Search Results (384)

Hydrogen energy offers high energy density and carbon-free combustion, making it a promising fuel for next-generation propulsion and power generation systems. Hydrogen offers approximately three times more energy per unit mass than natural gas, and its combustion yields only water as a byproduct, making it an exceptionally clean and efficient energy source. Materials used in hydrogen-fueled combustion engines must exhibit high thermal stability as well as resistance to corrosion caused by high-temperature water vapor. This study introduces a novel ceramic matrix composite (CMC) designed for such harsh environments. The composite is made of yttria-stabilized zirconia (YSZ) fiber-reinforced silicoboron carbonitride [Si(B)CN]. CMCs were fabricated via the polymer infiltration and pyrolysis (PIP) method. Multiple PIP cycles, which help to reduce the porosity of the composite and enhance its properties, were utilized for CMC fabrication. The Si(B)CN precursor formed an amorphous ceramic matrix, where the presence of boron effectively suppressed crystallization and enhanced oxidation resistance, offering superior performance than their counter part. Thermogravimetric analysis (TGA) confirmed negligible mass loss (≤3%) after 30 min at 1350 °C. The real-time ablation performance of the CMC sample was assessed using a hydrogen torch test. The material withstood a constant heat flux of 185 W/cm² for 10 min, resulting in a front-surface temperature of ~1400 °C and a rear-surface temperature near 700 °C. No delamination, burn-through, or erosion was observed. A temperature gradient of more than 700 °C between the front and back surfaces confirmed the material’s effective thermal insulation performance during the hydrogen torch test. Post-hydrogen torch test X-ray diffraction indicated enhanced crystallinity, suggesting a synergistic effect of the oxidation-resistant amorphous Si(B)CN matrix and the thermally stable crystalline YSZ fibers. These results highlight the potential of YSZ/Si(B)CN composites as high-performance materials for hydrogen combustion environments and aerospace thermal protection systems. Full article

This study investigates underground heat sources and develops effective strategies for mitigating heat hazards in coal mines, with a focus on the design and optimization of cooling systems. Using the 3107 fully mechanized mining face of Menkeqing Coal Mine as a case study, geological survey data and in situ measurements were combined to evaluate the severity of thermal hazards. Thermodynamic and heat transfer models were applied to quantify heat dissipation from multiple sources. Computational fluid dynamics (CFD) simulations, based on data-driven modeling and geometric reconstruction, tested different equipment layouts and spacing configurations to identify optimal cooling schemes. Field implementation of the designed cooling system confirmed its effectiveness, offering practical guidance for improving heat hazard control and cooling system optimization in deep coal mines. Full article

Under the background of energy transition, the Integrated Energy System (IES) of the park has become a key carrier for enhancing the consumption capacity of renewable energy due to its multi-energy complementary characteristics. However, the high proportion of wind and solar resource access and the fluctuation of diverse loads have led to the system facing dual uncertainty challenges, and traditional optimization methods are difficult to adapt to the dynamic and complex dispatching requirements. To this end, this paper proposes a new dynamic energy management method based on Deep Reinforcement Learning (DRL) and constructs an IES hybrid integer nonlinear programming model including wind power, photovoltaic, combined heat and power generation, and storage of electric heat energy, with the goal of minimizing the operating cost of the system. By expressing the dispatching process as a Markov decision process, a state space covering wind and solar output, multiple loads and energy storage states is defined, a continuous action space for unit output and energy storage control is constructed, and a reward function integrating economic cost and the penalty for renewable energy consumption is designed. The Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms were adopted to achieve policy optimization. This study is based on simulation rather than experimental validation, which aligns with the exploratory scope of this research. The simulation results show that the DDPG algorithm achieves an average weekly operating cost of 532,424 yuan in the continuous action space scheduling, which is 8.6% lower than that of the DQN algorithm, and the standard deviation of the cost is reduced by 19.5%, indicating better robustness. Under the fluctuation of 10% to 30% on the source-load side, the DQN algorithm still maintains a cost fluctuation of less than 4.5%, highlighting the strong adaptability of DRL to uncertain environments. Therefore, this method has significant theoretical and practical value for promoting the intelligent transformation of the energy system. Full article

