Search Results (650)

Rigid-body chain following on piecewise analytic paths is a fundamental subroutine in motion planning and multibody simulation. The problem is nontrivial when only the leader trajectory of the first node is available: enforcing fixed inter-node distances reduces to circle–curve intersection, which is generally multi-valued and becomes particularly challenging near non-smooth junctions. We present a Dichotomy Geometric Path Search (DGPS) framework that converts each constraint into a one-dimensional root-finding task and resolves the branch selection through no-backtracking ordering: at every time step, the admissible solution for the current node is the nearest feasible root in the past relative to its immediately preceding node. DGPS combines backward bracketing with bisection, achieving robust convergence. Compared with the inverse Jacobian method, which maps end-effector velocities to joint velocities via explicit inversion, the proposed approach avoids Jacobian inversion and globally coupled nonlinear solves. We further characterize the local structure of the zero set and establish monotonicity/uniqueness conditions that justify stable root selection across piecewise junctions. Extensive tests on representative piecewise trajectories (line–arc–line, polylines with corners, piecewise sinusoids, and time reparameterization) show that DGPS enforces distance constraints to near machine precision, produces interpretable speed/acceleration transients around non-smooth events, and exhibits computational costs consistent with iteration difficulty. The results support DGPS as a general, efficient solver requiring only the prescribed leader trajectory. Full article

A design framework is presented for broad-crested weirs with exponential (power-law) head–discharge behavior and three practical control-section shapes: Rectangular, parabolic, and triangular. Unlike ideal-flow sizing, the approach explicitly accounts for real-flow effects through a velocity coefficient at the control section. Starting from the energy equation and the critical-depth condition, analytical relations are obtained for the control-section depth, the critical depth, and the velocity and discharge coefficients. These relations are coupled with geometry-specific critical-flow expressions to derive a general, dimensionless design equation that links the required contraction ratio to the approach-velocity coefficient, the control-section velocity coefficient, and the head exponent n. The core innovation of the framework is a general dimensionless design equation that directly yields the required control-section area ratio $A^{*}$/$A_o$, i.e., the geometric contraction relative to the approach section, for a specified design head and approach-velocity condition. The method provides direct design parameters for each section family: Rectangular width, parabolic parameter, and triangular head angle. A short quantitative check against representative classical experimental ratios shows very good agreement with measured values. For the applied design example based on a trapezoidal approach section and conservative lower-bound $C_v$ values, neglecting real-flow effects underpredicts the required contraction ratio by about 28–39%, depending on the selected section shape. The developed framework provides a transparent, theoretically grounded, and practical tool for the hydraulic design of broad-crested weirs. Full article

Addressing the thermal management challenges of prismatic aluminum shell lithium battery modules in electric vehicles under high-rate charge–discharge conditions, this study proposes a multi-objective optimization design method for integrated biomimetic liquid cooling plates. By integrating various highly efficient heat transfer structures from nature, including fractal-tree-like networks, leaf vein branching systems, and spider web radial distribution, a novel biomimetic liquid cooling plate topology was constructed. A multi-physics coupled numerical model considering electrochemical heat generation, thermal conduction, convective heat transfer, and thermal stress deformation was established. The NSGA-II algorithm was employed to globally optimize 12 design variables including channel geometric parameters, operating conditions, and structural dimensions, achieving collaborative optimization objectives of maximum temperature minimization, temperature uniformity maximization, pressure drop minimization, and structural lightweighting. The weight coefficients for the four optimization objectives were determined through the Analytic Hierarchy Process (AHP) with verified consistency (CR = 0.02 < 0.10), ensuring rational priority allocation aligned with automotive safety standards. The optimization results demonstrated that compared to the initial design, the optimal solution reduced the maximum temperature under 3C discharge conditions by 9.9% to 34.7 °C, decreased the temperature difference by 31.3% to 3.3 °C, lowered the pressure drop by 24.6% to 2150 Pa, reduced structural mass by 4.0%, and decreased maximum stress by 16.7%. Quantitative comparison with single biomimetic structures under identical boundary conditions showed that the integrated design achieved a 3.3% lower maximum temperature and 25.7% better flow uniformity than the best-performing single structure, demonstrating the synergistic advantages of multi-biomimetic integration. These synergistic performance improvements can be attributed to the hierarchical multi-scale architecture where fractal networks provide macro-scale flow distribution, leaf vein branches ensure meso-scale coverage, and spider web radials achieve micro-scale thermal matching. Long-term cycling tests conducted at 1C/1C rate with 25 ± 1 °C ambient temperature showed that the optimized design maintained a capacity retention rate of 92.3% after 1000 charge–discharge cycles, demonstrating excellent durability. The complex biomimetic channel structure can be fabricated using selective laser melting technology with minimum feature sizes below 0.3 mm, indicating promising manufacturing feasibility. The research findings provide theoretical guidance and technical support for the engineering design of high-performance battery thermal management systems. Full article

