Performance prediction of tubular solar air heater with finned semi-Cylindrical absorber and swirl flow using Kolmogorov-Arnold networks and Walrus Optimizer

Faculty Science Year: 2025
Type of Publication: ZU Hosted Pages:
Authors:
Journal: Solar Energy Materials and Solar Cells Elsevier Volume:
Keywords : Performance prediction , tubular solar , heater with    
Abstract:
In this study, a tubular solar air heater with swirl-flow and find absorber supported by radial and longitudinal fins (SAH) was used to improve thermohydraulic performance. The suggested SAH has been compared to the plain duct SAH without fins at a typical air flow rate of 0.01–0.050 kg/s. The results of this study show that radial fins improve SAH performance characteristics when compared to a plain heater or a heater with longitudinal fins. Furthermore, increasing the number of radial fins and airflow rate improves SAH performance. SAH with five radial fins and an airflow rate of 0.050 kg/s achieves maximum thermal and thermohydraulic performance. The experimental analysis showed that the pressure loss in the proposed SAH with swirl tubes increases by 2.64 % and 16.44 % for AMFR 0.010 kg/s and 0.030 kg/s, respectively. Additionally, radial fins record the highest-pressure loss values, with an increase in pressure loss of almost 11.97 % with maximum average temperature difference about 9.75 °C. Also, this study also demonstrates the effectiveness of various Kolmogorov-Arnold Networks (KAN) models combined with optimization algorithms in predicting the outlet temperature and pressure loss of the investigated heater. The analysis, based on experimental field data, uses five optimization techniques: KAN-CGO (Chaos Game Optimization), KAN-LASHDE (Lagrangian Adaptive Sheepdog Differential Evolution), KAN-AOA (Arithmetic Optimization Algorithm), KAN-HHO (Harris Hawks Optimizer), and KAN-WO (Walrus Optimizer). The KAN-WO model achieved an RMSE of 0.7557, demonstrating that its predictions closely match the experimental data. So, the KAN-WO model emerges as the most reliable for predicting outlet temperature and pressure loss, due to its superior performance, consistency, stability, and ability to generalize.
   
     
 
       

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