Search Results (8,531)

Thermal insulation is essential in lowering the energy consumption of buildings. However, many fossil-based insulation and exterior cladding materials are derived from petrochemical components, which often have adverse ecological impacts. This study explores the effectiveness of integrating sustainable thermal insulation solutions into building design to reduce energy consumption and minimize ecological impact. Focusing on an energy-efficient breakfast house located in Van, Turkey, the project was modeled using Autodesk-Revit software (2023). A comprehensive analysis was conducted by generating eighty alternative scenarios, combining two distinct wall structures, eight fiber-based natural insulation materials, and five wood-based exterior cladding materials. The energy performance of each scenario was evaluated using IES-VE software (2024.1), focusing on annual total energy consumption and CO₂ emissions, while accounting for regional climatic conditions and targeted indoor comfort levels. To further refine the selection of optimal materials, a hybrid evaluation was performed using multi-attribute decision approaches, including LODECI, MAXC, and DEPART. These methods provided a systematic framework for comparing the performance of wood-based insulation materials across multiple criteria. In order to verify the accuracy of the proposed multi-attribute decision models, a comparative analysis has been undertaken with other multi-attribute decision methods (COPRAS, ARAS and WASPAS). The study highlights the technical feasibility of incorporating cost-effective, eco-friendly fiber-based and wood-based materials into building envelopes, demonstrating their potential to significantly enhance energy efficiency and reduce environmental impact. By combining advanced simulation tools with robust decision-making methodologies, this research offers a scientifically grounded approach to sustainable architectural design, providing important outputs for future applications in energy-efficient construction. Full article

This study evaluates and compares the sustainability performance of selected historic, commercial, and institutional buildings in Istanbul to identify effective climate-responsive and energy-efficient design strategies. The objectives are to assess performance using LEED-based criteria, examine variations across building typologies, and outline implications for future sustainable design. Using an evaluation matrix, responses from 175 experts were analysed across key LEED categories for seven case study buildings. The comparative assessment reveals notable variations in sustainability performance across the seven evaluated buildings. ERKE Green Academy consistently achieved the highest mean scores (≈4.40–4.60), particularly in Sustainable Sites, Water Efficiency, Energy and Atmosphere, and Indoor Environmental Quality. This strong performance reflects its integration of advanced green technologies, optimised daylighting strategies, biophilic elements, and smart system controls. Modern commercial towers, such as the Allianz Tower and Sapphire Tower, recorded strong mean scores (≈4.20–4.50) across categories related to Integrative Design, Energy Efficiency, and Materials and Resources. Their performance is largely driven by intelligent façade systems, double-skin envelopes, automated shading, and high-performance mechanical systems that enhance operational efficiency. In contrast, heritage buildings including Hagia Sophia and Sultan Ahmed Mosque demonstrated moderate yet stable performance levels (≈4.00–4.40). Their strengths were most evident in Indoor Environmental Quality, where passive systems such as thermal mass, natural ventilation, and inherent spatial configurations contribute significantly to occupant comfort. Overall, the findings underscore the complementary value of combining traditional passive strategies with modern smart technologies to achieve resilient, low-energy, and user-responsive architecture. This study is novel as it uniquely demonstrates how traditional passive design strategies and modern smart technologies can be integrated to enhance climate-responsive and energy-efficient performance across diverse building typologies. The study recommends enhanced indoor air quality strategies, occupant education on system use, and stronger policy alignment with LEED standards. Full article

Host engineering is one of the most efficient approaches to maximizing the electroluminescent performance of organic light-emitting devices. Herein, two carbazole-based N,N′-Dicarbazolyl-4,4′-biphenyl (CBP) derivatives, (9-(4′-(9H-carbazol-9-yl)-[1,1′-biphenyl]-4-yl)-3-(3-(1-phenyl-1H-benzo[d]imidazol-2-yl)phenyl)-9H-carbazole (CBPmBI), and (9-(4′-(9H-carbazol-9-yl)-[1,1′-biphenyl]-4-yl)-9H-carbazol-3-yl)diphenylphosphine oxide (CBPPO), were designed as bipolar hosts for blue phosphorescent devices. By introducing the electron-withdrawing groups to the backbone of CBP, the bipolar hosts exhibited high triplet energy, enhanced thermal stability, and balanced charge transport. The device constructed with the blue guest emitter bis[2-(4,6-difluorophenyl) pyridinato-C2,N]iridium (III) (FIrpic) showed the excellent electroluminescence performance. For instance, the CBPPO-based devices achieved a maximum current efficiency of 28.0 cd/A, a power efficiency of 25.8 lm/W, and an external quantum efficiency of 14.4%. Notably, the external quantum efficiency retained at14.1% under the brightness of 5000 cd/m², featuring the negligible efficiency roll-off. Full article

