Search Results (35)

This paper presents a comprehensive review of forced convective heat-transfer phenomena in fluids, emphasizing the influence of fluid properties, tube geometries, and flow orientations under varying Prandtl numbers. Key governing parameters—including velocity, viscosity, thermal conductivity, density, specific heat, surface area, and flow regime (laminar or turbulent)—are expressed through dimensionless groups such as the Nusselt (Nu), Reynolds (Re), and Prandtl (Pr) numbers. The review encompasses heat-transfer characteristics of low-, medium-, and high-Prandtl-number fluids flowing through circular, square, triangular, and elliptical tubes in both horizontal and vertical orientations, aiming to critically evaluate the effectiveness and trends reported in previous studies. Where applicable, symmetry correlations—based on equivalent thermal and hydrodynamic behaviour along geometrically symmetric boundaries—were considered to interpret flow uniformity and heat-transfer distribution across cross-sectional profiles. Analysis reveals that over 84% of the reviewed studies emphasize on horizontal configurations and 55% on circular geometries, with medium-Prandtl-number fluids dominating experimental investigations. While these studies provide valuable insights, significant research gaps remain. Limited attention has been given to vertical orientations, where buoyancy effects may alter flow behaviour due to temperature and pressure gradients arising from variations in fluid density and viscosity, to non-circular geometries that enhance boundary-layer disruption, and to extreme-Prandtl-number fluids such as liquid metals and heavy oils, which are vital in advanced industrial applications. Bridging these gaps presents opportunities to design and optimize diverse engineering systems requiring efficient convective heat transfer. Practical examples include coolant flow in nuclear reactors, heat dissipation in high-performance CPUs, and high-speed airflow over automotive radiators. This review therefore underscores the need for future research extending forced-convection studies beyond conventional configurations, with particular emphasis on vertical orientations, complex geometries, and underexplored Prandtl-number regimes. Full article

We develop a first-principles theory that bridges three-dimensional (3D) confinement and two-dimensional (2D) in-plane correlations in counterion layers at oppositely charged interfaces. The system is controlled by two independent coupling constants. While a 3D parameter ($\gamma$) for perpendicular localization varies with the strength and direction of the applied electric field, a 2D parameter ($\Gamma$) for lateral correlations depends solely on system-specific conditions. This independence allows for strongly coupled yet noncrystalline liquid states. Our theoretical framework is based on a hybrid of density functional and statistical field theory, thereby yielding an extended Percus relation that, unlike its conventional counterpart for uniform 2D liquids, is valid for the spatially inhomogeneous density profiles. This extension is critical, as it establishes a direct connection between the 3D confinement and the resulting 2D in-plane structure. Numerical investigations of this relation reveal key in-plane structural features in the strong 3D coupling limit ($\gamma \to \infty$): a geometric length scale, the minimal inter-particle separation ($d_{\min }$), governs both the first peak of the radial distribution function and the wavelength ($\lambda$) of its oscillatory tail. These findings clarify that in-plane order in these strongly coupled counterion liquids is determined by a geometric constraint rather than any crystalline symmetry. Full article

Symmetry plays a critical role in preserving the structural balance and statistical integrity of datasets, particularly in privacy-preserving machine learning. Differential privacy introduces random noise to individual data points to ensure privacy while maintaining the overall symmetry of statistical distributions. However, excessive noise can reduce the utility of data, model accuracy, and computational efficiency. This study proposes a symmetry-preserving optimization framework for differentially private machine learning by integrating feature importance and t-SNE (t-distributed Stochastic Neighbor Embedding), UMAP (Uniform Manifold Approximation and Projection), and PCA (Principal Component Analysis), respectively. Feature importance derived from a random forest selects high-value features to improve data relevance. At the same time, t-SNE preserves geometric symmetry by retaining local and global structures more effectively than PCA or UMAP. Therefore, t-SNE is the best feature extraction method for dimensionality reduction in the proposed scheme. Experimental results demonstrate that the t-SNE method significantly enhances model performance under differential privacy, showing improved accuracy and reduced computational time compared to PCA and UMAP while preserving the underlying symmetry of the data distributions. Full article

