Search Results (216)

Solar energy is one of the most promising solutions for improving building energy efficiency. Among passive heating systems, the combination of a Trombe wall, phase change materials (PCM), and multi-alveolar structures (MAS) stands out. This configuration enhances the wall’s ability to absorb solar heat and distribute it evenly throughout the interior. This study evaluated thermal comfort by examining the effects of phase change materials and multi-alveolar structures combined with a Trombe wall on the thermal behavior of a building and improving the thermal inertia of brick walls. Numerical simulations using Visual FORTRAN were conducted to evaluate the thermal properties of different configurations under the climatic conditions recorded in Hammam Sousse, Tunisia. The results show that the integration of the Trombe wall and PCM has a significant impact on interior temperature stability, energy consumption, and overall thermal comfort. The combined effect of the MAS and PCM with the Trombe wall improved heat gain in winter and spring, reaching a low thermal damping factor of 40% in March, reducing heating power, and optimizing thermal comfort for occupants. Full article

Environmental contamination from industrial dyes, particularly Methylene Blue (MB), presents a growing challenge due to their toxicity and persistence in aquatic systems. This study explored the catalytic potential of cellulose acetate-stabilized nickel (CA/Ni) nanoparticles for the degradation of MB in aqueous solutions. CA/Ni was synthesized and characterized using FTIR and SEM, confirming its successful incorporation into the cellulose acetate matrix and uniform distribution across the membrane. UV-Vis spectrophotometry was employed to monitor the catalytic degradation of MB, revealing a significant decrease in absorbance at 665 nm over 28 min, indicating 68% degradation efficiency. Kinetic analysis showed that the degradation followed pseudo-first-order kinetics, with an apparent rate constant of 0.0348 min⁻¹ and an R² value of 0.9851, confirming excellent catalytic performance. The effects of temperature and pH on MB degradation were investigated, with the highest efficiency observed at 35 °C and a pH of 7. A room temperature (25 °C) and acidic conditions (pH 5) reduced the degradation rate to 52%. In comparison, a higher temperature (45 °C) and an alkaline pH (pH 9) resulted in a slight decline to 55%, likely due to changes in catalyst efficiency and MB solubility. These findings highlight the potential of Ni NP-stabilized membranes for wastewater treatment applications, providing a scalable and efficient approach to dye removal. Full article

This study developed cholesteric liquid crystal broadband reflective films using zinc oxide nanoparticles (ZnO NPs) and homotriazine UV-absorbing dye (UV-1577) to enhance infrared shielding. Unlike benzotriazole-based UV absorber UV-327, which suffers from volatility and contamination, UV-1577 exhibits superior compatibility with liquid crystals, higher UV absorption efficiency, and enhanced processing stability due to its larger molecular structure. By synergizing UV-1577 with ZnO NPs, we achieved a gradient UV intensity distribution across the film thickness, inducing a pitch gradient that broadened the reflection bandwidth to 915 nm and surpassing the performance of previous systems using UV-327/ZnO NPs (<900 nm). We conducted a detailed examination of the factors influencing the reflective bandwidth. These included the UV-1577/ZnO NP ratio, the concentrations of the polymerizable monomer (RM257) and chiral dopant (R5011), along with polymerization temperature, UV irradiation intensity, and irradiation time. The resultant films demonstrated efficient ultraviolet shielding via the UV-1577/ZnO NPs collaboration and infrared shielding through the induced pitch gradient. This work presents a scalable strategy for energy-saving smart windows. Full article