As sustainable green fuels for heavy-duty engines, using hydrogen doping with ammonia helps to mitigate greenhouse gas emissions. Based on the background of hydrogen production from ammonia reforming, the combustion and emission characteristics of hydrogen-doped ammonia engines are studied. By employing 3D-CFD numerical simulation, this study systematically explores the combined effects of the ignition timing, hydrogen energy ratio (HER), and equivalence ratio (Φ) on the premixed combustion and emission performances of ammonia–hydrogen blends. The findings indicate that at the operating conditions of HER = 4% and Φ = 1.0, the indicated mean effective pressure (IMEP) reaches its maximum at −40 °CA aTDC, with the indicated thermal efficiency (ITE) reaching 48.2%. However, to mitigate knock hazards, the ignition timing should be adjusted to −37.5 °CA aTDC. With HER increasing from 4% to 25%, the flame propagation velocity is markedly improved, and the combustion duration is notably reduced. As the equivalence ratio rises from 0.8 to 1.0, the combustion intensity is strengthened while the proportion of indicated work declines. Notably, the lean burn condition (Φ = 0.8) exhibits no knock risk and achieves the highest ITE (49.2%). In terms of emission characteristics, advanced ignition timing, higher HER, and lower equivalence ratio all promote NO_X formation. In contrast, N₂O emissions decrease as the combustion temperature rises and the combustion duration shortens. Unburned NH₃ is mainly distributed in the low-temperature areas inside the cylinder, and its emission amount decreases with the improvement of combustion completeness. Full article

Continuous particle exchange thermal machines require no time-dependent driving, can be realised in solid-state electronic devices, and can be miniaturised to nanometre scale. Quantum dots, providing a narrow energy filter and allowing to manipulate particle flow between the hot and cold reservoirs are at the heart of such devices. It has been theoretically shown that through mitigating passive heat flow, Carnot efficiency can be approached arbitrarily closely in a quantum dot heat engine, and experimentally, values of 0.7${\eta }_C$ have been reached. However, for practical applications, other parameters of a thermal machine, such as maximum power, efficiency at maximum power, and noise—stability of the power output or heat extraction—take precedence over maximising efficiency. We explore the effect of the internal microscopic dynamics of a quantum dot on these quantities and demonstrate that its performance as a thermal machine depends on few parameters—the overall conductance and three inherent asymmetries of the dynamics: entropy difference between the charge states, tunnel coupling asymmetry, and the degree of detailed balance breaking. These parameters act as a guide to engineering the quantum states of the quantum dot, allowing to optimise its performance beyond that of the simplest case of a two-fold spin-degenerate transmission level. Full article

This case study evaluates the economic and energy efficiency of retrofitting a hospital heating system in Krakow, Poland, by transitioning from a district-heating-only model to a bivalent hybrid system. The analyzed configuration integrates air-to-water heat pumps (HP), a 180 kWp photovoltaic (PV) installation, and a 120 kWh battery energy storage (ES) unit, while retaining the municipal district heating network as a peak load and backup source. Utilizing high-resolution quasi-steady-state simulations in Ebsilon Professional (10 min time step) and projected 2025 market data, the study compares three modernization scenarios differing in heat pump capacity (20, 40, and 60 kW). The assessment focuses on key performance indicators, including Net Present Value (NPV), Levelized Cost of Heating (LCOH), and Simple Payback Time (SPBT). The results identify the bivalent system with 40 kW thermal capacity (Variant 2) as the economic optimum, delivering the highest NPV (EUR 121,021), the lowest LCOH (0.0908 EUR/kWh), and a payback period of 11.94 years. Furthermore, the study quantitatively demonstrates the law of diminishing returns in the oversized scenario (60 kW), confirming that optimal sizing is critical for maximizing the efficiency of bivalent systems in public healthcare facilities. This work provides a detailed methodology and data that can form a basis for making investment decisions in similar public utility buildings in Central and Eastern Europe. Full article

