Calculation Efficiency Research Articles

Fast calculation of vehicle-road coupled response based on moving frequency response function

Vehicle–road coupled system is inherently time–varying, and its responses are traditionally calculated using time–domain methods which involves significant computational effort. Aiming to improve the efficiency of response calculation for the coupled system, this paper proposes a fast calculation method in frequency domain, based on the newly developed moving frequency response function (FRF). Firstly, considering the vibration characteristics of an infinitely long road, the road response is straightforwardly expressed using the road impulse response function (IRF). Subsequently, the concept of the road moving IRF is proposed and derived with respect to the moving observation points. The moving FRF is then obtained by applying Fourier transform, which allows the responses of the road moving observation points to be established in frequency domain for fast calculation under moving loads. Furthermore, by analyzing the vehicle–road coupled vibrations, based on the vehicle FRF and road moving FRF, a formula for the vehicle–road coupling force is derived in frequency domain, along with an expression for the responses at the vehicle–road contact points. Finally, the approach is illustrated in numerical simulations of vehicle–road coupled systems, and its computational efficiency and accuracy are verified through comparison with currently popular methods.

Application of simplifying jacobian matrix and introducing iterative threshold to improve newton-raphson method in three-phase power flow calculation

Abstract With the expansion of the scale and complexity of the power system, traditional three-phase power flow calculation methods are facing dual challenges of computational efficiency and accuracy. Power flow calculation is a mathematical method for analyzing the power flow of an electrical system. And the Newton-Raphson (NR) method is an effective algorithm for solving nonlinear problems. However, updating the Jacobian matrix in the NR method can take a lot of time. Based on the asymmetry in three-phase power systems and the time-consuming nature of the NR method, an enhanced version of the NR method is proposed in this paper that improves the efficiency of power flow calculations by simplifying the Jacobian matrix and optimizing the iteration speed. Furthermore, in order to minimize the number of iterations in the NR method, it is proposed to use the PO method to determine the ideal iteration threshold.

Ship Power System Network Reconfiguration Based on Swarm Exchange Particle Swarm Optimization Algorithm

As one of the important components of a ship, the ship’s integrated power system is an important safeguard for ships. In order to improve the service life of the ship’s power grid, the power system should be able to realize rapid reconstruction to ensure continuous power supply of important loads when the ship is attacked or fails suddenly. Therefore, it is of vital importance to study the reconfiguration technology of the ship’s integrated power system to ensure that it can quickly and stably cope with all kinds of emergencies in order to guarantee the safe and reliable navigation of the ship. This paper takes the ship’s ring power system as the research object and sets up the maximum recovery load and the minimum number of switching operations. The load is divided uniformly and the generator efficiency is balanced for the reconstruction of comprehensive function. It also sets up the system capacity, topology, and branch current limitations of the constraints to establish a mathematical model. The load branch correlation matrix method is used for branch capacity calculation and generator efficiency equalization calculation, and the load backup power supply path matrix is added on the basis of the matrix to judge the connectivity of some loads before reconfiguration. In this paper, for the network reconfiguration of the ship circular power system, which is a discrete nonlinear problem with multiple objectives, multiple time periods, and multiple constraints, we choose to use the particle swarm algorithm, which is suitable for global optimization, with a simple structure and fewer parameters; improve the particle swarm algorithm using the swarm exchange strategy by setting up two main and auxiliary swarms for global and local search; and exchange some of the particles with the golden ratio in order to keep the diversity of the populations. The simulation results of the network reconfiguration of the ship power system show that the improved algorithm can solve the power system network reconfiguration problem more effectively and provide a feasible reconfiguration scheme in a shorter time compared with the chaotic genetic algorithm under the same fault case test, and it also proves that the use of the swarm exchange particle swarm algorithm greatly improves the performance of reconfiguring the power grid of the ship.

An Improved Non-Monotonic Adaptive Trust Region Algorithm for Unconstrained Optimization

The trust region method is an effective method for solving unconstrained optimization problems. Incorrectly updating the rules of the trust region radius will increase the number of iterations and affect the calculation efficiency. In order to obtain an effective radius for the trust region, an adaptive radius updating criterion is proposed based on the gradient of the current iteration point and the eigenvalue of the Hessian matrix which avoids calculating the inverse of the Hessian matrix during radius updating. This approach reduces the computation time and enhances the algorithm’s performance. On this basis, we apply adaptive radius and non-monotonic techniques to the trust region algorithm and propose an improved non-monotonic adaptive trust region algorithm. Under proper assumptions, the convergence of the algorithm is analyzed. Numerical experiments confirm that the suggested algorithm is effective.

