Search Results (251)

The high energy consumption and spatiotemporal thermal asymmetry of data center cooling systems have become critical bottlenecks constraining their green and sustainable development. Traditional point-type temperature sensors suffer from insufficient spatial coverage, while conventional feedback control strategies exhibit delayed responses and limited adaptability under dynamic workloads. To address these challenges, this study proposes a real-time thermal symmetry management framework for data centers based on distributed fiber optic temperature sensing and model predictive control (MPC). The proposed system employs Brillouin scattering-based distributed sensing to continuously acquire high-density temperature measurements from thousands of points along a single optical fiber, enabling fine-grained perception of the three-dimensional thermal field. On this basis, a hybrid prediction model integrating thermodynamic physical equations with a Temporal Convolutional Network–Bidirectional Gated Recurrent Unit (TCN–BiGRU) deep neural network is developed to achieve accurate and stable spatiotemporal temperature forecasting. Furthermore, a symmetry-aware MPC controller is designed with the dual objectives of minimizing cooling energy consumption and suppressing thermal field deviations, thereby restoring temperature uniformity through rolling-horizon optimization. Experimental validation in a production data center demonstrates that the distributed sensing system achieves a measurement deviation of 0.12 °C, while the hybrid prediction model attains a root mean square error of 0.41 °C, representing a 26.8% improvement over baseline methods. The MPC-based control strategy reduces daily cooling energy consumption by 14.4%, improves the power usage effectiveness (PUE) from 1.58 to 1.47, and significantly enhances both thermal symmetry and operational safety. The Thermal Symmetry Index (TSI) decreased from 0.060 to 0.035, indicating a 41.7% improvement in spatial temperature distribution uniformity. The TSI is defined as the ratio of spatial temperature standard deviation to mean temperature, where lower values indicate better thermal uniformity; TSI < 0.03 represents excellent symmetry, 0.03–0.05 indicates good symmetry, and TSI > 0.08 suggests significant asymmetry requiring intervention. These results provide an effective and practical solution for intelligent operation, energy-efficient control, and low-carbon transformation of next-generation green data centers. Full article

Addressing the growing need for sustainable innovation in PVC materials, this study presents an illustrative framework that develops and demonstrates an ontological system that integrates lifecycle simulation using Granta EduPack, systematic literature analysis (including bibliometric, thematic, and content analytics) of peer-reviewed publications, and Protégé-based semantic reasoning, and their combination, in a holistic manner. Material and use-phase data for PVC, HDPE, PP, PET, and FRP cooling-tower components were sourced from ANSYS Granta EduPack Level-3 Polymer Sustainability 2023 R2 Version; 23.2.1, and a systematic analysis of the literature was then encoded as ontology classes, properties, and individuals following the Seven-Step ontology development method. Eco-audit simulations, standardised to a functional unit of 1 kg cooling tower fill material, reveal that the use phase dominates environmental impact (67 MJ primary energy, ~80% of total lifecycle), while material production and end-of-life recycling contribute ~15% and credits of ~900 MJ and 28 kg CO₂ via recycling offsets. Ontology reasoning with corrected SWRL rules and SPARQL queries classifies VirginPVCRef and PVC10ES as strong structural materials (tensile strength ≥ 40 MPa), identifies PVCRH40 as high-moisture-risk (water absorption > 0.10 g/g), and ranks hydro-thermal dechlorination (recyclability 0.90) over mechanical recycling (0.55). A systematic analysis of 40 Scopus-indexed publications (2015–2025) highlighted key themes in recycling technologies, LCA emissions, additive toxicity, ontology frameworks, machine learning integration, circular economy policy, and cooling-tower applications. Demonstrated via a simulation-based cooling-tower case study, hybrid PVC-FRP designs yield the highest justified Material Sustainability Performance Index (MSPI), outperforming PVC-only and FRP-only alternatives. This framework provides a conceptual decision-support tool for exploring PVC material optimisation, illustrating pathways to enhancing circularity and environmental responsibility in industrial applications. The proposed framework is, therefore, not intended as a validated decision-support tool, nor does it claim analytical optimisation or predictive performance but rather serves as a method of illustration that shows how domain knowledge can be formally structured using ontology principles linked to simulation representations, and that was examined for internal logical consistency. Full article

