Search Results (503)

Internal fins are commonly utilized as a passive technique to enhance natural convection, but their efficiency depends on complex interplay between fin design, material properties, and convective strength. This study presents an extensive numerical analysis of buoyancy-driven flow in square cavities containing a single horizontal fin on the hot wall. Over 9000 simulations were conducted, methodically varying the Rayleigh number (Ra = 10 to 10⁵), Prandtl number (Pr = 0.1 to 10), and fin characteristics, such as length, vertical position, thickness, and the thermal conductivity ratio (up to 1000), to assess their overall impact on thermal efficiency. Thermal enhancements compared to scenarios without fins are quantified using local and average Nusselt numbers, as well as a Nusselt number ratio (NNR). The results reveal that, contrary to conventional beliefs, long fins positioned centrally can actually decrease heat transfer by up to 11.8% at high Ra and Pr due to the disruption of thermal plumes and diminished circulation. Conversely, shorter fins located near the cavity’s top and bottom wall edges can enhance the Nusselt numbers for the hot wall by up to 8.4%, thereby positively affecting the development of thermal boundary layers. A U-shaped Nusselt number distribution related to fin placement appears at Ra ≥ 10³, where edge-aligned fins consistently outperform those positioned mid-height. The benefits of high-conductivity fins become increasingly nonlinear at larger Ra, with advantages limited to designs that minimally disrupt core convective patterns. These findings challenge established notions regarding passive thermal enhancement and provide a predictive thermogeometric framework for designing enclosures. The results can be directly applied to passive cooling systems in electronics, battery packs, solar thermal collectors, and energy-efficient buildings, where optimizing heat transfer is vital without employing active control methods. Full article

The city of Al Ain is a fast-developing area. With building typology varying from low-rise to mid-rise, sustainable design in buildings is needed. As the majority of the city’s population is Emirati Citizens, the percentage of expats is increasing. The expats tend to live in mid-rise buildings. One of the central midrise areas is AL Ain Square. This study aims to investigate how an optimized mashrabiya pattern can impact the energy and the Predicted Mean Vote (PMV) in a 3-bedroom apartment, fully oriented to the south, of an expat family. The methodology is as follows: case study selection, Weather analysis, Modeling/Validation of the base case scenario, Optimization of the mashrabiya pattern, Simulation of various scenarios, and Results. Analyzing the selected case study is the initial step of the methodology. This analysis begins with the district, building typology, and the chosen apartment. The weather analysis is relevant for using the mashrabiya (screen device) and the need to improve energy consumption and thermal comfort. The modeling of the base case shall be performed in Rhino Grasshopper. The validation is based on a one-year electricity bill provided by the owner. The optimization of mashrabiya patterns is an innovative process, where various designs are compared and then optimized to select the most efficient pattern. The solutions to the selected scenarios will then yield the results of the optimal scenario. This study is relevant to industry, academia, and local authorities as an innovative approach to retrofitting buildings. Additionally, the research presents a creative vision that suggests optimized mashrabiya patterns can significantly enhance energy savings, with the hexagonal grid configuration demonstrating the highest efficiency. This finding highlights the potential for geometry-driven shading optimization tailored to specific climatic and building conditions. Contrasting earlier mashrabiya studies that assess one static pattern, we couple a geometry-agnostic evolutionary solver with a utility-calibrated EnergyPlus model to test thousands of square, hexagonal, and triangular permutations. This workflow uncovers a previously undocumented non-linear depth perforation interaction. It validates a hexagonal screen that reduces annual cooling energy by 12.3%, establishing a replicable, grid-specific retrofit method for hot-arid apartments. Full article

