Beijing Transportation Research Center

With the development of urbanization and smart transportation, the accessibility of urban agglomeration has been improved. However, the growth effects vary across counties, and the level of accessibility improvement under the same investment also differs. This study first analyzes common strategies for optimizing accessibility, and constructs a Multi-objective Bilevel Optimization Model (MOBOM) which incorporates time efficiency and economic benefits into the objective function, considering the spatial heterogeneity of accessibility on economic growth, then uses Genetic Algorithm (GA) for model solving. The article take Guanzhong Plain Urban Agglomeration as an example, and generated a set of 30 optimized roads, referring to the "Guanzhong Plain Urban Agglomeration Development Plan". According to the research conclusion, this study suggests that peripheral counties such as Taibai and Lingtai should focus on improving internal transportation by constructing county-level roads and increasing network density. Inner counties like Weibin District and Houma City should prioritize external connectivity by upgrading transit corridors and enhancing capacity. Core urban areas such as Yanta District should invest in major infrastructure, including high-speed rail, to drive overall accessibility improvement. This research provides strategic insights for accessibility improvements and transportation planning level of urban agglomeration based on spatial heterogeneity.

Abstract To address the design challenge of lightweight helical springs with fixed geometric dimensions and stiffness varying with deflection, this paper proposes a segmented variable-stiffness composite helical spring design method based on plyangle optimization. First, the theoretical stiffness calculation formula for segmented composite helical springs is derived. On this basis, a segmented design method is developed. Combined with finite element modeling, it enables the design of two types of variable-stiffness composite helical springs suitable for different operating conditions using a single mold. Furthermore, based on simulation data, a Physics-Informed Neural Network (PINN) is constructed as a surrogate model capable of rapidly predicting the stiffness of composite helical springs. This approach significantly reduces computational cost and facilitates the efficient design of variable-stiffness composite helical springs.

This paper investigates the tensile behavior of cotton ring-spun yarn through experimental testing, numerical simulation, and theoretical calculation. Firstly, scanning electron microscope testing of the microscopic geometric morphologies of yarns was performed for the development of basic finite element (FE) models. Then, the influences of tensile speed and yarn length on the tensile properties of yarn were studied using tensile experiments. Numerical simulations were further performed to investigate the effects of yarn diameter, twist angle, and friction between fibers on the tensile modulus of yarn. Finally, a modified 'rule-of-mixtures' equation was proposed to effectively calculate the tensile modulus of yarn through incorporating the friction correction factor. The experimental results show that the tensile modulus and strength of tested yarn are significantly affected by the yarn structure and are not sensitive to the yarn length and tensile speed. Furthermore, the tensile moduli of yarns obtained from the numerical simulations show a good fitting accuracy with those obtained from experimental tests when the friction coefficient is set to 0.5 in the FE models. The simulation results show that the twist angle and friction coefficient are two key factors affecting the tensile modulus of yarn. The modified 'rule-of-mixtures' equation presents better accuracy for the calculation of the tensile modulus of yarn compared with the traditional 'rule-of-mixtures' equation, which can be used to replace the FE modeling and simulation and reduce the computational cost. This work will provide a deeper understanding of the mechanical properties of cotton ring-spun yarns and enhance their application in the textile industry.

The structural performance of industrial robotic arms is a key factor affecting production efficiency and accuracy. In this paper, a six-degree-of-freedom industrial robotic arm is designed for the sorting task of small new energy batteries, which is combined with ANSYS for static analysis and base topology optimization, and MATLAB is used to complete the kinematic simulation analysis. The results of static analysis show that the maximum total deformation of the manipulator is 1.06 mm and the maximum equivalent stress is 19.4 MPa. After topology optimization, the weight of the base is reduced by 16.45%, and the maximum total deformation and other effects are the same as the optimization basics, which is in line with the design requirements. The working space covers the theoretical area, and after comparing the results of third, fifth and seventh polynomial interpolation, it is found that the seventh polynomial interpolation trajectory is smooth, which meets the needs of sorting accuracy. The research verifies the rationality of the structural design of the robotic arm, realizes the lightweight of the base, and makes up for the poor structural flexibility of the traditional sorting equipment and the large terminal positioning error of such equipment. In the future, dynamic analysis can be combined to evaluate the performance of the robotic arm under dynamic and complex working conditions.