Search Results (1,270)

This paper investigates the potential of localized air-curtain microclimate control to reduce HVAC energy consumption in electric vehicles while maintaining occupant thermal comfort. The study compares conventional full-cabin cooling with driver-focused and passenger-focused air-curtain configurations under controlled ambient conditions of 32 °C. The experimental framework combines analytical airflow and heat-transfer modeling with comparative HVAC performance evaluation using power consumption, time to reach thermal comfort, and Predicted Mean Vote (PMV) analysis. The results show that the air-curtain configurations reduce HVAC power consumption from 3.2 kW for conventional cooling to 2.3 kW and 2.5 kW for the driver- and passenger-focused configurations, corresponding to energy savings of approximately 22–28%. In addition, localized airflow significantly accelerates thermal comfort attainment, reducing stabilization time from 8 min to 4–5 min while maintaining PMV values within acceptable comfort limits. The findings demonstrate that occupant-centered air-curtain microclimate strategies can improve HVAC energy efficiency, reduce auxiliary energy demand, and support more sustainable and range-efficient operation of next-generation electric vehicles. Full article

To clarify how injection-induced cooling and reservoir properties jointly control rock damage during hydraulic fracturing of deep shale reservoirs, this study develops a coupled thermo–hydro–mechanical phase-field model incorporating fracture pressurization, matrix seepage, heat transfer, thermoelastic stress redistribution, and tensile damage evolution. The hydraulic fracture component is verified against the classical KGD analytical benchmark, and the thermal damage component is benchmarked against a ceramic quenching experiment. The phase-field formulation is constructed using tensile-compressive strain-energy decomposition so that only the tensile part of the elastic energy contributes to damage evolution, while the compressive stiffness is retained. The results show that low-temperature fluid injections produce a steep but spatially limited cooling zone near the fracture wall. The constrained contraction of the cooled rock generates additional thermoelastic tensile stress, strengthens fracture-tip stress localization, and accelerates phase-field damage accumulation. In the baseline case, thermal cooling increases the peak tensile stress near the fracture tip along profile c from 10.2 MPa in the hydraulic-only case to 22.5 MPa at t = 2 h, while the phase-field damage value increases from 0.03 to 0.77. Five-case sensitivity analyses show that, as α_T increases from 0.5 × 10⁻⁵ to 1.5 × 10⁻⁵ 1/°C, the fracture-tip tensile stress at t = 2 h increases from approximately 18.6 MPa to 25.7 MPa, and the damage value increases from approximately 0.80 to 0.96. As permeability increases from 0.0001 mD to 0.01 mD, the pore pressure at 2 m from the fracture wall increases from approximately 50.4 MPa to 71.2 MPa, and the tensile stress along profile c increases from approximately 16.4 MPa to 21.8 MPa. These results demonstrate that coupled thermal and hydraulic effects govern fracture initiation, localization, and propagation tendency during thermally assisted hydraulic fracturing in deep shale reservoirs. Full article

The “Two Joint Operations” policy aims to promote coordinated development between thermal power and renewable energy through institutional incentives and constraints. Under this framework, government participation significantly affects cooperative relationships and benefit realization among energy enterprises. To analyze this coordination mechanism, this study constructs a tripartite cooperative game model involving the government, thermal power enterprises, and renewable energy enterprises. Social Network Analysis (SNA) is introduced to characterize inter-agent cooperative structures, while network centrality indicators are incorporated into key parameters such as coordination efficiency, renewable energy integration capability, and institutional execution efficiency. Furthermore, the Shapley value method is structurally weighted based on network relationships to reflect differences in participants’ actual roles within the coordination system. The results indicate that embedding social network analysis (SNA) structures can effectively release the synergistic effects of institutional optimization and policy coordination, enhance system coordination efficiency, and promote a steady increase in total coalition payoff with a stable positive response pattern. Both thermal power and renewable energy enterprises benefit from the coordination improvement, while renewable energy entities exhibit higher sensitivity, indicating significant heterogeneity in agent responses. The government’s payoff remains generally stable across scenarios. Parameter analysis further verifies that the model demonstrates good structural stability and mechanism robustness. By introducing a network-structure-based analytical perspective, this study provides a potential reference for related research. Full article