This study presents a morphing Non-Uniform Rational B-Spline (NURBS) optimization method for enhancing sports car aerodynamics, with performance evaluation conducted in the vehicle’s symmetry plane. The morphing approach enables precise, smooth deformations of rear-end and spoiler geometries while preserving shape continuity, allowing controlled aerodynamic modifications suitable for comparative analysis. Flow simulations were carried out in ANSYS Fluent 2022 using the Reynolds-Averaged Navier–Stokes (RANS) equations with the standard k-ε turbulence model, selected for its stability and accuracy in predicting boundary-layer evolution, wake behavior, and flow separation in external automotive flows. Three configurations were assessed: the baseline model, a spoiler-equipped version, and two NURBS-morphed designs. The symmetry-plane evaluation ensured bilateral balance across all variants, enabling direct comparison of drag and lift performance. The results show that the proposed morphing strategy achieved notable lift reduction and favorable drag-to-lift ratios while maintaining manufacturability. The findings demonstrate that combining NURBS-based morphing with symmetry-plane aerodynamic assessment offers an efficient, reliable framework for vehicle aerodynamic optimization, bridging geometric flexibility with robust computational evaluation. Full article

Symmetry plays a key role in the study of stress and strain analysis of spherical tanks, as described in detail in the main text. The inherent geometric symmetry of a spherical tank–being uniform in all directions from its center–allows for significant simplification of finite element models. This radial symmetry means that the stress and strain fields under uniform internal pressure are also symmetrical, reducing the computational domain to a small, representative portion of the tank rather than the entire structure. By using these symmetry principles, the study not only ensures the accuracy of its predictions but also achieves a high degree of computational efficiency, making complex engineering problems easier and more accessible. The application of symmetry, therefore, is not just a theoretical concept but a practical tool that underlies the methodology and success of this analysis. This study investigates the mechanical behavior of a spherical tank subjected to internal fluid pressure, utilizing the finite element method (FEM) as a primary analytical tool. Spherical tanks are widely used for the storage of various fluids, including liquefied natural gas (LNG), compressed gases, and water. Their design is critical to ensure structural integrity and safety. This research aims to provide a comprehensive stress and strain analysis of a typical spherical tank, focusing on the hoop and meridian stresses, and their distribution across the tank’s geometry. A 3D finite element model of a spherical tank will be developed using commercial FEA software. The model will incorporate realistic material properties (e.g., steel alloy) and boundary conditions that simulate the support structure and internal fluid pressure. The analysis will consider both linear elastic and potentially non-linear material responses to explore the tank’s behavior under various operational and overpressure scenarios. The primary objectives of this study are as follows: (1) determine the maximum principal stresses and strains within the tank wall, (2) analyze the stress concentration at critical points, such as support connections and nozzle penetrations, and (3) validate the FEM results against classical analytical solutions for thin-walled spherical pressure vessels. The findings will provide valuable insights into the structural performance of these tanks, highlighting potential areas of concern and offering a robust numerical approach for design optimization and safety assessment. This research demonstrates the power and utility of FEM in engineering design, offering a more detailed and accurate analysis than traditional analytical methods. Full article