Accurate correction of radial lens distortion is a fundamental requirement in computer vision and photogrammetry, as geometric inaccuracies directly affect 3D reconstruction, mapping, and geospatial measurements, particularly in high-precision imaging systems. In this study, we propose a fully analytical, non-iterative method for truncated inverse modeling of radial lens distortion, applicable to general radial distortion polynomials that contain constant terms. Unlike classical truncated Lagrange series reversion, which relies on recursive expansion and combinatorial series construction, the proposed formulation determines inverse distortion coefficients directly through a system of constrained algebraic inverse polynomials. This enables deterministic computation of inverse parameters without iterative refinement, numerical root finding, or combinatorial complexity. The method was evaluated using ultra-wide-angle smartphone camera imagery exhibiting severe barrel distortion modeled by an eighth-degree depressed radial distortion polynomial. Its performance was compared with a commonly used iterative inverse modeling approach. The analytical formulation demonstrated improved numerical stability and substantially reduced reprojection errors when correcting highly nonlinear distortion profiles, achieving sub-pixel accuracy in image rectification. In contrast, the iterative approach exhibited instability and significantly larger reprojection errors under identical conditions. These results demonstrate that the proposed framework provides a general, robust, and repeatable solution for inverse radial distortion modeling, particularly for high-order polynomial models. The method offers clear practical advantages for camera calibration pipelines in photogrammetry, remote sensing, robotics, and other applications requiring high-fidelity imaging. Full article

This study presents an ASME-based structural assessment of the head–shell junction in a 60 m³ pressurized railway tank wagon subjected to an internal pressure of 0.45 MPa, combining classical shell theory with finite element analysis (FEA) in accordance with ASME Section VIII Division 2 stress categorization and linearization procedures. An analytical model based on the moment theory of shells of revolution was developed to describe displacement and rotation compatibility at the ellipsoidal head–cylindrical shell junction, allowing for the determination of contour interaction loads governing membrane–bending coupling in the discontinuity region. The calculated contour loads (Q₀ = 795 N/mm, M₀ = 13,350 N·mm/mm) indicate localized membrane–bending interactions caused by geometric discontinuity. Finite element simulations using axisymmetric (2D) and full 3D models were evaluated through the ASME VIII-2 stress linearization procedure, enabling comparison between analytical predictions and numerical results. The maximum equivalent stress according to the Coulomb–Tresca criterion reached 115 MPa (2D) and 117 MPa (3D), with less than 2% deviation, confirming the adequacy of the axisymmetric model. Stress linearization shows that the maximum combined primary membrane and bending stress (109.5 MPa) remains well below the ASME allowable limit of 308 MPa, while the discontinuity influence zone extends approximately 120–150 mm from the junction. The results confirm compliance with ASME VIII Division 2 requirements and demonstrate that the combined analytical–numerical approach provides a reliable method for evaluating stress concentration effects in railway tank wagons. Full article

