Search Results (222)

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article

The growing global demand for electricity, driven by the development of electromobility, data centers, and smart technologies, necessitates innovative approaches to energy generation. Wind power, as a clean and renewable energy source, plays a pivotal role in the global transition towards low-carbon power systems. This paper presents a comprehensive review of generator technologies used in wind turbine applications, ranging from conventional synchronous and asynchronous machines to advanced concepts such as low-speed direct-drive (DD) generators, axial-flux topologies, and superconducting generators utilizing low-temperature superconductors (LTS) and high-temperature superconductors (HTS). The advantages and limitations of each design are discussed in the context of efficiency, weight, reliability, scalability, and suitability for offshore deployment. Special attention is given to HTS-based generator systems, which offer superior power density and reduced losses, along with challenges related to cryogenic cooling and materials engineering. Furthermore, the paper analyzes selected modern generator designs to provide references for enhancing the performance of grid-synchronized hybrid microgrids integrating solar PV, wind, battery energy storage, and HTS-enhanced generators. This review serves as a valuable resource for researchers and engineers developing next-generation wind energy technologies with improved efficiency and integration potential. Full article

High-titanium steel contains an elevated titanium content, which promotes the formation of abundant non-metallic inclusions in molten steel at high temperatures, including titanium oxides, sulfides, and nitrides. These inclusions adversely affect continuous casting operations and generate substantial internal/surface defects in cast slabs, ultimately compromising product performance and service reliability. Therefore, stringent control over the size, distribution, and population density of inclusions is imperative during the smelting of high-titanium steel to minimize their detrimental effects. In this paper, samples of high titanium steel (0.4% Ti, 0.004% N) casting billets were analyzed by industrial test sampling and full section comparative analysis of the samples at the center and quarter position. Using the Particle X inclusions, as well as automatic scanning and analyzing equipment, the number, size, location distribution, type and morphology of inclusions in different positions were systematically and comprehensively investigated. The results revealed that the primary inclusions in the steel consisted of TiN, TiS, TiC and their composite forms. TiN inclusions exhibited a size range of 1–5 µm on the slab surface, while larger particles of 2–10 μm were predominantly observed in the interior regions. Large-sized TiN inclusions (5–10 μm) are particularly detrimental, and this problematic type of inclusion predominantly concentrates in the interior regions of the steel slab. A gradual decrease in TiN inclusion number density was identified from the surface toward the core of the slab. Thermodynamic and kinetic calculations incorporating solute segregation effects demonstrated that TiN precipitates primarily in the liquid phase. The computational results showed excellent agreement with experimental data regarding the relationship between TiN size and solidification rate under different cooling conditions, confirming that increased cooling rates lead to reduced TiN particle sizes. Both enhanced cooling rates and reduced titanium content were found to effectively delay TiN precipitation, thereby suppressing the formation of large-sized TiN inclusions in high-titanium steels. Full article

The increase in data center facilities has led to higher energy consumption and a larger carbon footprint, prompting improvements in thermal environments for energy efficiency and server lifespan. Existing literature studies often overlook categorizing equipment for power usage effectiveness (PUE), addressing power efficiency measurement limitations and employee thermal comfort. These issues are addressed through an investigation of the PUE metric, a comparative analysis of various data center types and their respective cooling conditions, an evaluation of PUE in relation to established thermal standards and an assessment of employee thermal comfort based on defined criteria. Thirty-nine papers and ten websites were reviewed. The results indicated an average information technology (IT) power usage of 44.8% and a PUE of 2.23, which reflects average efficiency, while passive cooling was found to be more applicable to larger-scale data centers, such as Hyperscale or Colocation facilities. Additionally, indoor air temperatures averaged 16.5 °C with 19% relative humidity, remaining within the allowable range defined by ASHRAE standards, although employee thermal comfort remains an underexplored area in existing data center research. These findings highlight the necessity for clearer standards on power metrics, comprehensive thermal guidelines and the exploration of alternative methods for power metrics and cooling solutions. Full article