In this study, the influence of thermal loading in a supersonic flight environment on the mechanical stiffness of elastic structures and the corresponding aeroelastic stability limits is investigated analytically. Recognizing that elevated temperatures inherently alter constituent elastic properties, a temperature-dependent continuous elasticity framework is incorporated directly into the governing differential operators of the structural domain. The macro-mechanical behavior of representative panel- and wing-type elements is modeled utilizing the Euler–Bernoulli beam formulation, while high-speed supersonic aerodynamic effects are represented through linearized first-order piston theory. The continuous spatial displacement fields are discretized by means of a modal expansion, and the coupled aeroelastic system is subsequently transformed into a finite set of dynamic state-space equations using the Ritz–Galerkin truncation method. The numerical and analytical outputs demonstrate that aerothermal softening not only induces continuous erosion in the material stiffness but also directly modulates the aeroelastic pole trajectories, thereby prematurely contracting the safe supersonic flight envelope. The primary novelty of the proposed framework lies in the derivation of explicit analytical expressions that directly map temperature-dependent stiffness variations onto supersonic aeroelastic instability boundaries. Because this approach is formulated in a generalized analytical form, it can be applied across diverse material systems, geometric profiles, and thermal conditions with reduced computational overhead compared to full fluid–structure interaction solvers, thereby providing a theoretical basis for preliminary stability assessment of supersonic aerospace configurations operating under high-temperature conditions. Full article

This study presents an Indoor Environmental Quality (IEQ)-aware framework for residential valuation by integrating low-cost IoT sensing, transparent scoring, and hedonic price modeling. The analysis uses a dataset of 244 apartments across 12 districts in Tehran. It combines indicators of thermal comfort, particulate exposure, lighting, acoustics, stability, exceedance, and uncertainty with conventional housing covariates (area, age, bedrooms, floor level, renovation status, amenities, and accessibility proxies). Results show that pooled IEQ–price relationships are weak and confounded, whereas controlled specifications produce modest but consistent improvements in explanatory fit after IEQ features are introduced. Conventional location and structural attributes remain the dominant determinants of price per square meter. Still, IEQ contributes a non-redundant information layer that improves within-segment differentiation and interpretability for inspection and listing workflows. Methodologically, the framework extends beyond average comfort metrics by incorporating volatility, threshold exceedance duration, and sensor uncertainty, enabling uncertainty-aware reporting rather than single-point scoring. In practice, the workflow supports portable sensing, reproducible analytics, and privacy-preserving edge aggregation, suitable for PropTech deployment. The findings support a cautious but actionable conclusion: IEQ should be treated as an incremental valuation signal rather than a standalone pricing determinant. In this context, IEQ is conceptualized as a supplementary attribute block that may add explanatory value beyond conventional housing covariates rather than as a standalone pricing determinant. Full article

Real-time telemetry is essential for performance optimization and safety in motorcycle racing, yet commercial solutions remain proprietary, expensive, and poorly extensible. This paper presents the design, implementation, and experimental evaluation of an open-architecture embedded telemetry unit built around the STM32H745 dual-core microcontroller. The system integrates a u-blox ZED-F9P RTK-GNSS receiver, a Bosch BNO085 9-DoF IMU with on-chip sensor fusion, a CAN-FD interface for powertrain data acquisition, and a SIM7600E-H 4G/LTE module for real-time remote streaming, all housed in a 3D-printed vibration-resistant enclosure. The firmware employs deterministic dual-core task partitioning: the Cortex-M7 core handles sensor fusion and CAN-FD at high frequency, while the Cortex-M4 core manages 4G communication and microSD logging. We explicitly delimit the scope of the evidence presented: CAN-FD powertrain acquisition and end-to-end operational reliability are experimentally validated on real circuit data spanning four campaigns, over 100 laps, and 5.8 h of logging—with sustained acquisition of 13 powertrain channels at speeds up to 185 km/h and zero system resets or data-integrity errors. In contrast, RTK positioning accuracy (2.5 cm CEP), sensor-fusion latency (sub-2 ms at the 99th percentile), 4G-uplink reliability, and thermal margins are characterized through manufacturer specifications, Monte Carlo simulation, and analytical models, with a fully instrumented end-to-end measurement campaign identified as the immediate next step. The 50 Hz effective positioning rate combines 25 Hz GNSS with IMU interpolation. With a bill of materials of approximately EUR 265, the platform offers an order-of-magnitude cost reduction over commercial alternatives while providing full openness and extensibility for distributed intelligence applications. Full article

