Search Results (1,538)

This study investigates the residual web crippling behavior of pultruded glass fiber-reinforced polymer (P-GFRP) hollow sections after exposure to elevated temperatures. The primary objective is to evaluate the combined influence of temperature and loading configuration on web crippling capacity, failure mechanisms, and structural performance, and to develop practical prediction models for engineering applications. A total of twenty pultruded GFRP hollow section specimens were exposed to temperatures of 24 °C, 200 °C, 250 °C, 300 °C, and 350 °C and tested under four loading configurations: End Ground (EG), Interior Ground (IG), End Two Flange (ETF), and Interior Two Flange (ITF). In addition to web crippling tests, tensile, SEM-EDS, TGA-DSC, DMA, and FT-IR analyses were conducted to investigate the mechanical, thermal, and microstructural degradation mechanisms. The results showed that elevated temperatures significantly reduced the web crippling capacity, with strength losses reaching up to 80% at 350 °C due to matrix degradation, fiber–matrix debonding, and loss of structural integrity. Among the investigated loading configurations, IG exhibited the highest load-carrying performance, whereas ETF experienced the greatest capacity reduction. A temperature-dependent reduction factor and unified empirical prediction equations were developed and demonstrated good agreement with the experimental results, with experimental-to-predicted ratios ranging from 0.97 to 1.15. The findings provide valuable insight into the post-fire behavior of pultruded GFRP hollow sections and offer practical guidance for the design, assessment, and fire safety evaluation of GFRP structural members exposed to elevated-temperature environments. Full article

While double-layer insulation structures are widely adopted, their thermal performance is critically dependent on the thermophysical behavior of the interstitial air cavity, a variable often oversimplified in current design practices. This article moves beyond generic material descriptions to investigate the specific mechanism of heat transfer transition within sealed air gaps sandwiched between graphite polystyrene boards. The innovation of this experiment lies in the rigorous isolation of air gap thickness as the primary independent variable within a 1 × 1 × 1 m closed building model, instrumented with high-precision GPRS temperature and humidity sensors to capture real-time thermal gradients under the authentic climate conditions of Anyang, Henan. The results demonstrate a non-monotonic relationship between gap thickness and effective thermal resistance, governed by the competition between molecular conduction and buoyancy-driven natural convection. Specifically, the data validates that a 20 mm air gap represents the statistically significant optimum, thereby maximizing insulation efficiency while minimizing radiative heat loss. Using this optimized structure reduces steady-state heat flux compared to monolithic equivalents and aligns with the energy conservation target. Unlike previous studies limited by simulation assumptions or short-term testing, this research provides empirically verified, long-term field data that bridges the gap between theoretical fluid dynamics and practical building envelope engineering. These findings offer a robust, physics-based reference for optimizing double-layer insulation systems in the Central Plains, directly supporting the low-carbon retrofitting of existing building stocks. Full article

Frequent seasonal phase transitions in cold and arid lakes require different remote sensing indices for frozen and open-water periods, complicating the use of traditional empirical indices for automated monitoring. To address this challenge, this study proposes an intelligent indexing framework integrating the heuristic reasoning of Large Language Models (LLMs) with Random Forest (RF) feature selection. Leveraging the Google Earth Engine (GEE) and Landsat 8 data from Ulansuhai Lake, five LLMs such as Gemini and ERNIE were employed to generate candidate spectral indices based on typical sample spectra. Optimal band combinations were identified via RF importance, and Land Surface Temperature (LST) was incorporated as a physical constraint for unified cross-seasonal classification and determine the optimal threshold. Results show that the LLM-derived ERNIE-WISI and Gemini-WISI exhibit high robustness. During the freezing period, ERNIE-WISI significantly outperformed other indices, achieving an Overall Accuracy (OA) of 89% and a Kappa of 0.86. Spatially, it yielded snow and ice mapping with clear textures and low commission errors. During the non-freezing period, ERNIE-WISI achieved an OA of 95% with a Kappa of 0.84. While Gemini-WISI achieved an OA of 94% with a Kappa of 0.80, performing comparably to MNDWI. Notably, ERNIE-WISI effectively suppressed background interference in complex landscapes like narrow channels and aquaculture areas, maintaining high geometric fidelity and spatial continuity. A key advantage of ERNIE-WISI is its consistent performance without seasonal threshold adjustments. Aligned with the AI for Science paradigm, this methodology bridges AI-driven heuristic discovery and physical remote sensing, offering a robust, transferable solution for long-term dynamic lake monitoring in extreme environments, thereby facilitating sustainable water management. Full article