Deformation and internal force of multitower suspension bridges: Numerical modeling and analytical analysis

An analytical model for predicting the deformation and internal forces of multitower suspension bridges is proposed in this paper. Equilibrium differential equations and deformation coordination equations are established based on deflection theory and coupling analysis of bridge components, in which the vertical stiffness contribution of girders and the longitudinal stiffness of all towers are considered. The equations for the deformation and internal forces of the bridge components, including towers, hangers, cables, and girders, are derived taking into account the live loads to which the multitower suspension bridge is subjected. The equations capture the mechanical characteristics of the bridge components, and the results can be obtained explicitly with the proposed model, notably improving the calculation efficiency. The applicability and efficiency of the proposed model are verified by two finite element model examples featuring two and three towers. A parametric analysis is performed to investigate the impact of major bridge parameters using the proposed model. The analysis results indicate that the longitudinal stiffness ratio of the mid-tower to the main cable plays an important role in the key design indices, including girder deflection and safety factor of anti-slipping of the saddle. A suitable stiffness range for the mid-tower is obtained by the proposed model.

Spectral element method for random vibration analysis of the vehicle-track coupling system

In this paper, we propose a new random vibration analysis model for the vertical vehicle-track coupling system, which combines the symplectic method and the spectral element method (SEM) to enhance calculation efficiency and accuracy. The model assumed that the vehicle is composed of a multi-rigid body with 10 DOFs, and its motion equation is derived using the traditional frequency analysis method. Tracks are considered periodic structures, and the part between sleeper support points is considered a substructure. The motion equation of the periodic track-substructure is established by spectral element method (SEM), and the propagation of vibration wave between the substructures is calculated by the symplectic method. The frequency responses of the vehicle-track coupling system are calculated by using the German low-interference irregularity spectrum and short-wave irregularity spectrum as excitation. Then, the rationality of this method is verified by comparing it with the results from the traditional symplectic method and simulations using Universal Mechanism software. The comparison shows that this method surpasses the traditional symplectic method in high-frequency accuracy while maintaining high computational efficiency. Although the finite element method also has the capability for high-frequency analysis, this method can calculate the high-frequency vibration of an infinitely long track using just one element, making it more suitable for the efficient analysis of vehicle-track coupling systems. Finally, the model is applied to analyze the distribution characteristics of the vibration response of the coupled system in the frequency domain.

Modeling of Multibody Lubrication Dynamics in Cylindrical Roller Bearings

As a typical multibody system, rolling bearing consists of several lubricated frictional pairs which transmit motions and forces among components. In this paper, a multibody lubrication dynamics (MLD) model of cylindrical roller bearings (CRBs) is developed by coupling the bearings dynamics sub-model and several lubrication sub-models of each frictional pairs in real-time. The lubrication sub-models are discretized by using finite difference and solved by numerical iteration methods of Gauss-Seidel with line relaxation and discrete convolution-fast Fourier transform (DC-FFT) technique. What’s more, a parallel algorithm is also designed to enhance the calculation efficiency. Its results are validated by the experimental results in literature, and further compared by the results from other dynamics models. It is revealed that the film formation changes not only friction but also geometrical constraints and pressure distribution, which may introduce additional load inside bearing and increase rolling friction under high-speed conditions. As the dynamic oil film stiffness and damper are took into account by MLD, the cage has a lower whirl frequency and forces. The curve-fitting formulas of EHL film thickness are overestimates the thickness value, and thus change the load distribution and friction between roller and raceway.

Systematic Uncertainties from Gribov Copies in Lattice Calculation of Parton Distributions in the Coulomb gauge

Abstract Recently, it has been proposed to compute parton distributions from boosted correlators fixed in the Coulomb gauge within the framework of Large-Momentum Effective Theory. This method does not involve Wilson lines and could greatly improve the efficiency and precision of lattice QCD calculations. However, there are concerns about whether the systematic uncertainties from Gribov copies, which correspond to the ambiguity in lattice gauge-fixing, are under control. This work gives an assessment of the Gribov copies’ effect in the Coulomb-gauge-fixed quark correlators. We utilize different strategies for the Coulomb-gauge fixing, selecting two different groups of Gribov copies based on the lattice gauge configurations. We test the difference in the resulted spatial quark correlators in the vacuum and a pion state. Our findings indicate that the statistical errors of the matrix elements from both Gribov copies, regardless of the correlation range, decrease proportionally to the square root of the number of gauge configurations. The difference between the strategies does not show statistical significance compared to the gauge noise. This demonstrates that the effect of the Gribov copies can be neglected in the practical lattice calculation of the quark parton distributions.

