Search Results (679)

This paper investigates the influence of rolling resistance on fuel consumption in Class 8 heavy-duty vehicles, with a focus on a modeling approach through variations in the rolling resistance coefficient ($C_{rr}$) across different driving scenarios. Leveraging TruckSim’s multibody modeling approach for vehicle dynamics and MATLAB/Simulink co-simulation capability, the study provides insights into how tire rolling resistance affects energy efficiency under varying conditions while enabling controlled, repeatable comparisons across various scenarios. Results show that across the evaluated scenarios, increases in $C_{rr}$ impact the vehicle’s speed, fuel consumption, engine torque, and crankshaft spin. Specifically, increasing $C_{rr}$ from 0.004 to 0.013 was found to lead up to 68% higher fuel consumption in high demand scenarios. These findings aim to guide efforts to optimize tire design and vehicle performance that help achieve improved fuel efficiency. Full article

The location of the rider centre of mass (CoM) is especially relevant in bicycles and motorcycles due to the large human-to-vehicle mass ratio. This work illustrates two alternative methods for the experimental identification of the longitudinal and lateral coordinates of the rider CoM position as a function of the posture. The first method uses a set of load cells and provides accurate and reliable results. However, riders’ must firmly hold their configuration for the time necessary to stabilise the force measurements, which may be uncomfortable in configurations such as lean-out. The second method utilises an optical system which captures the rider attitude. This information is then used to feed a multibody model, which is used to estimate the CoM coordinates. Full article

Background and Clinical Significance: Traumatic brain injuries (TBI), most frequently caused by falls, represent a major source of morbidity and mortality and pose significant challenges in forensic investigations, especially when events are unwitnessed or testimonies conflict. Despite advances in imaging and autopsy, reconstructing the mechanism of head trauma often remains impossible. The objective of this study is to assess how biomechanical modeling can support forensic practitioners by narrowing the range of plausible scenarios and strengthening evidence-based interpretation in complex medico-legal contexts, without seeking to establish legal causality or certainty. Case Presentation: This case report investigates forensic biomechanics as a decision-support tool using a combined multibody and finite element (FE) modeling approach. An initial set of twenty-five scenarios, derived from witness statements and investigative data, was reconstructed to simulate potential fall- and assault-related mechanisms. Multibody simulations with the human facet model were first performed to estimate head impact velocities and orientations. These parameters were then applied to an FE head model to evaluate tissue response. Conclusions: Skull fracture patterns and intracerebral von Mises stress distributions were analyzed and systematically compared with clinical, radiological, and autopsy findings. Although simulated stress magnitudes were generally lower than injury thresholds reported in the literature, several scenarios reproduced fracture propagation and intracerebral stress patterns consistent with the documented lesions, including corpus callosum involvement. This multidisciplinary approach highlights the growing role of biomechanics in forensic investigations and forensic anthropology. Full article

Accurate prediction of excavation forces is critical for the design reliability and operational safety of Mars surface sampling systems. This study establishes an analytical modeling framework to describe the excavation mechanics of Martian soil, focusing on the formation mechanism and evolution of resistance. Soil deformation and failure processes are qualitatively identified using particle image velocimetry (PIV) and discrete element method (DEM) simulations. Based on limit equilibrium theory, the passive earth pressure is derived, and the scoop is divided into seven force-bearing regions for three-dimensional force decomposition. The analytical model is validated against multibody dynamics–discrete element method (MBD–DEM) co-simulation. The results indicate that excavation resistance exhibits a distinct single-peak evolution, maximizing near the maximum excavation depth. Notably, the inner bottom surface and cutting edge dominate resistance during penetration, contributing approximately 56% and 30% of the total force, respectively. The resistance mechanism transitions after soil emergence due to the gravitational effect of retained soil. Consequently, this framework provides a physically interpretable and quantitatively validated approach for force prediction, offering theoretical support for sampling scoop design and optimization in future Mars missions. Full article

Discrete Body Dynamics (DBD) is a recently developed approach for solving multibody dynamics problems that aims to improve the numerical treatment of systems with joint compliance. Conventional multibody formulations typically rely on kinematic constraints, which can increase numerical complexity and sensitivity, particularly in closed-chain systems. In this work, DBD is presented as a unified framework that combines a new modeling approach with a new numerical solution strategy. Mechanical joints are modeled explicitly using sets of springs and dampers, replacing ideal constraints and transforming the governing equations from a differential-algebraic system into a purely differential one. Based on this modeling framework, the numerical solution avoids global matrix operations and relies on element-wise computations, resulting in linear computational complexity with respect to the number of bodies. The numerical performance of the DBD method is investigated using a set of closed-chain benchmark systems, which are known to be challenging for conventional constraint-based solvers. The analysis examines the influence of joint stiffness, system dynamics, time-step selection, and mechanism topology on numerical stability, energy dissipation, and computational efficiency. The results show that DBD maintains robust and accurate solutions across the examined scenarios and exhibits a well-defined operating region with low numerical dissipation. Across the examined compliant-joint benchmarks, DBD shows the potential for up to three orders of magnitude lower energy drift at comparable simulation-time-to-real-world time (SRT), or up to about one order of magnitude higher SRT at comparable energy drift, relative to ADAMS/View. These findings indicate that DBD is well suited for the simulation of realistic multibody systems with compliant joints, including closed-chain configurations. Full article