Estimating the position and orientation of a camera with respect to an observed scene remains a fundamental problem in computer vision, particularly in calibration procedures and multi-sensor vision systems. This paper revisits the planar Perspective–n–Point (PnP) problem with emphasis on rotation representation, initialization strategy, and optimization behavior. We propose the PnP-ProCay78 algorithm, which combines analytical elimination of translation via quadratic reconstruction error with nonlinear least-squares minimization of projection residuals in Cayley parameter space. A deterministic initialization scheme based on canonical directions of the reconstruction matrix eliminates the need for spectral search over the full solution space. Experimental evaluation on heterogeneous datasets acquired from high-resolution RGB cameras and low-resolution thermal cameras demonstrates that the proposed method achieves reprojection accuracy comparable to state-of-the-art OpenCV implementations such as SQPnP and IPPE. Convergence analysis in Cayley space reveals stable and rapidly contracting optimization trajectories, with consistent behavior across sensors of significantly different resolution and noise characteristics. The results indicate that a carefully chosen rotation parameterization combined with a transparent optimization framework can yield competitive numerical performance while maintaining geometric interpretability and structural simplicity. Full article

The borehole-to-surface electromagnetic (BSEM) method is widely employed in oil and gas exploration and downhole monitoring. However, the strength of the ground observation signals of the BSEM method is affected by the metal steel casing in the well. To investigate the response characteristics of the BSEM method under metal casing conditions, this study performed three-dimensional BSEM forward modeling based on a cylindrical grid. The finite volume method was adopted to discretize and solve the governing equations of the electromagnetic field, and the cylindrical grid was partitioned in accordance with the axisymmetric geometric features of the wellbore-casing system, thereby achieving high-precision adaptation to the well structure. To explore the impact of metal casing in an alternating electromagnetic field, four typical models were established: a linear source, a long metal wire, a metal casing, and a casing with a cement sheath. The characteristics of ground signals under low-frequency alternating emission conditions were systematically studied. By comparing the simulation results with the 1D analytical solution, this method was verified to have high numerical accuracy, which can accurately reflect the responses of a metal casing and multiple media interfaces to the alternating electromagnetic field. Based on comparative analysis, the differences in underground electromagnetic field distributions among different source models and their applicable ranges were clarified, and the applicable scenarios and effective detection depths of different models in actual monitoring were explored. This research provides numerical simulation cases to investigate the role of metal casings in BSEM observations, and also lays a theoretical foundation for the interpretation of downhole electromagnetic data, which is of positive significance for improving the effect of applying BSEM technology in oil and gas exploration. Full article

To address the critical issue of uneven flow allocation in district heating systems, this paper proposes a novel, systematic evaluation framework centered on the Flow Balance Index. The basic approach transforms discrete actual flow rates of users across the entire network into a continuous, normalized flow allocation curve. By analytically examining the geometric concavity of this curve, the overall imbalance level of the system is intuitively captured, which is further quantitatively represented by calculating the Flow Balance Index. The primary innovation of this method lies in shifting from local, point-based deviation metrics to a global, mathematical quantification of flow distribution balance by calculating the area of allocation deviation. To verify the effectiveness of this method, a parameterized branched heating network was constructed, simulating ideal balance, mild imbalance, and severe imbalance conditions. Within these simulated scenarios, the calculated Flow Balance Index successfully differentiated the varying degrees of global imbalance, yielding specific values of 0.16, 0.46, and 0.91, respectively. The results demonstrate that this method provides both an intuitive identification tool and an objective, scale-independent quantitative target for refined flow regulation strategies. Full article