Heat stress (HS) is at the top of the challenges facing modern dairy production, with annual losses according to global projections, under high-emission scenarios, reaching US$14.7–40.0 billion by the end of the century. This review emphasizes three interconnected topics that account for most of the proportion of the productive and reproductive losses during HS. First, the physiological consequences of HS are reviewed, with emphasis on the pair-fed thermal neutral (PFTN) paradigm, which established that reduced dry matter intake (DMI) accounts for only 35–50% of the observed milk yield decline, with the remainder arising from tissue-level effects of hyperthermia on mammary function, metabolism, and reproductive performance. Second, HS-induced microbiome disruption is examined as an active pathophysiological amplifier, whereby rumen dysbiosis compromises intestinal barrier integrity and drives systemic endotoxaemia, chronically amplifying the immune suppression already imposed by the thermal insult. Third, we focus on the integration of multi-omics platforms as a management approach, since single-omics analyses capture only a fraction of the biological complexity underlying the HS response. As the available datasets expand in coverage and scale, their integration through AI-driven analytical frameworks has the potential to substantially advance beyond the current fragmented picture, progressively building toward a systems-level model of thermal stress. Evidence-based mitigation strategies spanning environmental cooling, targeted nutritional supplementation, and genomic selection are critically evaluated within this framework, with emphasis on equity of access to evidence-based solutions across global dairy production systems. Full article

The energy recovery of biomass is frequently implemented through single-output systems or passive management schemes, resulting in underutilization of its thermodynamic potential and losses in economic value, climate benefits, and useful co-products. This study formalizes the concept of biopolygeneration as a diagnostic principle aimed at maximizing biomass utilization through the simultaneous production of multiple energy services and the valorization of secondary streams. A dimensionless metric, the Biopolygeneration Diagnostic Index (BDI), is proposed to quantify this concept. The index is bounded within $[0,1]$ and integrates five sub-indices: energy efficiency ($I_E$), thermal integration ($I_T$), energy self-sufficiency ($I_A$), exergetic quality of outputs ($I_Q$), and co-product valorization ($I_V$). Weights were determined using the Analytic Hierarchy Process ($w_1=0.40$, $w_2=0.24$, $w_3=w_4=0.14$, $w_5=0.08$; $\mathrm{CR}=0.007$). The BDI was evaluated using six cases, including five operating plants and one validated computational model representing five biomass conversion technologies in four countries. Results ranged from 0.453 for an engine without combined heat and power (CHP) to 0.733 for a cascade trigeneration system. Under identical feed conditions, the incorporation of CHP ($C1\to C2$) increased the BDI from 0.453 to 0.715, representing a 57.7% improvement attributable solely to heat recovery. Current limitations include the small validation sample ($n=6$) and the reconstruction of $I_A$ and $I_V$ from technological characteristics due to the absence of standardized reporting in the literature. Although these sub-indices account for only 22% of the total weighting ($w_{I_A}+w_{I_V}=0.22$), the present results should be considered a proof of concept rather than a fully empirical validation. The BDI provides a thermodynamically consistent framework for comparing bioenergy systems across technologies and supports technical, regulatory, and investment decision making. Broader validation using larger measurement-based datasets is required before claims of universality can be established. Full article

It has been observed in prior research that high thermal impact—resulting from a large temperature difference between hot water vapor and cold liquid water—can enhance the thermal performance of cavitation-induced low-energy nuclear reactions (LENRs) in water, with an estimated increase in the coefficient of performance (COP) of approximately 50% for every 100 °C temperature rise. The temperature of the hot water vapor is primarily determined by the boiler output, which typically represents the highest temperature source and plays a dominant role in reactor performance. In this study, a flow oscillator was designed as an thermal conditioning component for these potential LENR reactor systems using linear flow network analysis (LFNA) to generate flow resonance that elevates the hot vapor temperature, thereby increasing thermal impact and improving LENR performance. LFNA is based on the linearization of the fluid flow equations governing mass and momentum transport and utilizes a fluid-electric circuit analogy. For a fluid flow system, various components can be modeled using analogs of electrical resistance, capacitance, and inductance (R, C, and L), allowing the system behavior to be analyzed similarly to an RLC circuit. Through this analogy, flow resonance phenomena can be predicted, potentially enabling the generation of high-temperature and high-pressure responses that are beneficial to LENR processes. The analytical model was experimentally validated and subsequently applied in the LENR reactor design. The analytical result shows that an output temperature difference exceeding 350 °C can be achieved using a 0.5 m pulse tube at a 46 Hz triggering frequency with 20 kPa perturbation, which indicates a potential COP enhancement of 175% based on prior studies. The result provides a potential mechanism to significantly enhance the thermal impact conditions and promote LENR performance in water-based reactor systems. Full article