AgriRover was developed to address key operational constraints faced by smallholder vineyards in Peru, including sandy and saline soils, labor shortages, and limited access to advanced agricultural machinery. The platform features an articulated, all-wheel-drive chassis designed to ensure mobility and stability on loose terrain while minimizing soil compaction. This study presents the simulation-driven development of a digital pre-twin, conceived as a virtual prototype prepared for future sensor integration but currently operating without real-time data feedback. The pre-twin was implemented in MATLAB/Simulink (vers. 2024b) using a multibody dynamics model and evaluated through eight scenario-based simulations, varying field geometry, soil type, and slope conditions. The results show stable operation on slopes up to 10°, wheel sinkage values ranging between approximately 20 and 45 mm depending on terrain conditions, and a moderate battery state-of-charge reduction across most scenarios, with higher power demand observed on sandy soils. A scenario-based comparison indicates a potential reduction of approximately 50% in total monitoring time relative to manual field scouting, while advanced sensing, autonomous navigation, and AI-based analytics remain part of future developments. The current pre-twin provides a validated, low-cost foundation for context-specific phenological monitoring and early-stage precision agriculture applications in developing regions. Full article

Nonlinear energy harvesting systems based on multibody structures constitute a promising solution for autonomous devices powered by ambient vibrations. This paper presents the modeling and control of a nonlinear energy harvester employing a double pendulum configuration and BLDC motors operating as generators. The primary objective of the study was to develop a control strategy that enables the maximization of harvested power while simultaneously improving the energy conversion efficiency during the charging of the battery supplying the target system. The developed model incorporates the mechanical equations of motion of the double pendulum, an electrical model of the BLDC motors, and two independently controlled buck–boost converters, each connected to one joint of the pendulum. In addition, a perturb-and-observe (P&O) maximum power point tracking (MPPT) algorithm was implemented, which utilizes a portion of the computational resources of the target system’s microcontroller and allows for dynamic adjustment of the electrical loads seen by the generators. Simulation results obtained in the Simulink environment confirm that the application of independent power converters combined with local MPPT control leads to an increase in the total harvested power and ensures more stable battery charging under conditions of variable mechanical excitation. The obtained results demonstrate the effectiveness of the proposed approach and indicate its potential applicability in self-powered systems operating in environments characterized by irregular and stochastic vibrations. Full article

The calibration of suspension geometry involves highly nonlinear kinematic relationships and leads to challenging optimization landscapes that are difficult to explore efficiently with classical local methods. Quadratic Unconstrained Binary Optimization (QUBO) provides a unified discrete formulation that enables the use of a wide range of metaheuristic solvers, but its practical behavior in realistic engineering-inspired problems remains insufficiently benchmarked. This paper presents a computational benchmarking study of classical, parallel, and hybrid metaheuristic solvers applied to a QUBO-formulated double wishbone suspension geometry problem. A symbolic multi-body kinematic model is constructed and discretized into a large-scale QUBO instance capturing camber and caster tracking objectives across multiple roll conditions. Using a fixed low-resolution binary encoding, we systematically evaluate solver performance in terms of objective value, runtime, and time-to-solution trade-offs. The benchmark includes standard simulated annealing and tabu search, parallel simulated annealing, population-based annealing, bandit-controlled hybrid heuristics, and continuous-relaxation-based ADMM methods with and without annealing refinement. Extensive experiments conducted on a Euro-HPC pre-exascale system demonstrate that parallel and hybrid solvers can achieve substantial reductions in wall-clock time—often exceeding two orders of magnitude—while attaining objective values comparable to classical simulated annealing. The results reveal clear trade-offs between solution quality and computational efficiency, and highlight how solver structure influences performance on large QUBO instances derived from symbolic engineering models. Rather than focusing on final design optimality, this study provides a reproducible reference benchmark and practical insights into solver behavior for QUBO-based engineering optimization problems. Full article