Optimal shift control for new dual-clutch full-powershift transmission of heavy-duty tractor based on genetic algorithm–particle swarm optimization

The calculation of a linear quadratic regulator (LQR) control weighting matrix determines whether it can achieve optimal control. In order to solve the problem of complex calculation and low efficiency of this matrix, this paper proposed genetic algorithm–particle swarm optimizatio algorithm (GAPSO) combining genetic algorithm and particle swarm algorithm to solve the weighting matrix of LQR clutch oil pressure control strategy, so as to control the design efficiency and precision of the strategy. Subsequently, the shifting quality of a tractor was improved. A dynamic model of the shifting process was established using a comprehensive indicator that combined jerk and sliding friction work as a quadratic function. An LQR-weighted matrix calculated using GAPSO enabled optimal control strategy designing for clutch hydraulic pressure. Furthermore, the shift process from the 11th to 12th gear was analyzed via simulations during plowing operations. The simulation results show that the genetic operation of crossover and variation is introduced into PSO, and the optimization ability of PSO is improved effectively. Compared with the clutch oil pressure control strategy calculated by GAPSO with LQR weighted matrix and LQR clutch oil pressure control strategy calculated by PSO with LQR weighted matrix, the clutch heat load is slightly increased, the smoothness of shifting is greatly improved, and the shifting quality of tractors is improved. Overall, this study provides valuable insights for designing and computationally analyzing optimal clutch LQR control.

A Harmonic Response Topology Optimization Method Based on Hybrid Cellular Automata

Abstract Hybrid cellular automaton (HCA) method is a topology optimization method with high convergence efficiency. Harmonic response topology optimization problem has been widely researched and common frequency-domain methods have been applied to solve this problem. Despite this, little work has so far been undertaken to utilize HCA method to improve the calculation efficiency of harmonic response topology optimization. In this paper we present a methodology which applies HCA method to solve harmonic response topology optimization problem. Mode displacement method (MDM), mode acceleration method (MAM) and full method (FM) are common frequency-domain methods which are respectively utilized as harmonic response analysis methods in this study. The examples under rotating loads demonstrate that the feasibility of this methodology at one specific frequency excitation and multiple frequencies excitation. This paper presents and implements a high convergence efficiency method for harmonic response topology optimization, this method can converge in 20–30 iterations. This method can extend the application range of HCA method and improve the optimization efficiency of harmonic response topology optimization.

High-efficient sample point transform algorithm for large-scale complex optimization

Decomposition algorithms and surrogate model methods are frequently employed to address large-scale, intricate optimization challenges. However, the iterative resolution phase inherent to decomposition algorithms can potentially alter the background vector, leading to the repetitive evaluation of samples across disparate iteration cycles. This phenomenon significantly diminishes the computational efficiency of optimization. Accordingly, a novel approach, designated the Sample Point Transformation Algorithm (SPTA), is put forth in this paper as a means of enhancing efficiency through a process of mathematical deduction. The mathematical deduction reveals that the difference between sample points in each iteration loop is a simple function related to the inter-group dependent variables. Consequently, the SPTA method achieves the comprehensive transformation of the sample set by establishing a surrogate model of the difference between the sample sets of two cycles with a limited number of sample points, as opposed to conducting a substantial number of repeated samplings. This SPTA is employed to substitute the most time-consuming step of direct calculation in the classical optimization process. To validate the calculation efficiency, a series of numerical examples were conducted, demonstrating an improvement of approximately 75 % while maintaining optimal accuracy. This illustrates the advantage of the SPTA in addressing large-scale and complex optimization problems.

Modal analysis of key components of crusher based on digital simulation technology

The modal characteristics of the crusher rotor and shell constitute the crucial factors influencing vibration and noise. Based on the principle of simplification, the rotor component model was established. Through mesh optimization, the model accuracy and calculation efficiency can be ensured, and the calculation of natural frequency and modal shapes was completed based on ANSYS. To verify the accuracy of the finite element model, the modal test was carried out by the hammering method. Sensors were set in three different directions to obtain the frequency response function and the modal assurance criterion matrix mode confidence criterion. Using the same research method, the modal characteristics of the shell model were simulated and analyzed. The research results show that the modal parameters identified by the modal test are basically consistent with the simulation model. The natural frequencies of the rotor and the shell are quite different from the excitation frequency of the motor, and resonance problems will not occur when the crusher is proper functioning.

