Search Results (335)

To systematically examine the effects of ambient wind speed on the fire behavior of inclined tunnel cables, this paper determines the combustion characteristics of ZR-RVV cable combustion parameters using synchronous thermal analysis and cone calorimetry. A 1:20 scaled tunnel platform was established based on Froude similarity criterion to conduct combustion experiments under varying wind speeds (0–0.7 m/s) and inclination angles (−30°–30°). Results indicate the ignition time of the cable decreases gradually with increasing external heating radiation intensity (25–50 kW/m²), with ignition at 295.1 °C. A modified Richardson number (Ri*) is introduced to quantitatively identify the dominant flow regime. It is confirmed that when |θ| ≈ 20°, Ri* ≈ 1, and the fire behavior transitions from “domination” (Ri* < 0.5) to “buoyancy-driven stack effect domination” (Ri* > 2). This critical inclination angle provides decisive guidance for fire source localization, smoke control, and exhaust design. Increasing ambient wind speed significantly reduces the fire temperature and dilutes the smoke; at a wind speed of 0.7 m/s, the maximum temperature drop at the ceiling monitoring point reaches 67%, while CO/CO₂ concentrations decrease correspondingly. The findings provide a theoretical basis for smoke exhaust design and fire monitoring in tunnel fire protection. Full article

Rapid changes in meteorological conditions can lead to frequent switching of wind turbine operating states, causing wind power sequences to exhibit pronounced non-stationarity and multimodal characteristics. As a result, conventional single prediction models often struggle to simultaneously maintain forecasting accuracy and stability under different operating conditions. To address this issue, this paper proposes a wind power forecasting method based on the Convolutional Normalized Transformer Encoder and Multi-Task Learning (CNTE-MTL). First, operating samples of wind turbines are divided into different operating conditions according to typical meteorological variables, such as wind speed, wind direction, and ambient temperature, to characterize differences in meteorology-driven operating patterns. Then, wind power forecasting under different meteorological conditions is formulated as multiple related subtasks, and a multi-task learning framework consisting of a shared feature extraction network and condition-specific prediction heads is constructed. In this framework, the shared feature extraction network employs one-dimensional convolution to extract local temporal fluctuation information and combines it with a Transformer encoder to capture long-term dependency features. The condition-specific prediction heads further characterize the differentiated power evolution patterns under different meteorological conditions, thereby enabling the sharing of common cross-condition information and differentiated modeling. Short-term forecasting, long-term forecasting, supplementary comparative experiments, and ablation experiments are conducted based on SCADA data from an actual wind farm. The results show that the proposed CNTE-MTL model achieves an RMSE of 0.0165 and an $R^2$ of 0.9689 in the one-month short-term forecasting experiment, and an RMSE of 0.0072 and an $R^2$ of 0.9980 in the three-month long-term forecasting experiment, outperforming comparative models such as CNTE, Informer, Transformer, TCN, and LSTM. The ablation experiments further verify the effectiveness of meteorology-driven operating condition division, the shared feature extraction network, and the condition-specific prediction heads in improving forecasting performance. Full article

During CO₂ pipeline transportation, factors such as third-party interference, pipeline corrosion, and material defects may cause pipeline rupture and CO₂ leakage, posing a threat to the safety of surrounding personnel. Therefore, it is of great significance to study the leakage and dispersion characteristics of CO₂ pipelines. Based on the similarity theory, this study established an offshore platform experimental system, measured the CO₂ concentration variation patterns at different positions on the offshore platform during leakage and dispersion, and identified the influence laws of leakage direction (0°~90°), leakage pressure (1.5~3 MPa), leakage time (1~4 min), and environmental wind speed (0~0.5 m/s) on the leakage and dispersion characteristics of pipeline stations. The results show that when leakage pressure increases, the reading of sensor No. 15 remains unaffected and the maximum concentration is measured at a certain distance from the leakage port; leakage duration has minimal impact; ambient wind speed mainly affects near-field concentration; increasing leakage orifice diameter significantly increases far-field concentration; all sensor readings are zero during vertical leakage; and sensor No. 15 shows the highest reading during 45° upward leakage, while sensor No. 5 shows the highest reading during horizontal leakage. The research results can provide guidance for CO₂ transportation and storage on offshore platforms. Full article