Diamond is an attractive substrate candidate for GaN high-electron-mobility transistors (HEMT) to enhance heat dissipation due to its exceptional thermal conductivity. However, the thermal boundary resistance (TBR) at the GaN–diamond interface poses a significant bottleneck to heat transport, exacerbating self-heating and limiting device performance. In this work, TCAD simulations were employed to systematically investigate the effects of thermal boundary layer (TBL) thickness (d_TBL) and thermal conductivity (κ_TBL) on the electrothermal behavior of GaN-on-diamond HEMTs. Results show that increasing the TBL thickness (5–20 nm) or decreasing its thermal conductivity (0.1–1.0 W/(m·K)) leads to elevated hotspot temperatures and degraded electron mobility, resulting in a notable deterioration of I–V characteristics. The nonlinear dependence of device performance on κ_TBL is attributed to Fourier’s law, where heat flux is inversely proportional to thermal resistance. Furthermore, the co-analysis of substrate thermal conductivity and interfacial quality reveals that interface TBR has a more dominant impact on device behavior than substrate conductivity. Remarkably, devices with low thermal conductivity substrates and optimized interfaces can outperform those with high-conductivity substrates but poor interfacial conditions. These findings underscore the critical importance of interface engineering in thermal management of GaN–diamond HEMTs and provide a theoretical foundation for future work on phonon transport and defect-controlled thermal interfaces. Full article

Urban green spaces (UGS) serve as critical mitigators of urban heat islands (UHIs), yet the scale-dependent mechanisms through which UGS morphology regulates thermal effects remain insufficiently understood. This study investigates the multi-scale relationships between UGS spatial patterns and cooling effects in Macao, employing morphological spatial pattern analysis (MSPA) to characterize UGS configurations and geographically weighted regression (GWR) to examine city-scale thermal interactions, complemented by patch-scale buffer analyses of area, perimeter, and landscape shape index effects. Results demonstrate that high-UGS-integrity areas significantly enhance cooling capacity (area with proportion of core ≥35% showing optimal performance), while fragmented elements (branches, edges) exacerbate UHIs, with patch-scale analyses revealing nonlinear threshold effects in cooling efficiency. A tripartite classification of UGS by cooling capacity identifies strong mitigation types with optimal shape metrics and cooling extents. These findings establish a tripartite UGS classification system based on cooling performance and identify optimal morphological parameters, advancing understanding of thermal regulation mechanisms in urban environments. This research provides empirical evidence for UGS planning strategies prioritizing core area conservation, morphological optimization, and seasonal adaptation to improve urban climate resilience, offering practical insights for sustainable development in high-density coastal cities. Full article

In this study, natural kaolin was modified with hexadecyltrimethylammonium bromide (HDTMA-Br) at two levels corresponding to 50% and 90% of its cation exchange capacity. The resulting materials, designated as HKR-50 and HKR-90, were used as adsorbents for the mycotoxins ochratoxin A (OCHRA) and zearalenone (ZEN). The characterization of the HKRs with several methods (X-ray diffraction, DRIFT spectroscopy, thermal analysis (DTA/TG), SEM, zeta potential measurements, and the determination of the point of zero charge and textural properties) confirmed the presence of surfactant ions on the organokaolinites’ surfaces. The adsorption of ZEN and OCHRA by HKRs followed nonlinear adsorption isotherms, suggesting a complex adsorption mechanism. The adsorption capacities of ZEN and OCHRA were similar for HKR-50 and HKR-90 at pH 3, with higher adsorption observed for ZEN (~13.0 mg/g for HKR-50 and HKR-90 for ZEN and ~8.0 mg/g for HKR-50 and HKR-90 for OCHRA). At pH 7, the adsorption of ZEN and OCHRA was lower than at pH 3, especially for OCHRA, but slightly increased with increased amounts of surfactant on the kaolinite surface (8.5 mg/g for HKR-50 and 10.8 mg/g for HKR-90 for ZEN and 2.6 mg/g for HKR-50 and 4.1 mg/g for HKR-90 for OCHRA). Special attention was paid to the safety assessment of the natural kaolin and HKR-90, and toxicological tests confirmed the safety of both materials, as no adverse effects were observed in rats. Full article