This paper presents a comprehensive review and theoretical analysis of integrating Cyclic Solvent Injection (CSI) with multilateral well (MLW) technologies to enhance heavy oil recovery. Given that many MLW configurations inherently exhibit symmetrical geometries, CSI–MLW integration offers structural advantages for fluid distribution. CSI offers a non-thermal mechanism for oil production through viscosity reduction, oil swelling, and foamy oil behaviour, but its application is often limited by poor sweep efficiency and non-uniform solvent distribution in conventional single-well configurations. In contrast, MLW configurations are effective in increasing reservoir contact and improving flow control but lack solvent-based enhancement mechanisms. In particular, symmetrical MLW configurations, such as dual-opposing laterals and evenly spaced fishbone laterals, can facilitate balanced solvent distribution and pressure profiles, thereby improving sweep efficiency and mitigating early breakthrough. By synthesizing experimental findings and theoretical insights from the existing literature, laboratory studies have reported that post-CHOPS CSI using a 28% C₃H₈–72% CO₂ mixture can recover about 50% of the original oil in place after six cycles, while continuous-propagation CSI (CPCSI) has achieved up to ~85% OOIP in 1D physical models. These representative values illustrate the performance spectrum observed across different CSI operational modes, underscoring the importance of operational parameters in governing recovery outcomes. Building on this foundation, this paper synthesizes key operational parameters, including solvent composition, pressure decline rate, and well configuration, that influence CSI performance. While previous studies have extensively reviewed CSI and MLW as separate technologies, systematic analyses of their integration remain limited. This review addresses that gap by providing a structured synthesis of CSI–MLW interactions, supported by representative quantitative evidence from the literature. The potential synergy between CSI and MLW is highlighted as a promising direction to overcome current limitations. By leveraging geometric symmetry in well architecture, the integrated CSI–MLW approach offers unique opportunities for optimizing solvent utilization, enhancing recovery efficiency, and guiding future experimental and field-scale developments. Such symmetry-oriented designs are also central to the experimental framework proposed in this study, in which potential methods, such as the microfluidic visualization of different MLW configurations, spanning small-scale visualization studies, bench-scale experiments on fluid and chemical interactions, and mock field setups with pipe networks, are proposed as future avenues to further explore and validate this integrated strategy. Full article

This study develops a vibration model for functionally graded material (FGM) plates with embedded planar cracks. Based on thin plate theory and von Kármán-type geometric nonlinear strain assumptions, the kinetic and potential energies of each region are derived. Displacement field trial functions are constructed according to boundary conditions, and the Ritz method is employed to determine natural frequencies and vibration modes under small deformation conditions. The investigation focuses on how crack parameters and material gradient coefficients affect vibration characteristics in exponentially graded FGM plates. The results show that natural frequencies decrease with increasing crack length, while crack presence alters nodal line patterns and mode symmetry. During free vibration, the upper and lower surfaces of the crack region exhibit relative displacement. Material gradient effects induce thickness–direction asymmetry, causing non-uniform displacements between the plate’s upper and lower sections. Full article

Urban safety critically depends on effective street lighting systems; however, rapidly expanding cities, such as Astana, face considerable challenges in maintaining these systems due to the inefficiency, high labor intensity, and error-prone nature of conventional manual inspection methods. This necessitates an urgent shift toward automated, accurate, and scalable monitoring systems capable of quickly identifying malfunctioning streetlights. In response, this study introduces an advanced computer vision-based approach for automated detection and analysis of street lighting conditions. Leveraging high-resolution dashcam footage collected under diverse nighttime weather conditions, we constructed a robust dataset of 4260 carefully annotated frames highlighting streetlight poles and lamps. To significantly enhance detection accuracy, we propose the novel YOLO-CSE model, which integrates a Channel Squeeze-and-Excitation (CSE) module into the YOLO (You Only Look Once) detection architecture. The CSE module leverages the inherent symmetry of streetlight structures, such as the bilateral symmetry of poles and the radial symmetry of lamps, to dynamically recalibrate feature channels, emphasizing spatially repetitive and geometrically uniform patterns. By modifying the bottleneck layer through the addition of an extra convolutional layer and the SE block, the model learns richer, more discriminative feature representations, particularly for small or distant lamps under partial occlusion or low illumination. A comprehensive comparative analysis demonstrates that YOLO-CSE outperforms conventional YOLO variants and state-of-the-art models, achieving a mean average precision (mAP) of 0.798, recall of 0.794, precision of 0.824, and an F1 score of 0.808. The model’s symmetry-aware design enhances robustness to urban clutter (e.g., asymmetric noise from headlights or signage) while maintaining real-time efficiency. These results validate YOLO-CSE as a scalable solution for smart cities, where symmetry principles bridge geometric priors with computational efficiency in infrastructure monitoring. Full article