Offshore wind turbine blades in cold marine environments are exposed to low-temperature, high-humidity, and saline-droplet conditions, under which the melting behavior, interfacial sliding, and de-icing energy demand of saline ice differ from those of freshwater ice. Existing studies on combined phase-change coating–electrothermal de-icing have mainly focused on freshwater icing. Here, a glass-fiber-reinforced polymer (GFRP) NACA0018 airfoil was tested in a recirculating low-temperature icing wind tunnel to evaluate an n-tetradecane phase-change microcapsule/polyurethane (PCMS-PUR) coating combined with electrothermal heating at a salinity of 3%. Operating parameters, including heat flux density (8, 10, and 12 kW/m²), ambient temperature (−5, −10, and −15 °C), and incoming wind speed (3, 6, and 9 m/s), were systematically varied under a constant water flow rate (60 mL/min) and spray pressure (0.3 MPa) to characterize the evolution of ice morphology, temperature response, and de-icing energy consumption. During electrothermal de-icing, saline ice was more prone to interfacial softening and lubricating meltwater-layer formation, resulting in a dominant whole-block sliding detachment mode rather than gradual local melting. The PCMS-PUR coating further promoted interfacial melting and advanced ice destabilization through latent-heat release and thermal buffering. When the heat flux density increased from 8 to 12 kW/m², the de-icing energy consumption of the uncoated and coated blades decreased by 45.08% and 42.53%, respectively. The maximum energy-saving efficiency of the combined system reached 16.27% at 9 m/s. These findings clarify the de-icing behavior and energy-saving potential of combined phase-change coating–electrothermal systems under saline icing and provide guidance for the design of low-energy de-icing systems for offshore wind turbine blades. Full article

The present research, using finite element method simulation, studies the heat dissipation of a fin-type passive cooling system installed on monocrystalline photovoltaic panels under different environmental conditions associated with altitude. For this purpose, three scenarios at different altitudes were analyzed: Manta (14 m.a.s.l.), Puyo (926 m.a.s.l.), and Ambato (2724 m.a.s.l.). A model simulated using the finite element method, validated in a previous investigation, was used to simulate these three cases. The model was meshed, and the boundary conditions used were obtained from meteorological data averaged over one year. The variables used in this stage were irradiance, ambient temperature, and wind speed in the time range from 08:00 to 17:00. The numerical model used in the simulation considered the mechanisms of conduction in the panel layers, mixed convection toward the surrounding air, and thermal radiation from the exposed surfaces. The results show that, in the city of Ambato, the heat sink presents its best thermal performance. Under conditions of minimum ambient temperature and solar irradiance, a maximum percentage reduction of 3.11% in the photovoltaic panel temperature was obtained, while under conditions of maximum ambient temperature and solar irradiance, the reduction reached 11.11%. This reveals that, when higher panel temperatures occur, the heat sink exhibits better performance. In general, the results showed a reduction in temperature when this heat dissipation mechanism was used. It is evident that the effectiveness of these systems depends not only on geometry or materials, but also on the atmospheric conditions associated with altitude. It is concluded that the heat dissipation capacity of passive cooling mechanisms is influenced by the meteorological conditions of the area, such as ambient temperature, solar irradiance, and wind speed, which may vary according to the altitude at which the system is located. Full article