The mechanism for the upscale deposition of energy into the atmosphere from molecules and photons up to organized wind systems is examined. This analysis rests on the statistical multifractal analysis of airborne observations. The results show that the persistence of molecular velocity after collision in breaking the continuous translational symmetry of an equilibrated gas is causative. The symmetry breaking may be caused by excited photofragments with the associated persistence of molecular velocity after collision, interaction with condensed phase surfaces (solid or liquid), or, in a scaling environment, an adjacent scale having a different velocity and temperature. The relationship of these factors for the solution to the Navier–Stokes equation in an atmospheric context is considered. The scale invariant version of Gibbs free energy, carried by the most energetic molecules, enables the acceleration of organized flow (winds) from the smallest planetary scales by virtue of the nonlinearity of the mechanism, subject to dissipation by the more numerous average molecules maintaining an operational temperature via infrared radiation to the cold sink of space. The fastest moving molecules also affect the transfer of infrared radiation because their higher kinetic energy and the associated more-energetic collisions contribute more to the far wings of the spectral lines, where the collisional displacement from the central energy level gap is greatest and the lines are less self-absorbed. The relationship of events at these scales to macroscopic variables such as the thermal wind equation and its components will be considered in the Discussion section. An attempt is made to synthesize the mechanisms by which winds are generated and sustained, on all scales, by appealing to published works since 2003. This synthesis produces a view of the general circulation that includes thermodynamics and the defining role of turbulence in driving it. Full article

The use of both structural steel and reinforced concrete is common in civil and military infrastructure projects. Anchorage plays a crucial role in these systems, serving as the key element that connects structural components and secures attachments within complex composite structures. This research focuses on evaluating the performance of steel–concrete column connections under the combined effects of lateral loading and fire exposure. Additionally, the study investigates the use of carbon fiber-reinforced polymers (CFRP) for strengthening and repairing these connections. The research methodology combines experimental testing and finite-element modeling to achieve its objectives. First, experimental investigation was carried out to test two groups of steel-reinforced concrete column specimens, each group made of three specimens. The first group specimens were designed based on special moment frame (SMF) detailing, and the other group specimens were designed based on intermediate moment frame (IMF) detailing. These two types of design were selected based on seismic demands, with SMFs offering high ductility and resilience for severe earthquakes and IMFs providing a cost-effective solution for moderate seismic zones, both benefiting from ongoing innovations in connection detailing and design approaches. Then, finite-element analysis was conducted to model the test specimens. High-fidelity finite-element modeling was conducted using ANSYS program, which included three-dimensional coupled thermal-stress analyses for the six tested specimens and incorporated nonlinear temperature-dependent materials characteristics of each component and the interfaces. Both the experimental and numerical results of this study show that fire has a more noticeable effect on displacement compared to the peak capacities of both types of specimens. Fire exposure results in a larger reduction in the initial residual lateral stiffness of the SMF specimens when compared to IMF specimens. While the effect of CFRP wraps on initial residual lateral stiffness was consistent for all specimens, it caused more improvement for the IMF specimen in terms of post-fire ductility when compared to SMF specimens. This exploratory study confirms the need for further research on the effect of fire on the concrete–steel anchorage zones. Full article

Elevated thermal conditions, rock formations exhibit distinct mechanical behaviors that significantly deviate from their characteristics under ambient temperature environments. This phenomenon raises critical questions regarding the applicability of conventional failure criteria in accurately assessing wellbore stability and maintaining the structural integrity of subsurface infrastructure within geothermal environments. Based on the least absolute deviation method, this paper studies the response characteristics of rock strength at different temperatures and evaluates the prediction performance of six commonly used strength criteria under various temperature and stress environments. The experimental findings reveal a pronounced nonlinear dependence of rock strength on confining pressure elevation. A comparative analysis of failure criteria demonstrates hierarchical predictive performance: the Hoek–Brown (HB) criterion achieves superior temperature-dependent strength prediction fidelity, outperforming the modified Griffith (MGC), Mohr–Lade (ML), and modified Wiebols–Cook (MWC) criteria by 12–18% in accuracy metrics. Notably, the Zhao–Zheng (ZZ) and conventional Mohr–Coulomb (MC) criteria exhibit statistically significant deviations across the tested thermal range. The HB criterion’s exceptional performance in high-temperature regimes is attributed to its dual incorporation of nonlinear confinement effects and thermally activated microcrack propagation mechanisms. The implementation of this optimized model in Well X’s borehole stability analysis yielded 89% alignment between predictions and field observations, with principal stress variations remaining within 7% of critical failure thresholds. These mechanistic insights offer critical theoretical and practical references for thermo-hydro-mechanical coupling analysis in enhanced geothermal systems and deep subsurface containment structures. Full article