To improve the mechanical stability and service durability of solar road structures, this study systematically investigates the mechanical response characteristics of photovoltaic panels with different geometric shapes—including triangles, rectangles, squares, regular pentagons, and regular hexagons—under consistent boundary and loading conditions using the discrete element method (DEM). All panels have a uniform thickness of 10 cm and equivalent surface areas to ensure shape comparability. Side lengths vary among the shapes: square panels with sides of 0.707 m, 1.0 m, and 1.5 m; triangle 1.155 m; rectangle (aspect ratio 1:2) 0.707 m; pentagon 1.175 m; and hexagon 0.577 m. Results show that panel geometry significantly influences stress distribution and deformation behavior. Although triangular panels exhibit higher ultimate bearing capacity and failure energy, they suffer from severe stress concentration and low stiffness. Regular hexagonal panels, due to their geometric symmetry, enable more uniform stress and displacement distributions, offering better stability and crack resistance. Size effect analysis reveals that larger panels improve load-bearing and energy dissipation capacity but exacerbate edge stress concentration and reduce overall stiffness, leading to more pronounced “thinning” deformation and premature failure. Failure mode analysis further indicates that shape governs crack initiation and path, while size determines crack propagation rate and failure extent—revealing a coupled shape–size mechanical mechanism. Regarding assembly, honeycomb arrangements demonstrate superior mechanical performance due to higher compactness and better load-sharing characteristics. The study ultimately recommends the use of small-sized regular hexagonal units and optimized splicing structures to balance strength, stiffness, and durability. These findings provide theoretical guidance and parameter references for the structural design of solar roads. Full article

In this study, we study the trapping of linear water waves by infinite arrays of three-dimensional fixed periodic structures in a three-layer fluid. Each layer has an independent uniform velocity field with respect to the fixed ground in addition to the internal modes along the interfaces between layers. Dynamical stability between velocity shear and gravitational pull constrains the layer velocities to a neighbourhood of the diagonal $U_1=U_2=U_3$ in velocity space. A non-linear spectral problem results from the variational formulation. This problem can be linearized, resulting in a geometric condition (from energy minimization) that ensures the existence of trapped modes within the limits set by stability. These modes are solutions living the discrete spectrum that do not radiate energy to infinity. Symmetries reduce the global problem to solutions in the first octant of the three-dimensional velocity space. Examples are shown of configurations of obstacles which satisfy the stability and geometric conditions, depending on the values of the layer velocities. The robustness of the result of the vertical column from previous studies is confirmed in the new configurations. This allows for comparison principles (Cavalieri’s principle, etc.) to be used in determining whether trapped modes are generated. Full article

This research introduces an innovative probabilistic method for examining torsional stress behavior in spherical shell structures through Monte Carlo simulation techniques. The spherical geometry of these components creates distinctive computational difficulties for conventional analytical and deterministic numerical approaches when solving torsion-related problems. The authors develop a comprehensive mesh-free Monte Carlo framework built upon the Feynman–Kac formula, which maintains the geometric symmetry of the domain while offering a probabilistic solution representation via stochastic processes on spherical surfaces. The technique models Brownian motion paths on spherical surfaces using the Euler–Maruyama numerical scheme, converting the Saint-Venant torsion equation into a problem of stochastic integration. The computational implementation utilizes the Fibonacci sphere technique for achieving uniform point placement, employs adaptive time-stepping strategies to address pole singularities, and incorporates efficient algorithms for boundary identification. This symmetry-maintaining approach circumvents the mesh generation complications inherent in finite element and finite difference techniques, which typically compromise the problem’s natural symmetry, while delivering comparable precision. Performance evaluations reveal nearly linear parallel computational scaling across up to eight processing cores with efficiency rates above 70%, making the method well-suited for multi-core computational platforms. The approach demonstrates particular effectiveness in analyzing torsional stress patterns in thin-walled spherical components under both symmetric and asymmetric boundary scenarios, where traditional grid-based methods encounter discretization and convergence difficulties. The findings offer valuable practical recommendations for material specification and structural design enhancement, especially relevant for pressure vessel and dome structure applications experiencing torsional loads. However, the probabilistic characteristics of the method create statistical uncertainty that requires cautious result interpretation, and computational expenses may surpass those of deterministic approaches for less complex geometries. Engineering analysis of the outcomes provides actionable recommendations for optimizing material utilization and maintaining structural reliability under torsional loading conditions. Full article