In this investigation, a comprehensive validation framework for an integrated electrochemical-thermal model that addresses critical thermal management challenges in lithium-ion batteries (LIBs) is presented. The two-dimensional numerical model combines the Newman–Tiedemann–Gu–Kim (NTGK) electrochemical-thermal battery framework with the enthalpy-porosity approach for phase change material (PCM) battery thermal management systems (BTMSs). Rigorous validation against benchmarks demonstrates the model’s exceptional predictive capability across a wide range of operating conditions. Simulated temperature distribution and voltage capacity profiles at multiple discharge rates show excellent agreement with the experimental data, accurately capturing the underlying electrochemical-thermal mechanisms. Incorporating Capric acid (with a phase transition range of 302–305 K) as the PCM, the thermal management model demonstrates significantly improved accuracy over existing models in the literature. Notable error reductions include a 78.3% decrease in the Mean Squared Error (0.477 vs. 2.202), a 53.4% reduction in the Root Mean Squared Error (0.619 vs. 1.483), and a 55.5% drop in the Mean Absolute Percentage Error. Statistical analysis further confirms the model’s robustness, with a high coefficient of determination (R² = 0.968858) and well-distributed residuals. Liquid fraction evolution analysis highlights the PCM’s ability to absorb thermal energy effectively during high-discharge operations, enhancing thermal regulation. This validated model provides a reliable foundation for the design of next-generation BTMS, aiming to improve the safety, performance, and lifespan of LIBs in advanced energy storage applications where thermal stability is critical. Full article

In the automotive industry, the increasing stringent standards to reduce fuel consumption and pollutant emissions has driven significant advancements in turbocharging systems. The centrifugal compressor, as the most widely used power-absorbing machinery, plays a crucial role but remains one of the most complex components to study and design. While most numerical studies rely on adiabatic models, this work analyses several Computational Fluid Dynamics (CFD) models with conjugate heat transfer (CHT) of varying complexity, incorporating real solid components. This approach allowed a sensitivity analysis of the performance obtained from the different models compared to the adiabatic case, highlighting the effects of internal heat exchange losses. Moreover, an analysis of the temperature distribution of the wheel was conducted, along with a thermal assessment of the various heat flux contributions across the different components, to gain a deeper understanding of the performance differences. The impact of including the seal plate has been evaluated and different boundary conditions on the seal plate have been tested to assess the uncertainty in the results. Finally, the influence of heat exchange between the shroud and the external environment is also examined to further refine the model’s accuracy. One of the objectives of this work is to obtain a correct temperature profile of the rotor for a subsequent thermo-mechanical analysis. Full article

In this paper, we propose a multi-mode switchable ultra-wideband terahertz absorber based on patterned graphene and VO₂ by designing a graphene pattern composed of a large rectangle rotated 45° in the center and four identical small rectangles in the periphery, as well as a VO₂ layer pattern composed of four identical rectangular boxes and small rectangles embedded in the dielectric layer. VO₂ can regulate conductivity via temperature, the Fermi level of graphene depends on the external voltage, and the graphene layer and VO₂ layer produce resonance responses at different frequencies, resulting in high absorption. The proposed absorption microdevices have three modes: Mode 1 (2.52–4.52 THz), Mode 2 (3.91–9.66 THz), and Mode 3 (2.14–10 THz), which are low-band absorption, high-band absorption, and ultra-wideband absorption. At 2.96 THz in Mode 1, the absorption rate reaches 99.98%; at 8.04 THz in Mode 2, the absorption rate reaches 99.76%; at 5.04 THz in Mode 3, the absorption rate reaches 99.85%; and at 8.4 THz, the absorption rate reaches 99.76%. We explain the absorption mechanism by analyzing the electric field distribution and local plasma resonance, and reveal the high-performance absorption mechanism by using the relative impedance theory. In addition, absorption microdevices have the advantages of polarization insensitivity, incident angle insensitivity, multi-mode switching, ultra-wideband absorption, large manufacturing tolerance, etc., and have potential research and application value in electromagnetic stealth devices, filters and optical switches. Full article