Power system parameters are susceptible to multiple influencing factors such as environmental conditions and load current, with line parameters being notably affected. This compromises the accuracy of power flow calculation and fault analysis, and can significantly undermine the reliability of protection schemes. To address these limitations, this study proposes an integrated correction method for power system line parameters via a framework that combines soil resistivity inversion and multi-factor sag calculation. First, based on fault-recording data from external line faults, sequence impedance parameters are calculated using a two-terminal impedance difference subtraction strategy, followed by the inversion of soil resistivity along the transmission corridor. Second, considering the spatial inhomogeneity of the transmission corridor, a sliding-window statistical method is applied to segment the line, and a piecewise series model is employed to correct the zero-sequence impedance parameter. Finally, a conductor temperature and sag model based on the heat balance equation is established. By coupling ambient temperature, wind speed, solar radiation, and mechanical load, the ground capacitance and susceptance parameters are dynamically corrected. Simulation results demonstrate that the proposed framework can systematically achieve dynamic correction of power system line parameters and significantly reduce calculation errors. The developed method provides an effective technical pathway for enhancing the accuracy of power system simulation and improving the reliability of protection schemes. Full article

To address the difficulty of directly measuring the internal conductor temperature and the complex influence of external environmental factors on gas-insulated switchgear (GIS), a three-dimensional thermal–fluid multiphysics coupling model was developed for a 110 kV three-phase common-enclosure GIS disconnect switch. The model incorporates contact resistance heating, natural convection of SF₆ gas, wind speed, and solar radiation. The effects of contact resistance and environmental factors on the temperature field distribution were systematically investigated. The results show that an increase in contact resistance significantly raises the conductor temperature, while higher wind speeds effectively reduce the temperature rise of the equipment. Solar radiation substantially increases the enclosure temperature, whereas ambient temperature has little influence on temperature rise. Based on the enclosure temperature rise, a conductor temperature-rise prediction model and a multi-factor correction model were established. Validation results indicate that all models achieved coefficients of determination greater than 0.98, with prediction errors controlled within ±2 °C. The proposed method enables the accurate prediction of conductor temperature under complex environmental conditions and provides technical support for condition monitoring and overheating fault diagnosis of GIS equipment. Full article

Parabolic solar dish–Stirling (PSDS) technologies are among the most efficient solar-to-electric conversion options, but their system-level modeling remains challenging because optical losses, receiver heat losses, package leakage, and Stirling engine non-idealities are strongly coupled under variable operating conditions. This study develops a modular, energy-consistent system-level framework that couples dish receiver optics and thermal behavior, hot-end package losses, and a non-ideal Stirling engine under variable charge (Qu-mode) control. The key novelty is a receiver engine heat-matching formulation in which receiver temperature, useful heat, working gas charge/mean pressure, and engine output emerge from a closed energy balance rather than from prescribed hot-side temperature, fixed heat input, or prescribed mean pressure. The framework was benchmarked in stages against the Mendoza receiver formulation, GPU-3/LeRC Stirling engine data, and EuroDish dispatch-level measurements. At the integrated EuroDish level, it reproduced heat input, cooler rejection, and net electrical output with mean absolute percentage errors of 2.90%, 4.07%, and 4.28%, respectively, while preserving explicit traceability of optical, receiver, package, engine, generator, and parasitic losses. A receiver formulation comparison showed that the final receiver treatment reduced the cooler rejection MAPE from 8.11% to 4.07% relative to the Mendoza-type receiver swap baseline. A limited-input transferability study for representative pressure-controlled dish–Stirling platforms retained peak power and efficiency within a ±10% envelope for the quantitatively assessed cases. Parametric studies further showed a broad engine speed optimum, a heat exchanger sizing trade-off governed by conductance and pumping/friction losses, stronger sensitivity to ambient temperature than wind over the tested EuroDish range, and cooling boundary effects that redirect fixed thermal input from electricity to rejected heat. The resulting framework provides a compact predictive basis for loss diagnosis, design studies, and control-oriented evaluation of PSDS units. Full article

