This study presents the first investigation of TiO2-MgO hybrid nanofluids in water for Ground Heat Exchangers (GHEs) with advanced divergent and convergent helical geometries, including variable-pitch configurations. Addressing a gap in prior work that largely relied on simplified single-phase assumptions, a comparative analysis between single-phase and Euler-Euler multiphase Computational Fluid Dynamics (CFD) models is performed, capturing interphase slip, particle interactions, and volume fraction (phi)-dependent effects. Simulations are carried out for Reynolds numbers ranging from 1000 to 5000, complemented by an exergy analysis to evaluate the thermo-hydraulic and thermodynamic performance. Using a hybrid TiO2-MgO (50:50)-water nanofluid with 2% volume fraction, the divergent variable-pitch configuration increases thermal- hydraulic performance by 4.3% over the convergent case at the same Reynolds number. Adding 2% hybrid nanoparticles in the divergent heat exchanger increases the Nusselt number by up to 5.2% and total heat transfer by up to 6.3% compared to pure water in a convergent exchanger. Multiphase modeling at Re = 5000 reveals that for volume fractions up to 1%, thermal conductivity predictions are within-5.16% (single-phase) and +14.14% (multiphase) of experimental values, with Nusselt number deviations of-6.30% to-1.02% (single-phase) and-3.19% to +4.60% (multiphase). Exergy efficiency improvements align with the observed heat transfer gains, demonstrating that nanoparticle enhancement is achieved at moderate pumping penalties. Trade-offs were quantified via a pumping index (Delta p & centerdot;Vin) and a thermal-capacity index (Nu & centerdot;Aw): The divergent-pitch coil raised Nu by 24% relative to a uniform-pitch helix. These findings validate multiphase CFD as a more accurate predictive tool at higher volume fraction (phi) and confirm the engineering relevance of TiO2-MgO nanofluids for scalable, high-efficiency geothermal heat exchanger applications.

Solar-based cogeneration systems offer a promising pathway for low-carbon, decentralized development in rural areas of the Middle East and North Africa (MENA). While fully integrated triple-generation systems remain unimplemented, key components, such as PV mini-grids, PV-powered desalination units, solar pumps, and solar water heaters, have proven effective. PV/T-based systems can generate 9-55 kW of electricity, 18-143 kW of thermal energy, and 8-14 m3 of freshwater per day, aligning with typical rural demands and leveraging the region's high solar irradiation and brackish groundwater resources. To realize this potential, challenges such as solar intermittency, efficiency losses, high upfront costs, and fossil fuel subsidies must be addressed through field pilots, advanced control strategies, anti-scaling/anti-soiling solutions, cost-effective thermal storage, and innovative financing mechanisms.

The sun offers a vast and renewable supply of clean, free energy. Photothermal-driven thermal energy storage systems enable the efficient use of solar energy to provide a reliable energy supply for off-grid residential buildings. In this study, the theoretical performance of a solar photothermal-driven thermal energy storage system, based on a photovoltaic- thermal (PV/T) collector and a salt-gradient solar pond (SGSP), was investigated to meet the electricity demand of one four-person household and the heat demand of six four-person households. The system, with a collector area of 50 m2 and a mass flow rate of 100 kg/h, generates an annual average of 5.7644 MWh of electricity and 41.753 MWh of thermal energy. The SGSP provides the majority of the system's thermal energy throughout the year, supplying on average about 67% of the required heat. Under the worst-case climatic scenario on December 1, the system can produce 84.81 kWh of heat and 9.373 kWh of electricity, which corresponds to approximately 1.349 times the heat demand of six households and 1.53 times the electricity demand of one four-person household. On that day, approximately 70% of the total thermal energy was supplied by the SGSP. Furthermore, about 50% of the total heat generated (42.24 kWh) was produced during periods without solar radiation by the SGSP.

The convergence of freshwater scarcity, energy limitations, environmental degradation, and economic pressures presents a critical challenge for sustainable development in arid and remote regions. Addressing these interconnected crises requires integrated technologies capable of delivering clean energy and freshwater with low environmental impact and acceptable cost. This study experimentally investigates a standalone photovoltaic/thermal-driven stepped solar still (PV/T–SSS) as a sustainable desalination solution that simultaneously produces electricity, recovers thermal energy, and generates freshwater. The proposed configuration improves resource utilization by thermally regulating the PV module, supplying recovered heat to the desalination unit, and operating without external energy input, forming a compact, low-emission, cost-effective trigeneration platform. A comprehensive 4E framework covering energy, exergy, economic, and environmental analyses is applied to evaluate system performance at different coolant flow rates of 110, 190, and 220 cm³/min. Results show that reducing the flow rate from 220 cm³/min to 110 cm³/min increases annual freshwater productivity by approximately 24% (482.9 L/m²·yr) while decreasing unit water cost by nearly 19% (0.171 $/L·m²). At this condition, the SSS attains energy and exergy efficiencies exceeding 80% and 1.6%, respectively, and the overall system energy efficiency approaches 25%. Lifecycle assessment indicates a low specific carbon footprint of 5.61 kg CO₂/m³, confirming that operational optimization enables simultaneous thermodynamic, economic, and environmental sustainability.

