An air-based photovoltaic thermal collector (PVTC) is a system that generates both electricity and heat using air flowing over a photovoltaic (PV) module. This system offers the advantage of easy maintenance; however, it suffers from lower thermal efficiency compared to other PVTCs, mostly owing to the low heat capacity of air. Thus, this study introduces a novel PVTC incorporating dual ducts and semicircular turbulators, which were experimentally evaluated under actual weather conditions in the Republic of Korea. The proposed PVTC was compared with two other types of PVTC: one is a single-duct PVTC with semicircular turbulators, and the other is a dual-duct PVTC without turbulators. The results showed that the thermal efficiency of the proposed PVTC increased by approximately 88.7% compared to the single-duct PVTC with a turbulator and by 9.3% compared to the dual-duct PVTC without a turbulator. The electrical efficiency showed a slight decrease of about 7.2% compared to the single-duct PVTC but an increase of 1.4% compared to the dual-duct PVTC without a turbulator. Overall, the total efficiency of the proposed PVTC increased by 54.2% and 7.7% compared to the single-duct PVTC and the dual-duct PVTC without a turbulator, respectively. These experimental results demonstrate that attaching dual ducts and semicircular turbulators to an existing PVTC increases the daily thermal energy output, which ultimately enhances the total daily energy output.

Indirect evaporative coolers (IECs) use the latent heat of water evaporation to cool air. This system has the advantage of operating at low power without a compressor and does not increase the absolute humidity of the air. However, an IEC is difficult to use on its own because its cooling capacity is limited by the theoretical constraint of the wet-bulb temperature of the ambient air. Therefore, an air conditioning unit (ACU) was integrated with an IEC and experimentally evaluated in this study. The dry and wet channels of the IEC were integrated with an ACU evaporator and a condenser, unlike previous studies where IECs were integrated solely with either an evaporator or a condenser. This reduced the cooling load on the evaporator and helped the condenser to dissipate heat to improve the performance of the existing ACU. In addition, the IEC was equipped with baffles to improve its performance. To assess the extent of the performance improvement due to integration with the IEC, comparisons were also performed under the same experimental conditions with an ACU only. The results showed that under conditions with an indoor temperature of 32 °C, integrating the IEC with the ACU increased the average cooling capacity by 13.1% and decreased the average power consumption by 8.60% during the test period, compared to using only the ACU. Consequently, the average coefficient of performance (COP) increased by 19.5% compared to using only the ACU under the same conditions.

A solar air heater (SAH) converts solar energy into heated air without causing environmental pollution. It features a low initial cost and easy maintenance due to its simple design. However, owing to air’s poor thermal conductivity, its thermal efficiency is relatively low compared to that of other solar systems. To improve its thermal performance, previous studies have aimed at either enlarging the heat transfer surface or increasing the convective heat transfer coefficient. In this study, a novel SAH with fins and sequentially placed obstacles on the fin surface—designed to achieve both surface extension through a finned channel and enhancement of the heat transfer coefficient via the obstacles—was investigated using computational fluid dynamics analysis. The results confirmed that the obstacles enhanced heat transfer performance by up to 2.602 times in the finned channel. However, the obstacles also caused a pressure loss. Therefore, the thermo-hydraulic performance was discussed, and it was concluded that the obstacles with a relative height of 0.12 and a relative pitch of 10 yielded the maximum THP values among the investigated conditions. Additionally, correlations for the Nusselt number and friction factor were derived and predicted the simulation values with good agreement.

In this study, supervised machine learning (ML) regression models are employed to predict water uptake during the sorption process in a sorption reactor for thermal energy storage applications. Two main methods are used to study sorption storage systems: experimental studies and numerical simulations. Experimental studies involve physical testing and measurements but are often costly and time-consuming. Numerical simulations are more flexible and cost-effective, though they can require significant computational resources for large or complex systems. To address these challenges, researchers are increasingly employing various machine learning techniques, which offer strong potential for data analysis and predictive modeling. In this study, CFD-based sorption simulations are integrated with machine learning models to predict the spatiotemporal evolution of water uptake. Several ML techniques including support vector regression (SVR), Random Forest, XGBoost, CatBoost (gradient boosting decision trees), and multilayer perceptron neural networks (MLPs) are evaluated and compared. A fixed-bed reactor equipped with fins and tubes is considered within a closed adsorption thermal storage system. Numerical simulations are conducted for three different fin lengths (10 mm, 25 mm, and 35 mm) to generate a comprehensive dataset for training the ML models and capturing the complex temporal evolution of water uptake, thereby enabling predictions for unseen fin geometries. The results indicate that neural network-based models achieve superior predictive performance compared to the other methods. For water uptake training, the mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination $\left(R^2\right)$ are approximately 2.83, 4.37, and 0.91, respectively. The predicted water uptake shows close agreement with the numerical simulation results. For the prediction cases, the MAE, MSE, and $R^2$ values are approximately 1.13, 1.2, and 0.8, respectively. Overall, the study demonstrates that machine learning models can accurately predict water uptake beyond the training dataset, indicating strong generalization capability and significant potential for improving thermal management system design. Additionally, the proposed approach reduces simulation time and computational cost while providing an efficient and reliable framework for modeling complex sorption processes in thermal energy storage systems.