Search Results (12,984)

Geothermal exploration and monitoring often require dense temperature observations in terrains where wired networks are impractical and battery replacement for in situ sensors is costly. This paper proposes an infrared-assisted, temperature-aware access scheme for a UAV-enabled backscatter IoT network tailored to geothermal hotspot sensing. A rotary-wing UAV equipped with a thermal infrared camera and an RF transceiver first surveys the area to construct a surface temperature map and identify candidate hotspots, and then hovers above a selected hotspot to perform periodic frames consisting of wireless energy transfer followed by backscatter uplink collection. Ground sensors harvest RF energy, measure their local temperature, and autonomously activate only when both the harvested energy exceeds a threshold and the measured temperature falls within a target interval broadcast by the UAV, thereby concentrating channel access on thermally relevant nodes. We develop a system model that couples a geothermal-like thermal field, RF energy harvesting, and framed slotted backscatter access, and introduce hotspot-oriented performance metrics including effective hotspot throughput, task completion time, and energy per hotspot report. The simulation results show that the proposed temperature–energy-gated access significantly increases the fraction of successfully decoded packets originating from hotspot regions and improves the energy efficiency of geothermal monitoring compared with full activation and purely energy-based activation. Full article

The increasing deployment of Internet of Things (IoT) sensing infrastructures and distributed renewable energy resources is enabling the emergence of Renewable Energy Communities (RECs), which require intelligent, adaptive, and decentralized energy management strategies. This study proposes a sensor-driven reinforcement learning (RL) framework for the coordinated management of residential RECs, aiming to jointly optimize thermal comfort, economic savings, and environmental sustainability. Each household is equipped with a network of IoT sensors monitoring indoor temperature, energy production and consumption, battery state of charge, and user presence, which collectively define a discretized state space for a tabular Q-learning agent controlling heating systems and programmable appliances. A stochastic simulation environment is developed to realistically reproduce weather variability, building thermal dynamics, user activity profiles, and photovoltaic generation. To address the instability typical of multi-agent learning, a two-stage training strategy is adopted: agents are first pre-trained at single-house level using synthetic sensor data and are subsequently deployed within the full community, where coordination is achieved through shared reward components without explicit inter-agent communication. Performance is evaluated on a heterogeneous Renewable Energy Community (REC) composed of eleven households, including both prosumers and consumers. The simulation results show that the proposed approach significantly outperforms rule-based control strategies, achieving lower energy consumption, improved thermal comfort stability, and higher global reward. Moreover, pre-trained agents maintain stable and cooperative behavior when operating concurrently at community level, with limited sensitivity to exploration. These findings demonstrate that sensor-driven, lightweight reinforcement learning represents a viable and scalable solution for decentralized energy management in IoT-enabled Renewable Energy Communities. Full article

The prevalence of high-rack warehouses and large-space facilities with high ceilings poses significant challenges to traditional automatic sprinkler systems, which often exhibit activation delays and limited suppression efficacy. This study investigates the spatio-temporal evolution and distribution characteristics of fire-induced thermal smoke flow through a hybrid approach combining full-scale fire experiments and numerical simulations. A physical hypothesis is proposed: the ceiling temperature field approximately follows a two-dimensional Gaussian distribution. Through parametric numerical simulations under varied ambient temperatures, fire identification criteria were calibrated, encompassing a sustained increase in the average temperature rise within high-temperature zones, the attainment of a predefined threshold, and the spatial stabilization of the Gaussian distribution center. Subsequently, a precise algorithm for rapid fire identification and source localization was developed. Experimental validation demonstrates that the proposed algorithm significantly outperforms traditional passive-activation closed sprinklers, advancing fire detection by 46–67 s. Furthermore, the fire source localization error is maintained within half of the sprinkler spacing. The algorithm also exhibits robust environmental adaptability and generalizability across a wide ambient temperature range, providing a technical foundation for active-actuation fire suppression. Full article