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally segmented temperature sequences. Among all evaluated predictors, the Bidirectional Long Short-Term Memory (BiLSTM) network achieved the highest prediction fidelity, yielding an RMSE of 0.018 °C, a 35.7% improvement over the conventional Long Short-Term Memory (LSTM) (RMSE = 0.028 °C) due to its ability to simultaneously encode forward and backward temporal dependencies inherent in cyclic electrochemical operation. While CatBoost exhibited the strongest performance among classical regressors (RMSE = 0.022 °C), outperforming Random Forest, Gradient Boosting, Support Vector Regression, XGBoost, and LightGBM, it remained inferior to BiLSTM because it lacks the capacity to represent bidirectional electrothermal dynamics. This performance hierarchy confirms that LIB thermal evolution is not dictated solely by historical load sequences; it also depends on forthcoming cycling patterns and entropic interactions, which unidirectional and memoryless models cannot capture. The resulting hybrid physics-data-driven framework provides a reliable surrogate for real-time LIB thermal estimation and can be directly embedded within BMS to enable proactive intervention strategies such as predictive cooling activation, current derating, and early detection of hazardous thermal conditions. By coupling physics-based decomposition with deep sequential learning, this study establishes a validated foundation for next-generation LIB thermal-management platforms and identifies a clear trajectory for future work extending the methodology to module- and pack-level systems suitable for industrial deployment. Full article

Lithium-ion batteries (LIBs) dominate energy storage for electric vehicles (EVs) due to their high energy density, long cycle life, and low self-discharge. However, high costs, complex manufacturing, and the requirement for advanced battery management systems (BMSs) constrain their broader deployment. Therefore, extending the utility of LIBs through reuse is essential for economic and environmental sustainability. Retired EV batteries with 70–80% state-of-health (SOH) can be repurposed in battery energy storage systems (BESSs) to support power grids. Effective reuse depends on accurate and rapid assessment of SOH and state-of-safety (SOS), which relies on precise state-of-charge (SOC) detection, particularly for aged LIBs with elevated thermal and electrochemical risks. This review systematically surveys SOC, SOH, and SOS detection methods for second-life LIBs, covering model-based, data-driven, and hybrid approaches, and highlights strategies for a fast and reliable evaluation. It further examines power electronics topologies and control strategies for integrating second-life LIBs into power grids, focusing on safety, efficiency, and operational performance. Finally, it analyzes key factors within the closed-loop supply chain, particularly reverse logistics, and provides guidance on enhancing adoption and supporting the establishment of circular battery ecosystems. This review serves as a comprehensive resource for researchers, industry stakeholders, and policymakers aiming to optimize second-life utilization of traction LIBs. Full article

The rapid advancement of high-performance electronics has intensified the demand for wide-bandgap semiconductor materials capable of operating under high-power and high-temperature conditions. Among these, silicon carbide (SiC) has emerged as a leading candidate due to its superior thermal conductivity, chemical stability, and mechanical strength. However, the high cost and complexity of SiC wafer fabrication, particularly in slicing and exfoliation, remain significant barriers to its widespread adoption. Conventional methods such as wire sawing suffer from considerable kerf loss, surface damage, and residual stress, reducing material yield and compromising wafer quality. Additionally, techniques like smart-cut ion implantation, though capable of enabling thin-layer transfer, are limited by long thermal annealing durations and implantation-induced defects. To overcome these limitations, ultrafast laser-based processing methods, including laser slicing and stealth dicing (SD), have gained prominence as non-contact, high-precision alternatives for SiC wafer exfoliation. This review presents the current state of the art and recent advances in laser-based precision SiC wafer exfoliation processes. Laser slicing involves focusing femtosecond or picosecond pulses at a controlled depth parallel to the beam path, creating internal damage layers that facilitate kerf-free wafer separation. In contrast, stealth dicing employs laser-induced damage tracks perpendicular to the laser propagation direction for chip separation. These techniques significantly reduce material waste and enable precise control over wafer thickness. The review also reports that recent studies have further elucidated the mechanisms of laser–SiC interaction, revealing that femtosecond pulses offer high machining accuracy due to localized energy deposition, while picosecond lasers provide greater processing efficiency through multipoint refocusing but at the cost of increased amorphous defect formation. The review identifies multiphoton ionization, internal phase explosion, and thermal diffusion key phenomena that play critical roles in microcrack formation and structural modification during precision SiC wafer laser processing. Typical ultrafast-laser operating ranges include pulse durations from 120–450 fs (and up to 10 ps), pulse energies spanning 5–50 µJ, focal depths of 100–350 µm below the surface, scan speeds ranging from 0.05–10 mm/s, and track pitches commonly between 5–20 µm. In addition, the review provides quantitative anchors including representative wafer thicknesses (250–350 µm), typical laser-induced crack or modified-layer depths (10–40 µm and extending up to 400–488 µm for deep subsurface focusing), and slicing efficiencies derived from multi-layer scanning. The review concludes that these advancements, combined with ongoing progress in ultrafast laser technology, represent research opportunities and challenges in transformative shifts in SiC wafer fabrication, offering pathways to high-throughput, low-damage, and cost-effective production. This review highlights the comparative advantages of laser-based methods, identifies the research gaps, and outlines the challenges and opportunities for future research in laser processing for semiconductor applications. Full article