Vertical wind shear plays a crucial role in the organization and persistence of mesoscale convective systems, yet its geometrical and topological effects remain challenging to quantify. In this study, we introduce a shear-induced anisotropic metric, denoted $d_S$, which embeds the direction and magnitude of environmental wind shear directly into the framework of persistent homology. The metric deforms the ambient geometry by weighting distances differently along and across the shear direction, enabling topological descriptors to respond dynamically to the flow environment. We establish the analytical properties of $d_S$, and demonstrate its compatibility with Vietoris–Rips filtrations. The method is applied to the Corsican bow–echo event of 18 August 2022, where shear vectors are derived from ERA5 reanalysis data. Two complementary topological analyses are performed: a transport analysis on $H_0$ using Wasserstein distances, and a structural analysis on $H_1$ persistent generators under parallel and perpendicular shear metrics. The results reveal distinct topological evolutions associated with different shear orientations, highlighting the sensitivity of persistent homology to shear-induced deformation. Overall, the framework provides a mathematically consistent bridge between dynamical meteorology and topological data analysis, extending persistent homology to anisotropic metric spaces. Full article

The balanced performance of fiber-reinforced flexible (FRF) pipes is essential for maintaining dimensional stability and structural integrity in pipelines. However, current theoretical approaches face challenges in simultaneously incorporating end effects, geometric nonlinearity, and material nonlinearity, resulting in a persistent reliance on engineering experience when determining balanced fiber winding angles. This work proposes a semi-analytical method for evaluating the balanced performance of thick-walled FRF pipes, based on the strain energy density function, with governing equations established by integrating finite deformation theory and the principle of minimum potential energy. A displacement trial function is adopted to approximate the actual displacement field, with its coefficients determined iteratively using the Newton–Raphson method. An eight-coefficient displacement trial function demonstrates effectiveness in characterizing the pipe’s deformation characteristics under the maximum working internal pressure, capturing key deformation features such as radial inward expansion with outward restraint gradient, nonlinear axial deformation, and axial end warping. The proposed method is validated against both experimental results and finite element simulations, and an analysis of the fiber winding angle’s influence on balanced performance is conducted, thereby establishing a theoretical basis for the design of self-balanced thick-walled FRF pipes. Full article

Stiffened panels are fundamental structural components that maintain the integrity of engineering structures subjected to torsional loading, making accurate strength assessment essential in design and evaluation. Conventional finite element method (FEM) analyses often involve complex geometric modeling and extensive pre-processing, which can reduce efficiency and limit practical applicability. This study presents a structured FEM-based framework supported by a Python-based interface that streamlines model generation while preserving analytical rigor. The interface assists in geometry definition, meshing, material assignment, and boundary condition implementation, thereby improving consistency and reducing pre-processing time without altering the numerical formulation. Nine stiffened panel configurations were investigated by combining three plate thicknesses with three longitudinal stiffener geometries. The results indicate that increasing plate thickness significantly enhances torsional resistance. Stiffener geometry also markedly influences structural response: the 80 × 80 × 8 stiffener provides the highest resistance under general torsional loading, whereas the 100 × 65 × 9 stiffener exhibits superior performance under pure torque conditions. Overall, the results demonstrate that the proposed framework provides a consistent and efficient approach for evaluating the torsional strength of stiffened panels. Full article

This paper presents a topology optimization approach that enables the creation of void structures that reduce part weight while meeting stress constraints for additive manufacturing, using an optimization packing problem. The problem is aimed at maximizing a total area of elliptical voids within an irregular polygonal domain, subject to minimum-distance constraints. Geometric feasibility conditions are expressed analytically using the phi-function technique, ensuring exact enforcement of 3D printing standards. A corresponding nonlinear programming mathematical model is constructed. A stress condition is incorporated in the model using an equivalent mechanical stress computed from the resulting geometry. A solution strategy is proposed that integrates geometric design and solid mechanics within a unified optimization approach. To solve the constrained optimization problem, a local optimization algorithm is developed, based on feasible directions method. Gradients of geometric constraints and the objective function are computed analytically, while the stress gradient is estimated numerically using a finite difference approximation. This permits the simultaneous consideration of geometric and mechanical constraints without requiring an explicit stress function. Numerical experiments demonstrate that the approach produces optimized designing parts with controlled peak stress and achieves competitive performance compared with known topology optimization techniques. Full article