Data centers contribute to roughly 1% of global energy consumption and 0.3% of worldwide carbon dioxide emissions. The cooling system alone constitutes a substantial 50% of total energy consumption for data centers. Lowering Power Usage Effectiveness (PUE) of data center cooling systems from 2.2 to 1.4, or even below, is one of the critical issues in this thermal management area. In this work, a digital twin system of an Intelligent Data Center (IDC) prototype is designed to be capable of real-time monitoring the temperature distribution. Moreover, aiming to lower PUE, Deep Q-Learning Network (DQN) is further established to make optimization decisions of thermal management during cooling down of the local hotspot. The entire process of thermal management for IDC can be real-time visualized in Unity, forming the virtual entity of data center prototype, which provides an intelligent solution for sustainable data center operation. Full article

The production of AISI 304 stainless steel (a corrosion-resistant alloy prone to solidification defects from high alloy content) particularly benefits from twin-roll strip casting—a short-process green technology enabling sub-rapid solidification (the maximum cooling rate exceeds 1000 °C/s) control for high-performance steels. However, the internal phenomena within its molten pool remain exceptionally challenging to monitor. This study developed a multiscale numerical model to simulate coupled fluid flow, heat transfer, and solidification in AISI 304 stainless steel twin-roll strip casting. A quarter-symmetry 3D model captured macroscopic transport phenomena, while a slice model resolved mesoscopic solidification structure. Laboratory experiments had verified that the deviation between the predicted temperature field and the measured average value (1384.3 °C) was less than 5%, and the error between the solidification structure simulation and the electron backscatter diffraction (EBSD) data was within 5%. The flow field and flow trajectory showed obvious recirculation zones: the center area was mainly composed of large recirculation zones, and many small recirculation zones appeared at the edges. Parameter studies showed that, compared with the high superheat (110 °C), the low superheat (30 °C) increased the total solid fraction by 63% (from 8.3% to 13.6%) and increased the distance between the kiss point and the bottom of the molten pool by 154% (from 6.2 to 15.8 mm). The location of the kiss point is a key industrial indicator for assessing solidification integrity and the risk of strip fracture. In terms of mesoscopic solidification structure, low superheat promoted the formation of coarse columnar crystals (equiaxed crystals accounted for 8.9%), while high superheat promoted the formation of equiaxed nucleation (26.5%). The model can be used to assist in the setting of process parameters and process optimization for twin-roll strip casting. Full article

As the demand for advanced cooling solutions increases with the rise in artificial intelligence and high-performance computing, efficient thermal management becomes critical, particularly for data centers and electronic systems. Triply Periodic Minimal Surface (TPMS) heat sinks have shown superior thermal performance over conventional designs by enhancing heat transfer efficiency. In this study, a novel Fischer–Koch-S (FKS) TPMS heat sink was experimentally tested with four porosity configurations, 0.6 (identified as P6), 0.7 (identified as P7), 0.8 (identified as P8), and a gradient porosity ranging from 0.6 to 0.8 (identified as P678) along the flow direction, under a mass flow rate range of 0.012 to 0.019 kg/s. Key thermal parameters including surface temperature, thermal resistance, heat transfer coefficient, and Nusselt number were analyzed and compared to the conventional straight-channel heat sink (SCHS) using numerical modeling. Among all configurations, the P6 design demonstrated the best performance, with surface temperature differences ranging from 13.1 to 14.2 °C at 0.019 kg/s and a 54.46% higher heat transfer coefficient compared to the P8 design at the lowest mass flow rate. Thermal resistance decreased consistently with an increasing mass flow rate, with P6 achieving a 31.8% reduction compared to P8 at 0.019 kg/s. The P678 gradient design offered improved temperature uniformity and performance at higher mass flow rates. Nusselt number ratios confirmed that low-porosity and gradient TPMS designs outperform the SCHS, with performance advantages increasing as the mass flow rate rises. To further enhance the experimental process, a deep learning model based on a Temporal Convolutional Network (TCN) was developed to predict steady-state surface temperatures using early-stage time-series data, to reduce test time and enable efficient validation. Full article