This paper presents a systematic review of linear motor-based electromagnetic suspension, a key technology for reconciling vehicle comfort, handling stability, and energy consumption. The review focuses on two core areas: actuator configuration and control strategy. In configuration design, a comparison of moving-coil, permanent magnet synchronous (PMSLM), and switched-reluctance linear motors identifies the PMSLM as the mainstream approach due to its high-power density and performance. Key design challenges for meeting stringent vehicle operating conditions, such as mass-volume optimization, thermal management, and high reliability, are also analyzed. Regarding control strategy, the review outlines the evolutionary path from classical to advanced and intelligent control. It also examines the energy-efficiency trade-off between vibration suppression and energy recovery. Furthermore, the paper summarizes three core challenges for industrialization: nonlinear issues like thrust fluctuation and friction, the coupling of electromagnetic–mechanical–thermal multi-physical fields, and bottlenecks related to high costs and reliability verification. Finally, future research directions are envisioned, including new materials, sensorless control, and active safety integration for autonomous driving. Full article

The construction sector has a very high share in solving the energy demand of the world and global warming problems. Therefore, it had to increase studies on building materials-based heat storage and thermal insulation. Foam concrete is one of them, but its thermal and mechanical properties need to be improved. So, in this study, calcined marl was used as a replacement material to evaluate its thermal performance in the production of foam mortars. The aims of this study are to determine the physical, mechanical, and thermal properties of foam mortars produced with blended cements containing calcined marl at 0, 10, 30, and 50% ratios and to obtain novel and optimum design data for the foam concrete market. In conclusion, the optimum calcined marl replacement ratio is up to 30% in terms of both thermal performance and mechanical properties of foam mortars. Due to calcined marl, this study presents a foam mortar design with economic and low-carbon. And, thanks to the mixed designs of foam mortars prepared with blended cement containing novel calcined marl additive, it is observed that they improve the thermal insulation and heat storage ability of foam mortars and provide sufficient strength. Full article

Reducing the fuel consumption of tractors has consistently been a critical challenge that the agricultural machinery industry must address. To investigate the energy consumption during the plowing process of tractors and enhance their economic efficiency, this study conducted comparative experiments under varying plowing speeds and depths. In this experiment, the CAN bus protocol was utilized for the collection of engine operational data, such as rotational speed and fuel flow. A GPS positioning system was adopted to measure the plowing speed of the tractor and combined with the data from the tractor coasting test, and then the energy consumption for operating the plow was determined. In addition, a tension sensor was installed on the three-point hitch to measure the horizontal pull force exerted by the five-share plow during plowing, thereby facilitating the calculation of the energy consumption of agricultural machinery. The findings indicate that when the tractor’s plowing speed is maintained at 5.7 km/h, both the average fuel consumption and the fuel consumption per unit area increase as the plowing depth increases. If the plowing depth is fixed at 23 cm, the average fuel consumption rises with an increase in plowing speed, whereas the fuel consumption per unit area decreases. The experimental data show that during the actual tillage operation of the tractor, the brake thermal efficiency of diesel engines ranges from 21.76% to 28.57%. The energy consumed by agricultural implements accounts for only 11.79% to 17.04% of the total fuel energy. The energy consumed in operating the tractor-drawn plow accounts for merely 7.87% to 13.66% of the diesel engine output energy. Approximately 23.24% to 38.69% of the effective power of the diesel engine is lost during the transmission process. This study provides valuable insights for optimizing the performance of tractors during operation. Full article

