Search Results (449)

Thermal gradients induced by friction frequently trigger buckling deformation of the friction elements, especially in heavy-duty helicopters. Nevertheless, the subsequent influence of such post-buckling deformation on transient thermal characteristics during helicopter successive shifting remains insufficiently addressed in existing research. In the present work, a gap model for friction pairs with conical separate discs is first proposed. Subsequently, a comprehensive thermal-fluid-dynamic model incorporating spline friction, split springs, and time-varying thermal parameters is developed to investigate the transient thermal characteristics of wet clutches with conical separate discs in successive shifting. A corresponding qualitative analysis is performed to explore the transient thermal response and influence mechanisms of operating parameters, including shifting interval, rotation speed and control oil pressure. The results indicate that a rise in the control oil pressure from 1.5 MPa to 1.9 MPa facilitates a 42.65% increase in the maximum radial temperature gradient and augments the maximum axial temperature gradient by 24.35%. Meanwhile, an increase in rotation speed accelerates heat dissipation but compromises the uniformity of the temperature field. Additionally, extended shifting intervals under inadequate heat dissipation exacerbates thermal buildup, driving a persistent and significant escalation in the temperature of friction elements. The conclusions can provide a theoretical basis for the optimal design, condition monitoring, and fault diagnosis of aviation clutches. Full article

This study introduces a dynamic, multi-output stacking framework for real-time forecasting of HVAC cooling-coil behavior in air-handling units. The dynamic model encodes short-horizon system memory with input/target lags and rolling psychrometric features and enforces leakage-free, time-aware validation. Four base learners—Random Forest, Bagging (DT), XGBoost, and ANN—are each optimized with an Optuna hyperparameter tuner that systematically explores architecture and regularization to identify data-specific, near-optimal configurations. Their out-of-fold predictions are combined through a Ridge-based stacker, yielding state-of-the-art accuracy for supply-air temperature and chilled water leaving temperature (R² up to 0.9995, NRMSE as low as 0.0105), consistently surpassing individual models. Novelty lies in the explicit dynamics encoding aligned with coil heat and mass-transfer behavior, physics-consistent feature prioritization, and a robust multi-target stacking design tailored for HVAC transients. The findings indicate that this hyperparameter-tuned dynamic framework can serve as a high-fidelity surrogate for cooling-coil performance, supporting set-point optimization, supervisory control, and future extensions to virtual sensing or fault-diagnostics workflows in industrial AHUs. Full article

Dynamic social and legal transformations drive technological innovation and the transition of energy and heating sectors toward renewable sources and higher efficiency. Ensuring the reliable operation of these systems requires regular inspections, fault detection, and infrastructure maintenance. Unmanned Aerial Vehicles (UAVs) are increasingly being used for monitoring and diagnostics of photovoltaic and wind farms, power transmission lines, and urban heating networks. Based on literature from 2015 to 2025 (Scopus database), this review compares UAV platforms, sensors, and inspection methods, including thermal, RGB/multispectral, LiDAR, and acoustic, highlighting current challenges. The analysis of legal regulations and resulting operational limitations for UAVs, based on the frameworks of the EU, the US, and China, is also presented. UAVs offer high-resolution data, rapid coverage, and cost reduction compared to conventional approaches. However, they face limitations related to flight endurance, weather sensitivity, regulatory restrictions, and data processing. Key trends include multi-sensor integration, coordinated multi-UAV missions, on-board edge-AI analytics, digital twin integration, and predictive maintenance. The study highlights the need to develop standardised data models, interoperable sensor systems, and legal frameworks that enable autonomous operations to advance UAV implementation in energy and heating infrastructure management. Full article