Traditional biochemical analysis methods are not only resource-intensive and time-consuming, but are increasingly inadequate for meeting the demands of modern production and quality testing. In recent years, hyperspectral imaging (HSI) technology has been widely applied as a non-destructive detection method for fruit and vegetable quality assessment. This study, based on HSI technology, systematically investigates the distribution patterns of jujube quality parameters under various drying temperature conditions. It focuses on analyzing six key quality indicators: L*, a*, b*, soluble solid content (SSC), hardness, and moisture content. HSI was used to acquire reflectance (R), absorbance (A), and Kubelka–Munk (K-M) spectral data of jujubes at various drying temperatures, followed by several spectral preprocessing methods, including standard normal variate (SNV), baseline correction (baseline), and Savitzky–Golay first derivative (SG1st). Subsequently, a nonlinear support vector regression (SVR) model was used to perform regression modeling for the six quality parameters. The results demonstrate that the SG1st preprocessing method significantly enhanced the predictive capability of the model. For the predictions of L*, a*, b*, SSC, hardness, and moisture content, the best inversion models achieved coefficients of determination $R_p^2$ of 0.9972, 0.9970, 0.9857, and 0.9972, respectively. To further enhance modeling accuracy, deep learning models such as bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and convolutional neural network–bidirectional gated recurrent unit (CNN-BiGRU) were introduced and compared comprehensively under the optimal spectral preprocessing conditions. The results demonstrate that deep learning models significantly improved modeling accuracy, with the CNN-BiGRU model performing particularly well. Compared to the SVR model, the $R_p^2$ values for L*, a*, b*, SSC, hardness, and moisture increased by 0.005, 0.007, 0.008, 0.011, 0.007, and 0.006, respectively; the $RPD$ values increased by 0.036, 0.04, 0.26, 0.462, 0.428, and 0.216. This study provides important insights into the further application of HSI technology in the quality monitoring and optimization of the jujube drying process. Full article

The rise in temperature worldwide, especially in hot regions with extreme weather conditions, has made climate change one of the critical issues that degrades the solar photovoltaic (PV) system performance. In this paper, a new design of solar cells based on plasmonic thin-film Silver (Ag) technology is introduced. The new design is characterized by enhancing thermal effects, optical power absorption, and output power significantly, thus compensating for the deterioration in the solar cells efficiency when the ambient temperature rises to high levels. The temperature distribution on a PV solar module is determined using a three-dimensional computational fluid dynamics (CFD) model that includes the front glass, crystalline cells, and back sheet. Experimental and analytical results are presented to validate the CFD model. The parameters of temperature distribution, absorbed optical power, and output electrical power are considered to evaluate the device performance during daylight hours in summer. The effects of solar radiation falling on the solar cell, actual temperature of the environment, and wind speed are investigated. The results show that the proposed cells’ temperature is reduced by 1.2 °C thanks to the plasmonic Ag thin-film technology, which leads to enhance 0.48% real value as compared to that in the regular solar cells. Consequently, the absorbed optical power and output electrical power of the new solar cells are improved by 2.344 W and 0.38 W, respectively. Full article

The feed industry is characterized by high energy consumption during the grinding stage, where hammer mills can account for up to 50% of total electricity usage; furthermore, efficiency analyses are based only on the classical equations reported in the literature. In this context, the present theoretical-applied research aimed to improve the efficiency of a plant operating below its nominal capacity. To achieve this, a comprehensive mathematical model was developed, integrating power and grain disintegration equations while overcoming the limitations of classical comminution theories. The model incorporates key factors such as feed rate, moisture content, absorbed power and hammer wear. Additionally, specific correction factors for temperature ($K_t$) and mechanical degradation ($K_d$) were introduced to accurately represent real operating conditions. The study was based on extensive measurements of electrical current, power factor, energy consumption, particle size distribution and thermal variations under different load conditions. The statistical analysis, which included ANOVA, ANCOVA and multiple regressions, demonstrated a predictive accuracy of 98% (R²) and a pseudo-R² of 89%. This high correlation allowed for an 18% reduction in energy consumption equivalent to 4 kWh/t and up to a 30% improvement in particle size uniformity, surpassing typical factory performance. The findings highlight that integrating operational, thermodynamic and wear-related factors enhances the robustness of the model, promoting more reliable energy-management practices in hammer mills. Consequently, the results confirm that the developed model serves as a scientifically robust, efficient and applicable tool for improving energy efficiency and reducing environmental impacts in the agri-food industry. Full article