This paper presents a full-scale case study on real-time corrosion monitoring in an underground reinforced-concrete potable water tank built in 1968. The study aims to demonstrate how continuous electrochemical monitoring can support durability assessment and predictive maintenance in ageing water-retaining infrastructure, where direct inspection is often limited and exposure conditions are spatially variable. Fourteen monitoring points were installed in beams, columns and domes subjected to different exposure conditions. Corrosion potential, concrete resistivity, corrosion current density and temperature were recorded every 3 h and used to assess the corrosion state of the reinforcement. The monitored durability indicators were reinforcement section loss, estimated from corrosion current density using Faraday’s law, and corrosion-induced crack-width evolution, used as a serviceability-related indicator for maintenance planning. The results show that beams remained predominantly passive, with corrosion current densities below 0.1 µA/cm² and incremental sectional losses below approximately 2 µm during the monitoring period. Columns showed the highest vulnerability, particularly at lower elevations subjected to prolonged immersion, with estimated incremental section losses reaching approximately 4–6 µm and a clear correlation between submerged time and corrosion progression. Domes exhibited intermediate behaviour, with occasional activation events associated with environmental fluctuations. A multivariable model combining resistivity and temperature was used to interpret corrosion kinetics, while Faraday-based section-loss estimates were coupled with empirical crack-width models to forecast serviceability indicators up to 2045. These forecasts are presented as scenario-based maintenance-support indicators rather than deterministic predictions of future damage, since corrosion propagation and crack development may evolve nonlinearly under changing exposure conditions. The proposed approach demonstrates how continuous corrosion monitoring can be linked to durability limit-state assessment, enabling risk-informed and performance-based maintenance of critical water infrastructure. Full article

Gas blending with hydrogen represents a core research direction for present and future energy transport systems. The throttling of natural gas and hydrogen mixtures through pressure-regulating valves inevitably induces thermodynamic temperature variations. Theoretical analyses and simulated thermal profiles demonstrate that hydrogen blending effectively counteracts the extreme expansion temperature drop post-throttling. This thermodynamic shift alleviates the localized microclimatic thermal conditions favorable to ice-plugging, validating the feasibility of hydrogen injection as a systematic thermal mitigation strategy for high-pressure pipeline networks. This study utilizes computational fluid dynamics software to model the flow field variations in pure hydrogen and gas–hydrogen mixtures under the influence of pressure-regulating valves. Employing a real gas equation of state across varying operational temperatures and pressure conditions, this research calculates and analyzes the flow field variations driven by the Joule–Thomson effect for pure hydrogen and mixtures with varying hydrogen blending ratios. The objective is to inform temperature regulation strategies for long-distance hydrogen–natural gas pipeline networks and to establish an empirical temperature fitting relationship for pure hydrogen. The numerical evaluation indicates a maximum relative error of 6.02% and a maximum absolute error of 0.06877 K. Furthermore, guided by the localized temperature variation patterns, the temperature rise results from 75 pure hydrogen simulation cases were extracted. A Multilayer Perceptron artificial intelligence algorithm was utilized to perform inverse calculation iterations on the thermal data and regulation results. Through the stochastic selection of initial parameters and repeated training iterations referencing the fitting formula, an optimized regulation sequence was obtained. This process drives the fluid temperature to approach the practical regulation target. Following the network training phase, the maximum absolute error between the calculated temperature regulation result and the target regulation temperature is recorded at 0.0556 K, providing a methodological reference for subsequent high-pressure hydrogen applications. Full article

Expanded snack extrusion is governed by tightly coupled interactions among raw material composition, moisture, barrel temperature, screw speed, feed rate, screw configuration, die geometry, and energy input. These variables affect not only final responses such as expansion ratio, bulk density, hardness, crispness, and water absorption or solubility indices, but also intermediate state variables including specific mechanical energy (SME), melt temperature, die pressure, melt viscosity, and bubble growth dynamics. As a result, modelling has become essential for product design, process optimisation, and scale-up. This review critically evaluates the major classes of models used to describe process–structure–quality relationships in the extrusion of expanded snacks. The literature shows that empirical regression and response surface methodology (RSM) remain the most widely applied tools because they are experimentally efficient and easy to interpret. However, mixture-process designs are more appropriate when formulation and operating variables are changed simultaneously, while phenomenological and mechanistic approaches provide better physical insight into expansion and structure development. More recently, machine-learning and interpretable artificial intelligence approaches have demonstrated strong predictive capability when large, well-curated datasets are available. Across model families, a consistent theme is that operating variables act on final product quality through intermediate process state variables rather than independently. On that basis, this review proposes a practical hybrid framework for expanded snack extrusion: a mixture-process quadratic model augmented with SME, die pressure, melt temperature and shear-related state variables, and structured in three levels linking (i) controllable inputs to state variables, (ii) state variables to measurable quality attributes, and (iii) quality attributes to a gold-standard product target or sensory-control criterion. Such a model offers a realistic balance between predictive performance, physical interpretability, experimental burden, and industrial usefulness, while also providing a clear pathway toward future digital twin and machine-learning-enabled optimisation. Full article