An accurate prediction of aircraft wheel and brake (W&B) temperatures is increasingly important for ensuring landing gear safety, supporting turnaround decision-making, and allowing for more effective condition monitoring. Although the thermal behavior of brake assemblies has been studied through component-level testing, analytical formulations, and numerical simulation, current understandings remain fragmented and limited in operational relevance. This paper discusses research across landing gear sensing, thermal modeling, and data-driven prediction to evaluate the state of knowledge supporting a non-intrusive, temperature-centric monitoring framework. Methods surveyed include optical, electromagnetic, acoustic, and infrared sensing techniques as well as traditional machine-learning methods, sequence-based models, and emerging hybrid physics–data approaches. The review synthesizes findings on conduction, convection, and radiation pathways; phase-dependent cooling behavior during landing roll, taxi, and wheel-well retraction; and the capabilities and limitations of existing numerical and empirical models. This study highlights four core gaps: the scarcity of real-flight thermal datasets, insufficient multi-physics integration, limited use of infrared thermography for spatial temperature mapping, and the absence of advanced predictive models for transient brake temperature evolution. Opportunities arise from emissivity-aware infrared thermography, multi-modal dataset development, and machine learning models capable of capturing transient thermal dynamics, while notable challenges relate to measurement uncertainty, environmental sensitivity, model generalization, and deployment constraints. Overall, this review establishes a coherent foundation for thermography-enabled temperature prediction framework for aircraft wheels and brakes. Full article

The grid-forming static var generator (SVG) is a key device that supports the stable operation of power grids with a high penetration of renewable energy. The cooling efficiency of its forced water-cooling system directly determines the reliability of the entire unit. However, existing wired monitoring methods suffer from complex cabling and limited capacity to provide a full perception of the water-cooling condition. To address these limitations, this study develops a wireless monitoring system based on multi-source information fusion for real-time evaluation of cooling efficiency and early fault warning. A heterogeneous wireless sensor network was designed and implemented by deploying liquid-level, vibration, sound, and infrared sensors at critical locations of the SVG water-cooling system. These nodes work collaboratively to collect multi-physical field data—thermal, acoustic, vibrational, and visual information—in an integrated manner. The system adopts a hybrid Wireless Fidelity/Bluetooth (Wi-Fi/Bluetooth) networking scheme with electromagnetic interference-resistant design to ensure reliable data transmission in the complex environment of converter valve halls. To achieve precise and robust diagnosis, a three-layer hierarchical weighted fusion framework was established, consisting of individual sensor feature extraction and preliminary analysis, feature-level weighted fusion, and final fault classification. Experimental validation indicates that the proposed system achieves highly reliable data transmission with a packet loss rate below 1.5%. Compared with single-sensor monitoring, the multi-source fusion approach improves the diagnostic accuracy for pump bearing wear, pipeline micro-leakage, and radiator blockage to 98.2% and effectively distinguishes fault causes and degradation tendencies of cooling efficiency. Overall, the developed wireless monitoring system overcomes the limitations of traditional wired approaches and, by leveraging multi-source fusion technology, enables a comprehensive assessment of cooling efficiency and intelligent fault diagnosis. This advancement significantly enhances the precision and reliability of SVG operation and maintenance, providing an effective solution to ensure the safe and stable operation of both grid-forming SVG units and the broader power grid. Full article

Ultra-precision single-point diamond turning (SPDT) remains the core process for fabricating optical-grade surfaces with nanometric roughness and sub-micrometer form accuracy. However, machining hard-to-cut or brittle materials such as high-entropy alloys, metals, ceramics, and semiconductors is limited by severe tool wear, high cutting forces, and brittle fracture. To overcome these challenges, a new generation of non-conventional assisted and hybrid SPDT platforms has emerged, integrating multiple physical fields, including mechanical, thermal, magnetic, chemical, or cryogenic methods, into the cutting zone. This review comprehensively summarizes recent advances in hybrid non-conventional assisted SPDT platforms that combine two or more assistive techniques such as ultrasonic vibration, laser heating, magnetic fields, plasma or gas shielding, ion implantation, and cryogenic cooling. The synergistic effects of these dual-field platforms markedly enhance machinability, suppress tool wear, and extend ductile-mode cutting windows, enabling direct ultra-precision machining of previously intractable materials. Recent key case studies are analyzed in terms of material response, surface integrity, tool life, and implementation complexity. Comparative analysis shows that hybrid SPDT can significantly reduce surface roughness, extend diamond tool life, and yield optical-quality finishes on hard-to-cut materials, including ferrous alloys, composites, and crystals. This review concludes by identifying major technical challenges and outlining future directions toward optimal hybrid SPDT platforms for next-generation ultra-precision manufacturing. Full article