Fused Filament Fabrication (FFF) is a widely adopted additive manufacturing technique, yet its mechanical performance is highly dependent on process parameters, particularly nozzle diameter and printing speed. This study evaluates the influence of these parameters on the tensile behavior of Acrylonitrile Butadiene Styrene (ABS) and Polylactic Acid (PLA), aiming to determine optimal conditions for enhanced strength. ASTM D638-Type IV specimens were printed using nozzle diameters ranging from 0.05 to 0.25 mm and speeds from 15 to 80 mm/s. For ABS, tensile strength increased from 56.46 MPa to 60.74 MPa, representing a 7.6% enhancement, as nozzle diameter increased, with the best performance observed at 0.25 mm and 45 mm/s, attributed to improved melt flow and interlayer fusion. PLA exhibited a non-linear response, reaching a maximum strength of 89.59 MPa under the same conditions, marking a 22.3% enhancement over the minimum value. The superior performance of PLA was linked to optimal thermal management that enhanced crystallinity and interlayer bonding. Fractographic analysis revealed reduced porosity and smoother fracture surfaces under optimized conditions. Overall, PLA consistently outperformed ABS across all settings, with an average tensile strength advantage of 47.5%. The results underscore the need for material-specific parameter tuning in FFF and offer practical insights for optimizing mechanical performance in applications demanding high structural integrity, including biomedical, aerospace, and functional prototyping. Full article

Modelling aging in the second life of lithium-ion batteries (LiBs) is challenging due to the complexity of degradation mechanisms that lead to capacity loss and internal resistance increase, as well as uncertainty and variability in the operational and environmental conditions to which the batteries are exposed. In this work, we propose a similarity-based approach for diagnosing the aging of LiBs in their second life, which combines time series analysis and machine learning to help identify trends and patterns in the aging process. This approach overcomes the intrinsic nonlinearity nature of the LiB aging trajectory in the second life while adapting to varying operational and environmental conditions. Knees or inflection points defining the first, second, and non-usable lives of the batteries are also identified, offering insights into degradation mechanisms and thus supporting thermal management and optimal user-pattern tasks to extend the LiBs’ lifetime. Full article

A two-dimensional time-dependent model is presented for upward-propagating internal gravity waves generated by an imposed thermal forcing in a layer of fluid with uniform background velocity and stable stratification under the anelastic approximation. The configuration studied is representative of a situation with deep or shallow latent heating in the lower atmosphere where the amplitude of the waves is small enough to allow linearization of the model equations. Approximate asymptotic time-dependent solutions, valid for late time, are obtained for the linearized equations in the form of an infinite series of terms involving Bessel functions. The asymptotic solution approaches a steady-amplitude state in the limit of infinite time. A weakly nonlinear analysis gives a description of the temporal evolution of the zonal mean flow velocity and temperature resulting from nonlinear interaction with the waves. The linear solutions show that there is a vertical variation of the wave amplitude which depends on the relative depth of the heating to the scale height of the atmosphere. This means that, from a weakly nonlinear perspective, there is a non-zero divergence of vertical momentum flux, and hence, a non-zero drag force, even in the absence of vertical shear in the background flow. Full article

Exploring the dynamics of nonlinear nanofluidic flow-induced vibrations, this work focuses on single-walled branched carbon nanotubes (SWCNTs) operating in a thermal–magnetic environment. Carbon nanotubes (CNTs), renowned for their exceptional strength, conductivity, and flexibility, are modeled using Euler–Bernoulli beam theory alongside Eringen’s nonlocal elasticity to capture nanoscale effects for varying downstream angles. The intricate interactions between nanofluids and SWCNTs are analyzed using the Differential Transform Method (DTM) and validated through ANSYS simulations, where modal analysis reveals the vibrational characteristics of various geometries. To enhance predictive accuracy and system stability, machine learning algorithms, including XGBoost, CATBoost, Random Forest, and Artificial Neural Networks, are employed, offering a robust comparison for optimizing vibrational and thermo-magnetic performance. Key parameters such as nanotube geometry, magnetic flux density, and fluid flow dynamics are identified as critical to minimizing vibrational noise and improving structural stability. These insights advance applications in energy harvesting, biomedical devices like artificial muscles and nanosensors, and nanoscale fluid control systems. Overall, the study demonstrates the significant advantages of integrating machine learning with physics-based simulations for next-generation nanotechnology solutions. Full article