Enhancing gait recognition in lower limb exoskeletons: adaptive feature selection and random forest with Bayesian optimization

Abstract To improve human-machine cooperation and enhance the accuracy of gait recognition in wearable lower limb exoskeletons, an enhancement method of gait recognition based on adaptive feature selection and an optimized machine learning algorithm was proposed. In this study, surface electromyography (sEMG) signals of rectus femoris, medialis femoris, lateralis femoris, semitendinosus and biceps femoris were recorded during level-ground walking. Then, time domain, frequency domain, time-frequency domain and nonlinear features were extracted. The integrated value of electromyography, variance, root mean square and wavelength were selected as the time domain features, and the mean power frequency and median frequency were selected as the frequency domain features. Wavelet packet energy was selected as the time-frequency domain feature. Nonlinear features including approximate entropy, sample entropy and fuzzy entropy of sEMG were extracted. To reduce feature dimension and improve the calculation efficiency, adaptive feature selection was performed by particle swarm optimization combined with sigmoid function. Then, the feature matrix was determined as the input for a random forest classifier to recognize different gait phases. An adaptive optimization mechanism based on Bayesian optimization was applied to random forest. Compared with random forest, the overall performance of the optimized model was improved. Its accuracy was increased by 3.57%. The feature selection and adaptive optimization mechanisms in gait recognition not only enhance the accuracy of random forest algorithms applied to sEMG for gait prediction, but also facilitate the flexibility and consistency required for lower limb exoskeleton gait control.

Sequential bias-corrected weighted least squares solution of mixed additive and multiplicative error models

In the era of big data, the number of observations in adjustment calculations may reach tens or even hundreds of thousands. When dealing with these large dataset problems, many matrix operations are often required. At this time, the dimensions of the matrix will be large, which will generate a great computational burden. At present, no research results have been published on the computational efficiency of bias-corrected weighted least squares (bcWLS) for mixed additive and multiplicative error models (MAMEM). Sequential adjustment (SEA) groups the observations for calculation and can provide the same computational precision while greatly improving computational efficiency. This paper applies the idea of SEA to the calculation of bcWLS and proposes an iterative solution for sequential bcWLS (SEbcWLS). Using three simulation experiments to verify the effectiveness of our method, it was found that when the number of observations is 10000, the effect is better when the number of groups does not exceed 100, achieving the same precision as the original method while having high computational efficiency. The calculation results of line fitting and plane fitting are not affected by the number of grouping groups. For DEM (Digital elevation model) experiments with strong nonlinearity, when the number of grouping groups is too large, the effect is not very good, but the calculation efficiency is also higher than the original method, and the difference in calculation results is not significant.

Autonomous trajectory planning method for aircraft based on ripple diffusion algorithm

Aiming at the problem that route optimization in dynamic continuous airspace (non-discrete road network) is difficult to ensure optimality and has low computational efficiency, a non-circular ripple diffusion algorithm is proposed. Firstly, considering the influence of time-varying airflow and obstacle area in dynamic airspace, the airspace wind speed model, route network and dynamic network weight model are established, and 6 airspace environments and 4 road network structures with the influence of time-varying airflow and obstacle area are constructed. Then, the effects of traditional dynamic path optimization algorithm, co-evolutionary path optimization algorithm and non-circular ripple diffusion algorithm on route optimization are compared in 1000 experiments, taking the optimized path length, flight time and calculation time as indicators. The simulation results show that the traditional dynamic path optimization algorithm has the worst effect under the three indicators, and the calculation time of non-circular ripple diffusion algorithm is significantly less than that of the other two algorithms, which effectively improves the engineering calculation efficiency.

Automatic High-Resolution Operational Modal Identification of Thin-Walled Structures Supported by High-Frequency Optical Dynamic Measurements.

High-frequency optical dynamic measurement can realize multiple measurement points covering the whole surface of the thin-walled structure, which is very useful for obtaining high-resolution spatial information for damage localization. However, the noise and low calculation efficiency seriously hinder its application to real-time, online structural health monitoring. To this end, this paper proposes a novel high-resolution frequency domain decomposition (HRFDD) modal identification method, combining an optical system with an accelerometer for measuring high-accuracy vibration response and introducing a clustering algorithm for automated identification to improve efficiency. The experiments on the cantilever aluminum plate were carried out to evaluate the effectiveness of the proposed approach. Natural frequency and damping ratios were obtained by the least-squares complex frequency domain (LSCF) method to process the acceleration responses; the high-resolution mode shapes were acquired by the singular value decomposition (SVD) processing of global displacement data collected by high-speed cameras. Finally, the complete set of the first nine order modal parameters for the plate within the frequency range of 0 to 500 Hz has been determined, which is closely consistent with the results obtained from both experimental modal analysis and finite element analysis; the modal parameters could be automatically picked up by the DBSCAN algorithm. It provides an effective method for applying optical dynamic technology to real-time, online structural health monitoring, especially for obtaining high-resolution mode shapes.