Human-perceived temperature (HPT) reflects the synergistic effects of multiple meteorological factors, and its extremes challenge human-managed and natural systems worldwide, especially in densely populated regions such as the Yangtze River Basin of China. However, detailed information on HPT at high temporal (e.g., hourly) and spatial resolution is severely lacking. In this study, we conduct a collaborative inversion for 12 HPT indices at a ~5 km spatial resolution and an hourly temporal resolution in the Yangtze River Basin from multi-source data (e.g., Himawari-8 images, meteorological stations, ERA5-Land reanalysis, and DEM data) using the LightGBM model. The model exhibited high predictive accuracy across all indices, achieving an average coefficient of determination (R²) of 0.981, root mean square error (RMSE) of 1.150 °C, and mean absolute error (MAE) of 0.860 °C. These results aligned well with observational data across spatial and temporal scales, effectively capturing the spatial heterogeneity and diurnal evolution of the region’s thermal environment. Our research provides a reliable data foundation for heat-health risk assessment and regional climate adaptation strategies. Full article

A novel quinoline-containing benzoxazine resin, 8HQ-fa, has been successfully synthesized at room temperature using sustainable raw materials, such as 8-hydroxyquinoline and furfurylamine as the phenol and amine source, respectively. The chemical structure of the hereinafter referred to as quinolinoxazine is fully characterized by Fourier transform infrared spectroscopy (FT-IR), ¹H and ¹³C nuclear magnetic resonance spectroscopy (NMR), as well as by 2D ¹H–¹H nuclear Overhauser effect spectroscopy (NOESY) and ¹H–¹³C heteronuclear multiple quantum correlation (HMQC) NMR. Thermal properties and polymerization behavior of the monomer are studied by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). The resulting polymer is also characterized in terms of its thermal and fire-related properties by DSC, TGA, and microscale combustion calorimetry (MCC). The resulting thermoset, poly(8HQ-fa), presents good thermal stability as evidenced by its T_g (201 °C), T_d5 and T_d10 (307 and 351 °C, respectively), and char yield (42%), and low flammability as determined by the LOI, heat release capacity, and total heat released values (34.3, 143 J/gK, and 10.8 kJ/g, respectively), making it a self-extinguishing thermoset. The combination of properties and advantages in the synthesis of 8HQ-fa, accompanied by a low polymerization temperature, suggests its great potential in the field of high-performance polymers. Full article

This study proposes a machine learning framework utilizing physical feature dimensionality reduction to address the problem of predicting the maximum excess temperature beneath the tunnel ceiling under the influence of multiple factors. First, theoretical analysis is used to systematically explore the impacts of various factors on the maximum excess temperature, including the heat release rate of the fire source, tunnel height, slope, and ambient air pressure. Physical relationships are established to identify key factors, remove redundant features, and construct a simplified feature vector set. Five typical machine learning models are selected: Random Forest (RF), Support Vector Regression (SVR), Fully Connected Neural Network (FCNN), Multi-Layer Perceptron (MLP), and Bayesian Neural Network (BNN). A hybrid data collection strategy combining scale model tests and CFD numerical simulations constructs a small-sample structured dataset with physical backgrounds. The models are evaluated regarding prediction accuracy, stability, and generalization ability. Results show that the Bayesian Neural Network (BNN) optimized by random search parameter optimization and Bayesian regularization significantly outperforms other comparative models in evaluation indices such as root mean square error (RMSE), and mean absolute error (MAE), and coefficient of determination (R²), making it the optimal model and algorithm combination for such tasks. This study provides a reliable quantitative analysis method for tunnel fire safety assessment and offers a new methodological reference for the research on fire dynamics in underground spaces. Full article