This study analyzes the energy consumption of unmanned aerial vehicles (UAVs) under the influence of external environmental factors, including ambient temperature, wind speed, and turbulence, as well as their effect on battery performance and flight endurance. The aim of the study is to improve the understanding of UAV energy efficiency under different climatic and operational conditions relevant to agricultural and infrastructure-monitoring missions. The results show that external factors substantially affect both UAV power demand and operating time. In particular, wind and turbulence increase energy consumption because of additional aerodynamic drag and the need for repeated stabilization efforts. The proposed framework integrates aerodynamic, thermal, and battery-related factors into a unified energy-consumption model and introduces empirical correction coefficients to improve the applicability of the calculations under different operating conditions. The study also discusses practical approaches to energy-aware mission planning, including route-level considerations and adaptation of flight parameters to environmental conditions. The obtained results indicate that UAV energy efficiency should be assessed not only from nominal battery parameters, but also with account for environmental loading and mission geometry. The proposed framework is relevant for both agricultural and rural infrastructure applications and may support comparative endurance estimation, preliminary route planning, and further development of UAV energy-assessment methods for operation in challenging climatic and operational environments. Full article

Due to their unique topography, deep open-pit coal mines are prone to temperature inversions, which, in turn, exacerbate dust pollution. To characterize this phenomenon, we combined field measurements with FLUENT-based numerical simulations to analyze how inversion layer properties and dust transport patterns respond to varying conditions. The results show that the temperature contrast between the pit walls is positively correlated with the inversion layer’s temperature difference, thickness, and strength. In contrast, ambient wind speed is negatively correlated with the layer’s temperature difference and strength, yet positively correlated with its thickness. Surface temperature has no significant effect on the inversion layer’s temperature difference or thickness and exhibits only a weak correlation with its strength. Furthermore, higher wall temperature contrasts lead to increased dust concentration, whereas stronger winds promote dispersion and lower concentrations. These findings confirm that temperature inversion intensifies pollution, with stronger inversions causing more severe contamination. Therefore, mitigating the formation of inversion layers is crucial for effective dust control in deep pits. Unlike previous phenomenological observations, this study provides novel quantitative data on the thermal-aerodynamic coupling within deep open pits. Specifically, it establishes exact mathematical correlations between discrete rock wall temperature differentials and inversion layer thickness, providing critical thresholds for predicting severe dust retention. Full article

This study evaluates the performance of a commercial silicon-based photovoltaic (PV) module under varying environmental conditions, including solar irradiance, module and ambient temperatures, humidity, and wind speed. Key performance indicators such as daily and lifetime energy output, CO₂ reduction, and potential income were analyzed. Machine learning techniques, including Linear Regression (LR), Artificial Neural Networks (ANN), Random Forest (RF), and XGBoost, were employed to predict photovoltaic (PV) efficiency under varying environmental conditions. The results indicate that solar irradiance is the primary driver of energy production, while elevated temperatures and high humidity reduce efficiency, and wind speed provides minor cooling benefits. Among the models, XGBoost achieved the highest predictive accuracy (Test R² = 0.9967), followed by RF and ANN, whereas LR underperformed due to a limited ability to capture nonlinear interactions. These findings highlight the critical influence of environmental and electrical factors on PV performance and demonstrate the effectiveness of advanced machine learning techniques, particularly XGBoost, in optimizing energy output and supporting sustainable energy planning. Full article

The increasing integration of renewable energy resources into modern microgrids requires reliable scheduling methods capable of managing uncertainty, seasonal variability, operating cost, and environmental impact. This study proposes a stochastic day-ahead scheduling approach for a representative grid-connected multi-energy microgrid comprising photovoltaic generation, wind generation, a microturbine, a fuel cell, an energy storage system, and utility-grid exchange. The proposed model was implemented and simulated in a MATLAB (2024b) environment. The Birds of Prey-Based Optimization algorithm is applied to determine the optimal 24 h dispatch schedule by minimizing a weighted objective function that combines operating and emission costs. Uncertainties in solar irradiance, wind speed, electrical load, ambient temperature, and electricity prices are modeled using probabilistic distributions and Monte Carlo simulations. To improve computational efficiency, 1000 generated scenarios are reduced to 10 representative scenarios using Fast Forward Selection based on Kantorovich distance. Seasonal case studies for winter, spring, summer, and autumn are used to evaluate the proposed method. Compared with five metaheuristic algorithms, the proposed approach achieves the lowest fitness value in all seasons, with reductions of 15.2%, 26.5%, 6.8%, and 23.9%, respectively. The results confirm improved economic and environmental microgrid operation under seasonal renewable uncertainty. Full article