The escalating global demand for renewable energy has positioned solar photovoltaics (PV) as a critical technology for achieving net-zero emissions. However, PV efficiency is strictly limited by thermal degradation, where elevated operating temperatures significantly reduce power output and accelerate material aging. This review systematically evaluates the integration of advanced phase change materials (PCMs) as a passive thermal management solution. We analyze the transition from material-level innovations—including nano-enhanced PCMs, 3D conductive frameworks, and shape-stabilization—to system-level hybrid architectures such as liquid—PCM, heat pipe-fin, and thermoelectric generator (TEG) integrations. Synthesis of recent empirical data (2024–2026) demonstrates that optimized PCM composites can achieve PV temperature reductions of up to 32 °C and electrical efficiency enhancements exceeding 19%. Furthermore, techno-economic assessments reveal that these systems can reduce the levelized cost of energy (LCOE) by 5–15% and achieve energy payback times as short as 1.5 years. Finally, this paper identifies critical research gaps in long-term outdoor durability, AI-driven predictive modeling, and sustainable bio-based encapsulation, providing a strategic roadmap for the commercialization of next-generation solar thermal management systems. Full article

Seasonal thermal stratification in deep reservoirs easily causes bottom hypoxia and a sharp decrease in oxidation–reduction potential (ORP), leading to the pulsed release of internal phosphorus from sediments. Under climate warming, this has become a hot issue for sustainable reservoir eutrophication control. Taking the Quanmin Reservoir in Southwest China as the research object, this study combined high-resolution profile monitoring and a Box–Behnken response surface experiment to construct a semi-empirical model coupling redox threshold effect and Arrhenius kinetics. Results showed that during thermal stratification, the water body below 18 m formed a significant redox gradient, resulting in a 21-fold vertical difference in phosphorus concentration. The response surface experiment confirmed that ORP dominates phosphorus release, and the temperature (T) effect is strictly redox-dependent: warming only promotes phosphorus release under anaerobic conditions (−50 mV), with a 26% increase in release amount when temperature rises from 10 °C to 30 °C, while temperature has a negligible effect under aerobic conditions (+30 mV). Model fitting yielded an ORP critical threshold of −17.2 ± 4.8 mV and a normalized steepness of 0.033 mV⁻¹, indicating joint control by diffusion and reaction. Based on these results, a synergistic regulatory mechanism of redox threshold and temperature was proposed, providing a quantitative basis for reservoir eutrophication management under climate warming. Maintaining ORP above −17 mV through bottom aeration can effectively block internal phosphorus release from the redox threshold perspective, though practical in situ application is constrained by aeration-induced water mixing and microbial variations, and such precise redox control may save energy, supporting the sustainability of reservoir ecosystems and long-term water quality security. Full article

Accurate estimation of the weighted mean temperature (Tm) is essential for retrieving precipitable water vapor (PWV) from ground-based Global Navigation Satellite System (GNSS) observations. Machine learning (ML) techniques excel in modeling nonlinear relationships among Tm time series, station geographic coordinates, and surface meteorological parameters, and recent studies have demonstrated that ML and neural network models outperform conventional linear Tm models. However, the full potential of surface meteorological measurements at GNSS stations for high-precision Tm retrieval remains to be fully explored. This study develops regional Tm empirical models using two ML methods—random forest (RF) and Temporal Mixture of Experts with Sequential Attention (TMESA)—to generate reliable real-time Tm estimates and enhance the accuracy of operational GNSS-PWV retrievals over China. A traditional linear model is adopted as the baseline to evaluate the performance improvements of the proposed models. The models are trained and tested using 10-year (2014–2023) hourly ERA5-derived Tm products and in-situ surface pressure, temperature, and relative humidity from 2377 meteorological stations, with Tm diurnal variations, station coordinates, and day of year integrated as auxiliary predictive features. Validation is conducted using 2024 ERA5 reanalysis data and radiosonde profiles from 120 stations across China. Results show that the RF model yields a bias (RMSE) of −0.11 K (2.67 K) against ERA5 and −0.21 K (2.67 K) against radiosonde data, while the TMESA model achieves superior performance with bias (RMSE) of −0.02 K (2.34 K) and 0.09 K (2.46 K), respectively, whose performance levels comparable to state-of-the-art studies. Compared with the traditional linear model, the RF model reduces Tm RMSE by 32% against ERA5 and 25% against radiosonde data, while the TMESA model achieves reductions of 40% and 33%, respectively. These findings confirm that the proposed ML models can provide high-accuracy Tm estimates for reliable GNSS-PWV retrieval. Future work will focus on the operational application of these models for near-real-time GNSS-PWV estimation. Full article

