Search Results (73)

To address the persistent challenges in optimizing the transmission performance of crank-rocker mechanisms—namely, the inaccuracies of local static evaluation models, the non-linear coupling constraints among multiple objectives, and the difficulties of navigating discontinuous and restricted solution spaces—this paper proposes a multi-objective collaborative design methodology grounded in kinematic and dynamic analysis. First, full-cycle mathematical models for transmission efficiency and transmission inertia are established, explicitly quantifying the impact of quick-return characteristics on inertial forces. Second, targeting the maximization of transmission efficiency alongside the minimization of transmission inertia and kinematic asymmetry, an adaptive multi-objective genetic algorithm is developed. Using a bearing life testing machine as the engineering baseline, virtual prototype simulations and multi-load physical bench tests are conducted to validate the proposed approach. Post-optimization results indicate that the full-cycle average transmission efficiency of the mechanism surges significantly from 73.6% to 91.96%, while the transmission inertial force is drastically curtailed by 72.28%. Concurrently, the advance-to-return time ratio, an indicator of kinematic asymmetry, is reduced to 1.0229. Additionally, the torque fluctuations at the output shaft are notably mitigated, and the overall operational noise level is reduced by 4 to 6 dB. This research provides a highly effective theoretical and engineering paradigm for achieving the globally collaborative optimum of planar mechanisms under complex physical constraints. Full article

Actual construction errors and accumulated damage seriously affect the spatial structural form of the tensile membrane structure, making it difficult to characterize the full-life spatial form of the physical tensile membrane structure based on the original theoretical model. There are still significant challenges in achieving the virtual–real correspondence of digital twin results based on the original theoretical design model. It is necessary to propose a high-precision digital twin model construction method that adapts to the full life cycle of the tensile membrane structure. For this reason, a refined digital twin model reconstruction and morphological deviation visualization method for the umbrella-shaped tensile membrane structure combined with 3D point clouds, computational geometry algorithms, and 3D3S finite element simulation modeling is proposed in this paper. Firstly, three-dimensional point cloud data of the umbrella-shaped membrane structure test bench were acquired, and the point cloud data were accurately registered based on the Iterative Closest Point (ICP) algorithm. Secondly, the physical membrane surface reconstruction was obtained by applying the Screened Poisson Surface Reconstruction (SPSR) algorithm. Subsequently, the 3D3S theoretical model of the umbrella-shaped tensile membrane structure was updated according to the contour of the measured membrane surface reconstruction model. Finally, global spatial geometric morphological deviation was compared among the theoretical models and the reconstructed physical model. Furthermore, different environmental load combinations were analyzed to support hazard warning of the refined digital twin model of the physical umbrella-shaped tensile membrane structure. The results show that the maximum spatial form deviation of the original theoretical model was 43.56 mm, whereas the maximum form deviation of the updated theoretical model was only 0.05 mm. The updated theoretical model accurately reflected the spatial form characteristics of the physical membrane structure and satisfied the requirement for digital twin physical–virtual consistency. In addition, the global stress distribution between the original/updated theoretical models differed markedly, and the updated theoretical model is more suitable for identifying weak areas and evaluating safety performance of the actual umbrella-shaped tensile membrane structure under different ultimate environmental loads. Full article

In the space sector, test benches are facilities used to perform ground testing of rocket engines with solid, liquid, or hybrid propellants. These tests typically occur during the stages of development, validation, qualification, certification, and acceptance of the rocket engine assembly. Due to the periodicity and complexity related to these stages, it may be necessary to carry out several test campaigns in order to obtain the expected results. This requires skill and knowledge from the technical team involved, since the occurrence of operational nonconformities in actuator components installed in this test bench constitutes a situation with sufficient potential to put human lives at risk and/or cause material losses. In this context, this work presents a proposal for a Graphical User Interface to be integrated into the architecture of a system that performs the operational self-testing of these actuators. The virtual resources established for the windows of the Graphical User Interface are expressive and are related to the procedures defined to perform the self-testing of actuator activation, steady-state operation, and shutdown. The validation of the functionality of this Graphical User Interface is obtained through tests carried out on a prototype that was developed considering the main blocks contained in the mentioned system architecture. The satisfactory results observed in these tests suggest that the Graphical User Interface is suitable for the purpose for which it is intended. Full article

