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Efficient cooling systems are critical for maximizing the electrical efficiency of Photovoltaic (PV) solar panels. However, conventional temperature probes often fail to capture the spatial variability in thermal patterns across panels, impeding accurate assessment of cooling system performance. Existing methods for quantifying cooling efficiency lack precision, hindering the optimization of PV system maintenance and renewable energy output. This research introduces a novel approach utilizing deep learning techniques to address these limitations. A U-Net architecture is employed to segment solar panels from background elements in thermal imaging videos, facilitating a comprehensive analysis of cooling system efficiency. Two predictive models—a 3-layer Feedforward Neural Network (FNN) and a proposed Convolutional Neural Network (CNN)—are developed and compared for estimating cooling percentages from individual images. The study aims to enhance the precision and reliability of heat mapping capabilities for non-invasive, vision-based monitoring of photovoltaic cooling dynamics. By leveraging deep regression techniques, the proposed CNN model demonstrates superior predictive capability compared to traditional methods, enabling accurate estimation of cooling efficiencies across diverse scenarios. Experimental evaluation illustrates the supremacy of the CNN model in predictive capability, yielding a mean square error (MSE) of just 0.001171821, as opposed to the FNN’s MSE of 0.016. Furthermore, the CNN demonstrates remarkable improvements in mean absolute error (MAE) and R-square, registering values of 1.2% and 0.95, respectively, whereas the FNN posts comparatively inferior numbers of 3.5% and 0.85. This research introduces labeled thermal imaging datasets and tailored deep learning architectures, accelerating advancements in renewable energy technology solutions. Moreover, the study provides insights into the practical implementation and cost-effectiveness of the proposed cooling efficiency monitoring system, highlighting hardware requirements, integration with existing infrastructure, and sensitivity analysis. The economic viability and scalability of the system are assessed through comprehensive cost-benefit analysis and scalability assessment, demonstrating significant potential for cost savings and revenue increases in large-scale PV installations. Furthermore, strategies for addressing limitations, enhancing predictive accuracy, and scaling to larger datasets are discussed, laying the groundwork for future research and industry collaboration in the field of photovoltaic thermal management optimization.

The thermal management and reduction of energy consumption in cooling systems have become major trends with the continued growth of high heat dissipation data centers and the challenging energy situation. However, the existing studies have been limited to studying the influences of individual factors on energy saving and thermal management and have not been systematically summarized. Thus, this paper reviews the key factors in achieving thermal management and reducing energy consumption in each cooling system, the corresponding research, and optimization methods. To achieve these goals, in this paper, literature surveys on data center cooling systems are investigated. For data center air cooling, thermal management is mainly related to the uniform distribution of hot and cold air. Adjusting the porosity of perforated tiles can reduce energy consumption. For liquid cooling and free cooling systems, climate conditions, cooling system structural design, coolant type, and flow rate are key factors in achieving thermal management and reducing energy consumption. This paper provides the power usage effectiveness (PUE) values of the cooling systems in some cases. A summary of the key factors can provide directions for research on thermal management and energy reduction, and a summary of previous research can provide a basis for future optimization.

A composite phase change material (PCM) was prepared by incorporating paraffin (PA) with expanded graphite (EG) and nano-metal particles to improve the thermal conductivity and reduce the leakage performance of PA once it melts and, consequently, develop a more efficient PCM for a personal phase change cooling system. A series of experiments was carried out by a scanning electron microscope, a differential scanning calorimeter, a hot-disk thermal analyzer, and leakage tests on the composite PCM with various mass fractions of EG and metals (i.e., Cu, Al, Ni, and Fe). Through comprehensive consideration of the thermal conductivity, leakage, and homogeneity, a composite PCM with the optimal proportion (PA-EG11%-Cu1.9%) was screened out. Its thermal conductivity was significantly improved nine times, while the phase change enthalpy showed a minimal decrease. In addition, the relationships of the composite PCM with its temperature and density were systematically investigated. The experimental results are important for determining the proper package density of PCM for application into a personal cooling system because its weight is crucial for the system design and benefits the performance comparison of various PCMs prepared under various conditions. Lastly, the heat storage efficiency of the PA–EG–Cu material was investigated using heat storage tests. Cooling performance clearly improved compared to the PCM without nano-particles added.

