Search Results (19,146)

This study analyses the potential of using sunflower husks as an energy source by producing bio-pellets and evaluating their combustion process in residential settings. As one of the leading sunflower producers in the European Union, Bulgaria generates significant agricultural residues with high, yet underutilized, energy potential. This study employs a combination of experimental data and numerical modelling aided by ANSYS 2024 R1 to analyse the combustion of sunflower husk pellets in a hot water boiler. The importance of balanced air distribution for achieving optimal combustion, reduced emissions, and enhanced thermal efficiency is emphasized by the results of a comparison of two air supply regimes. It was found that a secondary air-dominated air supply regime results in a more uniform temperature field and a higher degree of oxidation of combustible components. These findings not only confirm the technical feasibility of sunflower husk pellets but also highlight their commercial potential as a sustainable, low-cost energy solution for agricultural enterprises and rural heating providers. The research indicates that there are business-to-business (B2B) market opportunities for biomass producers, boiler manufacturers, and energy distributors who wish to align themselves with EU green energy policies and the growing demand for solutions that support the circular economy. Full article

Traditional forced-air cooling systems suffer from excessive energy consumption and noise pollution. This study proposes an innovative passive cooling strategy through developing aqueous radiative cooling coatings made from a combination of TiO₂-decorated carbon nanotube (TiO₂-CNT) hybrids and polyacrylic acid (PAA), designed to simultaneously enhance the heat dissipation and improve the mechanical strength of the coating films. Based on CNTs’ exceptional thermal conductivity and record-high infrared emissivity, bead-like TiO₂-CNT architectures have been prepared as the filler in PAA. The TiO₂ nanoparticles were in situ grown on CNTs, forming a rough surface that can produce asperity contacts and enhance the strength of the TiO₂-CNT/PAA composite. Moreover, this composite enhanced heat dissipation and achieved remarkable cooling efficiency at a small fraction of the filler (0.1 wt%). The optimized coating demonstrated a temperature reduction of 23.8 °C at an operation temperature of 180.7 °C, coupled with obvious mechanical reinforcement (tensile strength from 13.7 MPa of pure PAA to 17.1 MPa). This work achieves the combination of CNT and TiO₂ nanoparticles for strong radiative cooling coating, important for energy-efficient thermal management. Full article

LPG fuel supply systems are increasingly important for improving energy efficiency and reducing carbon emissions in the shipping industry. The primary objective of this research is to investigate the heat transfer phenomena to enhance the thermal performance of double-pipe heat exchangers (DPHEs) in LPG fuel supply systems. This study investigates the heat transfer performance of a glycol–steam double-pipe heat exchanger (DPHE) within an LPG fuel supply system under varying operating conditions. A computational model and methodology were developed and validated by comparing the numerical results with experimental data obtained from commissioning tests. Additionally, the effects of turbulence models and parametric variations were evaluated by analyzing the glycol–water mixing ratio and flow direction—both of which are critical operational parameters for DPHE systems. Numerical validation against the commissioning data showed a deviation of ±2% under parallel-flow conditions, confirming the reliability of the proposed model. With respect to the glycol–water mixing ratio and flow configuration, thermal conductance (UA) decreased by approximately 11% in parallel flow and 13% in counter flow for every 20% increase in glycol concentration. Furthermore, parallel flow exhibited approximately 0.6% higher outlet temperatures than counter flow, indicating superior heat transfer efficiency under parallel-flow conditions. Finally, the heat transfer behavior of the DPHE was further examined by considering the effects of geometric characteristics, pipe material, and fluid properties. This study offers significant contributions to the engineering design of double-pipe heat exchanger systems for LPG fuel supply applications. Full article

Millisecond lasers, with their high processing efficiency and large power, are widely used in manufacturing fields such as aerospace. This study aims to investigate the effects of different processing parameters on the micro-hole processing of 316 heat-resistant steel using millisecond lasers. Through the control variable method, the study examines the impact of pulse energy, pulse count, and pulse width on the quality of micro-holes, including the entrance diameter, exit diameter, and taper. Furthermore, combined with orthogonal experiments and COMSOL Multiphysics 6.2 simulations, the study explores the influence of pulse width on the formation of blind holes. The experimental results show that when the pulse energy is 2.2 J, the taper is minimal (2.2°), while the taper reaches its peak (2.4°) at 2.4 J pulse energy. As the pulse count increases to 55–60 pulses, the exit diameter stabilizes, and the taper decreases to 1.8°. Blind holes begin to form when the pulse width exceeds 1.2 ms. When the pulse width is 1.2 ms, pulse energy is 2.4 J, and pulse count is 50, the entrance diameter of the blind hole reaches its maximum, indicating that longer pulse widths result in more significant energy reflection and thermal accumulation effects. COMSOL simulations reveal that high-energy pulses cause intense melt ejection, while longer pulse widths exacerbate thermal accumulation at the micro-hole entrance, leading to blind hole formation. This study provides important process references for laser processing of through-holes and blind holes in heat-resistant steel. Full article