This study numerically evaluates PCMs in fabric-based emergency tents in Sistan and Baluchestan, Iran, using a passive thermal-management strategy for hot desert conditions. A two-stage CFD framework is applied: steady-state external airflow is solved with the SST k-omega turbulence model in ANSYS Fluent to obtain surface convective coefficients, while transient heat transfer inside the tent is computed via the enthalpy-porosity (mushy-zone) method, incorporating solar radiation, phase change, and buoyancy effects. Models were validated against benchmark data for PCM melting, external forced convection, and natural convection (similar to 7% deviation), confirming simulation reliability in capturing coupled thermal phenomena. Results show that PCM integration reduces the maximum indoor temperature from 326.75 K to 312.25 K (14.5 K/4.4% lower) and the daily average from 317.89 K to 308 K (9.89 K/3.1% lower). The peak vertical temperature difference decreases by 31%, from 17.24 K to 11.88 K. Roofs fully melted, with the West Roof storing 23.85 MJ and other PCM layers storing 16.08-19.33 MJ, while the West Wall reached similar to 60% melting, yielding a total of 71.55 MJ of latent heat storage, more clearly capturing the system's overall thermal behavior. Internal air velocities remained below 0.03 m & centerdot;s(-1), indicating limited natural mixing.

Controlling the nozzle wall temperature using regenerative cooling is a highly effective approach in liquid rocket engines. The selection of coolant is critical to determining the cooling system's thermal performance. In this study, the transient and steady-state heat transfer characteristics of a regenerative cooling channel are numerically investigated using two benchmark coolants, water and Ethyl-alcohol, under different coolant mass flow rates. A two-dimensional axisymmetric conjugate heat transfer model is employed, in which the thrust chamber, cooling channel, and solid walls are fully coupled. For both coolants, the maximum gas-side wall temperature occurs upstream of the nozzle throat, and increasing the coolant mass flow rate significantly reduces both gas-side and coolant-side wall temperatures throughout the thrust chamber. Specifically, for water, the maximum wall temperature decreases from 657 K to 541 K as the mass flow rate increases from 0.5 to 1.5 kg/s, whereas for Ethyl-alcohol, it decreases from 957 K to 693 K. The time required to reach steady-state conditions for Ethyl-alcohol is approximately 1.8 times longer than that of water, while reducing the coolant mass flow rate from 1.0 to 0.5 kg/s nearly doubles the steady-state time. Moreover, the coolant mass flow rate strongly influences the peak heat flux and convective heat transfer coefficient upstream of the throat. Increasing the mass flow rate enhances heat transfer, so that at 1.5 kg/s the maximum heat flux is 21.0 MW/m² for water and 19.7 MW/m² for Ethyl-alcohol. Correspondingly, the convective heat transfer coefficient attains a peak value of approximately 7.8 kW/m²·K near the throat and increases with increasing coolant mass flow rate. The results provide quantitative insights into transient thermal behavior and heat transfer mechanisms in regenerative cooling channels, offering helpful guidance for the thermal design and optimization of rocket engine thrust chambers.

The thermal safety limitations of lithium‑ion batteries remain a major challenge in high‑power electric‑vehicle applications. This study numerically evaluates a porous double‑layer minichannel battery thermal management system (BTMS) and examines five flow configurations under 1C–3C discharge rates. Three‑dimensional conjugate heat‑transfer simulations reveal that flow arrangement has minimal influence in clear channels, whereas porous channels exhibit strong configuration‑dependent behavior. Porous channels exhibit strong configuration‑dependent behavior; the porous–parallel design achieves the best cooling performance, reducing peak temperature to 32.7 °C and limiting temperature non‑uniformity to 7.6 °C. Although counter‑type configurations yield higher Nusselt numbers, they introduce significant thermal non‑uniformity. A Response Surface Methodology framework identifies porosity and Reynolds number as the dominant parameters governing thermohydraulic performance, with optimal conditions occurring near ε≈0.8 and Re≈300. Further balance analysis shows that significant temperature reduction is accompanied by increased hydraulic errors, while intermediate configurations partially compensate for these losses with limited thermal compromise. Trade‑off analysis further reveals that achieving an ∼11.5 K reduction in peak temperature requires nearly a fourfold increase in pressure drop, while intermediate configurations recover ∼34% of this penalty with minimal thermal compromise. The results provide a data‑driven guideline for designing high‑efficiency BTMS architectures capable of meeting the stringent thermal demands of next‑generation EV batteries.