Climate change has increased the frequency of extreme weather events, posing a major threat to the sustainable development of agriculture and farmers’ welfare. Based on provincial meteorological data and China Family Panel Studies (CFPS) data from 2014 to 2022, this study systematically investigates the impact of climate shocks on farmers’ welfare, heterogeneity characteristics, and the buffering role of off-farm employment, using a two-way fixed-effect model. The results show that climate shocks significantly reduce farmers’ welfare, with greater welfare losses in northern regions, major grain-producing areas, and plain areas. Extreme low temperatures, extreme high temperatures, and drought are the three dominant climate hazards. In response to climate shocks, off-farm employment effectively buffers welfare losses. This study clarifies the logic of changes in farmers’ welfare and livelihood adaptation mechanisms under climate change, providing micro-empirical support for improving differentiated climate adaptation policies, strengthening agricultural risk management systems, enhancing agricultural system resilience, and promoting high-quality and sustainable agricultural development. However, constrained by the matching precision between micro-level data and meteorological indicators, future research should further refine the measurement of climate shock exposure at the individual farmer level. Full article

Hydrate formation and erosion-related pipe wear are critical operational challenges in natural gas pipelines. This study evaluates a pyrocondensate-based liquid composition modified with fine clay particles as a dual-function formulation for apparent hydrate-onset control and erosion-wear mitigation. The liquid phase contains pyrocondensate solvent, heavy gasoline fraction, and white oil. Laboratory screening was performed for composition ranges including 60/20/20 and 65/15/20 with formulations at reported dosages of 20–25 g/1000 m³. Under the applied procedure, visible hydrate formation for model gas samples was suppressed down to −24 °C with the 60/20/20 formulation. For a field gas sample from well No. 422, the 65/15/20 formulation shifted the observed apparent hydrate-onset temperature from +6 °C to approximately −20 °C at a dosage of 20 g/1000 m³. To mitigate erosion-wear, fine clay particles were added at 10 wt.% of the liquid composition. Laboratory tests demonstrated increased visual erosion-onset time in gas–liquid–solid flows. A preliminary four-month field application on well No. 422 recorded no hydrate formation or visible erosion-related complications. The results demonstrate the empirical potential of this dual-function composition under the investigated conditions. Full article

The South-to-North Water Diversion Project (SNWDP) in China involves a vast number of resettlers with far-reaching impacts. As a crucial carrier of resettlers’ daily lives, the indoor thermal comfort of resettlement housing directly affects their physical and mental health. However, existing empirical and field studies have paid limited attention to the thermal comfort and thermal adaptation of the resettlers. This study focuses on resettlers of the SNWDP, employing a combination of questionnaires and on-site measurements to analyze thermal benchmarks and thermal adaptation behavior data. The study introduces the concept of relative deprivation theory from social psychology, compares the correlations between vertical and horizontal deprivation and thermal perception across different relocation modes, and validates the predictive performance of commonly used thermal comfort models. The results show that as the relocation distance increases, the summer indoor thermal neutral temperature rises sequentially, and both the sensitivity to temperature changes and the width of the comfort zone also increase. Regarding thermal adaptation behaviors, the short-distance group primarily relies on passive adjustments such as using electric fans and reducing clothing, while the long-distance group significantly shifts toward active mechanical cooling like air conditioning. The sense of relative deprivation has a significant impact on the thermal comfort of medium- and long-distance resettlers, and its correlation even exceeds that of physical factors such as air temperature and black globe temperature. Among all groups, the ePMV and ePTS models modified by the expectancy factor exhibit the best predictive performance, with the smallest average deviation from the actual Thermal Sensation Vote (TSV), making them the optimal evaluation models for indoor thermal comfort of resettlers in the SNWDP. The findings provide theoretical guidance for creating healthy and comfortable indoor thermal environments in resettlement areas and for the sustainable development of subsequent phases of the SNWDP. Full article