Electroencephalography (EEG) is a noninvasive tool used by healthcare professionals to measure brain electrical activity. EEG analysis can indicate various anomalies linked to different brain pathologies, including seizures. Traditionally, the analysis is confined to two-dimensional displays and relies exclusively on the visual modality, limiting a comprehensive overview. EEG analysis through visualisation is challenging and time-consuming, and artificial intelligence (AI) is increasingly used to aid the process of seizure detection. However, the educational value of AI-assisted seizure detection models depends on the explainability of the underlying models. Explainable AI can help learners understand the features and patterns associated with seizure detection and also support informed use of AI-based decision support systems. M2EEG-VR leverages the focus and immersive capabilities of virtual reality (VR) with the aim of developing a multi-modal platform for EEG seizure detection analysis with a human-in-the-loop. The ability to understand EEG and seizure patterns is key to addressing and effectively treating many neurological conditions. Neonatal seizure detection is particularly challenging where seizure patterns are subtle and context dependent. This study advances toward multi-modal analysis by encoding EEG signals into auditory representations using AI that aids in the acoustic detection of the presence of neonatal seizures in EEG. The platform also introduces a 3D brain model with a spatial mapping of seizure regions. In a user study (N = 20, 4 prior EEG experience, 16 no prior EEG experience), participants achieved higher seizure detection accuracy in the combined visual and auditory condition (mean = 7.6 ± 1.2) than in visual-only or audio-only modes. These preliminary findings suggest that a multi-modal environment may improve the accuracy of detection. However, further controlled studies are needed to ascertain the performance benefits. Usability was rated excellent (SUS = 83 ± 11), and task load remained moderate (NASA-TLX = 36.6). The findings suggest that VR multi-modal interaction can reduce cognitive load and enhance the explainability of complex EEG data in a focused virtual environment. The analysis of the diagnostic accuracy showed that participants without prior EEG knowledge performed similarly across all modalities to those with prior EEG knowledge. This implies that the accessibility barrier is reduced for novice users using the tool for the EEG review/detection task. This, together with high usability and moderate task load scores, indicates that the tool may be suitable for medical training applications. A multi-modal EEG in VR may prove useful in education and also be used as a test bench to further explore AI with human-in-the-loop paradigms for seizure detection. Full article

Nowadays, numerical simulation methods are advanced and widely used in industry, enabling the modeling of complex systems from printed circuit boards (PCBs) to full power converters. Among many isolated topologies, the phase-shift full-bridge (PSFB) topology is a well-established solution for isolated DC–DC conversion in electric vehicles. Therefore, this paper proposes a broadband electromagnetic compatibility (EMC) modeling methodology for a custom-designed 1 kW gallium nitride (GaN)-based PSFB converter intended for an electric vehicle (EV) DC powertrain. Moreover, the approach combines full-wave electromagnetic simulation with circuit-level simulation, including parasitic effects from PCB layout, power harnesses, and discrete components. Thus, the virtual prototype is assessed within a complete virtual test bench compliant with the standard Comité International Spécial des Perturbations Radioélectriques (CISPR) 25 over the 150 kHz–108 MHz range to capture common-mode (CM) and differential-mode (DM) conducted electromagnetic interference (EMI). Results show that the converter achieves efficiencies of 97.26% in standalone mode and 97.03% when integrated into the full DC powertrain. However, the conducted EMI assessment reveals that both CM and DM emissions exceed CISPR 25 Class 2 limits across the entire spectrum, with excess levels reaching up to 72 dBµV. Therefore, power harnesses significantly increase EMI levels at low frequencies due to the distributed inductance and stray capacitance. Finally, this study demonstrates the value of virtual prototyping for simulation-based EMI prediction in early-stage power converter design. Full article

Deep neural networks can fit nonlinear bearing vibration responses, but their learned parameters are difficult to relate to contact deformation, rolling element angular position, and other acceleration-generating mechanisms. To improve physical traceability in data-driven bearing dynamics identification, this study develops a physics-informed SINDy-NN with a mechanism-guided feature library. This paper presents a novel approach for constructing a physics-informed SINDy-NN (Sparse Identification of Nonlinear Dynamics-based Neural Network) and demonstrates its application in identifying bearing dynamics. A 5-DoF (five Degrees of Freedom) bearing dynamics model is built, and the primary components influencing the acceleration response are analyzed. This analysis forms the basis for defining a physics-explainable basis function library for the SINDy-NN. For comparison, widely used polynomial and Fourier libraries are also employed to evaluate modeling accuracy and convergence speed. Furthermore, to address the limited number of bearing data, virtual states are generated by applying multiple finite differences to the acceleration signal, expanding the dimensionality of the model and enabling the use of a Multi-Input–Multi-Output (MIMO) model in SINDy-NN. Finally, experimental data from the FEMTO bearing test bench are utilized for validation. The results demonstrate that the physics-informed SINDy-NN offers superior modeling efficiency, with sufficient accuracy and improved interpretability compared to general SINDy-NN. Full article