Accurate fault diagnosis of mining hoisting head sheave systems is critical for ensuring operational safety in harsh underground environments. This study proposes a multi-source fault diagnosis framework that fuses vibration and acoustic information using a Convolutional Neural Network and Random Forest (CNN-RF). To support mechanism understanding and validate the experimental platform, finite element and multi-body dynamics simulations (ANSYS/ADAMS) are employed for physical verification and fault signature analysis, while the CNN-RF model is trained and tested exclusively using experimentally acquired vibration and acoustic data. For feature construction, vibration signals are transformed into time–frequency representations (including STFT, CWT, and generalized S-Transform (GST)), and acoustic signals are characterized using Mel-Frequency Cepstral Coefficients (MFCCs). Experimental results demonstrate that vibration–acoustic fusion improves diagnostic performance compared with single-modality baselines; the best performance is achieved by GST+MFCC with the proposed CNN-RF classifier, reaching an accuracy of 98.96%. Future work will conduct cross-condition validation under varying speeds and loads and investigate missing-modality robustness to further assess generalization and deployment reliability. Full article

The Laser Interferometer Space Antenna (LISA) is an ESA mission designed to detect gravitational waves from space. To initiate the science phase, six test masses (TMs) are precisely handled and released into near-perfect free fall by dedicated mechanisms known as the Grabbing, Positioning, and Release Mechanisms (GPRMs). The stringent requirements on the noise level affecting the TMs’ release acceleration are extremely ambitious, motivating the need to experimentally verify the feasibility of achieving such performance. To this end, a dedicated precursor mission, LISA Pathfinder (LPF), flew from 2015 to 2017 to test key technologies. However, during the LPF mission, most release tests exhibited anomalous release velocities, often exceeding the requirements. In addition, the TM repositioning tests also revealed a bi-stable behavior in the TM rotations, which depend on the repositioning direction. This effect is produced by an unexpected non-rectilinear motion of the GPRM end effector, characterized by a micrometric side motion at the reversal of its axial motion. The bi-stable behavior also contributes to a TM-GPRM end effector misalignment, producing unwanted contacts and increasing the probability of a non-compliant TM release. Previous analyses identified asymmetric friction forces in the side-guiding system of the GPRM end effector as the primary cause of this behavior. Starting from the LPF flight experience, the GPRM delta development project in view of LISA led to a redesign of the mechanism architecture, supported by numerical analyses and multi-body models. Since the rectilinearity of the end-effector motion has been identified as critical for flight operation, alternative side-guiding concepts are developed, analyzed, and tested experimentally to evaluate their impact on the overall mechanism performance. The correlation of the models with ground and flight experimental data strengthens the understanding of the guiding system behavior, providing pivotal insights for selecting the GPRM design baseline for LISA. Full article

Rotary tillage blades are critical soil-engaging components in conservation tillage systems but are prone to adhesion of soil particles under cohesive soil conditions, which increases tillage resistance, degrades tillage quality, and lowers operational efficiency. To address these issues, this study proposed a collaborative strategy that combines parameter optimization of rotary tillage blades with a bionic microtexture design to reduce adhesion and resistance and improve operation performance. A coupled soil–wheat straw–rotary tillage blade model based on the Discrete Element Method (DEM) and Multibody Dynamics (MBD) was established in loessial soil environment. The structure and working parameters of the rotary tillage blade were optimized using a Box–Behnken experimental design. On this basis, a bionic microtexture design was introduced on regions prone to adhesion of the rotary tillage blade, inspired by the non-smooth convex hull microstructure on the head surface of the dung beetle. The results indicated that the optimal parameter combination (rotational speed 244 r·min⁻¹, tillage depth 110 mm, and bending angle 122°) reduced soil adhesion mass and tillage resistance by 74.47% and 23.44%, respectively. After applying the bionic microtexture, the corresponding reductions further increased to 82.93% and 28.35%. Moreover, the bionic-optimized rotary tillage blade outperformed the original design in disturbance depth and range and exhibited improved energy consumption performance. Overall, the results demonstrated that coupling parameter optimization with bionic microtexture design substantially enhanced adhesion and resistance reduction and improved soil-disturbance performance, thereby providing theoretical support for the development of high-performance rotary tillage blades. Full article

Parameter estimation plays an important role in improving the accuracy, control, and diagnostic performance of mechanisms, particularly in parallel mechanisms such as the Stewart platform, which are increasingly used in high-precision automation, advanced manufacturing, and machine-centric applications. This paper presents a multibody–based framework for generalized dynamic modeling and inertial parameter estimation of parallel robotic manipulators, demonstrated on the DeltaLab-SMT EX800 Stewart platform. A systematic constrained multibody dynamic formulation is developed using an iterative kinematic–dynamic coupling scheme to compute generalized coordinates and their time derivatives under prescribed motion trajectories. The proposed identification manifold is experimentally validated on the physical test rig, in which the platform motion is executed via the control/DAQ system, while inertial measurements are acquired using an external 6-axis motion sensor to obtain direct acceleration data from the moving platform. Platform acceleration measurements are mapped through the inverse dynamics of the multibody model to derive the corresponding generalized forces, providing a practical and cost-effective alternative to direct force measurement with transducers. A Kalman filter is subsequently employed to combine the measured and the model-predicted data, yielding optimally filtered estimates of the inertial coordinates for accurate parameter identification. Inertial parameters are estimated using particle swarm optimization and bench marked against a gradient-based Levenberg–Marquardt approach, with comparison in terms of convergence behavior, robustness, and estimation accuracy. The results support the proposed framework as a measurement-informed benchmark methodology for parameter estimation of parallel manipulators. Full article

