Search Results (76)

The separable heat pipe, with its highly efficient heat transfer and flexible layout features, has become an innovative solution to the heat dissipation problem of batteries, especially suitable for the directional heat dissipation requirements of high-energy-density battery packs. However, most of the number–value models currently studied examine the flow of refrigerant working medium within the pump as an isentropic or isothermal process and are unable to effectively analyze the heat transfer characteristics of different internal regions. Based on the laws of energy conservation, momentum conservation, and mass conservation, this study establishes a steady-state mathematical model of the pump-driven microchannel-separated heat pipe. The influence of factors—such as the phase state change in the working medium inside the heat exchanger, the heat transfer flow mechanism, the liquid filling rate, the temperature difference, as well as the structural parameters of the microchannel heat exchanger on the steady-state heat transfer and flow performance of the pump-driven microchannel-separated heat pipe—were analyzed. It was found that the influence of liquid filling ratio on heat transfer quantity is reflected in the ratio of change in the sensible heat transfer and latent heat transfer. The sensible heat transfer ratio is higher when the liquid filling is too low or too high, and the two-phase heat transfer is higher when the liquid filling ratio is in the optimal range; the maximum heat transfer quantity can reach 3.79 KW. The decrease in heat transfer coefficient with tube length in the single-phase region is due to temperature and inlet effect, and the decrease in heat transfer coefficient in the two-phase region is due to the change in flow pattern and heat transfer mechanism. This technology has the advantages of long-distance heat transfer, which can adapt to the distributed heat dissipation needs of large-energy-storage power plants and help reduce the overall lifecycle cost. Full article

Traditional empirical correlations for predicting saturated flow boiling heat transfer coefficients (HTC) often struggle with accuracy and generalizability, particularly across different refrigerants, heat exchanger geometries, and operating conditions. To address these limitations, this study investigates the application of machine learning for more robust HTC prediction. A comprehensive dataset was compiled, consisting of 22,608 data points from over 140 published studies, covering 18 pure refrigerants under diverse experimental setups. The primary goal was to evaluate the performance of different machine learning approaches—Wide Neural Network (WNN), Linear Regression (LR), and Support Vector Machine (SVM)—in predicting HTCs across varying tube types and heat exchanger configurations. The results indicate that the WNN model achieved the highest predictive accuracy, with a Root Mean Square Error (RMSE) of 1.97 and a coefficient of determination (R²) of 0.91, corresponding to less than 5% prediction error for all refrigerants. These outcomes confirm that machine learning models can effectively capture the complex thermofluid interactions involved in boiling heat transfer. This work demonstrates that data-driven methods provide a reliable and generalizable alternative to empirical correlations. Full article

Prefabrication in a controlled factory setting may improve the energy performance of modular buildings compared to traditional site-built facilities. However, few studies report empirical evidence to support this premise in full-scale operational buildings. Since energy efficiency standards in the United States are driven by building code, the compliance path chosen and field verification through site inspection, an investigation of how site-built and modular projects satisfy code requirements is critical to understanding long-term energy consumption. Therefore, this study investigated and compared Energy Code Compliance (ECC) among 55 commercial multifamily buildings (25 modular and 30 site-built) in four American Society of Heating, Refrigerating and Air-Conditioning Engineers climate zones (3B, 3C, 4A and 4C). For climate zone 3, ECC analyses indicated that modular slightly exceeded site-built construction. For zone 4, site-built construction slightly exceeded modular. Nearly all buildings met or exceeded the prescriptive energy code requirements for each climate zone regardless of whether a performance or trade-off compliance path was utilized. Field observations suggest that envelope construction quality in modular buildings could be higher. Results provide insights for researchers exploring energy use in buildings, as well as the basis for a nuanced understanding of normalized operational energy consumption in an ongoing longitudinal study of the same 55 multifamily buildings. Full article

With the integration of numerous distributed energy resources (DERs) and buildings with diverse energy demands, the inherent vulnerability of agricultural microgrids poses escalating security threats. Harnessing the regulatory capabilities of diverse building loads and energy storage systems to mitigate voltage excursions caused by DER generation in microgrids is of significant importance. Therefore, a data mechanism fusion-driven microgrid planning method is proposed in this paper, aiming to enhance the security of microgrids and optimize the utilization of DERs. A comprehensive agricultural microgrid model that incorporates intricate constraints of various types of buildings is established, including greenhouses, refrigeration houses and residences. Based on this model, a site selection and capacity determination planning methodology is proposed, taking into account wind turbines (WTs), photovoltaics (PVs), electric boilers (EBs), battery energy storage systems (BESSs), and heat storage devices. To address the limitations of traditional greenhouse models in accurately predicting indoor temperatures, a temperature field prediction method for greenhouses is proposed by leveraging a generalized regression neural network (GRNN) to train and modify the model indicators. Case studies based on a modified IEEE 33-bus system verified the effectiveness and rationality of the proposed method. Full article

