Search Results (355)

Within this paper, the feasibility of reducing test rig runs in constant velocity joint (CVJ) development by combining data from different sources (simulation and test rig) for artificial intelligence (AI) models has been investigated. Therefore, a case study on CVJ efficiency prediction using a random forest regressor, a decision-tree-based algorithm, was conducted using a data set of 95,798 points derived from both test rigs (52,486 points) and multi-body simulations (43,312 points). The amount of test rig data in the training data set of the regression model was iteratively reduced from 100% to 12.5% to investigate the need of expensive test rig data. Additionally, clustering models related to KMeans-algorithm were performed, to achieve further improvements of the AI models and more information about the data. Furthermore, regression and clustering models were performed with data dimensionally reduced by principal component analysis (PCA) to improve model complexity and performance. The number of principal components for the regression model was reduced from 65 to 5 components to investigate their influence on the models predictions. The study showed that combining data from different sources has a positive impact on the predictions of AI models and the confidence of their results, even though the R2-Score of the trained regression models did not change significantly, ranging from 0.927% to 0.9497%. Full article

Developing the analytical model of a multibody system is often the initial step in control and optimization. The analytical model (equations of motion) describes a system’s time evolution under specified forcing conditions. Although developing these equations is easy for simple systems, this process becomes more complex for systems composed of multiple bodies. Deriving equations of motion for complex multibody systems requires specialized expertise in multibody dynamics, is time-consuming, and is susceptible to error. To address this issue, this paper presents an open-source, easy-to-use, systematic framework to derive symbolic equations of motion in both Python and MATLAB using the joint coordinate formulation. This formulation results in a set of ordinary differential equations that use the minimum set of coordinates needed to model a system. The symbolic representation provides better insight into the influence of design parameters on system performance, facilitates sensitivity analysis and parameter studies, and supports direct implementation of control and optimization routines. The tool enables numerical simulation for specified parameter sets, is modular for straightforward integration with other tools and libraries, and allows incorporation of hydrodynamics, mooring, and other external forces. The result is a reproducible, extensible pipeline for modeling, simulation, and design of complex multibody systems. The proposed tool is versatile and can be applied to domains such as robotics, control, and design. In addition, we integrated external libraries that provide capabilities for modeling offshore systems such as underwater robots and marine energy converters. Full article

The transfer of aircraft on deck relies on the traction system, which is easily affected by the offshore environment. Violent ship motion in the complex marine environment poses a great threat to the aircraft traction process, such as the tire sideslip, off-ground phenomena, the aircraft overturning, traction rod fatigue fracture, and so on. Therefore, it has merits in both academia and engineering practice to study the dynamic behaviors of the ship-borne aircraft towing system under high sea states. Considering the intricate coupling motions of the hull roll, pitch, and heave, the dynamic analysis of the towing system with rod are carried out based on the multibody dynamics theory. The influence of the sea state level and the traction speed on the dynamic characteristics of the towing system is investigated. The results indicate that noticeable tire sideslip occurs under sea state 3, with the peak lateral tire force increasing by approximately 250% compared with sea state 2. Under sea state 4, intermittent off-ground phenomena are observed, accompanied by a further increase of about 22% in lateral tire force. These findings provide quantitative insights into the dynamic characteristics and operational limits of rod-traction systems for ship-borne aircraft in rough marine environments. Full article

E-scooters have become a widely adopted form of urban mobility, increasing the need to understand how vibration exposure affects comfort and safety. While most studies have examined the effects of speed, pavement roughness, and overall tire stiffness, none have evaluated how differing stiffness curves between the front and rear wheels influence rider comfort. This article uses real stiffness curves for rigid and inflatable tires at various pressures (30 psi, 60 psi, and rigid) to assess how front–rear stiffness asymmetry affects vibration transmission across speeds (10–20–30 km/h) and two roughness levels (low and high). The analysis, following the standard UNE-ISO 2631-1:2008 and supported by a multiple-regression model (adjusted R² = 93.84%, homoscedastic residuals), shows that speed and roughness dominate the comfort response (98.9%), while tire stiffness offers a secondary (1.1%) but useful tuning parameter, inducing comfort index variations exceeding 14% between front–rear pressure combinations under typical urban conditions (~20 km/h, low roughness). In this case, the most favorable configuration corresponds to inflatable tires with slightly higher front pressure (+2.9–4.35 psi), whereas solid tires consistently yield the poorest comfort. These findings underscore the role of front–rear stiffness management in improving ride quality and provide practical guidance for optimal inflation strategies in urban e-scooters. Full article