This study investigates the quantitative relationship between material composition and the performance of foam concrete based on 170 validated experimental datasets extracted from the existing literature. The statistical approach combined with machine learning modeling was employed to systematically analyze and predict key performance indicators. Pearson correlation analysis was used to identify the parameters affecting mechanical and thermal properties. The analysis revealed that the water-to-cement ratio (W/C) and cement content were the most influential factors for mechanical properties, while density and the coarse-to-fine aggregate ratio (Cag/Fag) had the greatest impact on thermal conductivity. To overcome the limitations of traditional empirical models in capturing complex nonlinear relationships, a predictive framework with eight machine learning algorithms was established. Among these, Neural Network Regression exhibited the highest accuracy for mechanical property prediction, with a coefficient of determination of R² = 0.987 for compressive strength and R² = 0.932 for flexural strength. For thermal conductivity, support vector regression achieved the best predictive performance with R² = 0.933. Error analysis demonstrated significant differences in prediction accuracy across performance indicators: compressive strength was the easiest to predict, followed by flexural strength, while thermal conductivity was the most challenging. Based on practical engineering requirements, a hierarchical model selection strategy was proposed. Specifically, Neural Network Regression is prioritized for mechanical properties, and support vector regression is prioritized for thermal properties. Decision Tree Regression is recommended as a general-purpose model. The predictive model used in this study provides reliable technical support for the optimization and engineering application of foam concrete, enhancing both prediction accuracy and practical efficiency. Full article

The thermal data of the hypersonic wind tunnel field accurately reflect the aerodynamic performance and key parameters of the aircraft model. However, the prediction of the temperature in hypersonic wind tunnels has problems such as a large delay, nonlinearity and multivariable coupling. In order to reduce the influence brought by temperature changes and improve the accuracy of temperature prediction in the field control of hypersonic wind tunnels, this paper first combines kernel principal component analysis (KPCA) with phase space reconstruction to preprocess the temperature data set of wind tunnel tests, and the processed data set is used as the input of the temperature-prediction model. Secondly, support vector regression is applied to the construction of the temperature prediction model for the hypersonic wind-tunnel temperature field. Meanwhile, aiming at the problem of difficult parameter-combination selection in support vector regression machines, an Improved African Vulture Optimization Algorithm (IAVOA) based on adaptive chaotic mapping and local search enhancement is proposed to conduct combination optimization of parameters in support vector regression. The improved African Vulture Optimization Algorithm (AVOA) proposed in this paper was compared and analyzed with the traditional AVOA, PSO (Particle Swarm Optimization Algorithm) and GWO (Grey Wolf Optimizer) algorithms through 10 basic test functions, and the superiority of the improved AVOA algorithm proposed in this paper in optimizing the parameters of the support vector regression machine was verified in the actual temperature data in wind-tunnel field control. Full article

Climate change has significantly affected marine fisheries. In recent years, marine heatwaves (MHWs) have intensified concurrently with increasing sea surface temperature (SST), particularly along the coast of Japan in the Northwest Pacific. Although the relationships between MHWs and large-scale climate patterns are well established, the long-term effects of MHWs on fisheries remain uncertain. Considering thermal adaptability, we analyzed the catches of warm- and cold-water species from commercially important fisheries in adjacent sea regions around Japan, correlating them with regional SSTs and MHW indices. Our results show that regional SSTs exhibited a persistent increasing trend, with major shifts occurring around 1988/89 and 1998/99. Pronounced interannual–decadal variabilities were observed in the leading principal components (PCs) of different species groups, with step changes concentrated in 1989~1992, 1999~2003, and 2009~2012. Notably, there was a significant negative response of cold groups to warming SSTs. Among warm-water species, only the Japanese sardine (Sardinops melanostictus) catch exhibited a strong correlation with climate change. Gradient forest analysis and threshold generalized additive models (TGAMs) further revealed the nonlinear, threshold-driven responses of the fish groups to environmental variability, which occurred after step changes in both the environmental factors and catches. Matching analysis between the annual change rates of catches and MHW indices confirmed the detrimental effects of strong MHWs on marine fisheries. Full article