Search Results (60)

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

Fluids that exhibit self-rewetting properties, such as aqueous long-chain alcohol solutions, display a unique quadratic relationship between surface tension and temperature and are marked by a positive gradient. This characteristic leads to distinctive patterns of thermocapillary convection and associated interfacial dynamics, setting self-rewetting fluids apart from normal fluids (NFs). The potential to improve heat transfer using self-rewetting fluids (SRFs) is garnering interest for use in various technologies, including low-gravity conditions and microfluidic systems. Our research aims to shed light on the contrasting behaviors of SRFs in comparison to NFs regarding interfacial transport phenomena. This study focuses on the thermocapillary convection in SRF layers with a deformable interface enclosed inside a closed container modeled as a square cavity, which is subject to nonuniform heating, represented using a Gaussian profile for the heat flux variation on one of its sides, in the absence of gravity. To achieve this, we have enhanced a central-moment-based lattice Boltzmann method (LBM) utilizing three distribution functions for tracking interfaces, computing two-fluid motions with temperature-dependent surface tension and energy transport, respectively. Through numerical simulations, the impacts of several characteristic parameters, including the viscosity and thermal conductivity ratios, as well as the surface tension–temperature sensitivity parameters, on the distribution and magnitude of the thermocapillary-driven motion are examined. In contrast to that in NFs, the counter-rotating pair of vortices generated in the SRF layers, due to the surface tension gradient at the interface, is found to be directed toward the SRF layers’ hotter zones. Significant interfacial deformations are observed, especially when there are contrasts in the viscosities of the SRF layers. The thermocapillary convection is found to be enhanced if the bottom SRF layer has a higher thermal conductivity or viscosity than that of the top layer or when distributed, rather than localized, heating is applied. Furthermore, the higher the magnitude of the effect of the dimensionless quadratic surface tension sensitivity coefficient on the temperature, or of the effect of the imposed heat flux, the greater the peak interfacial velocity current generated due to the Marangoni stresses. In addition, an examination of the Nusselt number profiles reveals significant redistribution of the heat transfer rates in the SRF layers due to concomitant nonlinear thermocapillary effects. Full article

This study numerically examines laminar natural convection within a square cavity that has a horizontally attached adiabatic fin on its heated vertical wall. The analysis employed the finite element method to investigate how fin position, length, thickness, and thermal conductivity affect heat transfer behavior over a broad spectrum of Rayleigh numbers (Ra = 10 to 10⁶) and Prandtl numbers (Pr = 0.1 to 10). The findings indicate that the geometric configuration and the properties of the fluid largely influence the thermal disturbances caused by the fin. At lower Ra values, conduction is the primary mechanism, resulting in minimal impact from the fin. However, as Ra rises, convection becomes increasingly significant, with the fin positioned at mid-height (Y_fin = 0.5), significantly improving thermal mixing and flow symmetry, especially for high-Pr fluids. Extending the fin complicates vortex dynamics, whereas thickening the fin improves conductive heat transfer, thereby enhancing convection to the fluid. A new fluid-focused metric, the normalized Nusselt ratio (NNR), is introduced to evaluate the true thermal contribution of fin geometry beyond area-based scaling. It exhibits a non-monotonic response to geometric changes, with peak enhancement observed at high Ra and Pr. The findings provide practical guidance for designing passive thermal management systems in sealed enclosures, such as electronics housings, battery modules, and solar thermal collectors, where active cooling is infeasible. This study offers a scalable reference for optimizing natural convection performance in laminar regimes by characterizing the interplay between buoyancy, fluid properties, and fin geometry. Full article