This study investigates the exergoeconomic performance of a milk pasteurization system powered by geothermal energy, operating across geothermal source temperatures (GSTs) ranging from 80 °C to 110 °C. The system uses geothermal heat as its primary energy source, while the cooling process is supported by a vapor compression refrigeration cycle driven by electricity generated through an Organic Rankine Cycle (ORC). The analysis was carried out in three stages: determining system parameters for each GST level, conducting detailed energy and exergy analyses, and performing an exergoeconomic evaluation using the specific exergy costing (SPECO) method. The results show that both energy and exergy efficiencies decline as GST increases. Energy efficiency varies between 88.30% and 78.53%, while exergy efficiency ranges from 72.86% to 58.02%. In parallel, unit-specific manufacturing costs increase with higher GST. Electricity production costs range from 610 to 900 USD·MWh⁻¹, and the cost of pasteurized milk varies between 3.76 and 6.53 USD·ton⁻¹. These findings offer practical insights into how geothermal source temperature affects the thermodynamic and economic performance of such systems, contributing to the broader understanding of sustainable dairy processing technologies. Full article

Heating, ventilation, air conditioning, and refrigeration (HVAC&R) systems account for a significant share of global energy demand, prompting intensive research into advanced thermal enhancement techniques. Among these, nanofluids—colloidal suspensions of nanoparticles in base fluids—have shown promise in boosting heat transfer performance. This review provides a structured and critical evaluation of nanofluid applications in HVAC&R systems, synthesizing research published from 2015 to 2025. A total of 200 peer-reviewed articles were selected from an initial pool of over 900 through a systematic filtering process. The selected literature was thematically categorized into experimental, numerical, hybrid, and AI/ML-based studies, with further classification by fluid type, performance metrics, and system-level relevance. Unlike prior reviews focused narrowly on thermophysical properties or individual components, this work integrates recent advances in artificial intelligence and hybrid modeling to assess both localized and systemic enhancements. Notably, nanofluids have demonstrated up to a 45% improvement in heat transfer coefficients and up to a 51% increase in the coefficient of performance (COP). However, the review reveals persistent gaps, including limited full-system validation, underexplored real-world integration, and minimal use of AI for holistic optimization. By identifying these knowledge gaps and research imbalances, this review proposes a forward-looking, data-driven roadmap to guide future research and facilitate the scalable adoption of nanofluid-enhanced HVAC&R technologies. Full article

The global energy crisis, driven by factors such as increased demand, limited fossil fuel resources, and growing environmental concerns created an urgent need for energy-efficient solutions across all sectors. Among these, refrigeration systems, which are used extensively in both domestic and commercial settings, are responsible for a sizeable amount of global energy consumption. Finding ways to reduce energy used in the refrigeration could play a crucial role in mitigating the energy crisis. Phase Change Materials (PCMs) have emerged as a promising technology to enhance the energy efficiency of refrigeration systems. By storing and releasing energy in the form of latent heat, PCMs optimize energy conversion rate of the processes, reduce power consumption, and lower the overall environmental impact. The present research focus Calcium Chloride Hexahydrate (CCH) as the PCM which acts as an intermediary between the heat sources to achieve optimal effectiveness. To improve system performance and optimize PCM quantity, two novel system configurations were assessed in the mass proportions of 1 kg and 2 kg of PCM with water. The incorporation of PCZ enhanced the overall heat energy utilisation, recovery of waste heat, and greater system output. And actual COP of the refrigeration system was meet out with the domestic refrigerator in ranges of 1.0759 to 1.1537. The above two novel system were proved that a vital role in removal of waste latent heat into lateral use in the ranges of 110.8 kJ (min.) into 226.8 kJ (max.). Finally proposed system was avoided global warming temperature raise because of uses of waste heat into lateral uses in the refrigeration systems. Full article