Efficient solid-liquid mass transfer and separation are central to wastewater treatment, environmental process engineering, and multiphase transport, yet clarifier performance remains highly sensitive to inlet hydraulics, flow distribution, and solids loading. This study presents two baffle-free radial-inlet configurations that passively decelerate and radially disperse influent, coupled with a hybrid computational fluid dynamics (CFD)-machine learning (ML) surrogate for rapid performance prediction. Two-dimensional axisymmetric transient simulations using a mixture multiphase model with k-epsilon turbulence closure compared a baseline, a baffled clarifier, and the proposed geometries across inlet orientations and particulate conditions. The optimized baffle-free configuration achieved a peak separation efficiency of 98.07% with a concurrent pressure drop of only 50.44 kPa, outperforming the baffled design (97.07% efficiency at 51.15 kPa) and the baseline case (84.89% efficiency). Separation efficiency increased substantially with particle diameter and peaked at an inlet solids fraction of 0.003 before declining at higher loadings. The ML surrogate, trained on CFD data, reduced the mean absolute error for efficiency prediction from 4.58% to 1.93% while accurately predicting pressure drop. The hybrid CFD-ML framework offers a practical tool for optimizing mass transfer, flow uniformity, and energy efficiency in clarifier design.

Concentrated photovoltaic (CPV) technology plays a pivotal role in the progression towards a zero-carbon built environment (ZCBE). The incorporation of CPV technology within building designs not only enhances the energy output of PV cells but also facilitates photovoltaic thermal applications. Nonetheless, the non-uniform irradiance field produced by the concentrator results in a corresponding non-uniform temperature distribution, which adversely impacts the electrical performance of the PV cells. To address this issue, a model of a non-uniform composite physical field PV cell has been developed, and the model's reliability has been established through a comparison of experimental data with simulation outcomes. Following the validation of the model, the effects of different PV cell surface structures (different PV cell distributions, busbar distributions and numbers) on the power output of CPV is investigated. Furthermore, the analysis encompassed the radiation distribution within the emitter region and the series losses occurring in the finger and busbar regions under varying surface structures of the PV cells. The results show that the difference in the electrical efficiency of the CPV for different PV cell distributions is 0.30%; the maximum difference in the electrical efficiency of the CPV for different busbar distributions is 0.98%; and the maximum difference in the electrical efficiency of the CPV for different numbers of busbars is 0.37%. Consequently, optimizing the structural design of PV cells to accommodate the specific non-uniform composite physical field generated by concentrated light may represent a viable strategy for enhancing the efficiency of photovoltaic energy generation.

Underwater solar cells (UWSCs) have emerged as a key clean energy solution for coastal regions. However, the electrical performance of solar cells in real underwater environments is affected by multiple factors. Existing studies lack a detailed analysis of irradiation attenuation-thermal effects in shallow water depths as well as comprehensive energy loss evaluations. In this study, we developed an opto-electro-thermal coupling model based on irradiation analysis, which was validated through outdoor experiments. The c-Si solar cells achieved a breakthrough efficiency of 38.7% under shallow water depths, outperforming current land-based photovoltaic solutions. Compared to terrestrial solar cells, UWSCs generated up to 12.9% more energy annually. Based on this, we conducted an in-depth analysis of the energy losses in UWSCs and clarified the underlying reasons for their superior performance compared to land-based systems, identifying an optimal "thermo-optical window" governed by water depth. Additionally, we predicted the annual power generation potential of UWSCs in typical regions and analyzed the levelized cost of electricity in representative water types, revealing the trend of "thermo-optical economic trade-off." This study provides strategic direction and guidance for the efficient deployment of UWSCs in marine renewable energy development.