The building sector significantly contributes to global energy consumption and carbon emissions, primarily due to the extensive use of carbon-intensive materials such as concrete and steel. Mass timber construction, particularly using cross-laminated timber (CLT), offers a promising low-carbon alternative. This study aims to calculate the embodied carbon emissions and biogenic carbon storage of a CLT-based affordable housing project, 340+ Dixwell in New Haven, Connecticut. This project was designed using a honeycomb structural system, where mass timber floors and roofs are supported by mass timber-bearing walls. The authors are not aware of a prior study that has evaluated the life cycle impacts of honeycomb mass timber construction while considering Timber Use Intensity (TUI). Unlike traditional post-and-beam systems, the honeycomb design uses nearly twice the amount of timber, resulting in higher carbon sequestration. This makes the study significant from a sustainability perspective. This study follows International Standard Organization (ISO) standards 14044, 21930, and 21931 and reports the results for both lifecycle stages A1–A3 and A1–A5. The analysis covers key building components, including the substructure, superstructure, and enclosure, with timber, concrete, metals, glass, and insulation as the materials assessed. Material quantities were extracted using Autodesk Revit^®, and the life cycle assessment (LCA) was evaluated using One Click LCA (2015)^®. The A1 to A3 stage results of this honeycomb building revealed that, compared to conventional mass timber housing structures such as Adohi Hall and Heartwood, it demonstrates the lowest embodiedf carbon emissions and the highest biogenic carbon storage per square foot. This outcome is largely influenced by its higher Timber Use Intensity (TUI). Similarly, the A1-A5 findings indicate that the embodied carbon emissions of this honeycomb construction are 40% lower than the median value for other multi-family residential buildings, as assessed using the Carbon Leadership Forum (CLF) Embodied Carbon Emissions Benchmark Study of various buildings. Moreover, the biogenic carbon storage per square foot of this building is 60% higher than the average biogenic carbon storage of reference mass timber construction types. Full article

Bike-sharing has been widely recognized for addressing the “last-mile” problem and improving commuting efficiency. While prior studies emphasize how the built environment shapes feeder trips, the effects of station types and spatial heterogeneity on bike-sharing and metro integration remain insufficiently explored. Taking the urban core area of Shenzhen as a case study, this paper examines how the built environment influences such integration during morning peak hours and how these impacts differ across station types. First, we proposed a “3Cs” (convenience, comfort, and caution) framework to capture key built environment factors. Metro stations were classified into commercial, residential, and office types via K-means clustering. Subsequently, the ordinary least squares (OLS) regression model and the multiscale geographically weighted regression (MGWR) model were employed to identify significant factors and explore the spatial heterogeneity of these effects. Results reveal that factors influencing bike-sharing–metro integration vary by station type. While land-use mix and enclosure affect bike-sharing usage across all stations, employment and intersection density are only significant for commercial stations. Furthermore, these influences exhibit spatial heterogeneity. For instance, at office-oriented stations, population shows both positive and negative effects across areas, while residential density has a generally negative impact. These findings enhance our understanding of how the built environment shapes bike-sharing–metro integration patterns and support more targeted planning interventions. Full article

The growing complexities, power densities, and cooling demands of modern electronic systems and batteries—such as three-dimensional integrated circuit chip packaging, printed circuit board assemblies, and electronics enclosures—have pushed the urgency for efficient and dynamic thermal management strategies. Traditional numerical methods like computational fluid dynamics (CFD) and the finite element method (FEM) are computationally impractical for large-scale or real-time thermal analysis, especially when dealing with complex geometries, temperature-dependent material properties, and rapidly changing boundary conditions. These approaches typically require extensive meshing and repeated simulations for each new scenario, making them inefficient for design exploration or optimization tasks. Physics-informed neural networks (PINNs) emerge as a powerful alternative approach that incorporates physical principles such as mass and energy conservation equations into deep learning models. This approach delivers rapid and adaptable resolutions to the partial differential equations that govern heat transfer and fluid dynamics. This review examines the basic principle of PINN and its role in thermal management for electronics and batteries, from the small unit scale to the system scale. We highlight recent advancements in PINNs, particularly their superior performance compared to traditional CFD methods. For example, studies have shown that PINNs can be up to 300,000 times faster than conventional CFD solvers, with temperature prediction differences of less than 0.1 K in chip thermal models. Beyond speed, we explore the potential of PINNs in enabling efficient design space exploration and predicting outcomes for previously unseen scenarios. However, challenges such as training convergence in fine-grained or large-scale applications remain. Notably, research combining PINNs with LSTM networks for battery thermal management at a 2.0 C charging rate has achieved impressive results—an R² of 0.9863, a mean absolute error (MAE) of 0.2875 °C, and a root mean square error (RMSE) of 0.3306 °C—demonstrating high predictive accuracy. Finally, we propose future research directions that emphasize the integration of PINNs with advanced hardware and hybrid modeling techniques to advance thermal management solutions for next-generation electronics and battery systems. Full article