Accurate multi-energy load forecasting is essential for the low-carbon, efficient, and resilient operation of park-level Integrated Energy Systems (PIESs), where cooling, heating, and electricity networks interact closely and increasingly incorporate renewable energy resources. However, forecasting in such systems remains challenging due to complex cross-energy coupling, high-dimensional feature interactions, and pronounced nonlinearities under diverse meteorological and operational conditions. To address these challenges, this study develops a novel three-stage hybrid forecasting framework that integrates Recursive Feature Elimination with Cross-Validation (RFECV), a Multi-Task Long Short-Term Memory network (MTL-LSTM), and Random Forest (RF). In the first stage, RFECV performs adaptive and interpretable feature selection, ensuring robust model inputs and capturing meteorological drivers relevant to renewable energy dynamics. The second stage employs MTL-LSTM to jointly learn shared temporal dependencies and intrinsic coupling relationships among multiple energy loads. The final RF-based residual correction enhances local accuracy by capturing nonlinear residual patterns overlooked by deep learning. A real-world case study from an East China PIES verifies the superior predictive performance of the proposed framework, achieving mean absolute percentage errors of 4.65%, 2.79%, and 3.01% for cooling, heating, and electricity loads, respectively—substantially outperforming benchmark models. These results demonstrate that the proposed method offers a reliable, interpretable, and data-driven solution to support refined scheduling, renewable energy integration, and sustainable operational planning in modern multi-energy systems. Full article

This paper proposes a hybrid integer optimization method based on the Whale Optimization Algorithm (WOA) for the asymmetric central air conditioning chiller system of a 530-m super high-rise building in Guangzhou. Firstly, a three-hidden-layer multilayer perceptron (MLP) chiller model based on 16,276 sets of measured data and a gradient boosting regression cooling tower model based on 21,369 sets of operating condition data were constructed, achieving high-precision modeling of the energy consumption of all equipment in the chiller system. Secondly, a hybrid encoding strategy of “threshold truncation + continuous relaxation” was proposed to integrate discrete on-off states and continuous operating parameters into WOA, and a three-layer constraint repair mechanism was designed to ensure the physical feasibility of the optimization process and the safe operation of equipment. Verification across three load scenarios—low, medium, and high—showed that the optimized system’s energy efficiency ratio (EER) increased by 15.01%, 12.61%, and 11.86%, respectively, with energy savings of 12.91%, 11.18%, and 10.58%. The annual rolling optimization results showed that the average EER increased from 5.07 to 5.88 (16.1%), with energy savings ranging from 8.59% to 18.92%. Sensitivity analysis indicated that pump quantity is the most influential parameter affecting system energy consumption, with an additional pump reducing it by 1.1%. The optimization method proposed in this paper meets the minute-level real-time scheduling requirements of building automation systems and provides an implementable solution for energy-saving optimization of central air conditioning chiller systems in super high-rise buildings. Full article

Low-light image enhancement (LLIE) requires modeling spatially extensive and interdependent degradations across large pixel regions, while directly equipping diffusion-based LLIE with heavy global modules inside the iterative denoising backbone leads to prohibitive computational overhead. To enhance long-range context modeling without inflating the per-step cost of diffusion, we propose ECA-Diff, a diffusion framework augmented with a deep External Context-Aware Encoder (ECAE). A latent-space context network built with hybrid Transformer–Convolution blocks extracts holistic cues from the input, generates multi-scale context features once, and injects them into the diffusion backbone as lightweight conditional guidance across all sampling steps. In addition, a CIELAB-space Luminance-Adaptive Chromaticity Loss regularizes conditional diffusion training and mitigates the cool color cast frequently observed in low-luminance regions. Experiments on paired and unpaired benchmarks show that ECA-Diff consistently outperforms recent state-of-the-art LLIE methods in both full-reference (PSNR/SSIM/LPIPS) and no-reference (NIQE/BRISQUE) metrics, with the external context path introducing only modest overhead relative to the baseline diffusion backbone. These results indicate that decoupling global context estimation from the iterative denoising process is an effective way to boost diffusion-based LLIE and provides a general compute-once conditioning paradigm for low-level image restoration. Full article