With the continuous expansion of the electric vehicle market, lithium-ion batteries have also been rapidly developed, but this has brought about concerns over the safety of lithium-ion batteries. Research on the correlation mechanism between the expansion and safety of lithium-ion batteries is a key step in the construction of a battery life cycle safety evaluation system. In this paper, the physicochemical mechanism of early safety faults in batteries was analyzed from three dimensions of electricity, heat, and force. The interactions of electrochemical side reactions, thermal runaway chain reactions, and mechanical fault mechanisms were analyzed, and the core induction of early safety risk was explored. A battery coupling model based on electrical, thermal, and mechanical dimensions was built, and the accuracy of the coupling model was verified by a variety of test conditions. Based on the coupling model, the stress distribution of the battery under different safety boundary conditions was simulated, and then the average expansion force of the battery surface was calculated through the stress distribution results. Through this process, a multi-parameter database based on the test and simulation data was obtained. According to the data of battery parameters at different times, an early safety classification method based on the battery expansion force was proposed, and a classification model between battery dimension data and safety level was proposed based on the nonlinear dynamic sparse regression method, and the classification accuracy was validated. From the perspective of fault warning, by establishing a multi-physical coupling model of electrical, thermal, and mechanical fields, the space-time evolution law of battery expansion under different working conditions can be dynamically monitored, and the fault criterion based on the expansion force can be established accordingly to provide quantitative indicators for safety risk classification warnings, and improve the battery’s reliability and durability. Full article

Power transformers are critical components of power grids, and their operational status characterization and fault diagnosis are crucial for power system reliability. Oil quality assessment is a crucial method for determining transformer status, and the detection of impurity particles in oil has historically been a key approach. However, recent field tests have revealed the presence of numerous impurity particles less than 5 μm in transformer oil. Current power standards do not address these micron-sized particles, and their sources and mechanisms of action are largely unresolved. Therefore, this paper designed a localized overheating experiment, incorporating microflow imaging technology, to investigate the generation patterns of impurity particles under localized overheating and their quantitative correlation with heat. Field oil samples were also collected and tested to further explore the potential application of these micron-sized particles in transformer overheating assessment. The research results show that insulating oil can decompose and produce impurity particles at temperatures as low as 140 °C. When the temperature is below 140 °C, the number of particles at different heat levels is not significantly different from that of the non-overheated oil sample. However, when the temperature exceeds 140 °C, the number of particles increases significantly with increasing heat. Among the generated particles, particles with a diameter of less than 5 μm account for over 50% of the total number, and their number increases significantly with increasing heat. Their morphology is characterized by a smooth, regular, and spherical shape. Field test results of overheated oil samples are consistent with laboratory tests. Micron-sized particles are highly sensitive to changes in overheating conditions and have the potential to be used as a new characteristic parameter of transformer overheating conditions. In summary, this paper reveals the formation mechanism of impurity particles in insulating oil under localized overheating conditions. It was found that insulating oil can also decompose and generate impurity particles at 140 °C, with the pyrolysis products mainly consisting of particles smaller than 5 μm in diameter, which are not currently considered a concern in existing standards. Further research indicates that these micron-sized particles exhibit high sensitivity to changes in overheating conditions, demonstrating potential application value as a novel characteristic parameter of transformer overheating. Full article

The rise in thermal runaway events within electric vehicle (EV) battery systems requires anticipatory models to predict critical safety failures during operation. This investigation develops a multi-physics digital twin framework that links electrochemical, thermal, and structural domains to replicate the internal dynamics of lithium-ion packs in both normal and faulted modes. Coupled simulations distributed among MATLAB 2024a, Python 3.12-powered three-dimensional visualizers, and COMSOL 6.3-style multi-domain solvers supply refined spatial resolution of temperature, stress, and ion concentration profiles. While the digital twin architecture is designed to accommodate different battery chemistries and pack configurations, the numerical results reported in this study correspond specifically to a lithium NMC-based 4S3P cylindrical cell module. Quantitative benchmarks show that the digital twin identifies incipient thermal deviation with 97.4% classification accuracy (area under the curve, AUC = 0.98), anticipates failure onset within a temporal margin of ±6 s, and depicts spatial heat propagation through three-dimensional isothermal surface sweeps surpassing 120 °C. Mechanical models predict casing strain concentrations of 142 MPa, approaching polymer yield strength under stress load perturbations. A unified operator dashboard delivers diagnostic and prognostic feedback with feedback intervals under 1 s, state-of-health (SoH) variance quantified by a root-mean-square error of 0.027, and mission-critical alerts transmitting with a mean latency of 276.4 ms. Together, these results position digital twins as both diagnostic archives and predictive safety envelopes in the evolution of next-generation EV architectures. Full article

