## 3 Results and Discussion
The rules ensure that the turbine operates within a defined range of physical conditions that are conducive to energy generation and do not compromise the canal's structural integrity. The rule base follows a logic structure that considers the following:  
* The worst-case scenarios first, where if either of the inputs is \"Low\", the success probability is also \"Low\". * A functional but limited system next, where both inputs are at least at a \"Mid\" level, resulting in a success probability that is moderate but not optimal. * The best-case scenarios last, where high levels of either one or both inputs lead to a high probability of successful operation. These rules are constructed using a combination of AND/OR operators, reflecting different possible scenarios that might be encountered in the canal's environment. The rules are modeled to cover a comprehensive range of conditions, ensuring that the fuzzy logic system can provide a nuanced assessment of the turbine installation's viability. The use of Mamdani-type fuzzy inference allows for a more intuitive, humanlike processing of information, enabling the system to handle the complexity and uncertainty of real-world applications in a structured manner. The flowchart based on which the rules processed by the fuzzy logic system are developed is presented in Figure 8.  
The displayed results in Figure 9 showcase the output from the fuzzy logic models for Turbine 1 and Turbine 2. These 3D plots graphically represent the relationship between the water depth (\"Deep\"), flow speed (\"Speed\"), and resulting \"SuccessProbability\" for each turbine model. The objective of these models is to validate the operational range of the turbines in conjunction with the potential water conditions in the Canal de Castilla. From a global perspective, the models reveal a majority of successful outcomes within the typical  
Figure 7: Comparative fuzzy logic models for axial turbine selection in the Canal de Castilla. operational range, indicating that both turbines are likely to perform well under the canal's usual conditions. However, Turbine 1 exhibits a slightly higher index of success across most cases compared to Turbine 2, though the difference in performance is relatively small. This suggests that while both turbines are viable options, Turbine 1 may offer a marginally better fit for the canal's specific environmental and operational parameters. Figure 10 illustrates the specific outputs of the fuzzy logic models for Turbine 1 and Turbine 2 when analyzing the conditions of 0.5 m depth and 1.9 m/s speed as base case conditions. The similarity in results for both turbines under these conditions is strikingly high, as depicted by the peak in the output membership functions on the success probability graphs. This denotes that at the given depth and speed, each turbine is predicted to operate with a high degree of success. The graphs demonstrate the fuzzy inference process, where the input values for depth and speed are processed through the membership functions and through the application of fuzzy rules, resulting in the aggregated output that predicts the success probability. The height of the peaks in the output graphs corresponds to the degree of confidence in successful operation, which in this case appears to be significant for both models. These findings suggest that with a depth of 0.5 m and speed of 1.9 m/s, both axial turbines are expected to perform well, indicating robustness in design and suitability for the canal's analysis conditions. The graphical outputs validate the operational feasibility of both turbines, reinforcing the potential for their application in the Canal de Castilla without the need for significant structural modification. Figure 8: Decision flowchart for axial turbine success probability based on fuzzy logic rules. Figure 9: Operational success probability of axial turbines in Canal de Castilla conditions.