Heating water in outdoor pools is common, particularly in regions with cool or temperate climates. Several factors, including solar radiation, ambient temperature, wind speed, and humidity, influence the pool water temperature. A key design challenge is to determine the collector surface area required to achieve the desired pool water temperature. In this study, a mathematical model was developed that accounts for the aforementioned factors. Under various operating conditions, thermal performance calculations were carried out. Climatic conditions at three locations across Europe, representing different climate regimes, were analyzed. The model was compared with results from the POLYSUN simulation software. Most of the calculations were performed for a pool surface area of 24 m². The calculations showed that wind speed above the pool water surface has a significant impact on heat losses. Locating the pool in a sheltered area results in a consistent reduction in heat losses. It was determined that, under the climatic conditions of Kraków, the installation of solar collectors with a surface area equal to 50% of the pool surface enables the maintenance of daytime water temperatures above 21 °C for approximately 100 days. In the absence of solar collectors, achieving such temperatures is not feasible. Full article

In evaluating energy consumption in building complexes, the influence of urban microclimate variations—primarily driven by the urban heat island (UHI) effect—is often overlooked, leading to modeling inaccuracies. This study develops a numerical simulation framework integrating Weather Research and Forecasting (WRF) and EnergyPlus to assess the energy consumption of university dormitories while accounting for regional microclimate conditions. This is because university dormitories serve as a key indicator for measuring the type of high-density residential buildings in China. The model incorporates dynamic microclimate variables, including ambient temperature, relative humidity, wind speed, solar radiation, and cloud cover, to simulate dormitory energy consumption profiles. Simulation results are validated against measured data, yielding an annual energy consumption error of −1.03%. Quantitative analysis indicates that ignoring the microclimate effect and directly using data from nearby meteorological stations or TMY data has a limited impact on the annual total energy consumption but has a significant impact on seasonal results. To improve the simulation accuracy of building complexes, more attention should be paid to temperature and relative humidity. Moreover, for areas with low occupant density and a high shape coefficient, energy consumption simulation should also consider the local microclimate factors. Full article

With global warming and accelerated urbanization, building air-conditioning (AC) releases more heat into the environment, exacerbating the urban heat island (UHI) effects and increasing building cooling energy consumption. Existing research has limited quantification of the impact of air-conditioning anthropogenic heat (ACAH) on the cooling energy consumption of different types. This study aims to explore the distribution characteristics of ACAH and its impact on residential building energy consumption. Firstly, typical residential buildings in the Pearl River Delta region were selected as a case study. Field experiments were conducted to measure temperature and humidity at 0.5 m, 1 m, 2 m, and 3 m from the outdoor unit, alongside ambient temperature and wind speed. Three grid densities were applied to verify the CFD model, with a prediction error of less than 0.3 °C at 0.5 m under a medium grid. The simulated temperature at 1 m from the outdoor unit under calm wind conditions was compared with field measurements to reveal the horizontal and vertical distribution characteristics of ACAH. Secondly, the effects of different building shapes, ambient temperatures, and wind speeds on the spatial distribution of ACAH were investigated. Finally, EnergyPlus (V23.1.0) was employed as the building energy simulation software, with its microclimate coupling interface implemented via Python scripts to quantify cooling energy consumption variations across different building floors under ACAH influence. Results indicated that ACAH exhibits significant horizontal non-uniformity, exerting the greatest impact within a 0.5 m radius (affected air temperature 4.3 °C higher than ambient). Vertically, localized heat accumulation occurs in the building’s central area, with air temperature 3.5 °C higher than at the bottom. Furthermore, compared to fixed meteorological conditions, the cooling energy consumption difference across floors considering ACAH reaches approximately 7.8%. This study provides accurate meteorological boundary conditions for building energy assessment and supports microclimate management in residential areas. Full article