Effective thermal management is critical for lithium-ion battery safety and performance, especially in electric vehicles, where thermal runaway poses significant risks. While hybrid cooling systems combining phase change materials (PCMs) and fins show promise, predicting their thermal behaviour remains a key challenge. This study develops a data-driven predictive framework to optimize a hybrid PCM-fin cooling system, integrating numerical modelling with advanced machine learning (ML). A pressure-based finite volume method solves the governing equations, incorporating phase change via the enthalpy-porosity approach, and is rigorously validated against experimental data for PCM melting and battery discharge. The core novelty lies in the comparative development of five ML models—Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest, XGBoost, and Gradient Boosting—to predict temperature distribution and system performance. The ANN model delivered RMSE <0.051 K, MAE < 0.04 K, and R2 > 0.94—markedly outperforming SVR and tree-based methods. The three-fin hybrid cooling system reduced peak surface temperature by 2.1 K, cut thermal gradients by 75.67 % during charging, and improved PCM melting uniformity by 9.5 % during discharge. This work provides a robust ML-assisted predictive framework for advanced battery thermal management. The validated ML-based temperature prediction framework offers a computationally efficient tool for the design, optimization, and real-time thermal management of lithium-ion battery systems, establishing a practical foundation for future multi-objective optimization and intelligent battery pack design.

The performance of solar chimney power plants is limited by insufficient quantitative insight into how geometric parameters affect buoyancy-driven flow, thermodynamic irreversibilities, and power output under site-specific climates such as Tabas, Iran. To address this gap, this study develops a validated computational framework integrating CFD simulations with a machine-learning-based Artificial Neural Network (ANN), implemented using a Multi-Layer Perceptron (MLP) architecture, to efficiently capture nonlinear geometry–performance interactions. The numerical framework solves the Reynolds-averaged Navier–Stokes equations with the standard k–ε turbulence model, the discrete ordinates radiation model, and the Boussinesq approximation for buoyancy-driven flow, while resolving coupled heat transfer mechanisms. The MLP is specifically employed to learn complex nonlinear interactions among geometric parameters, enabling accurate, low-cost prediction and sensitivity analysis across the design space. Results are reported relative to a baseline configuration (chimney height 200 m, collector radius 140 m, divergence angle 0°; power = 85.54 kW, entropy generation = 0.04562 W m−3 K−1). Increasing chimney height to 250 m raises power to 97.30 kW and lowers entropy generation to 0.04321 W m−3 K−1; lowering height to 100 m reduces power to 52.08 kW and raises entropy generation to 0.04926 W m−3 K−1. Expanding collector radius to 170 m increases power to 93.74 kW and entropy generation to 0.04669 W m−3 K−1, while reducing it to 80 m yields 66.88 kW and 0.02801 W m−3 K−1. Raising divergence angle to 0.4° boosts power to 116.22 kW and lowers entropy generation to 0.03397 W m−3 K−1, demonstrating strong geometric sensitivity. Diurnal simulations predict a peak power output of 267.3 kW at a pressure drop of 275 Pa. The MLP model achieves R2 > 0.99, indicating strong potential for site-specific performance analysis of future SCPP designs.

This study numerically investigates a synergetic strategy to enhance the thermal efficiency of a shell and spiral tube heat exchanger using hybrid nanofluids and a novel perforated inner tube. First, a comparative analysis was conducted among pure water, CuO-MoS₂/water, and MgO-Al₂O₃/water hybrid nanofluids. The results identified a 0.5% volume concentration of MgO-Al₂O₃/water as the superior working fluid for heat transfer enhancement compared to the other tested fluids. Second, a hollow perforated tube was introduced on the shell side to induce jet cross-flow. The numerical results demonstrate that this geometric modification significantly promotes fluid mixing and disrupts the thermal boundary layer. Specifically, the thermal efficiency of the perforated configuration improved by 199% compared to the unmodified baseline, while the non-perforated hybrid nanofluid case showed a 118% increase. Despite a higher pressure drop penalty at increased Reynolds numbers, the trade-off analysis confirms that the perforated design remains highly effective for low-flow regimes. These findings suggest that the integration of MgO-Al₂O₃ hybrid nanofluids and perforated tube geometry offers a high-performance solution for compact thermal management systems.