The residential building sector is a major contributor to global energy consumption and carbon emissions, making retrofit strategies essential for meeting climate targets. While many studies focus on reducing operational energy, few comprehensively evaluate the trade-offs between operational savings and the embodied carbon introduced by retrofit measures. This study addresses this gap by developing an integrated, novel scenario-based assessment framework that combines dynamic energy simulation and life cycle assessment (LCA) to quantify whole life carbon impacts. Applied to representative Irish housing typologies, the framework evaluates thirty retrofit scenarios across three intervention levels: original fabric, shallow retrofit, and deep retrofit incorporating multiple HVAC technologies and envelope upgrades. Results reveal that while deep retrofits deliver up to 80.2% operational carbon reductions, they also carry the highest embodied emissions. In contrast, shallow retrofits with high-efficiency air-source heat pumps offer near-comparable energy savings with significantly lower embodied impacts. Comparative analysis confirms that reducing heating setpoints has a greater effect on energy demand than increasing system efficiency, especially in low-performance buildings. Over a 25-year lifespan, shallow retrofits outperform deep retrofits in overall carbon efficiency, achieving up to 76% total emissions reduction versus 74% for deep scenarios. Also, as buildings approach near-zero energy standards, the embodied carbon share increases, highlighting the importance of LCA in design decision-making. This study provides a scalable, evidence-based methodology for evaluating retrofit options and offers practical guidance to engineers, researchers, and policymakers aiming to maximize carbon savings across residential building stocks. Full article

This study focuses on the numerical analysis of heat transfer in biological tissue. The proposed model is formulated using the Pennes equation for a two-dimensional cylindrical domain. The tissue undergoes laser irradiation, where internal heat sources are determined based on the Beer–Lambert law. Moreover, key parameters—such as the perfusion rate and effective scattering coefficient—are modeled as functions dependent on tissue damage. In addition, a fuzzy heat source associated with magnetic nanoparticles is also incorporated into the model to account for magnetothermal effects. A novel aspect of this work is the introduction of uncertainty in selected model parameters by representing them as triangular fuzzy numbers. Consequently, the entire Finite Pointset Method (FPM) framework is extended to operate with fuzzy-valued quantities, which—to the best of our knowledge—has not been previously applied in two-dimensional thermal modeling of biological tissues. The numerical computations are carried out using the fuzzy-adapted FPM approach. All calculations are performed due to the fuzzy arithmetic rules with the application of α-cuts. This fuzzy formulation inherently captures the variability of uncertain parameters, effectively replacing the need for a traditional sensitivity analysis. As a result, the need for multiple simulations over a wide range of input values is eliminated. The findings, discussed in the final Section, demonstrate that this extended FPM formulation is a viable and effective tool for analyzing heat transfer processes under uncertainty, with an evaluation of α-cut widths and the influence of the degree of fuzziness on the results also carried out. Full article

The VIIRS Nightfire (VNF) data product is particularly useful for monitoring of global natural gas flaring and estimation of flared gas volumes. Advantages of VIIRS include the collection of nightly global coverage with the inclusion of four daytime channels in the near and shortwave infrared that cover the wavelengths of peak radiant emissions from flares. VNF calculates flare temperatures, source areas, and radiant heat using physical laws. For more than a decade, the Earth Observation Group has estimated flared gas volumes based on radiant heat with a calibration based on reported annual flared and vented natural gas volumes from Cedigaz. The calibration was tuned with an exponent of 0.7 placed on the VNF source areas to achieve the highest regression correlation coefficient. The Cedigaz calibration has wide error bars attributed to unresolvable reporting errors in the Cedigaz data. In this paper we report on the development of an empirical calibration for estimating flared gas volumes based on VIIRS observations of flares running at low, medium, and high flared gas volumes. Tests were run with both single and double flares, with and without atmospheric correction. The new calibrations were applied to VIIRS detection profiles for metered flares located in the North Sea, Arabian Peninsula, and Gulf of Mexico. The results indicate the following: (1) the exponent is unnecessary and causes flared gas volumes to be overestimated for small flares and underestimated for large flares, (2) the calibration can be applied to sites having either single or multiple flares, and (3) flared gas volume estimates can be improved by applying an atmospheric correction to account for regional difference in band-specific transmissivity levels. The new calibration has a prediction interval (error bars) seventy times smaller than the Cedigaz calibration. Full article