The development of digital twins for railway freight vehicles represents a key step toward more efficient, data-driven maintenance and safety assessment. This study focuses on the creation of a digital twin of the T3000 articulated freight wagon, one of the most widespread intermodal transport solutions in Europe. Despite its relevance, the dynamic behavior of this vehicle type has been scarcely investigated so far in scientific literature. A dedicated onboard measurement layout was defined to enable comprehensive monitoring of vehicle dynamics and the interactions between adjacent wagons within the train. The experimental setup integrates inertial sensors and a 3D vision system, allowing for detailed analysis of both rigid-body and vibrational responses under real operating conditions. A high-fidelity multibody model of the articulated wagon was developed and tuned using the acquired data, achieving optimal agreement with experimental measurements in both straight and curved track segments. The resulting model constitutes a reliable and scalable digital twin of the T3000 wagon, suitable for predictive simulations and virtual testing. Future developments will focus on a deeper investigation of the buffer interaction through an additional experimental campaign, further extending the digital twin’s capability to represent the full dynamic behavior of articulated freight trains. Full article

The Lunar Gateway is a planned small space station that will orbit the Moon and serve as a central hub for NASA’s Artemis program to return humans to the lunar surface and to prepare for Mars missions. This work presents a hybrid optimization strategy for designing minimum-fuel transfers from an Earth orbit to a Lunar Near-Rectilinear Halo Orbit. The corresponding optimal control problem—crucial for missions to NASA’s Lunar Gateway—is characterized by a high-dimensional, non-convex solution space due to the multi-body gravitational environment. To tackle this challenge, a two-stage hybrid optimization scheme is employed. The first stage uses a Genetic Algorithm heuristic as a global search strategy, to identify promising feasible trajectory solutions. Subsequently, the initial solution guess (or guesses) produced by GA are improved by a local optimizer based on a Sequential Quadratic Programming method: from a suitable initial guess, SQP rapidly converges to a high-precision feasible solution. The proposed methodology is applied to a representative cargo mission case study, demonstrating its efficiency. Our numerical results confirm that the hybrid optimization strategy can reliably generate mission-grade quality trajectories that satisfy stringent constraints while minimizing propellant consumption. Our analysis validates the combined GA-SQP optimization approach as a robust and efficient tool for space mission design in the cislunar environment. Full article

To reduce the adverse effects of stern-shaft system vibration on ship performance, this work combined hydrodynamic excitations calculated for a semi-submerged propeller and established a multibody dynamics (MBDs) model of the stern shaft system that included a flexible shaft, propeller, and elastically damped support bearings. The MBDs model’s accuracy was verified through comparison between experimentally identified modal parameters and those computed by the model. It was found that the bearing stiffness and the hydrodynamic excitation frequency collectively determine the vibration amplitude and modal shape of the shaft system, based on an analysis of varied bearing stiffness and damping. Bearing displacement had a significant impact on shafting vibration. And the tie rod with a stiffness of 2.5 × 10⁷ N/m provided a noticeable vibration damping effect. The findings offered theoretical support for mitigating stern-shaft vibration in high-speed vessels subjected to hydrodynamic excitation from semi-submerged propellers. Full article

The aerial acrobat robot is a mechanical structure that achieves continuous acrobatic motion without electrical power by utilizing gravitational potential energy.The power of this motion is the rotational motion resulting from the imbalance of moments caused by both the masses, called counterbalance, and the weight of the robot. Or, it is the rotational motion resulting from the reciprocal energy conversion between the gravitational potential and kinetic energy of these two masses. Using the quasi-static single-link model mechanism, we derived a formula for the power moment that is important in the design of the mechanical structure to produce the aerial acrobat robot’s motion. This structure is mainly made of resin and is approximately 2 m long. Based on this structure, we developed a paper craft aerial acrobat robot compacted to about 0.27 m so that anyone can easily play with it. In the paper craft aerial acrobat robot based on the quasi-static single-link model, instability in the rotational behavior becomes apparent. To enhance the accuracy of the analysis of rotational moments, which are crucial in design, we develop a modeling method for a paper craft aerial acrobat robot using multibody dynamics. Furthermore, the theoretical solution for a simplified model of the paper craft aerial acrobat robot is constructed based on the double pendulum. The dynamic moments obtained by the modeling method of the paper craft aerial acrobat robot is verified by comparing the theoretical solution. Full article