This study focuses on a 155 mm 32CrNi3MoV steel barrel and presents a thermochemically coupled phase transformation and diffusion dynamics model. The model leverages the significant disparity between radial and axial temperature gradients to simplify the heat conduction problem to a one-dimensional transient formulation. The temperature field distribution during firing sequences is solved analytically, accounting for the dynamic shift in critical phase transformation temperatures under high heating rates. The evolution of the martensitic layer thickness under repeated thermal shock is subsequently calculated. A numerical model for the pulsed diffusion of C and N is established based on Fick’s second law, incorporating the competitive diffusion–phase transformation mechanisms that govern martensite/austenite interface migration. To quantitatively evaluate the synergistic contribution of C and N to austenite stabilization, a carbon equivalent (Ceq) model is introduced, with the weight coefficient of N relative to C determined to be 0.68 and the critical Ceq required to lower the martensite start temperature below 25 °C calculated as 1.15 wt%. Concurrently, the microstructure and elemental distribution within the austenite layer of the retired barrel are systematically characterized using multi-scale techniques. The results indicate that the austenite layer on the inner bore surface arises from the synergistic effects of cyclic thermal-shock-induced phase transformation and elemental diffusion. Based on the Ceq criterion, the austenite layer thickness increases rapidly during the initial ~100 firing cycles, after which the growth rate slows significantly: it reaches approximately 1.27 μm after the first cycle and 2.94 μm after 1000 cycles, with only 0.2 μm of additional thickening between 100 and 1000 cycles—consistent with the experimentally observed range of 1.52–4.16 μm. The martensitic layer formed during the first firing cycle exhibits low thermal conductivity, which impedes subsequent heat transfer and leads to stabilization of its thickness at a characteristic depth. Grain refinement induced by repeated thermal shock provide short-circuit diffusion paths for elemental diffusion, accelerating compositional homogenization within the austenite layer and resulting in a stepped concentration profile at the interface. This study provides a representative example of non-equilibrium coupled phase transformation–diffusion phenomena under extreme transient loading. The established thickness prediction model can provide guidance for service life assessment of large-caliber barrels, offering both theoretical foundations and practical engineering guidance for their material design and performance optimization. Full article

Accurate estimation of crop yield from remote sensing remains challenging due to the crop-specific nature of yield drivers and the difficulty of interpreting spectral indicators across agronomic systems. While many studies prioritise predictive accuracy through complex models, fewer explicitly examine the stability and physiological relevance of individual spectral and phenological indicators under controlled analytical conditions. This study investigates yield–spectral relationships in wheat and cotton using a unified Sentinel-2 indicator framework applied across multiple growing seasons in a Mediterranean agricultural environment. A consistent set of spectral and thermal indicators was derived from two phenologically targeted Sentinel-2 acquisitions per season and analysed using correlation analysis, univariate regression, constrained multivariate modelling, and recurrence analysis within an identical workflow for both crops. Distinct crop-specific patterns were observed. Wheat yield was most strongly associated with water-sensitive and canopy-related indicators, with NDWI-based metrics reaching Pearson correlations up to r = 0.85 and multivariate models explaining a substantial proportion of yield variability (up to R² ≈ 0.70) under controlled analytical conditions. In contrast, cotton yield variability was dominated by thermal accumulation, with growing degree day indicators showing correlations up to |r| = 0.59 and multivariate performance reaching R² = 0.74. Recurrence analysis indicated consistent recurrence of these indicator families across analytical stages under the examined conditions. Overall, the results indicate that parsimonious, physiologically interpretable indicator combinations can account for a meaningful proportion of yield variability without reliance on highly complex or high-dimensional modelling approaches, supporting crop-aware indicator selection for precision agriculture applications. Full article