To promote the green and low-carbon transition and achieve sustainable development in the transportation sector, virtual calibration technology was employed for the efficient and precise control of emissions from heavy-duty diesel engines and aftertreatment systems. A data-driven, semi-empirical and semi-physical simulation modeling method was proposed. By constructing core modules based on physical mechanisms and refining empirical parameters using experimental data, the method improves computational efficiency while maintaining the prediction accuracy of key parameters. Additionally, a collaborative architecture combining physical actuators and virtual sensor signals was introduced, laying the foundation for the validity of virtual calibration. By innovatively introducing a closed-loop system with real actuators and virtual sensors, the dynamic response characteristics of the control system are faithfully reproduced, providing a reliable environment for validating the results of virtual calibration. Under steady-state conditions, the results demonstrated an average relative error of 1.7% for brake-specific fuel consumption (BSFC) and 6.1% for NOx emissions. An open-loop system for the virtual calibration testing platform was constructed for steady-state calibration. Using the main injection timing and common rail pressure as independent variables, a D-optimal design was utilized to generate 43 sets of experimental points, from which a polynomial regression model was established (R² ≥ 98%). Under the constraints of NOx and pre-turbine temperature, fuel consumption in the low-load range is reduced by 0.5–3 g/kW·h, aftertreatment NOx emissions are reduced by 0.5–3 g/kW·h, and exhaust temperature is increased by 10 °C. This study establishes a complete development workflow consisting of “operating condition design-virtual optimization-bench validation,” significantly enhancing calibration efficiency and engineering applicability. This method shortens the calibration cycle and reduces the number of physical bench tests, providing the industry with a comprehensive calibration methodology tailored to engine operating conditions that is both reproducible and scalable. Full article

This study investigates the effect of ambient conditions, particularly the humidity of the intake air, on the operation of an ALCO V16 251F (USA) diesel engine. It evaluates the temperature at which air is delivered to the combustion chamber, after the cooling process is accomplished within the aftercooler heat exchanger. A theoretical analysis was conducted using AVL CRUISE software, a specialized computational tool developed by AVL that facilitates the simulation and virtual integration of engine subsystems. An experimental investigation on the operation of air coolers is difficult to perform on a test bench, due to the overall dimensions of heat exchangers used in large marine diesel engines. The simulation results show that the AVL CRUISE virtual cooler reproduces the temperature range of the ALCO aftercooler with small deviations of approximately 7% at the fluid outlets. In these conditions, simulation outcomes demonstrate that the engine’s original charge-air cooler can maintain a stable and controlled air temperature at the intake manifold inlet. For an absolute air humidity of 5%, only minor variations (less than 1%) were registered in the outlet air temperature from the aftercooler, accompanied by 0.7% decrease in cooling effectiveness and 0.6% decrease in heat-exchanger efficiency. These findings confirm that the charge-air cooler operates reliably under humid marine conditions and that the virtual model is suitable for further optimization studies. Full article

Minimal access cardiac surgery (MACS) can mitigate the increasing risk profile of cardiac surgery patients and is associated with improved postoperative outcomes. One of the ways to manage the steep learning curve of MACS is the use of surgical simulation training. We conducted a narrative review to identify the relevant literature discussing MACS simulation training. We identified 20 studies using our search strategy. Various platforms were represented: high-fidelity (n = 8), low-fidelity (n = 6), and animal studies (n = 6). Virtual reality (VR) appeared in two wet-lab studies as an adjunct. The surgical approach was video-assisted thoracoscopic surgery (VATS) in 11 and robotic-assisted thoracoscopic surgery (RATS) in nine. The most simulated procedure was minimal access mitral valve (MV) repair (n = 16). Most studies (n = 16) evaluated the impact of simulation training on the surgical skill of participants with varying baseline MACS experience. A small proportion of included studies (n = 4) carried out only fidelity testing. While some standardised assessment tools were used, there was considerable variation in how surgical skill and fidelity were assessed. There are an increasing number of publications on MACS simulation training, with equal focus on bench and animal models. MV procedures were the most simulated, suggesting a drive towards increasing the scope of minimal access MV training. Full article