To mitigate welding defects and optimize the microstructure of aluminum alloys, this study introduces a multi-pulse hybrid arc welding process. A comparative investigation was carried out between this novel process (AC/DC composite 1 kHz pulsed welding) and conventional methods (AC pulsed, AC/DC pulsed) during wire-fed overlay welding of 6061 aluminum alloy. Analyses were conducted on electrical signals, arc morphology, joint microstructure, and hardness. The results indicate that the AC/DC hybrid 1 kHz pulsed process combines the characteristics of both AC and DC pulsed signals with full-cross-section frequency pulse superposition, thereby optimizing arc welding process control. The frequency pulses induce a magnetoelectric effect, leading to significant arc constriction, which enhances arc energy density and arc pressure. This intensifies the fluid flow in the molten pool and accelerates cooling, thereby suppressing the growth of columnar grains and promoting the formation of fine equiaxed grains and an increased proportion of high-angle grain boundaries. Meanwhile, this process effectively reduces the number, area fraction, and overall porosity, and facilitates the distribution of a large amount of Al–Si eutectic structure along grain boundaries, enhancing the impediment to dislocation motion. The microstructural optimization significantly improves the hardness at the weld center to 73.1 HV, leading to enhanced mechanical properties. Full article

In the continuous demand for high-performance lithium-ion batteries (LIBs), thermal management control is, these days, crucial with respect to safety, performance, and longevity. As a promising passive solution, Phase Change Materials (PCMs) have been implemented to overcome the conventional battery thermal management (BTM) approaches, including air cooling, liquid cooling, or refrigerant-based systems. Their ability to transfer the heat during phase change processes makes them ideal candidates for further thermal buffers, thus allowing compact and energy-efficient temperature control without extra power consumption. This work encompasses the recent progress in PCM-based battery thermal management systems, with a particular focus on material selection, structural design, and experimental validation. Current advances in composite PCMs, including the use of high-conductivity additives, porous supports, and encapsulation methods, are here appraised in terms of their thermal conductivity, cycling stability, leakage prevention, and overall safety. Comparisons between organic, inorganic, and hybrid PCM types demonstrate the benefits and drawbacks of each class. Ongoing discussion is also directed towards challenges that include low thermal conductivity, limited heat storage capacity, scalability, cost, and flammability. Future development opportunities are also identified in the areas of multifunctional PCMs, hybrid passive–active cooling approaches, scalable processing, and life-cycle considerations. Full article

Slide burnishing (SB) is a cheap and effective method for improving the surface integrity (SI) and operational behaviour (wear, fatigue, corrosion) of metal components. As its name suggests, SB is implemented through tangential sliding friction and is based on severe plastic deformation of the surface. The review presented here is dedicated to SB implemented using a non-diamond-based deforming element and aims to systematise the achievements from recent decades regarding SB’s effects on the SI, fatigue, wear and corrosion behaviour of metal components. Depending on the burnishing conditions (lubrication, cooling, assisting and their main effects on the treated surface), and based on the difference between the concepts of method and process, a classification of the types of SB processes was made based on the SB method—that is, conventional, sustainable, minimum quantity lubrication-assisted, special, hybrid and combined processes involving SB. Based on this classification, a critical analysis was conducted, viewed through the prism of correlations between the SB, SI and operating behaviour. With sustainability issues becoming increasingly relevant across all industries, more attention is being paid to sustainable SB processes. Because the finite-element method is a powerful and inexpensive tool that can be applied to the analysis of burnishing processes, we used it to build adequate finite-element models of SB processes. At the end of the paper, we outline avenues for future research on SB. Full article

Prediction models for polymer injection molding quality often degrade due to shifts in operating conditions caused by variations in melting temperature, cooling efficiency, or machine conditions. To address this challenge, this study proposes a drift-aware dynamic quality-monitoring framework that integrates hybrid-feature autoencoder (HFAE) drift detection, sliding-window reconstruction error analysis, and a mixed-feature artificial neural network (ANN) for online quality prediction. First, shifts in processing parameters are rigorously quantified to uncover continuous drifts in both input and conditional output distributions. A HFAE monitors reconstruction errors within a sliding window to promptly detect anomalous deviations. Once the drift index exceeds a predefined threshold, the system automatically triggers a drift-event response, including the collection and labeling of a small batch of new samples. In benchmark tests, this adaptive scheme outperforms static models, achieving a 35.4% increase in overall accuracy. After two incremental updates, the root-mean-squared error decreases by 42.3% across different production intervals. The anomaly detection rate falls from 0.86 to 0.09, effectively narrowing the distribution gap between training and testing sets. By tightly coupling drift detection with online model adaptation, the proposed method not only maintains high-fidelity quality predictions under dynamically evolving injection molding conditions but also demonstrates practical relevance for large-scale industrial production, enabling reduced rework, improved process stability, and lower sampling frequency. Full article