Clamp band separation mechanisms are widely used in spacecraft interfaces, and the clamp band preload is a key factor governing both connection reliability and separation performance. The conventional torque-control method is susceptible to friction-induced preload non-uniformity in clamp band separation mechanisms. To overcome this limitation, thermal preloading has been proposed as an alternative installation method. In this paper, a thermo-mechanical analytical model is established for clamp band separation mechanisms during thermal preloading based on curved-beam and thin-shell theories. Theoretical analysis shows that the preload distribution can be divided into three characteristic zones: a stick zone, a slip zone, and a separation zone. In the stick zone, the preload remains constant and is mainly governed by thermal stress and structural relative stiffness. In the slip zone, friction dominates the load transfer, leading to a non-uniform preload distribution. In the separation zone, local disengagement occurs near the clamp band joint end due to the eccentricity-induced bending moment. The proposed model is validated by finite element simulations, and parametric studies are conducted to reveal the effects of friction coefficient and structural geometric parameters on preload distribution. Based on the theoretical model, a zoned-heating method is proposed to improve preload uniformity, providing a useful reference for optimizing the thermal preloading method. Full article

Energy pile technology integrates geothermal energy exploitation with pile foundation bearing, yet accurately evaluating its thermo-mechanical performance remains theoretically challenging. To address the limitations of traditional load-transfer methods for accurately locating the neutral plane—which cause inconsistencies between computed pile-head axial forces and applied loads, and calculation discontinuities at the neutral plane—this study proposes an improved method using an iterative algorithm to eliminate unbalanced forces. Furthermore, based on a non-linear load-transfer function for pile-soil displacement compatibility, a model with well-defined parameters is established to capture the impact of long-term temperature cycles on the evolution of shaft resistance. A comprehensive calculation method for pile axial force and shaft resistance under cyclic temperature effects is thereby established. The analytical method is systematically validated against classical numerical methods and field test data from the London and Kunshan energy piles. Subsequent analysis reveals that heating–cooling cycles induce additional pile settlement. With increasing thermal cycles, the pile’s mechanical response evolves and ultimately stabilizes. Finally, increasing the applied mechanical load progressively attenuates the impact of cyclic temperature variations on the pile’s load-bearing performance. Full article

This study analyzes and explores the influence of multiple physical mechanisms—namely the influences of heat and mass transfer, thermal radiation, and magnetic field effects on the peristaltic transport of a Rabinowitsch-type non-Newtonian fluid within an inclined channel. To accurately represent the intricate behavior of the fluid under these coupled physical phenomena, a nonlinear model was formulated that integrates thermal, magnetic, and radiative forces into its framework. The given coupled differential equations are transformed into ordinary differential equations (ODEs). Using assumptions of long-wavelength and low-Reynolds-number approximations, the governing equations were significantly simplified. The resulting set of equations was solved analytically using Mathematica, subject to appropriate boundary conditions for velocity, temperature, and concentration. Graphs for velocity, temperature and concentration are illustrated. Thermal radiation was incorporated into the energy equation via the Rosseland approximation, thereby enabling a more accurate characterization of heat transport within the system. Moreover, the rate of heat and mass transfer for different variables was also examined. These findings are essential for the progression of advanced fluid transport systems in biomedical engineering, chemical processing, and energy generation, improving the design and management of non-Newtonian fluid dynamics. Full article

The open-source Python package Geoloop introduces a novel, semi-analytical model for predicting the performance of deep (>500 m depth) vertical borehole heat exchangers (BHEs), with a focus on capturing depth-dependent variations in subsurface thermal properties, i.e., geothermal gradient and thermal conductivity. Conventional computationally efficient semi-analytical models based on load-aggregation of g-functions often assume uniform subsurface thermal properties. Geoloop addresses this gap by implementing a vertically stacked approach, allowing for realistic simulation of depth-variability in both the subsurface and borehole material properties. The model is benchmarked in the shallow domain against standard depth-uniform g-function implementations (up to 100 m depth) and for deeper conditions with a numerical finite volume model, demonstrating strong agreement and validating its accuracy and efficiency. Simulations for typical Dutch conditions show that deeper BHEs (up to 2000 m) can achieve significantly higher thermal power supply than shallower systems, and results in terms of resulting inlet/outlet temperatures for given heat extraction rates can strongly deviate (>4 °C) from results obtained by depth-uniform assumptions in thermal properties. Application of the model to the Dutch context reveals a non-linear increase in heat extraction potential with depth, surpassing values assumed in common practice by Dutch industry. The results highlight the importance of considering local geological heterogeneity and depth-dependent properties for accurate deep borehole heat exchanger (BHE) performance assessment and system optimization. Geoloop thus offers a robust, versatile platform for advancing the design and analysis of deep vertical BHE systems. Full article