The built environment is the key to creating vibrant urban spaces that contribute to the health and sustainability of cities. Studies have demonstrated that a reasonable built environment helps to stimulate urban vitality. Nevertheless, there are limitations to the understanding that three-dimensional (3D) built environment indicators from the ‘human perspective’ can substantially affect urban vitality. This study provides an empirical analysis of Xi’an, a city with both traditional historical blocks and a modern city landscape. By applying the ordinary least square model and the geographically weighted regression model, this study explores the impacts of the two-dimensional (2D) and 3D built environments on urban vitality. Results show: (1) the urban vitality exhibits significant spatial and temporal difference characteristics; (2) the 3D built environment exerts a greater influence on urban vitality than 2D; (3) taking weekdays for instance, the indicators of green space and road space (e.g., normalized difference vegetation index (−0.092), green view index (−0.104), road density (−0.021), and enclosure (−0.089)) are negatively correlated with urban vitality, while the indicators of building space and mixed function (e.g., building density, floor area ratio, points of interest (POI) mixing degree, and 3D mixing degree) present a positive effect. To improve urban vitality, the study provides suggestions from the perspective of 3D and human perception, which will contribute to the meticulous practice of urban design. Full article

This study is a comprehensive review on the natural convection heat transfer in horizontal and inclined closed rectangular enclosures with internal objects (including circular, square, elliptic, rectangular, and triangular cylinders, thin plates, as well as other geometries) at various heating conditions. The review examines the influence of various pertinent governing parameters, including the Rayleigh number, Prandtl number, geometries, inclination of enclosure, concentration of nanoparticles, non-Newtonian fluids, magnetic force, porous media, etc. It also reviews various numerical simulation methods used in the previous studies. The present review shows that the presence of inner objects at different heating conditions and the inclination of enclosures significantly changes the natural convection flow and heat transfer behavior. It is found that the existing studies within the scope of the present review are essentially numerical with the assumption of laminar flow and at relatively low Rayleigh numbers, which significantly restrict the usefulness of the results for practical applications. Furthermore, the majority of the past studies focused on single and two inner objects in simple shapes (circular, square, and elliptic) and assumed identical objects and uniformly distributed placements when multiple inner objects are presented. Based on the review outcomes, some recommendations for future research on this specific topic are made. Full article

This study explores the optimization of a Cu–Al₂O₃/water hybrid nanofluid within an irregular wavy enclosure under inclined periodic MHD effects. Hybrid nanofluids, with different mixture ratios of copper (Cu) and alumina (Al₂O₃) nanoparticles in water, are used in this study. Numerical simulations using the Galerkin residual-based finite-element method (FEM) are conducted to solve the governing PDEs. At the same time, artificial neural networks (ANNs) and response surface methodology (RSM) are employed to optimize thermal performance by maximizing the average Nusselt number (Nu_av), the key indicator of thermal transport efficiency. Thermophysical properties such as viscosity and thermal conductivity are evaluated for validation against experimental data. The results include visual representations of heatlines, streamlines, and isotherms for various physical parameters. Additionally, Nu_av, friction factors, and thermal efficiency index are analyzed using different nanoparticle ratios. The findings show that buoyancy and MHD parameters significantly influence heat transfer, friction, and thermal efficiency. The addition of Cu nanoparticles improves heat transport compared to Al₂O₃ nanofluid, demonstrating the superior thermal conductivity of the Cu–Al₂O₃/water hybrid nanofluid. The results also indicate that adding Al₂O₃ nanoparticles to the Cu/water nanofluid diminishes the heat transport rate. The waviness of the geometry shows a significant impact on thermal management as well. Moreover, the statistical RSM analysis indicates a high R² value of 98.88% for the response function, which suggests that the model is well suited for predicting Nu_av. Furthermore, the ANN model demonstrates high accuracy with a mean squared error (MSE) of 0.00018, making it a strong alternative to RSM analysis. Finally, this study focuses on the interaction between the hybrid nanofluid, a wavy geometry, and MHD effects, which can optimize heat transfer and contribute to energy-efficient cooling or heating technologies. Full article