Circular-Scan photoacoustic tomography (PAT) can provide high-resolution images of optical absorption, but its analytical reconstructions, such as delay-and-sum (DAS), are highly sensitive to scanning radius (SR) inaccuracies, which cause severe geometric distortions and artifacts. In this work, we propose a deep learning framework, termed smooth deconvolution ResNet (SD-ResNet), to correct DAS reconstruction degradation induced by SR errors. SD-ResNet uses an ImageNet-pretrained ResNet-50 encoder and a lightweight deconvolutional decoder with additional smoothing convolutions to suppress checkerboard artifacts and restore fine structural details. A paired training dataset is generated using k-Wave simulations driven by human thoracic computed tomography (CT) slices: for each phantom, radiofrequency data are simulated once, and DAS images reconstructed with the true SR serve as ground truth, whereas images reconstructed with biased SR values serve as inputs. This design provides structurally diverse training samples and enhances generalization. In silico experiments show that SD-ResNet effectively recovers image quality across a range of SR deviations. Phantom experiments with polyethylene microspheres further confirm that the proposed method can substantially reduce artifacts and recover correct source shapes under practical SR mismatches, offering a robust tool for SR-error-resilient PAT imaging. Full article

This study presents an analytical investigation into the free vibration behavior of perforated nanobeams resting on a Kerr-type elastic foundation within the framework of Eringen’s nonlocal elasticity theory. Specifically, Eringen’s nonlocal elasticity theory is employed to inherently capture small-scale effects, while the three-parameter Kerr model is utilized to provide a mathematically consistent representation of shear continuity and realistic surface interactions. In this context, the governing equations of motion for a perforated Euler–Bernoulli nanobeam are derived using Hamilton’s principle, incorporating both the nonlocal parameter and perforation geometric factors, namely, the filling ratio and the number of holes. The resulting equations are solved analytically via the Navier method for simply supported boundary conditions. The results indicate that the Kerr foundation model exhibits an intermediate behavior between the Winkler and Pasternak models, owing to the stiffness-reducing effect of its upper spring layer connected in series. A key finding is the “masking effect,” where high foundation stiffness significantly suppresses the frequency reduction caused by nonlocal small-scale effects. Furthermore, it is observed that in the absence of foundation support, the vibration behavior is governed by the competition between mass reduction and stiffness loss depending on the number of holes; however, foundation dominance stabilizes the system regardless of perforation geometry. Full article

Median crossover crashes are among the most severe roadway safety events due to their high-energy nature and strong association with fatal and incapacitating injuries, posing a substantial public health burden. This study develops a multimodal intelligent analytics framework to evaluate the cable median barrier performance in Tennessee by integrating structured crash data, roadway and traffic characteristics, post-impact vehicle responses, and unstructured police narratives. Across 6094 crashes on 576 cable barrier segments, 1196 involved barrier impacts and 914 included complete post-impact response information. Deep learning-based text mining using a BERT transformer model was applied to narrative descriptions from fatal, serious injury, and minor injury crashes to extract contextual indicators of loss of control, impact dynamics, and injury mechanisms. Safety effectiveness evaluation using Empirical Bayes methods showed substantial reductions after installation, including a 96% decrease in fatal crashes and an 88% reduction in serious-injury crashes. Vehicle–barrier interactions—classified as containment, redirection, rollover, or penetration—were modeled using a multinomial logit framework with marginal effects to assess the influence of geometric, operational, and vehicle-related factors. Reduced barrier offset, narrow shoulders, high traffic volumes, outer-lane departures, and heavy-vehicle involvement significantly increased the likelihood of rollover and penetration events, which are strongly linked to higher injury severity. Through fusing multimodal data and combining explainable statistical models with deep learning text analysis, this study provided a scalable, trustworthy approach to characterizing injury risk, aligning transportation safety analytics with emerging intelligent healthcare and big-data methodologies aimed at preventing severe and fatal trauma. Full article