## 6 Conclusions and Discussion  
In this study, in horizontal mantled hot water tanks, two obstacles with 60-degree obstacles parallel to each other were placed in the tank. The distances of the obstacles relative to each other and the effect of the distance of the first obstacle to the main input were investigated numerically and tried to be verified experimentally. Thermal performance, which was provided by obstacles placed at 60 degrees angle parallel to each other, was examined. For the normal tank, the average temperature of storage, for the main inlet velocity V = 0.036 m/s, was 315.8 K. As the speed increased, the average temperature of the storage decreased. It was 309.5 K when the main inlet velocity was V = 0.147 m/s. Obstacles contributed positively to thermal stratification. For obstacle position a = 60 \({}^{\circ}\), m = 50 mm, l = 100 mm tank, the stored water temperature was 312.26 K when the main inlet velocity was V = 0.036 m/s, and 305 K when the V was 0.147 m/s at the end of the two-hour period. For obstacle position a :60\({}^{\circ}\)-m: 50 mm-l: 200 mm tank, the stored water temperature was 312.7 K when the main inlet velocity was V = 0.036 m/s, and 304.5 K when the V was 0.147 m/s at the end of the two-hour period. For obstacle position a = 60\({}^{\circ}\), m = 100 mm, l = 100 mm tank, the average temperature of storage was 312.5 K when the main inlet velocity was V = 0.036 m/s, and 304.36 K when the V was 0.147 m/s at the end of the two-hour period. For obstacle position a = 60\({}^{\circ}\), m = 150 mm, l = 100 mm tank, the average temperature of storage was 313.18 K when the main inlet velocity was V = 0.036 m/s, and 304.26 K when the V was 0.147 m/s at the end of the two-hour period. For obstacle position a = 60\({}^{\circ}\), m = 150 mm, l = 100 mm tank, the average temperature of storage was 312.24 K when the main inlet velocity was V = 0.036 m/s, and 304.47 K when the V was 0.147 m/s at the end of the two-hour period. Consequently, a reduction in velocity, increasing the distance between the two obstacles, and increasing the distance of the first obstacle to the main inlet increased the thermal performance. One of the advantages of MNN is that it reduces the likelihood of inaccuracy, eliminating the necessity for the local linear behavior hypothesis. MNN training also has the benefit of being extremely quick without raising the computing cost of the conventional local learning technique. With MNN used as the learning algorithm for the designed ANN structure, the outlet temperatures of the horizontal jacketed tanks with obstacles placed inside were predicted. When the results were examined, ANN exhibited superior performance in the predictions for all obstacle models. These primary factors make the ANN structure a viable tool for predicting these properties. As a result, the temperature values obtained for different network inlet speeds and obstacle distances in horizontal jacketed hot water tanks with an obstacle placed inside were predicted using a designed feedforward artificial neural network model. The simulation results have shown that the prediction results of the robust and sensitive ANN are largely consistent with the real values. In light of the findings obtained from the study, the evaluation of the prognostic capabilities of the ANN developed using different amounts of neurons and input parameters has been conducted. The difference between the planned outputs and the expected results produced by the ANN has been graphically illustrated. Additionally, the performance measures have been meticulously chosen to evaluate the ANN's effectiveness. Furthermore, it has been shown that ANN serves as an effective resource for engineers, facilitating the analysis of applications associated with thermal performance. In later studies, improved thermal performance can be attained by augmenting the quantity of obstacles and adjusting the height of the obstacles accordingly, and research can be conducted in this area.