In recent years, the number of transistors on electronic chips has surpassed Moore’s law, resulting in overheating and energy consumption problems in data centers (DCs). Chip-level microchannel cooling is expected to address these challenges. Grooved heat sinks with droplet-shaped fins were introduced to modify the overall capability of the cooling system. The degree of impact of the distribution of grooves and fins was analyzed and optimized using the Taguchi method. Moreover, the coupling effect of flow and temperature fields was explained using the field synergy theory. The key findings are as follows: for thermal resistance, pump power, and overall efficiency, the influence degree is the number of combined units > number of fins in each unit > distribution of the combined units. The optimal configuration of 21 combined units arranged from dense to sparse with one fin in each unit achieves 14.05% lower thermal resistance and 8.5% higher overall efficiency than the initial heat sink. The optimal configuration of five combined units arranged from sparse to dense with one fin in each unit reduces the power energy consumption by 27.61%. After optimization, the synergy angle between the velocity vector and temperature gradient is reduced by 4.29% compared to the smooth heat sink. The coupling effect between flow and heat transport is strengthened. The optimized configuration can better balance heat dissipation and energy consumption, improve the comprehensive capability of cooling system, provide a feasible solution to solve the problems of local overheating and high energy consumption in DCs. Full article

Predicted natural ventilation (NV) often diverges from actual performance in dwellings. This discrepancy arises in part because most design tools do not account for how occupants actually operate windows. This study aims to determine how window geometry and orientation should be adjusted when occupant behavior is considered. Survey data from 150 Melbourne residents were converted into two window-operation schedules: Same Behavior (SB), representing average patterns, and Probable Behavior (PB), capturing stochastic responses to comfort, privacy, and climate. Both schedules were embedded in EnergyPlus and applied to over 200 annual simulations across five window-design stories that varied orientations, placements, and window-to-wall ratios (WWRs). Each story was tested across two living room wall dimensions (7 m and 4.5 m) and evaluated for air-change rate per hour (ACH) and solar gains. PB increased annual ACH by 5–12% over SB, with the greatest uplift in north-facing cross-ventilated layouts on the wider wall. Integrating probabilistic occupant behavior into window design remarkably improves NV effectiveness, with peak summer ACH reaching 4.8, indicating high ventilation rates that support thermal comfort and improved IAQ without mechanical assistance. These results highlight the potential of occupant-responsive window configurations to reduce reliance on mechanical cooling and enhance indoor air quality (IAQ). This study contributes a replicable occupant-centered workflow and ready-to-apply design rules for Australian temperate climates, adapted to different climate zones. Future research will extend the method to different climates, housing types, and user profiles and will integrate smart-sensor feedback, adaptive glazing, and hybrid ventilation strategies through multi-objective optimization. Full article

The enterprise or data center does not use the M2 SATA because of the cooling problem. Therefore, SSDs employ metal cases similar to the traditional 2.5” or 3.5” hard disk. The metal case is made of aluminum, which must be ground after the metal plate forming process. Conventionally, quality control is conducted to check the ground quality of aluminum cases manually. This method is not accurate as the data are difficult to digitize. To improve the quality control, speed, and efficiency. We established a digital imaging-based inspection system for the aluminum case grinding quality control. The inspection system consists of a digital industrial camera, a closed-circuit TV lens, a light-emitting diode (LED) light source, and a personal computer. If the loading and unloading time is ignored, the test time is less than five seconds for one case. When the tested case is uploaded to the inspection system, the camera captures and sends images to the computer. The image was processed to evaluate the quality and record the tested results. Then, the tested case is classified by a robot or an operator. Full article

In the context of the building sector, university campus buildings play a crucial role in promoting a green economic transition toward carbon neutrality, as universities are among the largest emitters of greenhouse gases. This research proposed a strategy for the operation and management of university campuses that focused on reducing energy consumption by optimizing the utilization of building spaces. To gather empirical data, a case study was conducted to examine the energy consumption of campus buildings based on their characteristics at Korea University. The results indicated that effective space utilization, achieved through the efforts of stakeholders, led to a reduction in heating and cooling energy consumption. To achieve this, the study classified university buildings by considering both physical variables and human-centered factors that affect energy consumption, analyzed space usage behavior, and compared heating and cooling energy consumption across buildings. This study expands current knowledge because its approach differs from previous research, which has generally focused on using simulation tools to analyze factors associated with the physical aspects of buildings—such as the energy performance of a building envelope or the energy-efficiency of facility systems. Full article