To study the mechanical properties of asymmetric K-shaped square tubular joints with built-in stiffening rib lap joints, axial tensile tests were carried out on one K-shaped joint without built-in stiffening ribs and four K-shaped joints with built-in stiffening ribs using an electro-hydraulic servo structural testing system. The effects of the addition of stiffening ribs and the welding method of the stiffening ribs on the mechanical properties were studied comparatively. The failure mode of the K-shaped joint was obtained, and the strain distribution and peak displacement reaction force in the nodal region were analyzed. A finite element analysis of the K-shaped joint was carried out, and the finite element results were compared with the experimental results. The results showed that the addition of transverse reinforcement ribs and more complete welds shared the squeezing effect of the brace on the chord. Arranging more reinforcing ribs in the fittings makes the chord more uniformly stressed and absorbs more energy while increasing the flexural load capacity of the fittings’ side plates. The presence of a weld gives a short-lived temperature increase in the area around the crack, and the buckling of the structure causes the surface temperature in the buckling area to continue to increase for some time. The temperature change successfully localized where the structure was deforming and creating cracks. The addition of the reinforcing ribs resulted in a change in the deformation pattern of the model, and the difference occurred because the flexural capacity of the brace with the added reinforcing ribs was greater than that of the side plate buckling. Full article

The northern slope of the Tibetan Plateau (TP) is the crucial affected area for dust originating from the Taklimakan Desert (TD). However, few studies have focused on the meteorological element responses to TD dust over different surface types near the TP. Satellite data and the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) were used to analyze the dust being transported from the TD to the TP and its effect from 30 July to 2 August 2016. In the TD, the middle-upper dust layer weakened the solar radiation reaching the lower dust layer, which reduced the temperature within the planetary boundary layer (PBL) during daytime. At night, the dust’s thermal preservation effect increased temperatures within the PBL and decreased temperatures at approximately 0.5 to 2.5 km above PBL. In the TP without snow cover, dust concentration was one-fifth of the TD, while the cooling layer intensity was comparable to the TD. However, within the PBL, the lower concentration and thickness of dust allowed dust to heat atmospheric continuously throughout the day. In the TP with snow cover, dust diminished planetary albedo, elevating temperatures above 6 km, hastening snow melting, which absorbed latent heat and increased the atmospheric water vapor content, consequently decreasing temperatures below 6 km. Surface meteorological element responses to dust varied significantly across different surface types. In the TD, 2 m temperature (T₂) decreased by 0.4 °C during daytime, with the opposite nighttime variation. In the TP without snow cover, T₂ was predominantly warming. In the snow-covered TP, T₂ decreased throughout the day, with a maximum cooling of 1.12 °C and decreased PBL height by up to 258 m. Additionally, a supplementary simulation of a dust event from 17 June to 19 June 2016 further validated our findings. The meteorological elements response to dust is significantly affected by the dust concentration, thickness, and surface type, with significant day–night differences, suggesting that surface types and dust distribution should be considered in dust effect studies to improve the accuracy of climate predictions. Full article

Computed Tomography–Tunable Diode Laser Absorption Spectroscopy (CT-TDLAS) is an effective diagnostic method for analyzing combustion flow fields within engines. This study proposes an adaptive reconstruction algorithm utilizing constrained polynomial fitting within the CT-TDLAS framework. Based on existing polynomial fitting models, the proposed algorithm integrates physical boundary constraints on temperature and concentration fields, optimizing integrated absorbance errors. This method significantly enhances reconstruction accuracy and computational efficiency, while also lowering computational complexity. The adaptive strategy dynamically adjusts the polynomial order, effectively mitigating issues of overfitting or underdetermination typically associated with fixed polynomial orders. Numerical simulations demonstrate reduced temperature reconstruction errors of 2%, 1.6%, and 2% for single-peak, dual-peak, and mixed distribution flow fields, respectively. Corresponding concentration errors were 2%, 1.8%, and 2.6%, which are all improvements over those achieved by the Algebraic Reconstruction Technique (ART). Experimental results using a McKenna flat-flame burner revealed an average reconstruction error of only 0.3% compared to thermocouple measurements for high-temperature regions (>1000 K), with a minimal central temperature difference of 6 K. For lower-temperature peripheral regions, the average error was 188 K, illustrating the practical applicability of the proposed algorithm. Full article