Current technical approaches find it challenging to reduce hydrogen production costs in combined cooling, heating, and power (CCHP) microgrids integrated with hydrogen refueling stations (HRS). Furthermore, the stability of such systems is significantly impacted by multiple uncertainties inherent on both the source and load sides. Therefore, this paper proposes a two-stage robust optimization for bi-level game-based scheduling of a CCHP microgrid integrated with an HRS. Initially, a bi-level game structure comprising a CCHP microgrid and an HRS is established. The upper layer microgrid can coordinate scheduling and the step carbon trading mechanism, thereby ensuring low-carbon economic operation. In addition, the lower layer hydrogenation station can adjust the hydrogen production plan according to dynamic electricity price information. Subsequently, a two-stage robust optimization model addresses the uncertainty issues associated with wind turbine (WT) power, photovoltaic (PV) power, and multi-load scenarios. Finally, the model’s duality problem and linearization problem are solved by the Karush–Kuhn–Tucker (KKT) condition, Big-M method, strong duality theory, and column and constraint generation (C&CG) algorithm. The simulation results demonstrate that the strategy reduces the cost of both CCHP microgrid and HRS, exhibits strong robustness, reduces carbon emissions, and can provide a useful reference for the coordinated operation of the microgrid. Full article

The formation, development, and spatial distribution of CUHIs are influenced by urban spatial heterogeneity, yet the scale and seasonal dependencies of the effects of urban morphology modulation on CUHIs have not been fully explored, needing further study. Based on multi-source data for the Yangtze-Huaihe River Valley (YHRV), this study employs the XGBoost model to systematically investigate the effects of two-dimensional (2D)/three-dimensional (3D) urban morphological indicators on CUHIs and their inherent scale–seasonal dependencies. Results show significant provincial heterogeneity in YHRV’s CUHIs: Shanghai exhibits the highest CUHI intensity (CUHII) across all seasons, with a peak of 1.55 °C in winter, followed by Zhejiang and Jiangsu. Seasonally, winter CUHII averages 0.6–0.8 °C (the highest), followed by autumn, while spring and summer have lower values. The effect of the modulation of urban morphology on CUHIs exhibits distinct spatiotemporal dependence: in winter and autumn, CUHII is mainly dominated by the percentage of landscape (PLAND) and largest patch index (LPI) at the 4 km buffer scale (correlation coefficients r = 0.475 and 0.406 for winter); in spring and summer, the 2 km buffer scale shows a more balanced regulatory role of multiple urban morphological indicators. Notably, 2D indicators of urban morphology are consistently more influential in regulating CUHIs than 3D indicators. The Hefei station case effectively validates the model’s sensitivity to changes in urban morphology. This study provides a quantitative basis for season–scale collaborative regulation of urban thermal environments in the YHRV. Future research will integrate climatic factors into XGBoost via screening, reconstruction, and interaction quantification to enhance its predictability for transient heat island processes. Full article

This work develops a Boundary Element Method (BEM) formulation for simulating bioheat transfer in perfused biological tissues using the Atangana–Baleanu fractional derivative in the Caputo sense (ABC). The ABC operator incorporates a nonsingular Mittag–Leffler kernel to model thermal memory effects while preserving compatibility with standard boundary conditions. The formulation combines boundary discretization with cell-based domain integration to account for volumetric heat sources, and a recursive time-stepping scheme to efficiently evaluate the fractional term. The model is applied to a one-dimensional cylindrical tissue domain subjected to metabolic heating and external energy deposition. Simulations are performed for multiple fractional orders, and the results are compared with classical BEM $(a=1.0)$, Caputo-based fractional BEM, and in vitro experimental temperature data. The fractional order $a\approx 0.894$ yields the best agreement with experimental measurements, reducing the maximum temperature error to 1.2% while maintaining moderate computational cost. These results indicate that the proposed BEM–ABC framework effectively captures nonlocal and time-delayed heat conduction effects in biological tissues and provides an efficient alternative to conventional fractional models for thermal analysis in biomedical applications. Full article