This study investigates the modulation of coercivity and magnetodielectric coupling in heat-treated, nickel-substituted lanthanum ferrite. LaFe_0.7Ni_0.3O₃ samples were synthesized by high-energy ball milling and sintered at temperatures between 1073 and 1473 K. Chemical composition, crystalline structural evolution, surface morphology, magnetic, dielectric, and electrical properties, as well as magnetodielectric coupling, were analyzed. The XPS spectra revealed the presence of adsorbed oxygen, associated with the high oxygen affinity of the material. This behavior is interpreted as a charge-compensation mechanism, related both to the formation of oxygen vacancies and to the partial oxidation of Fe³⁺ to Fe⁴⁺. XRD and Rietveld refinement confirmed a single-phase orthorhombic Pnma structure, and structural simulations revealed progressive octahedral distortions with increasing temperature, affecting the octahedral tilting and electronic bandwidth. Magnetic characterization revealed that thermal processing modifies the magnetic behavior, inducing weak ferromagnetism and a significant increase in coercivity, correlating with progressive densification, greater domain stability, and reduced microstrain. Impedance measurements revealed magnetodielectric coupling, the Maxwell–Wagner interfacial polarization mechanism, and reduced dielectric losses. These findings demonstrate that the coercivity and magnetodielectric response in cationic nickel-substituted lanthanum ferrite can be tuned through thermal processing. A semi-empirical magnetocrystalline anisotropy model is proposed to explain the coercivity evolution and associated multiferroic behaviors, thus contributing to the study of functional ferrites as sustainable alternatives to rare-earth magnetic materials with potential in sensors and memory devices. Full article

Driven by the urgent demand of the steel industry for utilizing low-grade, high-crystal-water iron ores, this study focuses on the thermal decrepitation problem in Guisha limonite pellet preparation caused by goethite dehydroxylation. Different from previous studies that mainly focused on single factors or single performance indicators, this work establishes a multi-factor experimental framework that simultaneously considers bentonite dosage, preheating temperature, and pellet size. This framework enables the strength–decrepitation trade-off of Guisha limonite pellets to be evaluated quantitatively rather than empirically. This work systematically investigated bentonite addition (0.8–1.6 wt%), preheating temperature (600–800 °C), and pellet diameter (9–13 mm). These factors were evaluated in terms of thermal cracking mass ratio and compressive strength. Their interactive effects on thermal cracking behavior and mechanical properties were quantitatively revealed. A target-oriented dual-window process control strategy was then proposed. The results show that thermal cracking intensifies with increasing preheating temperature and decreases with increasing bentonite content; compressive strength peaks at 1.2 wt% bentonite (approx. 1456 N). On this basis, a Min–Max normalization and weighted scoring method was adopted. A quantitative decision-making model was established for strength-prioritized and safety-prioritized objectives. The model identified two optimal process control windows at 1.2 wt% and 1.4 wt% bentonite. An optimized thermal regime—preheating at 700 °C, roasting at 1250 °C, and slow furnace cooling—was established. This regime provides directly referable process parameters. It also offers a decision-making framework for pellet production of similar ores. Full article

Cities in climatic transition zones face coupled radiative and evaporative stresses, and their carbon emission mechanisms differ significantly from those in humid regions. Taking Xi’an, a typical megacity in the transition zone, as a case study, this research utilises a 500 m × 500 m grid to integrate multi-source data for carbon emission accounting. By applying spatial autocorrelation and the Multi-scale Geographically Weighted Regression (MGWR) model, this study examines the spatial heterogeneity of carbon emissions and the mechanisms through which urban planning influences them. The results indicate that carbon emissions in Xi’an exhibit a “core–periphery” agglomeration pattern, with commercial land use exhibiting the highest emission intensity. Carbon emissions and land surface temperature are spatially coupled, consistent with a hypothesised positive feedback loop of the “dry heat island” effect. Morphological factors exhibit spatial non-stationarity: floor area ratio is positively associated with emissions in the old city centre, whereas mutual shading among super-high-rise buildings in the High-Tech Zone coincides with a weaker effect. Building density shows a positive association only where ventilation is limited. Land use mix and blue–green spaces show non-linear negative associations with emissions, with higher marginal benefits in arid–hot environments. This study proposes carbon reduction strategies for the renewal of old urban areas, business cores, and new ecological districts, providing empirical evidence and decision-making references for low-carbon spatial planning in cities within the climatic transition zone. Full article