With the global low-carbon energy transition, accurate prediction of thermal and physical parameters of deep rock masses is critical for geothermal resource development. To address the insufficient generalization ability of machine learning models caused by scarce measured data on granite thermal conductivity, this study focused on granites from the Gonghe Basin and Songliao Basin in Qinghai Province. A data augmentation strategy combining cubic spline interpolation and Gaussian noise injection (with noise intensity set to 10% of the original data feature range) was proposed, expanding the original 47 samples to 150. Thermal conductivity prediction models were constructed using Support Vector Machine (SVM), Random Forest (RF), and Backpropagation Neural Network(BPNN). Results showed that data augmentation significantly improved model performance: the RF model exhibited the best improvement, with its coefficient of determination R² increasing from 0.7489 to 0.9765, Root Mean Square Error (RMSE) decreasing from 0.1870 to 0.1271, and Mean Absolute Error (MAE) reducing from 0.1453 to 0.0993. The BPNN and SVM models also improved, with R² reaching 0.9365 and 0.8743, respectively, on the enhanced dataset. Feature importance analysis revealed porosity (with a coefficient of variation of 0.88, much higher than the longitudinal wave velocity’s 0.27) and density as key factors, with significantly higher contributions than longitudinal wave velocity. This study provides quantitative evidence for data augmentation and machine learning in predicting rock thermophysical parameters, promoting intelligent geothermal resource development. Full article

Amidst the background of accelerated global energy transition, the safety risk of lithium-ion battery energy storage systems, especially the fire hazard, has become a key bottleneck hindering their large-scale application, and there is an urgent need to build a systematic prevention and control program. This paper focuses on the fire characteristics and thermal runaway mechanism of lithium-ion battery energy storage power stations, analyzing the current situation of their risk prevention and control technology across the dimensions of monitoring and early warning technology, thermal management technology, and fire protection technology, and comparing and analyzing the characteristics of each technology from multiple angles. Building on this analysis, this paper summarizes the limitations of the existing technologies and puts forward prospective development paths, including the development of multi-parameter coupled monitoring and warning technology, integrated and intelligent thermal management technology, clean and efficient extinguishing agents, and dynamic fire suppression strategies, aiming to provide solid theoretical support and technical guidance for the precise risk prevention and control of lithium-ion battery storage power stations. Full article

In the context of the global response to climate change and the active promotion of energy transformation, a number of low-carbon policies coupled with the development of synergies to help power system transformation is an important initiative. However, the insufficient articulation of the green power market, tradable green certificate (TGC) market, and carbon emission trading (CET) mechanism, and the ambiguous policy boundaries affect the trading decisions made by its market participants. Therefore, this paper systematically analyses the composition of the main players in the electricity-CET-TGC markets and their relationship with each other, and designs the synergistic mechanism of the electricity-CET-TGC markets, based on which, it constructs the optimal profit model of the thermal power plant operators, renewable energy manufacturers, power grid enterprises, power users and load aggregators under the electricity-CET-TGC markets synergy, and analyses the behavioural decision-making of the main players in the electricity-CET-TGC markets as well as the electric power system to optimise the trading strategy of each player. The results of the study show that: (1) The synergistic mechanism of electricity-CET-TGC markets can increase the proportion of green power grid-connected in the new type of power system. (2) In the selection of different environmental rights and benefits products, the direct participation of green power in the market-oriented trading is the main way, followed by applying for conversion of green power into China certified emission reduction (CCER). (3) The development of independent energy storage technology can produce greater economic and environmental benefits. This study provides policy support to promote the synergistic development of the electricity-CET-TGC markets and assist the low-carbon transformation of the power industry. Full article

We report the development of highly luminescent, bovine serum albumin (BSA)-stabilized gold–silver bimetallic nanoclusters (Au-AgNCs@BSA) as a novel platform for high-sensitivity, ratiometric intracellular temperature sensing. Precise and non-invasive temperature sensing at the nanoscale is crucial for applications ranging from intracellular thermogenesis monitoring to localized hyperthermia therapies. Traditional luminescent thermometric platforms often suffer from limitations such as high cytotoxicity and low photostability. Here, we synthesized Au-AgNCs@BSA via a one-pot aqueous reaction, achieving significantly enhanced photoluminescence quantum yields (PL QYs, up to 18%) and superior thermal responsiveness compared to monometallic counterparts. The dual-emissive Au-AgNCs@BSA exhibit a linear ratiometric fluorescence response to temperature fluctuations within the physiological range (20–50 °C), enabling accurate and concentration-independent thermometry in live cells. Time-resolved PL and Arrhenius analyses reveal two distinct emissive states and a high thermal activation energy (E_a = 199 meV), indicating strong temperature dependence. Silver doping increases radiative decay rates while maintaining low non-radiative losses, thus amplifying fluorescence intensity and thermal sensitivity. Owing to their small size, excellent photostability, and low cytotoxicity, these nanoclusters were applied to non-invasive intracellular temperature mapping, presenting a promising luminescent nanothermometer for real-time cellular thermogenesis monitoring and advanced bioimaging applications. Full article