A hybrid Artificial Intelligence (AI) framework centered on metamodeling, integrating simulation data with hybrid data-driven techniques, was implemented to enhance the predictive accuracy and optimization of thermal load projections in three distinct climates in Morocco. Initially, 13 machine learning (ML) models were assessed to predict heating and cooling loads. The best-performing models from this stage were then selected for the subsequent phase to find out the optimal combinations of inputs to predict thermal loads. In this phase, an Integral Feature Selection (IFS) method was employed in conjunction with the best ML models. An extensive evaluation using advanced statistical measures was performed during the evaluation stage. The results reveal that, for each climate, numerous high-accuracy prediction pathways were identified for thermal load prediction, surpassing the confidence level of 99% for R². The results found here outperformed those reported by other researchers in thermal load predictions for Low-Energy Buildings (LEBs). Full article

Efficient cooling and heat recovery systems are becoming increasingly critical in large-scale commercial and industrial facilities, especially with the rising demand for sustainable energy solutions. Traditional air-conditioning and refrigeration systems often dissipate significant amounts of waste heat, which remains underutilized. This study addresses the challenge of harnessing low-potential waste heat from such systems to support fifth-generation district heating and cooling (5GDHC) networks, particularly in moderate-temperate regions like Flanders, Belgium. To evaluate the technical and economic feasibility of waste heat recovery, a methodology is developed that integrates established performance metrics—such as the energy efficiency ratio (EER), power usage effectiveness (PUE), and specific cooling demand (kW/t)—with capital (CapEx) and operational expenditure (OpEx) assessments. Empirical correlations, including regression analysis based on manufacturer data and operational case studies, are used to estimate equipment sizing and system performance across three operational modes. The study includes detailed modeling of data centers, cold storage facilities, and large supermarkets, taking into account climatic conditions, load factors, and thermal capacities. Results indicate that average cooling loads typically reach 58% of peak demand, with seasonal coefficient of performance (SCOP) values ranging from 6.1 to a maximum of 10.3. Waste heat recovery potential varies significantly across building types, with conversion rates from 33% to 68%, averaging at 59%. In data centers using water-to-water heat pumps, energy production reaches 10.1 GWh/year in heat pump mode and 8.6 GWh/year in heat exchanger mode. Despite variations in system complexity and building characteristics, OpEx and CapEx values converge closely (within 2.5%), demonstrating a well-balanced configuration. Simulations also confirm that large buildings operating above a 55% capacity factor provide the most favorable conditions for integrating waste heat into 5GDHC systems. In conclusion, the proposed approach enables the scalable and efficient integration of low-grade waste heat into district energy networks. While climatic and technical constraints exist, especially concerning temperature thresholds and equipment design, the results show strong potential for energy savings up to 40% in well-optimized systems. This highlights the viability of retrofitting large-scale cooling systems for dual-purpose operation, offering both environmental and economic benefits. Full article

With the continuous expansion of data centers, their carbon emission has become a serious issue. A number of studies are committing to reduce the carbon emission of data centers. Carbon trading, carbon capture, and power-to-gas technologies are promising emission reduction techniques which are, however, seldom applied to data centers. To bridge this gap, we propose a carbon-neutral architecture for distributed data centers, where each data center consists of three subsystems, i.e., an energy subsystem for energy supply, thermal subsystem for data center cooling, and carbon subsystem for carbon trading. Then, we formulate the energy management problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and develop a distributed solution framework using Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Finally, simulations using real-world data show that a cost saving of 20.3% is provided. Full article

Cloud Data Centers (CDCs) are an essential component of the infrastructure for powering the digital life of modern society, hosting and processing vast amounts of data and enabling services such as streaming, Artificial Intelligence (AI), and global connectivity. Given this importance, their energy efficiency is a top priority, as they consume significant amounts of electricity, contributing to operational costs and environmental impact. Efficient CDCs reduce energy waste, lower carbon footprints, and support sustainable growth in digital services. Consequently, energy efficiency metrics are used to measure how effectively a CDC utilizes energy for computing versus cooling and other overheads. These metrics are essential because they guide operators in optimizing resource use, reducing costs, and meeting regulatory and environmental goals. To this end, this paper reviews more than 25 energy efficiency metrics and more than 250 literature references to CDCs, different energy-consuming components, and configuration setups. Then, some real-world case studies of corporations that use these metrics are presented. Thereby, the challenges and limitations are investigated for each metric, and associated future research directions are provided. Prioritizing energy efficiency in CDCs, guided by these energy efficiency metrics, is essential for minimizing environmental impact, reducing costs, and ensuring sustainable scalability for the digital economy. Full article