Enhancing the energy efficiency of thermal systems reduces their consumption, lowers costs, and reduces undesired environmental impact, thus making these systems more sustainable. The current work introduces a passive method for heat transfer enhancement that is carried out using natural convection by nanofluid. This work introduces a computational study of the process of natural convection within a square cavity containing Cu/H₂O nanofluid. The cavity wall on the left side undergoes partial isothermal heating, while the opposing side is fully cooled isothermally, with all other boundaries maintained adiabatic. A mathematical model formulated based on a 2-D model was used to provide the solution for the system of governing equations of mass, momentum, and energy conservation, employing the finite element technique. A commercial CFD package is utilized to perform the computational simulation. The present investigation delves into the impact of the Rayleigh number, nanoparticle concentration, heater length, and heater location on the flow field and heat transfer characteristics. The model outcomes were displayed for a wide range of the pertinent parameters as 103 ≤ Ra ≤ 106, 0.25 ≤ l_h ≤ 1.0, 0.125 ≤ h_c ≤ 0.875, and 0.02 ≤ ϕ ≤ 0.10. Also, correlation equations relating the average Nusselt number to these crucial parameters are derived. These equations are simple and can be applied in practice easily in many fields, such as electric and electronic equipment cooling and thermal management of heat sources. Also, these equations gather all the parameters that affect the heat transfer process. They are shedding light on the intricate interplay between these parameters in the natural convection heat transfer process. Full article

Blowoff limits are essential in establishing the combustor operating envelope. Hence, there is a great demand for practical aero-engines to extend the blowoff limits further. In this work, the behavior of non-premixed swirling flames under fuel flow rate oscillations was investigated experimentally close to its blowoff limits. The methane flame was stabilized on the axisymmetric bluff body and confined in a square quartz enclosure. External acoustic forcing at 400 Hz was applied to the fuel flow to induce a fuel mass flow rate fluctuation (FMFRF) with varying amplitudes. A high-speed burst-mode laser and cameras ran at 20 kHz for OH*-chemiluminescence (CL), CH₂O-, and SO₂-PLIF measurements, offering the visualization of the two-dimensional flame structure and heat release distribution, temporally and spatially. The results show that the effect of FMFRF is predominantly along the central axis without altering the time-averaged flame structure and blowoff transient. However, the blowoff limits are extended due to the enhanced temperature and longer residence time induced by FMFRF. This work allows us to explore the mechanism of flame instability further. Full article

A configurable design of a microstrip square spiral RFID tag antenna, for a wide range of microwave frequencies in the S- and C-band, is presented. The design is parameterized in dimensions, and hence changing the design frequency (or frequencies) is easy, by changing only an initial value for the spiral geometry. A tag specimen was fabricated using a Cu electroplating technique according to the design for frequencies of interest in the areas of 2.4 and 5.8 GHz. The substrate material is 320 μm high-resistivity Si and the bridge dielectric is 15 μm polyimide PI2525. The steps of the microfabrication process involve metallic structure pattern transfer techniques with optical UV lithography procedures. The reflection coefficient and antenna gain of the specimen were measured inside an anechoic enclosure using a vector network analyzer (VNA) and a TEM horn test antenna over a frequency range of up to 6 GHz. Simulated and measured results, exhibiting reasonable agreement, are presented and discussed. Full article