In the current numerical simulation study of high-strength steel welding, ignoring the phase transformation plasticity effect in the coupling analysis led to a significant deviation between the simulated value of residual stress and the experimentally measured value. To investigate the influence mechanism of the Welding Residual Stresses (WRSs) of 30MnCrNiMo armor steel, the transformation plasticity (TP) coefficient (7.81 × 10^–5 MPa⁻¹) was measured via a Gleeble 3500, and a Finite Element Model (FEM) of thermal–metallurgical–mechanical coupling considering yield strength, volumetric strain and TP behavior in Solid-State Phase Transformation (SSPT) was developed. The results show that the volume expansion during the SSPT is the main factor for the shift in WRS from tensile to compressive. In contrast, the TP effect reduces the peak longitudinal tensile stress in the Heat-Affected Zone (HAZ) by 51 MPa. It also ultimately neutralizes the compressive component in this region. When the martensite fraction ranges from 0.12 to 0.45, transformation plastic strain becomes the dominant factor, leading to a characteristic evolution of longitudinal stress that initially decreases and subsequently increases. The FEM incorporating the TP effect successfully captures the dual reversals of residual stress in the HAZ. The average relative error between the simulated longitudinal stress and the experimental data obtained via X-ray diffraction (cosα method) is 8.8%. The TP coefficient database and the developed multi-field coupling model markedly enhance the predictive accuracy for WRS in 30MnCrNiMo steel, offering a robust theoretical foundation for the design of stress corrosion resistance and the service life assessment of welded joints in armored vehicles. Full article

Urban environments face increasing thermal stress from climate change and the Urban Heat Island effect, with significant implications for livability, public health, and energy sustainability. Outdoor thermal comfort is defined as the state in which conditions are perceived as acceptable, depends on interactions among meteorological, morphological, physiological, and behavioral factors. This review synthesizes the application of machine learning (ML) to outdoor thermal comfort assessment into a practice-oriented taxonomy. Research spans diverse climates and urban forms, using inputs across environmental and human domains. Supervised learning dominates. Regression approaches (linear regression, support vector regression, random forest, gradient boosting) and classification algorithms (decision trees, support vector machines, K-nearest neighbors, Naïve Bayes, random forest classifiers) are widely used to predict thermal indices such as the Physiological Equivalent Temperature and Universal Thermal Climate Index, or to classify subjective responses including thermal sensation, comfort, and acceptability. Unsupervised learning (clustering, principal component analysis) supports identification of microclimatic zones and perceptual clusters, while deep learning (multilayer perceptrons, convolutional and recurrent neural networks, generative adversarial networks) achieves superior accuracy for complex, high-dimensional, and spatiotemporal data. Algorithms such as random forests, support vector machines, and gradient boosting consistently show strong performance for both indices and subjective responses when integrating multi-domain inputs. Semi-supervised and reinforcement learning remain underexplored but offer promise for leveraging large-scale sensor data and enabling adaptive, real-time comfort management. The review concludes with a roadmap emphasizing explainable artificial intelligence, scalable surrogate modeling, and integration with simulation-based optimization and parametric design tools. Full article

Tropical daylight provision is inherently coupled with intensive solar heat gains, particularly in south-facing rooms that experience pronounced seasonal variations in solar altitude and exposure across different times of the year. When appropriately designed, external shading devices can mitigate solar heat gains while maintaining adequate indoor daylight availability. This study investigates the daylighting and thermal performance of a representative south-facing apartment room equipped with combined horizontal and vertical slat-based shading devices using a controlled, comparative simulation framework under tropical climate conditions. Parametric simulations were conducted using IES-VE to evaluate multiple shading configurations with varying slat positions, depths, and combinations under representative sky conditions and seasonal design days. The results demonstrate that mid-height horizontal slat configurations reduced front-zone Estimated Indoor Illuminance (EII) by up to 54.9%, while enhancing daylight penetration into deeper areas under direct sunlight conditions. Bottom horizontal slats further improved daylight distribution by reflecting sunlight into deeper zones, producing peak increases in EII of up to 26.8% in the middle zone and 19.7% in the rear zone under direct solar conditions. The addition of vertical slats further improved thermal performance by limiting lateral solar exposure without significantly diminishing the daylight-redirecting effects of horizontal elements. Selected integrated shading configurations achieved maximum reductions in operative temperature of up to 2.5 °C during peak afternoon periods compared with the base case within the adopted evaluation framework. However, under intermediate sky conditions without direct solar contribution, the daylighting and thermal benefits of slat-based shading were substantially reduced. Based on these findings, the study proposes a movable external shading system with adjustable horizontal and vertical slats for south-facing apartment rooms, intended to respond to changing solar conditions across the evaluated design days. Overall, this study provides mechanism-oriented insights to support the development of climate-responsive façade strategies for tropical high-rise residential buildings, with the aim of improving daylight distribution and reducing cooling demand. Full article