Contact–impact phenomena caused by joint clearances can significantly alter the dynamic response of high-speed mechanical systems, yet fewer studies combine analytical impact-force modeling, virtual prototyping, and experimental observations for multi-cylinder internal combustion engine mechanisms within a unified framework. This problem is scientifically important because the piston–connecting rod–crankshaft chain is subjected to rapid motion reversals, high transmitted loads, and local clearances that may generate shocks, force amplification, and vibration growth. The objective of this study is to evaluate the influence of mechanical impact on the dynamic response of a three-cylinder inline engine mechanism by combining analytical modeling, MSC Adams virtual prototyping, and experimental investigation. The mechanism was analyzed in two operating conditions: under load, using an experimentally derived gas pressure input, and without load at low speed imposed on the crankshaft, using a sectioned engine test bench. The loaded virtual model was studied at a crankshaft speed of 6000 rpm, with cylinder gas pressure peaks above 90 bar and engine torque oscillating around 170 Nm. A radial clearance of 0.03 mm was introduced in the connecting rod–piston joint to evaluate clearance-induced impacts. The results showed that the damping coefficient strongly influences the amplitude and harmonic content of the impact force. For the analyzed no-load case at low speed, the simulated impact force reached a maximum value of 3000 N. Experimentally, the worn connecting rod with 0.03 mm clearance exhibited markedly higher dynamic response than the clearance-free case, with the maximum longitudinal acceleration increasing from 17.77 to 48.26 m/s² at 1.341 Hz. The novelty of the study lies in the integrated analytical–virtual–experimental investigation of clearance-induced impact in a three-cylinder inline engine mechanism and in the comparative evaluation of its effects on joint forces and vibration signatures. In addition, compared to other models, the novelty lies in introducing and adapting the impact force damping component for mechanisms with rapid motion and high dynamic loads. Full article

This study investigates amplified hydraulic braking systems employed in high-performance motorsport applications, utilizing wedge mechanisms for self-energization. An analytical expression for the gain coefficient is derived from a simplified equilibrium analysis of the wedge-shaped pad, capturing the nonlinear dependency on both wedge angle and effective mean disc-pad friction. A previously validated coupled thermoelastic model for carbon-carbon (C/C) braking systems—developed in Dymola and Modelica using the finite volume method (FVM) and an analytical local friction formulation—is here adapted to wedge-amplified braking systems, with the aim of providing performance assessment during the design phase of new calipers at reduced computational cost compared to coupled thermoelastic finite element method (FEM) models. Several caliper configurations featuring different wedge angles are tested experimentally on a dynamometer. A reduction in the effective friction coefficient at high mean effective contact pressure—induced by pronounced wedge angles and reduced pad areas—is observed. To validate the thermoelastic model, simulated braking torque and disc surface temperature are compared against bench data. The model shows satisfactory predictive capability under various operating conditions and test cycles, with mean error indices on peak torque prediction below $5\%$ for the majority of the simulated cases. Finally, the validated model is used to virtually assess the performance of a new caliper prototype prior to its manufacturing and testing. Full article

The power-shift system employed in agricultural multi-range continuously variable transmissions (CVTs) features a complex structure and control logic, presenting significant challenges to the reliability of agricultural machinery. To enable timely detection of faults, constructing an intelligent fault diagnosis classifier to monitor the system’s health status is essential. Typically, fault samples utilized for classifier development originate from ideal bench tests, characterized by uniform patterns and limited diversity, thereby hindering the algorithm’s generalization capability. This study addresses this issue by proposing a generative adversarial network (GAN) model, integrated with a triple loss function and a novel generator architecture, to augment the fault dataset under laboratory conditions. The generator architecture comprises a variational autoencoder module and an oil pressure point attention mechanism, enabling the generation of diverse and fluctuating virtual samples. Building on this augmented dataset, a fault classifier based on one-dimensional ConvNeXt was developed. Experimental results indicate that the classifier achieves an accuracy of 99.73%. While classifier accuracy decreases with increasing noise levels, the GAN-generated dataset provides more comprehensive training, resulting in an accuracy approximately 3% higher than that achieved using the original dataset. Full article