In view of the shortcomings of existing methods for predicting the settlement of surrounding buildings caused by deep foundation pit construction, this study uses the monitoring data of a foundation pit project in Shanghai and divides the construction process of the pit into three working conditions, that is, enclosure construction, earthwork excavation, and basement support construction. The attention mechanism and residual update are integrated into the artificial neural network (ANN) model, and the root-mean-square error, average absolute error, and determination coefficient are used as the evaluation indices of the model. The artificial neural network prediction model LSTM-RA-ANN for building settlements in deep foundation pit construction was then established. The prediction performance of the model was also analysed under different working conditions, and the influences of the main factors (including the soil parameter, monitoring point location, activation function, hyperparameter, and input number) on the evaluation index was further explored. The results indicate that the performances of the established LSTM-RA-ANN model are closely related to the construction conditions, the predicted settlements agree well with the monitored ones in three working conditions with the greatest errors occurring at a later time of the working conditions, and the prediction accuracy of the great–small order corresponds to basement support, enclosure construction, and earthwork excavation respectively. The farther the monitoring point is from the edge of the pit, the better the model performance is. The activation function, initial learning rate, and maximum iteration batch have a great influence on the evaluation indices of the model, while the number of input points has little effect on the evaluation indices. These results may serve as a reference for the safe construction and normal operation of foundation pit engineering. Full article

The development of bio-insultation materials has attracted increasing attention in building energy-saving fields. In tropical and hot–humid climates, building envelope insulation is important for an energy efficient and comfortable indoor environment. In this study, several experiments were carried out on a bio-insulation material, which was prepared by using rice husk as a raw material. Square rice husk-based insultation panels were developed, considering the ASTM C-177 dimensions, to perform thermal conductivity coefficient tests. The thermal conductivity coefficient obtained was 0.073 W/(m K), which is in the range of conventional thermal insulators. In a second phase of this study, two experimental enclosures (chambers) were constructed, one with rice husk-based insulation panels and the second one without this insulation. The measures of the temperatures and thermal flows through the chambers were obtained with an electronic module based on the ARDUINO platform. This module consisted of three DS18B20 temperature sensors and four Peltier plates. Daily temperature and heat flux data were collected for the two chambers during the dry season in Panama, specifically between April and May. In the experimental chamber that did not have rice husk panel insulation on the roof, a flow of up to 28.18 W/m² was observed, while in the chamber that did have rice husk panels, the presence of a flow toward the interior was rarely observed. The rice husk-based insulation panels showed comparable performance with conventional insulators, as a sustainable solution that takes advantage of a local resource to improve thermal comfort and the reduction of the environmental impact. Full article

Smart farm environments, equipped with cutting-edge technology, require proficient techniques for managing poultry. This research investigates automated chicken counting, an essential part of optimizing livestock conditions. By integrating artificial intelligence and computer vision, it introduces a transformer-based chicken-counting model to overcome challenges to precise counting, such as lighting changes, occlusions, cluttered backgrounds, continual chicken growth, and camera distortions. The model includes a pyramid vision transformer backbone and a multi-scale regression head to predict precise density maps of the crowded chicken enclosure. The customized loss function incorporates curriculum loss, allowing the model to learn progressively, and adapts to diverse challenges posed by varying densities, scales, and appearances. The proposed annotated dataset includes data on various lighting conditions, chicken sizes, densities, and placements. Augmentation strategies enhanced the dataset with brightness, contrast, shadow, blur, occlusion, cropping, and scaling variations. Evaluating the model on the proposed dataset indicated its robustness, with a validation mean absolute error of 27.8, a root mean squared error of 40.9, and a test average accuracy of 96.9%. A comparison with the few-shot object counting model SAFECount demonstrated the model’s superior accuracy and resilience. The transformer-based approach was 7.7% more accurate than SAFECount. It demonstrated robustness in response to different challenges that may affect counting and offered a comprehensive and effective solution for automated chicken counting in smart farm environments. Full article