## 3 Results
### Response Surface
 \tag{6}\]  
Figure 14 shows the surface graphs obtained by evaluating the FFD's regression polynomial as a function of two factors. The surface graphs of the FCCD's regression polynomial are appreciably similar. To determine the geometric proportions that allow the maximum performance, the point at which the derivative value of the regression polynomials becomes zero must be found. Figure 15 offers a better appreciation of the surfaces stationary points shown in the previous figure. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Term**} & \multicolumn{4}{c}{**FFD**} & \multicolumn{4}{c}{**FCCD**} \\  & **Coefficient** & **Std. Error** & \(t\)**-Value** & \(p\)**-Values** & **Coefficient** & **Std. Error** & \(t\)**-Value** & \(p\)**-Values** \\ \hline Intercept & \(-49.703\) & \(7.6762\) & \(-6.475\) & \(0.0000571\) & \(-60.989\) & \(13.714\) & \(-4.4473\) & \(0.006719\) \\ \(O\): Tip diameter & \(1.0068\) & \(0.15629\) & \(6.4419\) & \(6.082\times 10^{6}\) & \(1.232\) & \(0.2805\) & \(4.3923\) & \(0.007073\) \\ \(P\): Inlet gap & \(0.049454\) & \(0.028425\) & \(1.7398\) & \(0.0999685\) & \(0.076752\) & \(0.041483\) & \(1.8502\) & \(0.123515\) \\ \(Q\): Outlet gap & \(0.1978\) & \(0.056851\) & \(3.4794\) & \(0.0028696\) & \(0.20925\) & \(0.082966\) & \(2.5221\) & \(0.053041\) \\ \(O:P\) & \(-0.000442\) & \(0.00028169\) & \(-1.5691\) & \(0.1350558\) & \(-0.0007616\) & \(0.00040546\) & \(-1.8783\) & \(0.119137\) \\ \(O:Q\) & \(-0.0021635\) & \(0.00056338\) & \(-3.8401\) & \(0.0013114\) & \(-0.0024408\) & \(0.00081092\) & \(-3.0099\) & \(0.029758\) \\ \(P:Q\) & \(0.002614\) & \(0.00028169\) & \(9.2796\) & \(4.579\times 10^{8}\) & \(0.0026814\) & \(0.00040546\) & \(6.6133\) & \(0.001189\) \\ \(O^{2}\) & \(-0.0050518\) & \(0.00079675\) & \(-6.3405\) & \(7.387\times 10^{6}\) & \(-0.0061646\) & \(0.0014303\) & \(-4.31\) & \(0.007643\) \\ \(P^{2}\) & \(-0.0009056\) & \(0.00019919\) & \(-4.5466\) & \(0.0002858\) & \(-0.0007783\) & \(0.00035758\) & \(-2.1765\) & \(0.081468\) \\ \(Q^{2}\) & \(-0.0018406\) & \(0.00079675\) & \(-2.3102\) & \(0.0336938\) & \(-0.0009675\) & \(0.0014303\) & \(-0.6764\) & \(0.528804\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Test statistics for each factor and experimental design results. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Term**} & \multicolumn{4}{c}{**FFD**} & \multicolumn{4}{c}{**FCCD**} \\  & **DOF** & **SS** & **MS** & \(F\)**-Value** & \(p\)**-Values** & **DOF** & **SS** & **MS** & \(F\)**-Value** & \(p\)**-Values** \\ \hline FO (\(O\), \(P\), \(Q\)) & 3 & \(5.59\times 10^{3}\) & \(1.86\times 10^{3}\) & \(30.549\) & \(4.52\times 10^{7}\) & 3 & \(3.08\times 10^{3}\) & \(1.03\times 10^{3}\) & \(12.185\) & \(9.78\times 10^{3}\) \\ TWI (\(O\), \(P\), \(Q\)) & 3 & \(6.30\times 10^{3}\) & \(2.10\times 10^{3}\) & \(34.44\) & \(1.92\times 10^{7}\) & 3 & \(4.74\times 10^{3}\) & \(1.58\times 10^{3}\) & \(18.774\) & \(3.75\times 10^{3}\) \\ PQ (\(O\), \(P\), \(Q\)) & 3 & \(4.04\times 10^{3}\) & \(1.34\times 10^{3}\) & \(22.07\) & \(4.26\times 10^{6}\) & 3 & \(3.80\times 10^{3}\) & \(1.27\times 10^{3}\) & \(15.031\) & \(6.18\times 10^{3}\) \\ Residuals & 17 & \(1.04\times 10^{3}\) & \(6.09\times 10^{5}\) & & & 5 & \(4.21\times 10^{4}\) & \(8.42\times 10^{5}\) & & \\ Lack of fit & 17 & \(1.04\times 10^{3}\) & \(6.09\times 10^{5}\) & & & 5 & \(4.21\times 10^{4}\) & \(8.42\times 10^{5}\) & & \\ Pure error & 0 & 0 & & & 0 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: ANOVA for the regression models adjusted for the FFD and the FCCD. (DOF), (SS), and (MS) refers to degrees of freedom, sum of squares, and mean squares, respectively. The coordinates of the global stationary points for each experimental design, in which all the factors of each model simultaneously have zero slope, are comparatively shown in Table 10. Substituting these coordinates in each regression polynomial results in a modeled \(C_{P}\).