Conformal cooling channels (CCCs) are a cooling passageway which follows the profile of the mold cavity or core to perform uniform cooling process effectively in the injection molding process. The production cost is closely related to productivity. To further improve productivity, the injection mold was equipped with CCCs to shorten the cooling time of the injection molded part. To investigate the relationship between the cooling channel layout and cooling efficiency of the CCCs, silicone rubber molds (SRMs) with different layouts of cooling systems were designed and constructed in this study. Simulation software was utilized to study the cooling performance. To verify the results of the simulation, SRM with different cooling systems were fabricated for low-pressure wax injection molding. It was found that the cooling time of the injection molded part is indeed affected by the total surface area of the heat exchange between the coolant and the SRM. The cooling system with four inlets and four outlets seems to be the optimum layout of the SRM in the case study in terms of the difficulty of mold making, total surface area of the heat exchange between the coolant and the SRM, and total cooling flow length of each segment. The saving in the cooling time about 2796 s and improvement of cooling efficiency about 76% can be obtained when the SRM with four inlets and four outlets was used for injection molding. The findings in this study can be used as a reference to design CCCs of injection mold built with AM technology.

Anthropogenic heat flux is the heat generated by human activities in the urban canopy layer, which is considered the main contributor to the urban heat island (UHI). The UHI can in turn increase the use and energy consumption of air-conditioning systems. In this study, two effective methods for water-cooling air-conditioning systems in non-domestic areas, including the direct cooling system and central piped cooling towers (CPCTs), are physically based, parameterized, and implemented in a weather research and forecasting model at the city scale of Hong Kong. An extreme high temperature event (June 23-28, 2016) in the urban areas was examined, and we assessed the effects on the surface thermal environment, the interaction of sea-land breeze circulation and urban heat island circulation, boundary layer dynamics, and a possible reduction of energy consumption. The results showed that both water-cooled air-conditioning systems could reduce the 2 m air temperature by around 0.5 °C-0.8 °C during the daytime, and around 1.5 °C around 7:00-8:00 pm when the planetary boundary layer (PBL) height was confined to a few hundred meters. The CPCT contributed around 80%-90% latent heat flux and significantly increased the water vapor mixing ratio in the atmosphere by around 0.29 g kg−1 on average. The implementation of the two alternative air-conditioning systems could modify the heat and momentum of turbulence, which inhibited the evolution of the PBL height (a reduction of 100-150 m), reduced the vertical mixing, presented lower horizontal wind speed and buoyant production of turbulent kinetic energy, and reduced the strength of sea breeze and UHI circulation, which in turn affected the removal of air pollutants. Moreover, the two alternative air-conditioning systems could significantly reduce the energy consumption by around 30% during extreme high temperature events. The results of this study suggest potential UHI mitigation strategies and can be extended to other megacities to enable them to be more resilient to UHI effects.

Proper thermal management of lithium-ion cells is vital for safety, longevity, and reliable performance, especially in high-power applications where heat generation is considerable. Among passive cooling strategies, phase change materials (PCMs) have shown great promise due to their ability to absorb large amounts of heat. However, while many studies have experimentally and numerically examined this topic, gaps still exist in the theoretical modeling of phase change-based cooling for cylindrical lithium-ion batteries. This study aims to enhance battery thermal management pack design by developing a comprehensive analytical solution for the coupled heat transfer in a cylindrical lithium-ion cell surrounded by PCM. The model incorporates realistic time-dependent boundary conditions, significantly improving its accuracy and applicability for battery pack design. By integrating the perturbation method for the PCM and the Green’s function method for the cell, the model predicts time-dependent temperature profiles and the position of the moving phase change front. Unlike numerical methods, this analytical framework provides rapid yet accurate insights, validated against experimental and computational results with an error margin of less than 5%. The results confirm that while PCM cooling effectively regulates the surface temperature, the battery core remains relatively hotter. Therefore, improving the thermal conductivity of the battery itself is crucial for maximizing the effectiveness of PCM cooling and achieving a more uniform temperature distribution. Furthermore, increasing the thermal conductivity and latent heat of the PCM can also help control the core temperature up to an optimal threshold.