Over the past few years, the combination of solar power with refrigeration technology has matured, providing a promising solution for sustainable cooling. However, a key challenge remains, namely the inherent intermittency of solar energy. Due to its uneven temporal distribution, it is difficult to ensure continuous 24 h operation when relying solely on solar energy. To address this issue, thermal energy storage technology has emerged as a viable solution. This paper presents a comprehensive systematic review of phase-change material (PCM) applications in solar refrigeration systems. It systematically categorizes solar energy conversion methodologies and refrigeration system configurations while elucidating the fundamental operational principles of each solar refrigeration system. A detailed examination of system components is provided, encompassing photovoltaic panels, condensers, evaporators, solar collectors, absorbers, and generators. The analysis further investigates PCM integration strategies with these components, evaluating integration effectiveness and criteria for PCM selection. The critical physical parameters of PCMs are comparatively analyzed, including phase transition temperature, latent heat capacity, specific heat, density, and thermal conductivity. Through conducting a critical analysis of existing studies, this review comprehensively evaluates current research progress within PCM integration techniques, methodological classification frameworks, performance enhancement approaches, and system-level implementation within solar refrigeration systems. The investigation concludes by presenting strategic recommendations for future research priorities based on a comprehensive systematic evaluation of technological challenges and knowledge gaps within the domain. Full article

The internal conditions of the high-temperature molten pool in an electro-fused magnesia furnace (EFMF) are difficult to measure, and the temperature distribution–energy conservation relationship in the EFMF cannot be effectively evaluated. Assuming that the feeding speed is constant, the heat absorbed by the newly added raw materials is equal to the rated power minus the heating power required to maintain thermal balance. Therefore, the EFMF can be approximately described by a steady-state model. In order to analyze the state of the molten pool of EFMF at different smelting stages, this study first constructed a three-dimensional steady-state multi-physics field numerical simulation model. The calculations show that the equivalent resistance of the molten pool varies approximately between 1 m$\Omega$ and 0.4 m$\Omega$. Furthermore, the equivalent reactance produced by the whole conductive circuit is almost of the same order as the resistance. The Reynolds number of the convection inside the molten pool exceeds ${10}^5$, which means that the flow inside the molten pool is forced convection dominated by the Lorentz force. Moreover, the turbulence makes the temperature uniformity of the molten pool (the temperature gradient near the solid–liquid interface is approximately within 300 K/m) far greater than that of the unmelted raw materials with very low thermal conductivity (the average temperature gradient reaches over 1000 K/m); the respective proportions of arc power and Joule heating power can be predicted by the model. When the molten pool size is small, the proportion of Joule heating power is high, reaching about 20% of the rated power (3700 kVA); as the molten pool size increases, the convection effect is relatively weakened, and the proportion of Joule heating power also decreases accordingly, only 5% to 10%; the model prediction and experimental estimation results are in good agreement, which makes it feasible to conduct a quantitative analysis of the power distribution in different smelting stages. Full article

Self-excited systems rely on stable external stimuli to initiate and sustain oscillations via internal processes. However, these oscillations can compromise system stability and increase friction, limiting their practical applications. To overcome this issue, we propose the light-fueled stable self-rolling of a liquid crystal elastomer (LCE)-based wheel. A photothermal response model based on an LCE was used to analyze the temperature distribution within the LCE rods. The driving torque for self-rolling is generated by the contraction resulting from the LCE’s photothermal response, which displaces the wheel’s center of mass. We then derived the equilibrium equations and identified the critical conditions for achieving stable self-rolling motion. Through the interaction between the temperature field and driving torque, the wheel achieves continuous and stable self-rolling by absorbing thermal energy to counteract damping dissipation. Numerical simulations revealed that the stable self-rolling velocity is influenced by several key parameters, including heat flux, the contraction coefficient, gravitational acceleration, the initial damping torque, and the rolling damping coefficient. The proposed LCE-based wheel enhances system stability and significantly reduces frictional losses. These characteristics make it a promising candidate for applications in autonomous drive systems, micro-transportation devices, and photothermal energy conversion technologies. Full article