Low-light image enhancement remains a challenging task in computer vision due to the complex interplay of noise, asymmetrical artifacts, illumination non-uniformity, and detail preservation. Existing methods such as traditional histogram equalization, gamma correction, and Retinex-based approaches often struggle to balance contrast improvement and naturalness preservation. Deep learning methods such as CNNs and transformers have shown promise, but face limitations in modeling multi-scale illumination and long-range dependencies. To address these issues, we propose WIGformer, a novel wavelet-based illumination-guided transformer framework for low-light image enhancement. The proposed method extends the single-stage Retinex theory to explicitly model noise in both reflectance and illumination components. It introduces a wavelet illumination estimator with a Wavelet Feature Enhancement Convolution (WFEConv) module to capture multi-scale illumination features and an illumination feature-guided corruption restorer with an Illumination-Guided Enhanced Multihead Self-Attention (IGEMSA) mechanism. WIGformer leverages the symmetry properties of wavelet transforms to achieve multi-scale illumination estimation, ensuring balanced feature extraction across different frequency bands. The IGEMSA mechanism integrates adaptive feature refinement and illumination guidance to suppress noise and artifacts while preserving fine details. The same mechanism allows us to further exploit symmetrical dependencies between illumination and reflectance components, enabling robust and natural enhancement of low-light images. Extensive experiments on the LOL-V1, LOL-V2-Real, and LOL-V2-Synthetic datasets demonstrate that WIGformer achieves state-of-the-art performance and outperforms existing methods, with PSNR improvements of up to 26.12 dB and an SSIM score of 0.935. The qualitative results demonstrate WIGformer’s superior capability to not only restore natural illumination but also maintain structural symmetry in challenging conditions, preserving balanced luminance distributions and geometric regularities that are characteristic of properly exposed natural scenes. Full article

The increasing demand for ultra-fast charging in electric vehicles (EVs) necessitates advancements in thermal management strategies to mitigate Joule heating, which arises due to electrical resistance in charging connectors and cable cores. This study presents a numerical analysis of immersion cooling performance for ultra-fast chargers under realistic charging conditions. The simulated results are validated by experiments with a maximum deviation of 5.5% at 600 A and 700 A currents. The novelty of this work lies in the consideration of a realistic charging cable length of 5 m, the evaluation of temperature characteristics in the charger connector, and the analysis of geometric symmetry in the charging cable and coolant configuration to ensure uniform heat distribution. Key operating conditions were systematically analyzed, including applied currents, ambient temperatures, coolant flow rates, cable core cross-sectional areas, and different types of coolants. Results indicate that increasing the applied current from 400 A to 800 A raised the connector temperature from 60.73 °C to 97.33 °C. As the ambient temperature increased from 20 °C to 50 °C, the connector temperature rose significantly from 42.71 °C to 74.99 °C, while the maximum cable core temperature increased from 65.26 °C to 100.61 °C. Increasing the cable core cross-sectional area from 20 mm² to 30 mm² reduced the connector temperature from 77.20 °C to 74.99 °C. Meanwhile, increasing the coolant flow rate from 2 LPM to 5 LPM had a negligible effect on the connector temperature. Among the three tested coolants, Novec 7500 exhibited the highest cooling efficiency, achieving the lowest contact temperature (74.76 °C) and the highest performance evaluation criteria (PEC) value of 3.8. This study provides valuable guidelines for enhancing symmetry-driven thermal management systems and demonstrates the potential of immersion cooling to improve efficiency, safety, and operational reliability in next-generation high-power EV chargers. Full article