Unconditional error analysis of weighted implicit-explicit virtual element method for nonlinear neutral delay-reaction–diffusion equation

In this paper, we develop a virtual element method in space for the nonlinear neutral delay-reaction–diffusion equation, while a weighted implicit-explicit scheme is utilized in time. The nonlinear term is adopted by using the Newton linearized method. The calculation efficiency is improved using an implicit scheme to analyze linear terms and an explicit scheme to deal with nonlinear terms. Then, the time–space error splitting technique and G-stability are creatively combined to rigorously analyze the unconditionally optimal convergence results of the numerical scheme without any restrictions on the mesh ratio. Finally, numerical examples on a set of polygonal meshes are provided to confirm the theoretical results. In particular, when the weighted parameter is taken θ=12 (or θ=1), the method degenerates into the Crank–Nicolson (or second-order backward differential formula (BDF2)) scheme.

A Non‐Iterative Wheel‐Rail Contact Geometry Determination Algorithm and Its Application

AbstractTo ensure a more accurate simulation of the wheel‐rail contact state in vehicle‐track interaction, a non‐iterative contact geometry determination algorithm is proposed. This algorithm decouples lateral movement and roll, independently calculates the minimum wheel‐rail gap on both sides, and overcomes the limitation of simultaneous wheel‐rail contact on both sides. It can be applied to single‐side and two‐side wheel‐rail separation problems. Additionally, by converting track irregularity and rail deformation into the reverse displacement of the wheel, it avoids real‐time calculation of the wheel‐rail spatial position, thereby improving calculation efficiency. The effectiveness of the algorithm is verified through Universal Mechanism software and field measurements. Subsequently, the influence of the measured wheel‐rail profile, rail cant, and rail gauge on the wheel‐rail contact characteristics is analyzed. Finally, vehicle shaking caused by track irregularity and wheel‐rail contact is examined from the perspective of wheel‐rail matching. The study demonstrates that the non‐iterative algorithm is suitable for online simulation by pre‐processing the contact geometry parameters. When the track gauge and rail cant increase, the distribution of wheel‐rail contact points becomes more concentrated. Furthermore, when the excitation frequency of wheel‐rail matching is close to the track irregularity excitation frequency, it causes vibration amplification, resulting in lateral vehicle shaking.

Assessing progressive collapse regions of reinforced concrete frame structures using Graph Convolutional Networks

In the progressive collapse, the structural topology changed dynamically as failures propagate. Representing reinforced concrete (RC) frames with graph data leverages the topology of beam–column joints and structural components, enabling graph neural networks (GNNs) to predict the failure propagation. Graph convolutional networks (GCNs), a specific type of GNN, offer superior computational efficiency for graph data to traditional GNNs. However, three challenges must be addressed for GCNs to rapidly assess collapse regions: (1) insufficient data, (2) lack of graph representations for structural collapse, and (3) absence of tailored GCN architectures. To address these gaps, this study focused on RC frames with the following initiatives: (1) an efficient and accurate method for generating progressive collapse data was developed; (2) joint-based representation and component-based representation were established, accompanied by a method for converting RC frames into these representations; (3) two GCN architectures were designed to identify failure paths and predicting collapse regions. The performance of the GCN models using various convolutional layer configurations was evaluated on the two graph representations, and the CGR-based model using SAGEConv layers exhibited the best performance with an accuracy and F1 score of 0.9839 and 0.8853, respectively. The proposed method exhibited over a 99 % improvement in calculation efficiency and approximately a 75 % reduction in memory usage compared with traditional FEM. Moreover, it captured the contribution of each component to progressive collapse resistance with better accuracy than previous approaches. Finally, this method was employed to predict the internal and external collapse regions of a real-world structure by incorporating a physics engine-based approach from prior studies.

An efficient predicting approach for blade frequency line-spectrum noise of marine centrifugal pumps

The paper introduces an innovative and efficient calculation method for the spectral flow noise of the pump system based on the spatial distribution dipole, which predicts the blade frequency radiation noise at the pipe flange of the pump system under different operating conditions. First, the unsteady flow simulation of the centrifugal pump is calculated using computational fluid dynamics (CFD). Then, the pressure on each segmented blade surface is decomposed into lift and drag forces. After that, each segment is decomposed into several dipole point sources based on the blade noise theory. Finally, the simplified dipole point sources are considered sound sources, and the blade frequency line spectrum noise at the pipe flange of the pump system is predicted using acoustic-structure coupling. The results indicate that the simplified method provides high prediction accuracy while significantly improving the computational efficiency of blade frequency line spectrum noise calculation for centrifugal pumps.