate to predefined installation rules with quantitative justifications. The computer code based on this method and developed in the frame of the ANALYST project has been delivered to SAFRAN E&P for further evaluation on their industrial use-cases. As a perspective, there would be a real interest to assess our method against geometries of increasing complexity with additional varying parameters. We think for example of two bundles of different lengths or no longer parallel with the possibility to crossing each other. Finally, it seems that the mathematical concepts developed in this article could be extended in order to evaluate EMS at the scale of a whole network in order to optimise the design of entire cable harnesses made of several branches of cable bundles. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Brute Force Monte Carlo** & **COBYLA/Classical MC** & **COBYLA/Kriging** \\ \hline \(d_{opt}\) & 30 cm & 30 cm (\(\pm 1\) m) & 32 cm (\(\pm 1\) m) \\ \hline number of calls to \(G\) & 12,000 & 1304 (\(\pm 200\)) & 501 (\(\pm 200\)) \\ \hline computational time & 250 h & 30 h & 12 h \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Brute force, COBYLA/classical MC, and COBYLA/Kriging methods with \(F=[100\pm 25]\) MHz, heights and lengths varying. Figure 6: \(G\) function distribution: brute force analysis with \(F=[100\pm 25]\) MHz, heights and lengths varying. **Author Contributions:** Conceptualization, J.M., I.J. and J.-P.P. ; methodology, J.M., I.J. and J.-P.P. ; software, J.M. and S.B. ; validation, J.M., I.J., S.B. and J.-P.P. ; manuscript writing, J.M. and I.J. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Clean Sky 2 Joint Undertaking under grant agreement No. 821128. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Acknowledgments:** This project received funding from the Clean Sky 2 Joint Undertaking under the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 821128. Uncertainty analysis was performed with the open-source Python library OpenTURNS [14]. **Conflicts of Interest:** The authors declare no conflict of interest.