The reliability of electromechanical systems is a critical factor in modern Industry 4.0, as unexpected failures in induction motors or gearboxes can cause costly downtime, productivity losses, and increased maintenance demands. Infrared thermography offers a non-invasive and real-time means of monitoring thermal behavior, yet its effective use for fault diagnosis remains challenging due to sensitivity to noise, environmental variability, and the need for robust feature extraction. This work proposes a novel end-to-end convolutional neural network (CNN) methodology for detecting and classifying faults in electromechanical systems through the processing of infrared thermography images. The method integrates an automatic preprocessing stage that isolates the Relevant Heating Areas (RHAs), preserving their geometric and thermal descriptors while filtering irrelevant background information. A tailored data augmentation strategy, including controlled noise injection, was designed to improve robustness under realistic acquisition conditions. The CNN architecture combines 3 × 3 and 5 × 5 kernels to capture both fine-grained and global heating patterns. Experimental validation is carried out under nine different faulty conditions, achieving 99.7% accuracy and demonstrating strong resilience against Gaussian blur and additive Gaussian noise. The results suggest that the method provides a scalable, interpretable, and efficient approach for fault diagnosis in electromechanical systems within Industry 4.0 environments. Full article

In recent years, hydrothermal geothermal resources have been predominantly exploited through well cluster systems, achieving extensive commercial implementation. The efficient development of such systems remains critically dependent on the comprehensive characterization of regional geological conditions, as multiple subsurface parameters—including stratal thickness, structural relief, porosity–permeability distribution, and fault architecture—exert substantial control over reservoir performance. However, the effective integration of these geological factors to optimize well network configurations, balancing economic viability, power output, and other evaluation metrics to enhance heat extraction efficiency and delay thermal breakthrough, remains unresolved. This study aimed to identify the optimal well cluster for hydrothermal geothermal resources in the X region. Geological, drilling, and well-logging data were compiled to construct a region-specific geological model. A coupled numerical model of fluid flow and heat transfer was developed for a five-spot well pattern. System performances under two commonly applied injection-to-production ratios (2:3 and 1:4) and spatial configurations between injection and production wells were quantified. A multi-criteria evaluation framework integrating heat extraction power, injection–production pressure difference, and production temperature decline was implemented to holistically assess well cluster. An integrated weighting strategy combining subjective expertise and objective analytical criteria was implemented alongside the TOPSIS method to systematically identify the optimal wellfield configuration. Results demonstrate that Pattern 11, comprising three injection wells and two production wells, achieved superior comprehensive performance among 15 patterns, with heat extraction power 24.13 MW, and production temperature decline <1 °C and injection–production pressure difference <3 MPa. Full article

Identifying the causes and mechanisms of heat hazards in mining operations is essential for effective heat hazard prevention and control. In recent years, hydrothermal phenomena have frequently occurred in the eastern part of the Chenghe Mining Area, located in the central Weibei Coalfield. However, research on the geothermal fluid migration patterns and heat generation mechanisms in this region remains limited. This study comprehensively explores the geothermal field characteristics in the area, based on well temperature logging data, rock thermal conductivity, temperature control models, temperature curve analysis, and numerical simulations. It reveals the key controlling factors and mechanisms behind the formation of geothermal anomalies in the region. The results show that the overall geothermal heat flow trend in the area is characterized by low heat in the northwest and high heat in the southeast. The formation of geothermal anomalies is primarily influenced by water-conducting faults and coal seams. Based on this, the temperature control models are classified into two types: the fault + deep circulating thermal water uplift model and the coal seam heat-resistant-folded temperature control model. Heat transfer occurs through groundwater convection along the F1 fault and its secondary faults, which transport heat. The heat generation mechanism in the study area involves the heating of groundwater during deep circulation, followed by the upward migration of the heated water along the F1 fault, which adds an additional heat source to the surrounding rock of the fault, creating localized thermal anomalies. The findings of this study provide direct guidance for safe production in the Chenghe Mining Area and offer a universal theoretical framework for understanding the causes of heat hazards in mining areas with strong tectonic activity in northwestern China. Full article