Nanocrystalline CeO₂ exhibits size-dependent lattice distortions linked to defect chemistry and surface effects. However, the relationships between the oxidation state, surface interactions, and nanoparticle structure remain unclear in the existing literature, particularly when inferred from conventional nanoparticle diffraction techniques, including powder X-ray diffraction. As a result, the atomistic origin of lattice expansion or contraction with the crystallite size of ceria nanoparticles is still debated. Here, synchrotron X-ray powder diffraction data are analyzed using Rietveld refinement supported by advanced peak profile modeling based on whole powder pattern modeling (WPPM), including thermal diffuse scattering (TDS). The latter provides direct access to information on lattice dynamics. Indeed, we simultaneously determine the size distributions of crystalline domains and their atomic displacements, which are then compared and quantitatively validated with molecular dynamics (MD) simulations. Reactive MD simulations further reveal that vacancy-rich surfaces induce lattice contraction at small particle sizes under vacuum, whereas water adsorption causes surface hydroxylation and lattice expansion. These results explain lattice parameter variations in nanocrystalline ceria through the interplay of surface chemistry and environment. This insight is critical for the correct interpretation of diffraction-derived structural parameters in oxide nanocatalysts used in redox and oxygen storage applications. Full article

Floating photovoltaic parks represent a significant innovation in the field of renewable energy, offering a sustainable alternative for electricity production in the context of the global transition to low-carbon sources. This study investigates the technical feasibility, energy performance, and environmental implications of a 20 MWp floating photovoltaic plant integrated with a 40 MWh battery energy storage system (BESS) in the Port of Constanța, a major logistics hub in the Black Sea region. A comprehensive modeling approach was developed, combining solar resource assessment, PV system simulation (PVsyst-like modelling), energy storage operation, hydrodynamic loading evaluation, and environmental impact screening. Results indicate an annual energy yield between 22.0 and 26.0 GWh, corresponding to a specific yield ranging between 1100 and 1300 kWh/kWp, depending on climatic variability and thermal performance assumptions. A sensitivity analysis was conducted to evaluate system performance under conservative, moderate, and optimistic solar yield scenarios specific to the Port of Constanța, within the realistic regional range of 1100–1300 kWh/kWp. The BESS enables peak-shaving and load-shifting, improving grid integration and reducing diesel generator usage in port operations. Hydrodynamic analysis indicates that three-point taut mooring systems can withstand local wave loads with acceptable safety factors under extreme storm scenarios. Environmentally, the system shows moderate impacts on underwater light availability and water temperature, which can be mitigated through careful siting and monitoring. Economically, the levelized cost of electricity (LCOE) is estimated at 0.046–0.052 €/kWh, competitive with terrestrial PV and aligned with European port decarbonization targets. The study demonstrates that FPV-BESS hybrid systems can play a central role in sustainable port transformation and offers a replicable framework for similar coastal infrastructures worldwide. Full article

Enhancing plant protein structure, functionality, and digestion-associated bioactivity is pivotal to advancing sustainable food applications. In this study, a controlled thermal treatment was applied to Xanthoceras sorbifolium seed meal protein (XSMP) to characterize alterations in structural features, functional performance, and digestion-related bioactivity. Structural analyses showed that moderate heating induced partial unfolding and disaggregation, leading to reduced particle size and improved colloidal stability, with optimal performance observed at 65 °C. Accordingly, foaming capacity and emulsifying activity index reached their highest values under moderate heat pretreatment (71.43% and 27.21 m²/g, respectively). Simulated in vitro gastrointestinal digestion revealed that moderate heat pretreatment enhanced protease accessibility and was associated with increased formation of low-molecular-weight fragments. As a result, digestion products from optimally treated XSMP exhibited significantly enhanced antioxidant activities during the intestinal phase, including higher reducing power, Fe²⁺-chelating capacity (up to 51.21%), and lipid peroxidation inhibition (82.83%). In contrast, insufficient unfolding at lower temperatures or excessive aggregation at higher temperatures reduced the susceptibility to digestive proteases and the associated functional performance. These findings demonstrate that controlled heat treatment provides a simple and eco-friendly strategy to enhance the functional potential of XSMP, supporting its application as a functional protein ingredient. Full article