The field of hard milling has recently witnessed growing interest in environmentally sustainable machining practices. Among these, Minimum Quantity Lubrication (MQL) has emerged as an effective strategy, offering not only reduced environmental impact but also economic benefits and enhanced cooling performance compared to conventional flood cooling methods. In hard milling operations, cutting temperature is a critical factor that significantly influences the quality of the finished component. Proper control of this parameter is essential for producing high-precision workpieces, yet measuring cutting temperature is often complex, time-consuming, and costly. These challenges can be effectively addressed by predicting cutting temperature using advanced Machine Learning (ML) models, which offer a faster and more efficient alternative to direct measurement. In this context, the present study investigates and compares the performance of Conventional Minimum Quantity Lubrication (CMQL) and Graphene-Enhanced MQL (GEMQL), with sesame oil serving as the base fluid, in terms of their effect on cutting temperature. The experiments are structured using a Taguchi L36 orthogonal array, with key variables including cutting speed, feed rate, MQL jet pressure, and the type of cooling applied. Additionally, the study explores the predictive capabilities of various advanced ML models, including Decision Tree, XGBoost Regressor, K-Nearest Neighbor, Random Forest Regressor, and CatBoost Regressor, along with a Hybrid Stacking Machine Learning Model (HSMLM) for estimating cutting temperature. The results demonstrate that the GEMQL setup reduced cutting temperature by 36.8% compared to the CMQL environment. Among all the ML models tested, HSMLM exhibited superior predictive performance, achieving the best evaluation metrics with a mean absolute error of 3.15, root mean squared error (RMSE) of 5.3, mean absolute percentage error of 3.9, coefficient of determination (R²) of 0.91, and an overall accuracy of 96%. Full article

Paraffin-based phase change materials (PCMs) have emerged as promising candidates for thermal energy storage (TES) applications due to their high latent heat, chemical stability, and low cost. However, their inherently low thermal conductivity and the risk of leakage during melting–solidification cycles significantly limit their practical performance. To address these limitations, numerous studies have investigated composite PCMs in which paraffin is incorporated into porous supporting matrices. Among these, diatomite has garnered particular attention due to its high porosity, large specific surface area, and chemical compatibility with organic materials. Serving as both a carrier and stabilizing shell, diatomite effectively suppresses leakage and enhances thermal conductivity, thereby improving the overall efficiency and reliability of the PCM. This review synthesizes recent research on paraffin–diatomite composites, with a focus on impregnation methods, surface modification techniques, and the influence of synthesis parameters on thermal performance and cyclic stability. The mechanisms of heat and mass transport within the composite structure are examined, alongside comparative analyses of paraffin–diatomite systems and other inorganic or polymeric supports. Particular emphasis is placed on applications in energy-efficient buildings, passive heating and cooling, and hybrid thermal storage systems. The review concludes that paraffin–diatomite composites present a promising avenue for stable, efficient, and sustainable phase change materials (PCMs). However, challenges such as the optimization of pore structure, long-term durability, and large-scale manufacturing must be addressed to facilitate their broader implementation in next-generation energy storage technologies. Full article

The increasing energy consumption of large datacenters, with cooling systems constituting a significant portion, calls for efficient thermal management strategies. Conventional computational fluid dynamics (CFD) methods, although accurate, are time-consuming for supporting real-time tasks in dynamic datacenter environments. Machine learning (ML)-based methods, particularly artificial neural network (ANN)-based surrogate models, have emerged as potential alternatives, but they struggle with generalization across diverse working conditions. Meanwhile, ML models’ performance in large datacenters still remains unclear. This research introduces a hybrid surrogate model combining ANNs and CNNs for the precise and rapid prediction of 3D temperature distributions in large datacenters. The proposed method incorporates an ANN for feature processing and a CNN for decoding spatial features, leveraging both to capture complex airflow patterns and temperature distributions under varying conditions. A dataset of 500 CFD-simulated temperature fields based on a real datacenter is established for model training and validation. The CFD method is evaluated by comparing the simulation results with experimental data. Results of the ML models’ performance indicate that the proposed hybrid surrogate model outperforms the conventional ANN model, reducing mean absolute error (MAE) by 87.44%. Additionally, the model is 300,000 times faster than CFD simulations, offering an efficient solution for further supporting real-time thermal management. Full article