A diffusion absorption heat transformer is a completely thermally driven heat upgrading technology with significant application potential in low-grade thermal energy recovery. However, existing diffusion absorption heat transformers have problems such as complex circulation processes, limited solution flow rates, and insufficient stability due to their reliance on bubble pumps. A jet pump was proposed for application in a diffusion absorption heat transformer cycle to replace the bubble pumps in the original diffusion absorption heat transformer cycle. In the novel cycle, without electricity consumption, the diffusant gas was used as the primary flow of the jet pump to transport the solution, and the diffusion generation of the refrigerant was realized in the jet pump for more efficient and stable thermal energy upgrading. The performance of the novel cycle with H₂O/LiBr/C₅H₁₀ or H₂O/HCOOK/C₅H₁₀ as working fluids was analyzed based on a constructed theoretical model validated by numerical simulation. It was found that the performance of the jet pump was sensitive to the generator temperature and the pressure difference of the cycle. Increasing the temperature of the jet pump and reducing the temperature of the absorber were conducive to improving the COP. As a potential absorbent substitute for LiBr, HCOOK also led to slightly better performance in most cases. Full article

The thermally driven ejector refrigeration system is generally used to recover industrial waste heat to improve the energy efficiency of industrial processes. However, for conventional single-stage ejector refrigeration systems (ERSs), the higher-pressure steam derived from high-temperature waste heat elevates the primary fluid pressure, resulting in significant pressure mismatch with the secondary fluid, which consequently leads to large irreversible losses and substantial degradation in system performance. To address this issue, a novel hybrid ejector refrigeration system (NHERS) is proposed and analyzed under design and off-design conditions using thermodynamics. The results indicate that under design conditions, compared to the conventional single-stage ejector refrigeration system, the proposed hybrid ejector refrigeration system can achieve increases of about 20.6% in the entrainment ratio, around 15.2% in the coefficient of performance (COP), and about 21.4% in exergetic efficiency. Analyzing its performance under off-design conditions to provide technical solutions for the flexible operation of the hybrid ejector refrigeration system proposed in this paper can broaden its application scenarios. Consequently, the proposed NHERS demonstrates remarkable superiority in energy conversion and transfer processes, showing certain application prospects in the field of medium- to high-temperature industrial waste heat recovery. Full article

The subcooling potential of a novel R717-based waste heat-driven multi-ejector chiller (HEC) integrated with an R744 refrigeration system was evaluated for use in supermarkets. The performance was compared with an R744 refrigeration system coupled to R718- and R600a-based HECs, an R744 system equipped with parallel compression (PC), and a standard R744 booster system (CB) in various warm and hot climatic locations. Integration of the R717-based HEC was found to improve the coefficient of performance by 3.7% at 27 °C to 12.1% at 45 °C compared to the R718, and by 1.6% at 27 °C to 7.6% at 45 °C compared to the R600a-based system. The energy-saving potential of the R717 system (6.2% to 9.4%) was also found to be higher than that of the R718 (0.7% to 2.8%) and R600a systems (2.5% to 6.6%). The use of the existing high-pressure controllers of the CB system was found to impose a relatively lower penalty on the system performance compared to the controllers of the PC system. Although the integration of the R718 system incurred a significantly lower additional investment, the recovery time of the R600a-based HEC (2.3–4.8 years) was found to be the shortest. Full article

The absorption refrigeration system (ARS) stands as a remarkable device that is capable of efficiently harnessing low-grade thermal energy and converting it into cooling capacity. The reverse electrodialysis (RED) system harvests the salinity gradient energy embedded in two solutions of different concentrations into electricity. An innovative RED–ARS integration system is proposed that outputs cooling capacity and electric energy, driven by waste heat. In this study, a comprehensive mathematical simulation model of a RED–ARS integration system was established, and an aqueous lithium bromide solution was selected as the working solution. Based on this model, the authors simulated and analyzed the impact of various factors on system performance, including the heat source temperature (90 °C to 130 °C), concentrated solution concentration (3 mol∙L⁻¹ to 9 mol∙L⁻¹), dilute solution concentration (0.002 mol∙L⁻¹ to 0.5 mol∙L⁻¹), condensing temperature of the dilute solution (50 °C to 70 °C), solution temperature (30 °C to 60 °C) and flow rate (0.4 cm∙s⁻¹ to 1.3 cm∙s⁻¹) in the RED stacks, as well as the number of RED stacks. The findings revealed the maximum output power of 934 W, a coefficient of performance (COP) of 0.75, and overall energy efficiency of 33%. Full article