Improving the ride comfort of commercial vehicles is crucial for driver health and operational safety. This study focuses on optimizing the parameters of a cab suspension system to improve its vibration isolation performance. Initially, nonlinear fitting was applied to experimental data characterizing air spring stiffness and damping, which informed the development of a multi-body rigid-flexible coupled dynamic model of the suspension system; its dynamic characteristics were subsequently validated through modal analysis. Road excitation data, filtered through the chassis suspension, were collected during vehicle testing, and displacement excitations for ride comfort simulation were reconstructed using virtual iteration technology. Thereafter, an integrated ISIGHT platform, combining ADAMS and MATLAB, was employed to systematically optimize suspension parameters and key bushing stiffness via a multi-island genetic algorithm. The optimization results demonstrated significant performance improvements: on General roads, the overall weighted root-mean-square acceleration was markedly reduced with enhanced isolation efficiency; on Belgian pave roads, resonance in the cab’s X-axis direction was effectively suppressed; and on Cobblestone roads, the pitch angle was successfully constrained within the design limit. This research provides an effective parameter matching methodology for performance optimization of cab suspension systems. Full article

Automated driving technologies have the capability to significantly increase road safety by decreasing accidents and increasing travel efficiency. This research presents a decision-making strategy for automated vehicles that models both lane changing and double lane changing maneuvers and is supported by a Deep Reinforcement Learning (DRL) algorithm. To capture realistic driving challenges, a highway driving scenario was designed using the professional multi-body simulation tool IPG Carmaker software, version 11 with realistic weather simulations to include aspects of rainy weather by incorporating vehicles with explicitly reduced tire–road friction while the ego vehicle is attempting to safely and perform efficient maneuvers in highway and merged merges. The hierarchical control system both creates an operational structure for planning and decision-making processes in highway maneuvers and articulates between higher-level driving decisions and lower-level autonomous motion control processes. As a result, a Duel Deep Deterministic Policy Gradient (Duel-DDPG) agent was created as the DRL approach to achieving decision-making in adverse driving conditions, which was built in MATLAB version 2021, designed, and tested. The study thoroughly explains both the Duel-DDPG and standard Deep Deterministic Policy Gradient (DDPG) algorithms, and we provide a direct performance comparative analysis. The discussion continues with simulation experiments of traffic complexity with uncertainty relating to weather conditions, which demonstrate the effectiveness of the Duel-DDPG algorithm. Full article

The study presents the design and simulation of a pneumatic drive unit intended for energy-efficient vehicle propulsion. The research focuses on developing a MATLAB 23.2/Simulink-based model that accurately represents the dynamic behavior of double-acting pneumatic actuators, including the interaction between pressure, force, torque transmission, and wheel rotation. The model integrates pneumatic circuit parameters with mechanical drivetrain components, allowing a comprehensive evaluation of system performance and compressed-air consumption. The simulation architecture is fully modular and parameterized, enabling rapid reconfiguration for different drive layouts and operating conditions. Results demonstrate that the proposed model provides a realistic representation of the physical processes in pneumatic systems, offering valuable insights for optimizing actuator control, gear ratios, and energy management strategies. Identified challenges include computational complexity and sensitivity to manually defined parameters, which highlight opportunities for further refinement. The developed model serves as a practical design and analysis tool for future engineers engaged in the development of sustainable pneumatic propulsion systems and educational simulations. Future work will address adaptive control algorithms, improved visualization using multibody dynamics, and optimization of air consumption under varying load conditions. Full article