Navigation planning and execution for underwater gliders operating in thermocline-affected environments is challenging due to the coupled influence of energy constraints, spatially distributed environmental disturbances, and limited control authority. Spatially varying thermocline structures act as structured environmental disturbances that degrade motion efficiency and tracking accuracy, and therefore must be explicitly considered in both path planning and control design. This paper proposes a hierarchical control-oriented decision framework for underwater glider navigation in thermocline regions. At the planning layer, a thermocline-aware multi-objective optimization problem is formulated to regulate the trade-off between navigation efficiency and cumulative environmental disturbance, characterized by total path length and cumulative thermocline exposure, respectively. A multi-objective artificial bee colony (MOABC) algorithm is employed to generate a set of Pareto-optimal reference trajectories that explicitly reveal this trade-off. At the execution layer, pitch angle regulation is formulated as a stochastic tracking control problem under environmental uncertainty. A Markov Decision Process (MDP) is constructed to model the coupled effects of pitch control on energy consumption and trajectory deviation, and a deep deterministic policy gradient (DDPG) algorithm is adopted to synthesize a feedback control policy for adaptive pitch regulation during path execution. Simulation results demonstrate that the proposed framework effectively reduces cumulative thermocline exposure and overall energy consumption while maintaining improved trajectory consistency compared with representative benchmark methods. These results indicate that integrating multi-objective planning with learning-based control provides an effective control-oriented solution for constrained underwater glider navigation in thermally stratified environments. Full article

In the context of global warming, the prediction of extreme precipitation events faces great challenges, especially the ensemble forecast of convective-scale heavy precipitation. Taking the heavy rainstorm in Zhengzhou on 20 July 2021 as an example, this paper aims to explore the performance of the convective-scale ensemble forecasting system based on the local breeding model cultivation method (LBGM) in extreme precipitation forecasting, and reveal the key physical mechanisms affecting the quality of forecasting. The traditional scoring (TS, Bias), neighborhood FSS and Contiguous Rain Area (CRA) methods were used to systematically evaluate the precipitation forecast, and the superior and inferior forecast members were diagnosed and analyzed by combining physical quantities such as isentropy vortex, relative vorticity, and water vapor flux divergence. The results show that: (1) the LBGM-EPS system can better capture the spatial distribution and intensity of heavy precipitation, which is better than the single deterministic forecast; (2) The CRA method is better than the traditional score in describing the spatial structure and intensity of precipitation, and can effectively identify the good and bad members of the forecast. (3) The reason why the dominant forecast members perform better is that the simulation of the dynamic-thermal structure of the mesoscale convective vortex is more reasonable, especially the coupling mechanism of the downward transmission of the high-level vortex and the convergence of water vapor at the lower level is better. The preliminary application of convective-scale ensemble forecasting based on the LBGM in this study has reference value for improving the prediction ability of extreme precipitation. Full article

Since the thermal reliability issues of terahertz traveling wave tubes (THz TWTs) severely limit their power capacity, we propose a thermal analysis and optimization process for THz TWTs in this paper. The measurement results from the accessible test points exhibited good consistency with those of the thermal analysis. Based on the analysis of the heat generation mechanisms of each component of the THz TWT, two novel thermal conduction structures were introduced that decreased the operating temperature of the output window from 81 °C to 34 °C and decreased the operating temperature of the slow wave structure (SWS) from 103 °C to 56 °C. According to the simulation results for the maximum allowable internal power dissipation under the optimized structure, these novel optimization strategies theoretically double the power capacity of the SWS. This work demonstrates an experimentally validated full-tube thermal model and establishes a transferable optimization principle based on identifying and eliminating thermal bottlenecks through a strategic heat conduction path design. This study provides an effective approach for improving the thermal reliability and power capacity of THz TWTs, providing technical support for the engineering application of high-power THz TWTs. Full article