Famatinite (Cu₃SbS₄) is an earth-abundant, nontoxic material with potential for thermoelectric energy generation applications. Herein, rapid, energy-efficient, and facile one-pot modified polyol synthesis was utilized to produce gram-scale quantities of phase-pure famatinite (Cu_2.7M_0.3SbS₄, M = Cu, Zn, Mn) nanoparticles (diameter 20–30 nm) with controllable and stoichiometric incorporation of transition metal dopants on the Cu-site. To produce pellets for thermoelectric characterization, the densification process by spark plasma sintering was optimized for individual samples based on thermal stability determined using differential scanning calorimetry and thermogravimetric analysis. Electronic transport properties of undoped and doped famatinite nanoparticles were studied from 225–575 K, and the thermoelectric power factor was calculated. This is the first time electronic transport properties of famatinite doped with Zn or Mn have been studied. All famatinite samples had similar resistivities (>0.8 mΩ·m) in the measured temperature range. However, the Mn-doped famatinite nanomaterials exhibited a thermoelectric power factor of 10.3 mW·m⁻¹·K⁻¹ at 575 K, which represented a significant increase relative to the undoped nanomaterials and Zn-doped nanomaterials engendered by an elevated Seebeck coefficient of ~220 µV·K⁻¹ at 575 K. Future investigations into optimizing the thermoelectric properties of Mn-doped famatinite nanomaterials are promising avenues of research for producing low-cost, environmentally friendly, high-performing thermoelectric materials. Full article

Thermal stimulation represents an effective method for enhancing reservoir permeability, thereby improving geothermal energy recovery in Enhanced Geothermal Systems (EGS). The phase-field method (PFM) has been widely adopted for its proven capability in modeling the fracture behavior of brittle solids. Consequently, a coupled thermo-mechanical phase-field model (TM-PFM) was developed in COMSOL 6.2 Multiphysics to probe thermal fracturing mechanisms in reservoir rocks. The TM-PFM was validated against the analytical solutions for the temperature and stress fields under steady-state heat conduction in a thin-walled cylinder, three-point bending tests, and thermal shock tests. Subsequently, two distinct thermal fracturing modes in reservoir rock under high-temperature conditions were investigated: (i) fracture initiation driven by sharp temperature gradients during instantaneous thermal shocks, and (ii) crack propagation resulting from heterogeneous thermal expansion of constituent minerals. The proposed TM-PFM has been validated through systematic comparison between the simulation results and the corresponding experimental data, thereby demonstrating its capability to accurately simulate thermal fracturing. These findings provide mechanistic insights for optimizing geothermal energy extraction in EGS. Full article

Building performance simulation is crucial for the design and optimization of sustainable buildings. However, the increasing complexity of building systems necessitates advanced modeling techniques capable of handling multi-domain interactions. This paper presents a novel application of the bond graph (BG) methodology to simulate and analyze the thermal behavior of an integrated trigeneration system within an experimental test cell. Unlike conventional simulation approaches, the BG framework enables unified modeling of thermal and hydraulic subsystems, offering a physically consistent and energy-based representation of system dynamics. The study investigates the system’s performance under both dynamic and steady-state conditions across two distinct climatic periods. Validation against experimental data reveals strong agreement between measured and simulated temperatures in heating and cooling scenarios, with minimal deviations. This confirms the method’s reliability and its capacity to capture transient thermal behaviors. The results also demonstrate the BG model’s effectiveness in supporting predictive control strategies, optimizing energy efficiency, and maintaining thermal comfort. By integrating hydraulic circuits and thermal exchange processes within a single modeling framework, this work highlights the potential of bond graphs as a robust and scalable tool for advanced building performance simulation. Full article