In practical applications of concentrator photovoltaic (CPV) cells, partial shading has emerged as a critical factor affecting performance stability due to inherently non-uniform irradiance distribution. This paper develops a simulation model based on the double-diode equivalent circuit to systematically evaluate shading effects on CPV performance. The model is validated against experimental measurements under six shading conditions (0%, 10%, 30%, 50%, 70%, and 90%), achieving excellent agreement with relative deviations below 3% for maximum power prediction. The effects of partial shading are comprehensively analyzed in terms of irradiance distribution, voltage and current density profiles, and internal current flow pathways. Results reveal a distinct nonlinear relationship between geometric shading ratio and effective irradiance loss, highlighting the necessity of using actual irradiance loss as the performance indicator. Non-uniform photocarrier generation induced by shading disrupts voltage and current density balance, extends current transport paths, enhances lateral diffusion, and significantly increases series resistance, thereby intensifying energy losses. Key electrical parameters including short-circuit current and maximum output power exhibit nearly linear decrease with increasing irradiance losses. Under equal irradiance losses, different shading distributions result in negligible performance differences (Pmax deviation <1.2%), confirming that total irradiance loss is the dominant factor governing CPV performance degradation. These findings provide quantitative guidance for minimizing irradiance losses and optimizing current transport paths in building-integrated CPV systems.

In recent years, researchers have focused on improving the efficiency of geothermal heat exchangers. This is crucial in advancing renewable energy and reducing greenhouse gas emissions. Research gaps in the design of helical geothermal heat exchangers include the determination of a suitable helix geometry and using hybrid nanofluids. Therefore, it is necessary to perform numerical simulations in this field to find the thermal performance of the above-mentioned modifications. This study presents a new geometry of a conical helical heat exchanger with half the length of the tubes in similar works that gives the same outlet temperatures in the range of ± 0.02

Pressure drop and increase of pumping power is a major issue associated with the applications of nanofluids in thermal systems. Accordingly, the accurate prediction of the pressure drop is important for the optimization of these systems. In this paper, predictions of three numerical models (the discrete phase model (DPM), two-phase mixture model, and the effective single-phase model) for the pressure drop of the alumina-water nanofluid flow through a tube were compared. The volume fraction of nanoparticles, phi, was varied from 0.1% to 6%. The nanofluid flows with the Re numbers of 450 and 900 were considered in the performed simulations. The results were compared with the available experimental data. Accordingly, it was concluded that the DPM provides more accurate results for phi>1%, while the mixture and single-phase models have better predictions for phi<1%. At phi = 1%, DPM provided more accurate predictions for the nanoparticle sizes smaller than or equal to 40 nm. However, the other two models led to better predictions for the nanoparticle sizes greater than or equal to 50 nm. Moreover, a sharp reduction in the nanofluid pressure drop was observed when the nanoparticle diameter rose from 40 nm to 50 nm at phi=1%.

Thermal insulation has a crucial effect on the thermal performance of a solar pond and heat extraction and is an effective parameter. The current study examines the impact of employing environmentally friendly insulation beneath a solar pond on its thermal performance, under the climatic conditions of Semnan City. For this purpose, a transient model was created to forecast the temperature variations in the solar pond, while examining the influential factors on the system's performance. Then, a solar pond was designed according to the maximum lower convection zone temperature and rapid warm-up under the metrological conditions of Semnan City. It was found that the highest reduction in ground heat loss occurred in January, with respective percentages of 26 % and 19 % for straw and wood bark insulations, respectively. This significant reduction in the ground heat loss in January has resulted in a notable temperature difference in the lower convective layer between uninsulated and insulated systems. During June, the temperature of lower convection zone temperature increased by 11.76 and 8.43 degrees for Straw and Wood bark insulations, respectively. After June month, the temperature of lower convection zone temperature increased between 3.33 and 11.73 degrees C for straw insulation and between 2.35 and 8.39 degrees C for the insulated system with Wood bark.

In practical applications of photovoltaic (PV) modules, the shading is an unavoidable issue. This study systematically investigates the impact of the shading on the performance of solar cells by combining experimental measurements with numerical simulations. Key performance parameters, including open circuit voltage (Voc), short circuit current (Isc), maximum output power (Pmax), and series resistance (Rs), are analyzed under different shading ratios. The experimental results demonstrate that the shading significantly reduces Isc and Pmax, both of which exhibit a linear decreasing trend as the shading ratio increases. Meanwhile, Voc shows only a slight decline, whereas Rs increases exponentially, indicating that series resistance plays a crucial role in power loss under the shading conditions. Furthermore, an equivalent transformation method is proposed, which converts regular shading patterns into equivalent strip-shaped shading along the coordinate axis, enabling rapid performance prediction under various shading conditions. Experimental validation confirms a high degree of agreement of the predicted and measured I-V and P-V characteristics, with relative deviations in key performance parameters remaining below 3%. Moreover, the proposed model exhibits high accuracy in predicting the impact of the irregular shading, such as the shading caused by leaves. These findings provide technical support for assessing the effect of shading on solar cell efficiency and offer as an effective modeling tool for evaluating the performance of solar cells under both uniform and non-uniform shading conditions.