Optimizing window insulation is crucial for reducing heat loss and energy use in industrial buildings in Northeast China’s severe cold regions. Based on six typical building prototypes identified via cluster analysis of field survey data, this study used DesignBuilder (Version 6.1.0.006) to simulate the influence of key parameters for insulation materials (type, thickness, emissivity) and installation methods (position, air cavity, operation). Simulations reveal that the energy-saving potential is inversely proportional to a building’s existing thermal performance, reaching a maximum of 10.3%. Regarding material selection, results indicate that reducing surface emissivity from 0.92 to 0.05 effectively substitutes for approximately 20 mm of physical insulation thickness. Transparent films prioritize daytime comfort, raising nighttime temperatures by 1.5 °C, whereas opaque panels excel at nighttime insulation with a 2.28 °C increase. Techno-economic analysis identifies low-emissivity foil combined with EPS or XPS as the most cost-effective strategy, achieving rapid payback periods of 0.6–3.2 years. Regarding installation, an external configuration with a 20 mm air cavity and vertical operation was identified as optimal, yielding 1.5–2.0% greater energy savings than an internal setup. This study provides tailored retrofitting strategies for industrial building windows in these regions. Full article

This study focuses on a proposed aluminum alloy radiant panel with 60° V-shaped grooves and integrated copper tubes. A numerical model of this novel grooved phase change material (PCM)-integrated radiant panel was established via Fluent 2022 R1 software. Through numerical simulations, the complete melting and solidification processes of two PCMs (n-hexadecane and LTXC-PCM-A-18) were analyzed, and differences in their phase change heat transfer performance were compared—revealing the role of the groove structure in enhancing PCM heat transfer and the material-structure compatibility. Results indicate that the groove structure effectively enhances convective heat transfer in the PCM liquid phase. During the melting stage, LTXC-PCM-A-18 exhibited a preheating rate of 0.00125 K/s, which is 67% higher than that of n-hexadecane (0.00075 K/s); its liquid fraction growth rate (0.0002 s⁻¹) was 2.67 times that of n-hexadecane, and the melting completion time was accelerated by 20% (2000 s). During solidification, LTXC-PCM-A-18’s initial cooling rate (0.0006 K/s) was 50% higher than that of n-hexadecane (0.0004 K/s), with a liquid fraction decay rate twice that of n-hexadecane. Additionally, its solidification temperature plateau was 1 K higher, providing superior thermal output stability. These findings reflect two distinct technical strategies: “steady-state temperature control” and “dynamic regulation.” n-Hexadecane exhibits smoother melting and solidification processes, making it suitable for continuous heating applications. In contrast, LTXC-PCM-A-18 demonstrates superior thermal responsiveness and phase change efficiency, aligning with intermittent heating requirements. This study provides quantitative guidance for PCM selection in grooved radiant panels. Full article

Thermoelectric systems configured in multistring arrays of thermoelectric generators (TEGs) represent a promising solution for energy harvesting in environments with non-uniform thermal gradients. However, the presence of multiple maximum power points (MPPs) in such configurations poses significant challenges to energy extraction efficiency. This study presents a comprehensive performance evaluation of four maximum power point tracking (MPPT) algorithms, Perturb and Observe (P&O), Incremental Conductance (InC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), applied to multistring thermoelectric generator (TEG) arrays under multiple and asymmetric thermal gradients. The simulated systems, modeled in MATLAB/Simulink, replicate real-world thermoelectric configurations by employing series-parallel topologies and eleven distinct thermal scenarios, including uniform, localized, and sinusoidal temperature distributions. The key contribution of this work lies in demonstrating the superior capability of metaheuristic algorithms (PSO and GA) to locate the global maximum power point (GMPP) in complex thermal environments, outperforming classical methods (P&O and InC), which consistently converged to local maxima under multi-peak conditions. Notably, PSO achieved the best average convergence time (0.23 s), while the GA recorded the fastest response (0.05 s) in the most challenging multi-peak scenarios. Both maintained high tracking accuracy (error ≈ 0.01%) and minimized power ripple, resulting in conversion efficiencies exceeding 97%. The study emphasizes the crucial role of algorithm selection in maximizing energy harvesting performance in practical TEG applications such as embedded systems, waste heat recovery, and autonomous sensor networks. Future directions include physical validation through prototypes, incorporation of dynamic thermal modeling, and development of hybrid or AI-enhanced MPPT strategies. Full article