Heat pumps are promising solutions for managing the increasing heating demand of residential houses, reducing the environmental impact when used with renewable energy. Accurate heat load predictions allow the heat pump to operate at the most efficient settings, maintaining comfortable temperatures while reducing excess energy use and lowering operating costs. Data-driven prediction solutions may have difficulty capturing the dynamics and nonlinearities of the thermodynamics involved. The physics-informed models combine the monitored observed data with theoretical knowledge of heat pumps and directly integrate physical constraints, allowing for better generalization and reducing the dependence on large volumes of data. However, they require detailed knowledge of the system topology and refrigerant parameters, which increases the model complexity. Therefore, in this paper, we propose a physics-informed neural network for predicting the heat load of heat pumps that integrates thermodynamics directly into the loss function of the neural network. We model the heat load as a function of the input variables, including the inlet temperature, outlet temperature, and water flow rate. We integrate the function during model training to reduce the model complexity. Our approach increases the accuracy of the predictions compared with data-driven models and generates prediction results that are consistent with the actual physical behavior of the heat pump. The results show superior prediction accuracy, with a 7.49% reduction in the RMSE and a 6.49% decrease in the MAPE, while the R² value shows an increase of 0.02%. Full article

The organic Rankine cycle (ORC) is a valuable method for harnessing low-temperature waste heat to generate electricity. In this study, two dual-pressure auto-cascade ORC systems driven by low-grade geothermal water are proposed in series and parallel configurations to ensure high thermal efficiency and power output. The energy and exergy analysis models for two systems are developed for comparative and parametric analysis, which uses a zeotropic refrigerant mixture of R134a and R245fa. The findings indicate that, with a heat source temperature of 393.15 K, the thermal efficiency and exergy efficiency of the series auto-cascade ORC reach 10.12% and 42.07%, respectively, which are 27% and 21.9% higher than those of the parallel auto-cascade ORC. However, the parallel cycle exhibits a higher net power output, indicating a better heat source utilization. The exergy analysis shows that evaporator 1 and the condenser possess the highest exergy destruction in both cycles. Finally, the parameter analysis reveals that the system performance is affected significantly by the heat source and heat sink temperature, the pinch temperature difference, and the refrigerant mixture concentration. These findings could provide valuable insights for improving the overall performance of ORCs driven by low-grade energy when using zeotropic refrigerant mixtures. Full article

This study explores the potential of machine learning models to predict evaporator heat transfer performance in Modular Refrigerated Display Cases (MRDCs). Six experimental datasets from MRDC systems were analyzed to compare the efficacy of six machine learning models: Linear Regression, Decision Tree Regression, Support Vector Machines (SVMs), Feedforward Neural Networks (FNNs), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM). The findings indicate that the ensemble tree-based models, LightGBM and RF, are particularly effective in predicting evaporator heat transfer performance. These models demonstrate high accuracy and robustness, effectively capturing the nonlinear relationship between the evaporator temperature and heat transfer coefficient. Moreover, LightGBM and RF exhibit notable stability and adaptability in scenarios of limited data availability and elevated noise levels. Their consistent predictive accuracy across different experimental conditions highlights their suitability for complex refrigeration systems. This research provides essential insights for optimizing MRDC evaporator performance, establishing a theoretical and data-driven foundation for energy-efficient enhancements and intelligent management within cold chain systems. Full article

Primary themes in intergenerational justice are a healthy environment, the perpetuation of Earth’s biodiversity, and the sustainable management of the biosphere. However, the current rate of species declines globally, ecosystem collapses driven by accelerating and catastrophic global heating, and a plethora of other threats preclude the ability of habitat protection alone to prevent a cascade of amphibian and other species mass extinctions. Reproduction and advanced biotechnologies, biobanking of germplasm and somatic cells, and conservation breeding programs (RBCs) offer a transformative change in biodiversity management. This change can economically and reliably perpetuate species irrespective of environmental targets and extend to satisfy humanity’s future needs as the biosphere expands into space. Currently applied RBCs include the hormonal stimulation of reproduction, the collection and refrigerated storage of sperm and oocytes, sperm cryopreservation, in vitro fertilization, and biobanking of germplasm and somatic cells. The benefits of advanced biotechnologies in development, such as assisted evolution and cloning for species adaptation or restoration, have yet to be fully realized. We broaden our discussion to include genetic management, political and cultural engagement, and future applications, including the extension of the biosphere through humanity’s interplanetary and interstellar colonization. The development and application of RBCs raise intriguing ethical, theological, and philosophical issues. We address these themes with amphibian models to introduce the Multidisciplinary Digital Publishing Institute Special Issue, The Sixth Mass Extinction and Species Sustainability through Reproduction Biotechnologies, Biobanking, and Conservation Breeding Programs. Full article