3D-Kagome lattice sandwich panels are mainly composed of upper and lower panels and a series of symmetrically and periodically arranged lattices, known for their excellent high specific stiffness, high specific strength, and energy absorption capacity. The inherent geometrical symmetry of the 3D-Kagome lattice plays a crucial role in achieving superior mechanical stability and load distribution efficiency. This structural symmetry enhances the uniformity of stress distribution, making it highly suitable for automotive vibration suppression, such as battery protection for electric vehicles. In this study, a polyurethane foam-filled, symmetry-enhanced 3D-Kagome sandwich panel is designed following an optimization of the lattice structure. A novel fabrication method combining precision wire-cutting, interlocking core assembly, and in situ foam filling is employed to ensure a high degree of integration and manufacturability of the composite structure. Its mechanical properties and energy absorption characteristics are systematically evaluated through a series of experimental tests, including quasi-static compression, three-point bending, and low-speed impact. The study analyzes the effects of core height on the structural stiffness, strength, and energy absorption capacity under varying loads, elucidating the failure mechanisms inherent to the symmetrical lattice sandwich configurations. The results show that the foam-filled sandwich panels exhibit significant improvements in mechanical performance compared to the unfilled ones. Specifically, the panels with core heights of 15 mm, 20 mm, and 25 mm demonstrate increases in bending stiffness of 47.3%, 53.5%, and 51.3%, respectively, along with corresponding increases in bending strength of 45.5%, 53.1%, and 50.9%. The experimental findings provide a fundamental understanding of foam-filled lattice sandwich structures, offering insights into their structural optimization for lightweight energy-absorbing applications. This study establishes a foundation for the development of advanced crash-resistant materials for automotive, aerospace, and protective engineering applications. This work highlights the structural advantages and crashworthiness potential of foam-filled Kagome sandwich panels, providing a promising foundation for their application in electric vehicle battery enclosures, aerospace impact shields, and advanced protective systems. Full article

In this research, eco-friendly PLA filaments were 3D-printed using FDM. Three geometric shapes with different orders of rotational symmetry were selected to create infill patterns: an equilateral triangle, a square, and a regular hexagon. Additionally, each of these three infill patterns was modified by rotating the basic shape used to form the infill pattern by 0°, 15°, and 30°. The objective of this study was to analyze how the order of rotational symmetry within the infill pattern affects the mechanical properties of the printed specimens. To ensure consistency, infill density was kept as uniform as possible across all samples produced. DMA and tensile tests were performed on the produced specimens. The obtained mean values in the tensile measurements were compared using the Kruskal–Wallis test. Dunn’s test was used for post hoc pairwise multiple comparisons. DMA showed that when comparing different infill patterns, the specimens with an order of rotational symmetry of 3 (triangle) showed the highest modulus of elasticity, and the specimens with a 15° rotation regardless of shape generally had the highest storage modulus. Statistical analysis showed that the maximum force of the infill pattern with an order of rotational symmetry of 3 (triangle) was the least affected by the rotation angle, while the infill pattern with an order of rotational symmetry of 4 (square) and a 0° rotation displayed a significantly higher value of the maximum force than other patterns. The infill pattern with an order of rotational symmetry of 6 (hexagon) was moderately affected by the angle of rotation. Given the numerous infill patterns utilized in FDM, the results of this research offered a new viewpoint and insights into optimizing the mechanical properties of 3D-printed infill patterns. Full article