## 3 Results
The regression analysis results for factors F, G, H, I, and J influencing temperature difference are provided in Table 4.  
The Pareto chart of standardized effects for pressure drop is presented in Figure 18. In this graph, it is observed that factor length of R11 has the highest impact on the uniformity index. Optimization has been performed to increase the uniformity index value while significantly reducing the pressure drop. Optimization has been carried out simultaneously for both the temperature difference and pressure drop based on the parameters specified in Table 5. Parameters affecting the efficiency of the fuel cell inlet and outlet geometries have been optimized based on data obtained from the pressure drop and UI values. With an increase in flow rates, extending the length of L1, and increasing the radius of R11, the UI value was increased by 3.68%, and the pressure drop was reduced by 7.69%. This optimization achieved pressure drop and UI increase values that current alternative designs could not reach. \begin{table}
\begin{tabular}{c c c c} \hline
**S** & **R-sq** & **R-sq (adj)** & **R-sq (pred)** \\ \hline
0.42338 & 89.41\% & 86.86\% & 87.5\% \\ \hline \end{tabular}
\end{table} 
Table 4: Model summary for uniformity index values. Figure 17: Graph depicting the signal-to-noise ratio of the uniformity index response based on factors and levels, obtained using the Minitab program (unit of \(X\)-axis is [mm]). In this study, validations of the models were performed with computational fluid dynamics results in Figure 19. For this purpose, a comparison between the design specified in the visual below--referred to as Design 20--and the optimized design is shown. The newly developed three-partitioned baffle heat exchanger was subsequently optimized using the Taguchi method. In the current study, the 1D model of the test bench was developed for investigating the performance of the system under different conditions. Therefore, the developed 1D model was correlated with the experimental data. The input current value of the test bench was a constant 5 Amper and the simulation time was 386 s. The ambient temperature was 10 \({}^{\circ}\)C. The results are presented in Figure 20. The maximum difference between the simulation results of the fan architecture and experiments was 6.5%, and for the compressor architecture the maximum error was 7.6%. Results also show the power of the fuel cell increased by 9.4%, meaning the compressor architecture boosted the power performance of the system. Figure 19: Comparison of pressure drop and UI CFD analysis results for the final geometry. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Target** & **Result** & **Optimization Result** & **Improvement** \\ \hline
**Pressure Drop (Pa)** & Minimum & 1522 Pa & 1405 Pa & 7.69\% \\ \hline
**UI** & Maximum & 0.897 & 0.93 & 3.68\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis results under the same conditions. Figure 18: Pareto chart of standardized effects for uniformity index. Using the validated mathematical model, a power variation graph corresponding to the variable current value was plotted. The profile of the variable current value, which is the input value of the model, was obtained by referencing the profile of the WLTC class 2 driving cycle and scaling it with a coefficient. The profile, adjusted to a maximum of 10 amperes, was compared to analyze the difference in the variable current profile between the two architectures. As seen in Figure 21, the architecture with a constant RPM compressor improved the power performance of the system by 10%. The simulation results presented in Figure 22 highlight the hydrogen consumption of different control configurations across a range of ambient temperatures (\(-20\)\({}^{\circ}\)C to 40\({}^{\circ}\)C). At low temperatures, the Compressor Variable (Comp V) configuration demonstrates the highest efficiency, achieving a 9.0% reduction in hydrogen consumption compared to the Fan Constant (Fan C) configuration.