District heating is critical for low-carbon urban energy systems, yet most networks remain centralized in both heat generation and data ownership, fossil-dependent, and poorly integrated with digital, customer-centric, and market-responsive solutions. While artificial intelligence (AI), the Internet of Things (IoT), and automation offer transformative opportunities, their adoption raises complex challenges related to business models, regulation, and consumer trust. This paper addresses the absence of a comprehensive synthesis linking technological innovation, business-model evolution, and institutional adaptation in the digital transformation of district heating. Using the PRISMA-ScR methodology, this review systematically analyzed 69 peer-reviewed studies published between 2006 and 2024 across four thematic domains: digital technologies and automation, business-model innovation, customer engagement and value creation, and challenges and implementation barriers. The results reveal that research overwhelmingly emphasizes technical optimization, such as AI-driven forecasting and IoT-based fault detection, whereas economic scalability, regulatory readiness, and user participation remain underexplored. Studies on business-model innovation highlight emerging approaches such as dynamic pricing, co-ownership, and sector coupling, yet few evaluate financial or policy feasibility. Evidence on customer engagement shows increasing attention to real-time data platforms and prosumer participation, but also persistent barriers related to privacy, digital literacy, and equity. The review develops a schematic conceptual framework illustrating the interactions among technology, business, and governance layers, demonstrating that successful digitalization depends on alignment between innovation capacity, market design, and institutional flexibility. Full article

The paper focuses on fault diagnosis in nonlinear analog circuits, a topic that has been explored in the literature for over 50 years but remains unresolved due to its complex nature. It reviews various aspects of nonlinear circuit diagnosis, including methods for assessing testability, selecting diagnostic tests, and conducting diagnostic processes through simulation before and after test methods. The paper also discusses the use of artificial intelligence tools in this context. Given the specific characteristics of nonlinear circuits used as DC-DC converters and the wealth of the existing literature—including review papers on the subject—this issue is addressed only briefly. The main aim of the paper is to identify research challenges in fault diagnosis for a general class of nonlinear circuits. To illustrate and discuss these challenges, the paper provides examples, including ambiguity in diagnostic equation solutions, multiple equilibrium points, and the effects of self-heating. These examples can serve as simple benchmarks for new proposals to advance comprehensive diagnosis methods for nonlinear circuits. Full article

The article presents the development and laboratory validation of a current optimization algorithm designed for voltage-source systems. The algorithm was implemented in a real laboratory setup using a synchronous generator driven by a DC motor to model a fragment of a power system. During tests, transient states were intentionally inducted to evaluate the algorithm’s performance. The proposed optimization method effectively reduced instantaneous current peaks by over 60% and the overall RMS current by approximately 4%, leading to lower power losses and decreased conductor temperatures. These improvements resulted in nearly 8% minimization of power losses and a noticeable reduction in heat generation during transient and fault conditions. The solution is particularly suitable for islanded networks with renewable and unstable energy sources. Full article

The neutral point of a 10 kV distribution network often adopts an arc suppression coil or high resistance grounding mode to ensure the reliability of the power supply. The single-phase grounding fault current is below 10 A, and the distribution network can continue to operate with the fault for up to 2 h. However, long-time arc faults may ignite cables and cause electrical fires, causing further damage to adjacent cables and seriously affecting the safety of the power grid. To study the combustion characteristics of a single-phase grounding fault of a distribution network cable under the action of a long-term small current arc, the cable fault ignition test was carried out by using the arc ignition method of welding tin wire fuses. Then, the temperature distribution of the cable channel in an electrical fire was simulated, based on an FDS simulation, and the damage of adjacent cables under typical layout was further analyzed. The results show that the 10 kV cable was quickly ignited by the high temperature arc within 0.04 s after the breakdown and damage of the cable. Flammable XLPE insulation melted or even dripped off at a high temperature in fire. Thus, the fire spread to both ends when burning. Under the condition of 4–10 A, the maximum flame temperatures above the arc fault point reached 725 °C, 792 °C, 812 °C and 907 °C, respectively. According to the network structure, some protection, such as fireproof tape, needs to be applied directly above the faulty cable when the fault current exceeds 6 A. Full article