The In-Wheel Motor represents a non-conventional propulsion architecture in which the electric motor is integrated into the wheel, offering advantages such as improved energy efficiency, individual torque control, and drivetrain simplification. In this study, two architectures, inboard and outboard, were developed using an original three-dimensional motor–brake–suspension–steering assembly model, in which disk brake position and In-Wheel Motor integration act as primary design drivers influencing vehicle dynamics. Both architectures were developed in CATIA V5 and exported to Altair Motion 2025 for multibody dynamics simulations. The study evaluates the impact of inboard versus outboard disk brake positioning on vehicle dynamics and provides a qualitative assessment of the associated architectures in terms of mechanical complexity, serviceability, sealing requirements, bearing load asymmetry, and packaging constraints. The results indicate that the inboard architecture exhibits more linear and stable kinematics and compliance (K&C) behavior compared to the outboard configuration, at the expense of increased mechanical complexity and reduced serviceability. By contrast, the outboard architecture preserves a simpler, more conventional MacPherson-like layout with a lower component count and improved service access but is dynamically outperformed under the imposed geometric constraints of the present study. Full article

This study investigates a duckbill-type hole seeder to elucidate the kinematic and force characteristics of hole formation under the dry sowing–wet emergence regime and to provide theoretical support for the optimization of key structural parameters. A bidirectional coupling simulation model based on the discrete element method (DEM) and multibody dynamics (MBD) was established to analyze the motion trajectories of the fixed and movable duckbills, the evolution of three-directional forces, and the associated soil–plastic film disturbance under different combinations of front and rear angles. The results indicate that soil disturbance during hole formation is dominated by vertical penetration and uplift, accompanied by forward cutting and lateral redistribution. The three-directional forces acting on the fixed duckbill exhibit a non-monotonic response with respect to the front angle, decreasing first and then increasing, while the force level during the expansion stage of the movable duckbill generally increases with the rear angle. Within the investigated parameter range, a front angle of 18° combined with a rear angle of 38° resulted in a relatively lower overall force level during penetration and expansion, which is favorable for stable hole formation. Field experiments conducted with this configuration showed an average seed placement deviation of 0.50 cm, satisfying the requirements for precision cotton planting under plastic mulch. The findings provide theoretical insight and methodological support for the structural optimization and engineering design of cotton hole seeders operating under the dry sowing–wet emergence regime. Full article

Unsteady atmospheric disturbances significantly compromise the stability of ornithopters, necessitating advanced turbulence-mitigation strategies. In contrast, natural flyers display remarkable aerodynamic adaptability through dynamic flow-control mechanisms such as covert feathers, enabling stability across unsteady flow regimes. Drawing inspiration from this biological phenomenon, this study presents the modeling and hybrid control of a kestrel-based ornithopter equipped with a Nature-Inspired Flow Control Device (NFCD) that replicates the adaptive feather deployment mechanism observed in kestrels. A reduced-order multibody bond-graph model (BGM) of the full ornithopter is developed, incorporating the main body, propulsion system, rigid wings, and the NFCD subsystem. The model captures key fluid-structure-interaction (FSI) effects between morphing feathers and surrounding airflow. A Linear Quadratic Regulator (LQR) ensures optimal performance under nominal gust conditions (≤3 m/s), while an H₂ controller activates during high-intensity gusts (≥4 m/s) to enhance disturbance rejection through electromechanical feather actuation. A gain-scheduled transition is employed in the intermediate gust range (3–4 m/s) to ensure a smooth transition between controllers. Simulations indicate up to 70% reduction in gust-induced oscillations and 32% gust-mitigation efficiency, achieved through feather actuation in the NFCD combined with hybrid control, stabilizing the ornithopter in less than 1.4 s under higher gust conditions. The close correspondence between simulated responses and previously reported findings validates the proposed approach. Overall, by merging biomimetic aerodynamics, nature-inspired flow control, and advanced control design, the LQR-H₂ governed NFCD provides a promising pathway toward gust-tolerant ornithopters capable of resilient and stable flight in unsteady atmospheric environments. Full article