Accessible and comfortable public transportation is a cornerstone of sustainable and inclusive urban mobility. However, there is a knowledge gap in how interior layout influences riders’ comfort perception under constant occupancy conditions. We conducted a pilot laboratory experiment in Valparaíso, Chile using a full-scale urban bus mock-up. Twenty-five participants each experienced four seating scenarios (yielding 100 total observations per outcome) that varied seat pitch (20, 30, 45 cm) and seat orientation (forward-facing vs. side-facing). Cumulative link mixed models were used to estimate seat pitch and orientation effects on the comfort outcomes, with participant-specific random intercepts. Increased seat pitch dramatically improved comfort ratings (e.g., virtually no participants felt comfortable at 20 cm, whereas nearly all did at 45 cm). Side-facing bench seating (longitudinal orientation) yielded significantly higher comfort, legroom, and ease-of-movement ratings than the forward-facing configuration at ~30 cm pitch (p < 0.001). Within the tested mock-up conditions, the results suggest that seat pitch is a major driver of perceived comfort and in-vehicle usability, and that a side-facing bench layout (tested at ~30 cm spacing) can improve perceived spaciousness relative to forward-facing seating. Because this is a small, non-probability pilot sample and a partial factorial design, these findings should be considered preliminary design sensitivities that warrant validation in larger, in-service studies before informing fleet-wide standards. Full article

This paper aims to address the problem of the low success rate of seedling picking and throwing, and the high damage rate of pot seedling, caused by the unclear interaction and parameter mismatch between the seedling picking end effector and the pot seedling during the seedling picking and throwing process of automatic transplanters. An EDEM–RecurDyn coupled simulation was conducted, through which the disturbance of substrate particles in the bowl body during the inserting, extracting, and transporting processes by the seedling picking end effector was visualized and analyzed. The force and motion responses of the particles during their interaction with the seedling picking end effector were explored, and the working parameters of the seedling picking end effector were optimized. A seedling picking end effector using two fingers and four needles is taken as the research object, a kinematic mathematical model of the seedling picking end effector is established, and the dimensional parameters of each component of the end effector are determined. Physical characteristic tests are conducted on Shanghai bok choy pot seedlings to obtain relevant parameters. A discrete element model of the pot seedling is established in EDEM 2022 software, and a virtual prototype model of the seedling picking end effector is established in Recurdyn 2024 software. Through EDEM-Recurdyn coupled simulation, the force and movement of the substrate particles in the bowl body during the inserting, extracting, and transporting processes of the seedling picking end effector under different operating parameters were explored, providing a theoretical basis for optimizing the working parameters of the end effector. The inserting and extracting velocity, transporting velocity, and inserting depth of the seedling picking end effector were used as experimental factors, and the success rate of seedling picking and throwing, and the loss rate of substrate, were used as evaluation indicators; single-factor tests and three-factor, three-level Box–Behnken bench tests were conducted. Variance analysis, response surface methodology, and multi-objective optimization were performed using Design-Expert 13 software to obtain the optimal parameter combination: when the inserting and extracting velocity was 228 mm/s, the transporting velocity was 264 mm/s, the inserting depth was 37 mm, the success rate of seedling picking and throwing was 97.48%, and the loss rate of substrate was 2.12%. A verification experiment was conducted on the bench, and the success rate of seedling picking and throwing was 97.35%, and the loss rate of substrate was 2.34%, which was largely consistent with the optimized results, thereby confirming the rationality of the established model and optimized parameters. Field trial showed the success rate of seedling picking and throwing was 97.04%, and the loss rate of substrate was 2.41%. The error between the success rate of seedling picking and throwing and the optimized result was 0.45%, indicating that the seedling picking end effector has strong anti-interference ability, and verifying the feasibility and practicality of the established model and optimized parameters. Full article

This study presents a methodology used in the design certification of a battery module for electric vehicle applications. The methodology combines virtual and physical techniques to assess the structure under mechanical shock testing, according to the standards’ requirements. Virtual prototyping is used to quantify parameters as stresses and deformations. Performed simulations using the virtual prototype are validated by testing the physical prototype, which allows for assessing various design parameters with a high level of confidence. The testing of a physical prototype is performed using specialized equipment—a mechanical shock test bench—which is developed and manufactured especially for this task. The presented methodology is demonstrated in an industrial use case, and the main contribution of this study is related to the way the combination of virtual and physical prototyping and testing is used. Full article