The continuous increase in power density of electronic devices imposes stringent requirements on the design of lightweight, high-efficiency heat sinks. To overcome the limitations of conventional single-gradient or monomaterial heat sinks—namely, their suboptimal heat-transfer efficiency and poor structural adaptability—this study proposes a dual-gradient, triply periodic minimal surface (TPMS)-based multimaterial heat sink architecture fabricated from CuCrZr and AlSi₇Mg. Thermal performance was quantified experimentally using infrared thermography, while the underlying flow-field mechanisms were investigated numerically via computational fluid dynamics (CFD) simulations employing the standard k–ε turbulence model. With the TPMS material volume ratio fixed at 3:3 (CuCrZr:AlSi₇Mg), the Z-axis gradient configuration P-Z4-5 delivered the best overall thermal performance, achieving a heat-transfer coefficient (HTC) of 1557.63 W·m⁻²·K⁻¹ and a thermal resistance as low as 1.83 K·W⁻¹ at an inlet velocity of 5 m·s⁻¹. In contrast, the Y-axis gradient configuration P-Y3-6 yielded the most uniform temperature distribution, exhibiting a maximum surface temperature difference of only 21.5 °C under the same inlet condition. Velocity and turbulence distribution analyses reveal that the dual-gradient design enhances both the narrow-tube effect and flow-induced disturbances; furthermore, increasing the inlet velocity from 5 m·s⁻¹ to 21.65 m·s⁻¹ significantly intensifies vorticity-driven fluid mixing. Among all configurations evaluated, P-Z4-5 exhibited the highest j/f factor (i.e., the ratio of Colburn j-factor to Fanning friction factor), followed by P-Z3.5-5.5 and P-Z3-6. These findings establish a promising new pathway for the development of high-performance, lightweight heat sinks tailored for next-generation high-power electronics. Full article

Injection molding is widely used for plastic parts, but its performance is limited by the cooling stage, which dominates cycle time and affects dimensional stability and energy consumption. Conformal cooling channels, which can be manufactured using additive technologies, improve thermal efficiency but introduce a high-dimensional design problem. This work proposes an integrated methodology for optimizing injection molds with conformal cooling channels that combines parametric CAD (Computer-Aided Drawing), simulation, non-linear principal component analysis, artificial neural network, and multi-objective evolutionary optimization. The workflow is applied to a case study with five cooling layouts. An initial set of 36 metrics related to temperature gradients, warpage, shrinkage, and energy is reduced to a small number of latent objectives, simplifying the search space while preserving the main physical trends. Artificial neural networks surrogates accurately reproduce numerical results, enabling exploration of the design space at a fraction of the computational cost. The optimization yields diverse Pareto-optimal solutions that balance cycle time, dimensional stability, and energy consumption, assisting the design of more sustainable injection molds. Sensitivity analysis identifies mold temperature and channel position/diameter as key design levers. The proposed methodology reduces dependence on expensive simulations and is readily transferable to industrial mold design. Full article

The oceanic wet–thermal and chloride salt environment creates extremely harsh service conditions for marine infrastructures. As a green construction material, geopolymer concrete has a promising application prospect in marine engineering due to its excellent durability. The impact resistance of geopolymer concrete subjected to wet–thermal and chloride salt environment is of great significance for the durability and quality of marine engineering structures. This study uses nano-SiO₂ (NS) and hybrid fibers (HF) to enhance the impact resistance of geopolymer gel concrete (GPC). Radial basis function (RBF) and back-propagation (BP) composite neural networks are used to predict the impact resistance of NS and HF-reinforced geopolymer gel concrete (NSHFGPC). The impact resistance of NSHFGPC specimens is characterized by two indicators: the cumulative number of repeated impact blows required to initiate the first visible crack (N₁) and the cumulative number of impact blows corresponding to ultimate failure (N₂). To evaluate the durability of NSHFGPC under oceanic conditions, specimens were exposed to a simulated marine environment within a simulation test chamber for 60 days prior to impact testing. The 60-day duration was selected to achieve a sufficient level of chloride penetration and matrix aging. Based on the resulting experimental database, an RBF-BP neural network was constructed to predict the material’s impact resistance. In this study, grid search and K-fold cross-validation were employed to select the optimal hyperparameters. Compared to standalone RBF and BP models, the RBF-BP network demonstrated superior performance, achieving R² values of 0.900 and 0.922. These results represent improvements of 20.18% and 11.18% over the standalone RBF model, respectively. Consequently, the RBF-BP algorithm serves as an experimental tool for predicting NSHFGPC impact resistance and guiding future mix design optimization. Full article