This paper proposes an integrated numerical–experimental methodology for the durability assessment and optimisation of a passenger-car rear axle. A dedicated rear-suspension durability test bench was designed to impose a controlled cyclic vertical excitation on a dependent axle, reproducing service-like translational and rotational amplitudes of the beam and stabiliser bar. A detailed flexible multibody model of the bench–axle system was developed in MSC ADAMS 2023 and used to tune the kinematic excitation and determine an equivalent design load at the wheel spindles, consistent with the stiffness of the suspension assembly. Experimental strain measurements at nine locations on the axle, acquired with strain-gauge instrumentation on the bench, were converted into stresses and used to validate an explicit dynamic finite element model in ANSYS. The FE predictions agree with the experiments within about 10% at the beam mid-span and correctly identify a critical region at the junction between the side plate and the arm, where peak von Mises stresses of about 104 MPa occur. The validated model then supports a response-surface-based optimisation of the safety-critical wheel spindle, yielding an optimised geometry in which spindle-fillet stresses remain around 180–185 MPa under a severe loading case corresponding to the maximum admissible wheel load at the bearings, while the associated increase in mass is modest and compatible with practical design constraints. Full article

The Space Solar Power Station/Satellite (SSPS) is a large-scale space-borne facility intended for the direct collection and conversion of solar energy in the extra-stratospheric region. The optimization of its light collection and conversion (LCC) structures, analysis of dynamic characteristics, and design of attitude control systems represent core technical bottlenecks impeding the advancement of SSPS. To address these issues, this study investigates a novel conceptual line-focusing SSPS. Firstly, a multi-objective collaborative optimization model is developed to optimize the structural parameters of the concentrator and photovoltaic (PV) array. Subsequently, based on the optimized parameters, a coupled multi-body dynamic model is formulated, incorporating gravity-gradient torque and other space-borne disturbance factors. Finally, a distributed Proportional–Integral–Derivative (PID) controller is proposed to achieve three-axis attitude stabilization of the SSPS. Simulation results demonstrate that the light collection efficiency achieves 81.9% with a power density of 4792.24 W/m²; concurrently, a balance between the geometric parameters of the LCC system and the aforementioned key performance indicators is attained, and the proposed controller possesses favorable anti-disturbance performance. Full article

Guided by principles of symmetry to achieve a proper balance among model consistency, accuracy, and complexity, this paper proposes a new approach for identifying the unknown parameters of nonlinear one-degree-of-freedom mechanical systems using nonlinear regression methods. To this end, the steps followed in this study can be summarized as follows. Firstly, given a proper set of input time histories and a virtual model with all parameters known, the dynamic response of the mechanical system of interest, used as output data, is evaluated using a numerical integration scheme, such as the classical explicit fixed-step fourth-order Runge–Kutta method. Secondly, the numerical values of the unknown parameters are estimated using the Levenberg–Marquardt nonlinear regression algorithm based on these inputs and outputs. To demonstrate the effectiveness of the proposed approach through numerical experiments, two benchmark problems are considered, namely a mass-spring-damper system and a simple pendulum-damper system. In both mechanical systems, viscous damping is included at the kinematic joints, whereas dry friction between the bodies and the ground is accounted for and modeled using the Coulomb friction force model. While the source of nonlinearity is the frictional interaction alone in the first benchmark problem, the finite rotation of the pendulum introduces geometric nonlinearity, in addition to the frictional interaction, in the second benchmark problem. To ensure symmetry in explaining model behavior and the interpretability of numerical results, the analysis presented in this paper utilizes five different input functions to validate the proposed method, representing the initial phase of ongoing research aimed at applying this identification procedure to more complex mechanical systems, such as multibody and robotic systems. The numerical results from this research demonstrate that the proposed approach effectively identifies the unknown parameters in both benchmark problems, even in the presence of nonlinear, time-varying external input actions. Full article

Physics-based simulations are now widely employed in mechanical engineering. Flexible Multibody dynamic Simulations (FMBSs) have proven to be effective in representing the behavior of complex structures with local damping and stiffness nonlinearities. However, due to the broad range of component flexibilities as well as contact behavior between structural elements, time integration analyses can result in high computational burden. The challenge addressed in this article concerns the implementation of an efficient model reduction procedure in order to provide an acceptable tradeoff between calculation time and loss of accuracy in the prediction of system responses and dynamic loads. In most FMBS commercial software, the behavior of linear elastodynamic components is taken into account via imported Craig–Bampton superelements. In this context, dynamic mode selection techniques have been shown to provide a better order reduction than the standard low-frequency truncation. This article provides a review of dynamic mode selection methods that can be found in the literature, followed by a comparison based on simulations of an aircraft engine stator integrated in the full industrial engine model and tested on a speed ramp-up with unbalance. Full article