Control of heat released during charge/discharge processes of lithium-ion batteries is very important for the improvement of efficiency of lithium-ion batteries. In this study, the thermal performance of a 20 Ah rectangular type battery pack is analyzed with two different cooling fluids, namely water and nanodiamond-Fe3O4 water/ ethylene glycol (ND- Fe3O4 W/EG) hybrid nanofluid. The cooling system has 5 to 25 number of fins while the Reynolds number is taken between 100 and 800. The volume fraction of the nanoparticles is between 0 and 2% while the discharge rates of 3C, 4C and 5C are considered. The findings suggest that increasing the Reynolds number and the nanoparticle volume ratio improves the temperature distribution and lowers the maximum temperature in the battery pack. When compared to water with the same Re number, ND-Fe3O4 + W/EG hybrid nanofluid with 2% volume ratio at 800 Re number provides improvement of maximum temperature by 23.1% while 70.35% improvement in temperature difference is achieved. Using higher number of fins improves the performance. There is a reduction of 13.8% of the temperature difference in the battery pack with 25 fin model as compared to 5 fin model.

The incorporation of conformal cooling systems has significantly enhanced the efficiency and quality of injection molding process. While several automated methods have been developed for creating conformal cooling channels in injection molds, the current optimization process for conformal cooling design parameters is hindered by labor-intensive iterative thermal simulation processes and the substantial reliance on empirical human knowledge. This paper presents an innovative machine learning method to assess the thermal performance of conformal cooling systems by employing a combination of a non-linear regression model and a neural network. By employing a logarithmic regression model describing the temperature graph and a neural network predicting the coefficients of the logarithmic regression model, the thermal performance of specified conformal cooling systems can be assessed and predicted precisely. This methodology empowers designers to evaluate the thermal efficiency of conformal cooling systems efficiently and effectively to further optimize the conformal cooling design parameters without relying on tedious manual thermal and fluid simulation processes.

Zuo, X.; Dong, M.; Gao, F., and Tian, S., 2020. The modeling of the electric heating and cooling system of the integrated energy system in the coastal area. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 1022–1029. Coconut Creek (Florida), ISSN 0749-0208. The accurate analysis of the coupling relationship of the electric heating and cooling system is the basis of its load calculation. Because the current method does not consider the analysis of the coupling relationship of the electric heating and cooling system in the design, the accuracy of the load calculation results is low. In order to solve this problem, the load modeling of the electric heating and cooling system of the comprehensive energy system in the coastal area is studied. Through the calculation of power network, natural gas network and comprehensive energy flow, the power flow value of electric heating and cooling system of comprehensive energy system is obtained. On this basis, according to the energy conversion efficiency of the equipment included in the energy hub and the distribution ratio of electric energy and gas, the coupling relationship of the electric heating and cooling system is obtained. According to the coupling relationship of the electric heating and cooling system, the load model of the electric heating and cooling system is constructed, and the optimal solution of the model is calculated by Grey Wolf algorithm to complete the modeling research of the electric heating and cooling system of the comprehensive energy system in the coastal area. The simulation results show that the model designed in this paper can obtain the accurate calculation results of the active power and reactive power of the electric heating and cooling system, and the calculation of the load of the electric heating and cooling system is time-consuming, accurate and reliable.