To address the high costs and inefficiencies of blind prospecting in deep geothermal exploration, this study develops a three-dimensional heat transfer model for quantitative prediction of geothermal enrichment targets. Unlike traditional qualitative or single-mechanism analyses, this research utilizes a finite element forward modeling approach based on step-faulted depressions (sedimentary basins/grabens) and uplifts (domes/uplift belts). We simulate temperature fields and heat flux distributions in multilayered systems incorporating four thermal conductivity types (A, K, H, Q). By systematically comparing the geometric heat flow convergence in depressions with the lateral diffusion in uplifts, this work reveals mirror and anti-mirror relationships between temperature fields and structural morphology at middle and deep levels, as well as local “hot spot” and “cold zone” effects. The results indicate that, in depressional structures, shallow high-temperature reservoirs (<2 km) are mainly concentrated in A- and K-types, while deeper reservoirs (>3 km) are enriched in Q- and H-types. In contrast, uplift structures are characterized by mid- to shallow-depth (<3 km) reservoirs predominantly in A- and K-types, with high temperatures at depth preferentially hosted in A- and H-types, and the highest temperatures observed in the A-type. Thermal conductivity contrasts, layer thicknesses, and structural morphology collectively control the spatial distribution of heat flux. A strong positive correlation between thermal conductivity and heat flux is observed at the central target area, significantly stronger than at the margins, whereas this relationship is notably weakened in Q-type. Crucially, low-conductivity zones display high geothermal gradients coupled with low terrestrial heat flow, disproving the axiom that “elevated geothermal gradients imply high heat flow,” thus establishing “high-gradient/low-heat-flow coupling zones” as strategic exploration targets. The model developed in this study demonstrates high simulation accuracy and computational efficiency. The findings provide a robust theoretical basis for reconstructing geothermal geological evolution and precise geothermal target localization, thereby reducing the risk of “blind heat exploration” and promoting the cost-effective and refined development of deep concealed geothermal resources. Full article

Temperature control plays a critical role in mitigating the lifespan degradation mechanisms and ensuring thermal safety of lithium-ion batteries (LIBs) and proton exchange membrane fuel cells (PEMFCs). However, current energy management strategies (EMS) for fuel cell hybrid electric vehicles (FCHEVs) generally lack comprehensive thermal effect modeling and thermal runaway fault diagnosis, leading to irreversible aging and thermal runaway risks for LIBs and PEMFCs stacks under complex operating conditions. To address this challenge, this paper proposes a thermo-electrical co-optimization EMS incorporating thermal runaway fault diagnosis actuators, with the following innovations: firstly, a dual-layer framework integrates a temperature fault diagnosis-based penalty into the EMS and a real-time power regulator to suppress heat generation and constrain LIBs/PEMFCs output, achieving hierarchical thermal management and improved safety; secondly, the distributional soft actor–critic (DSAC)-based EMS incorporates energy consumption, state-of-health (SoH) degradation, and temperature fault diagnosis-based constraints into a composite penalty function, which regularizes the reward shaping and guides the policy toward efficient and safe operation; finally, a thermal safe constriction controller (TSCC) is designed to continuously monitor the temperature of power sources and automatically activate when temperatures exceed the optimal operating range. It intelligently identifies optimized actions that not only meet target power demands but also comply with safety constraints. Simulation results demonstrate that compared to DDPG, TD3, and SAC baseline strategies, DSAC-EMS achieves maximum reductions of 39.91% in energy consumption and 29.38% in SoH degradation. With the TSCC implementation, enhanced thermal safety is achieved, while the maximum energy-saving improvement reaches 25.29% and the maximum reduction in SoH degradation attains 20.32%. Full article