A significant challenge in thermal device designs across diverse industries is optimizing heat dissipation rates to enhance system performance. Among different geometric configurations, a partially heated–cooled annular system containing magneto-nanofluids presents unique complexities due to the curvature ratio and strategic positioning of thermal sources–sinks, which substantially influences flow dynamics and thermal transfer mechanisms. The present investigation examines the buoyancy-driven heat transfer in an annular cavity containing a hybrid nanofluid under the influence of an inclined magnetic field and thermal source–sink pairs. Five different thermal source–sink arrangements and a wide range of magnetic field orientations are considered. The governing equations are solved using a finite difference approach that combines the Alternating Direction Implicit (ADI) method with relaxation techniques to capture the flow and thermal characteristics. An artificial neural network (ANN) is trained using simulation data to estimate the average Nusselt number for a range of physical conditions. Among different source–sink arrangements, the Case-1 arrangement is found to produce a stronger flow circulation and thermal dissipation rates. Also, an oblique magnetic field offers greater control compared with vertical or horizontal magnetic orientations. The network, structured with multiple hidden layers and optimized using a conjugate gradient algorithm, produces predictions that closely match the numerical results. Our analysis reveals that Case-1 demonstrates superior thermal performance, with approximately 19% greater heat dissipation compared with other chosen heating configurations. In addition, the Case-1 heating configuration combined with blade-shaped nanoparticles yields more than 27% superior thermal performance among the considered configurations. The outcomes suggest that at stronger magnetic fields $(Ha=50)$, the orientation angle becomes critically important, with perpendicular magnetic fields $(\gamma ={90}^{\circ })$ significantly outperforming other orientations. Full article

Urban heat exposure has become an increasingly critical environmental issue under the dual pressures of global climate warming and rapid urbanization, posing significant threats to public health and urban sustainability. However, conventional linear regression models often fail to capture the complex, nonlinear interactions among multiple environmental factors, and studies confined to single LCZ types lack a comprehensive understanding of urban thermal mechanisms. This study takes the central urban area of Zhengzhou as a case and proposes an integrated “Local Climate Zone (LCZ) framework + random forest-based multi-factor contribution analysis” approach. By incorporating multi-temporal Landsat imagery, this method effectively identifies nonlinear drivers of heat exposure across different urban morphological units. Compared to traditional approaches, the proposed model retains spatial heterogeneity while uncovering intricate regulatory pathways among contributing factors, demonstrating superior adaptability and explanatory power. Results indicate that (1) high-density built-up zones (LCZ1 and E) constitute the core of heat exposure, with land surface temperatures (LSTs) 6–12 °C higher than those of natural surfaces and LCZ3 reaching a peak LST of 49.15 °C during extreme heat events; (2) NDVI plays a dominant cooling role, contributing 50.5% to LST mitigation in LCZ3, with the expansion of low-NDVI areas significantly enhancing cooling potential (up to 185.39 °C·km²); (3) LCZ5 exhibits an anomalous spatial pattern with low-temperature patches embedded within high-temperature surroundings, reflecting the nonlinear impacts of urban form and anthropogenic heat sources. The findings demonstrate that the LCZ framework, combined with random forest modeling, effectively overcomes the limitations of traditional linear models, offering a robust analytical tool for decoding urban heat exposure mechanisms and informing targeted climate adaptation strategies. Full article

Resource maps for open-loop ground source heat pump (GSHP) systems were developed based on heating and cooling experiments to identify areas with potential for reduced operational costs. Experiments conducted at a public hall, where groundwater temperatures fluctuate seasonally, clarified the relationships between the coefficient of performance (COP) of a heat pump and three key parameters: groundwater temperature, flow rate, and energy consumption. Multiple regression analysis produced equations for estimating the energy consumption of both the heat pump and the water pump. Results indicate that groundwater temperature influences the COP, increasing it by 0.07969 per °C during heating and decreasing it by 0.1721 per °C during cooling. These equations enable the estimation of energy consumption in open-loop systems from groundwater temperature, groundwater depth, and building type. Resource maps developed for the Nobi Plain in central Japan reveal that annual energy consumption is lower in the northwestern region, where groundwater temperatures are generally lower, except in marginal zones for hospitals and offices. Full article