Bi₆Fe₂Ti₃O₁₈ (BFTO) ceramics were synthesized via a solid-state reaction route using stoichiometric amounts of Bi₂O₃, TiO₂, and Fe₂O₃ powders. A thermal analysis of the powder mixture was conducted to optimize the heat treatment parameters. Energy-dispersive X-ray spectroscopy (EDS) confirmed the conservation of the chemical composition following calcination. Final densification was achieved through hot pressing. The crystal structure of the sintered samples, examined via X-ray diffraction at room temperature, revealed a tetragonal symmetry for BFTO ceramics sintered at 850 °C. Electron backscatter diffraction (EBSD) provided detailed insight into the crystallographic orientation and microstructure. Broadband dielectric spectroscopy (BBDS) was employed to investigate the dielectric response of BFTO ceramics over a frequency range of 10 mHz to 10 MHz and a temperature range of −30 °C to +200 °C. The temperature dependence of the relative permittivity (ε_r) and dielectric loss tangent (tan δ) were measured within a frequency range of 100 kHz to 900 kHz and a temperature range of 25 °C to 570 °C. The impedance data obtained from the BBDS measurements were validated using the Kramers–Kronig test and modeled using the Kohlrausch–Williams–Watts (KWW) function. The stretching parameter (β) ranged from ~0.72 to 0.82 in the impedance formalism within the temperature range from 200 °C to 20 °C. Full article

Pulsed electric field (PEF) and high-pressure processing (HPP) are non-thermal treatments, developed to ensure preservation of food products whilst maintaining taste and valuable nutrients. In this study, we investigated their potential for the inactivation of 3 commercial exogenous pectinases (polygalacturonase, pectin transeliminase, pectin esterase) commonly used in juice processing for clarification of juices. The inactivation of these enzymes after processing is mandatory by European law. Clear apple juice was treated with both non-thermal processing methods, as well as with thermal pasteurization as the standard method. For HPP, 3 pressures (250, 450, and 600 MPa) and different holding times (from 2 to 12 min) were tested. For PEF, 3 electric field intensities (10, 13, and 15 kV/cm) and different specific energy values (from 121 to 417 kJ/kg). Standard thermal pasteurization resulted in a complete inactivation of all tested pectinases. HPP treatment only showed marginal effects on polygalacturonase and pectin transeliminase at the highest pressure and holding times, which are beyond levels used in industrial settings. For PEF, dependence upon high electric field strength and specific energy values was evident; however, here too, the effect was only moderate at the levels attainable within the scope of this study. Assuming a continued linear relationship, usable results could be achieved in an industrial setting, albeit under more extreme conditions. Full article

The junction temperature (T_j) is a key parameter reflecting the thermal behavior of Silicon carbide (SiC) MOSFETs and is essential for condition monitoring and reliability assessment in power electronic systems. However, the limited temperature sensitivity of switching characteristics makes it difficult for traditional single temperature-sensitive electrical parameters (TSEPs) to achieve accurate estimation. To address this challenge and enable practical thermal sensing applications, this study proposes an accurate, application-oriented T_j estimation method based on multivariate linear regression (MLR) using turn-off current fall time (t_fi) and fall loss (E_fi) as complementary TSEPs. First, the feasibility of using current fall time and current fall energy loss as TSEPs is demonstrated. Then, a coupled junction temperature prediction model is developed based on multivariate linear regression using t_fi and E_fi. The proposed method is experimentally validated through comparative analysis. Experimental results demonstrate that the proposed method achieves high prediction accuracy, highlighting its effectiveness and superiority in MLR approach based on the current fall phase characteristics of SiC MOSFETs. This method offers promising prospects for enhancing the condition monitoring, reliability assessment, and intelligent sensing capabilities of power electronics systems. Full article

Over the past few decades, lithium-ion batteries (LIBs) have gained significant attention due to their inherent potential for environmental sustainability and unparalleled energy storage efficiency. Meanwhile, polymer electrolytes have gained popularity in several fields due to their ability to adapt to various battery geometries, enhanced safety features, greater thermal stability, and effectiveness in reducing dendrite growth on the anode. However, their relatively low ionic conductivity compared to liquid electrolytes has limited their application in high-performance devices. This limitation has led to recent studies revolving around the development of poly(ionic liquids) (PILs), particularly imidazolium-mediated polymer backbones as novel electrolyte materials, which can increase the conductivity with fine-tuning structural benefits, while maintaining the advantages of both solid and gel electrolytes. In this study, a curated dataset of 120 data points representing eight different polymers was used to predict ionic conductivity in imidazolium-based PILs as well as the emerging ionene substructures. For this purpose, four ML models: CatBoost, Random Forest, XGBoost, and LightGBM were employed by incorporating chemical structure and temperature as the models’ inputs. The best-performing model was further employed to estimate the conductivity of novel ionenes, offering insights into the potential of advanced polymer architectures for next-generation LIB electrolytes. This approach provides a cost-effective and intelligent pathway to accelerate the design of high-performance electrolyte materials. Full article