The study investigates the combined effects of material selection, manufacturing constraints, and a dynamic stress constraint function on the resulting material distribution achieved through a structural optimization process, while ensuring full compliance with the relevant European assessment standards for railway bogie. A high-fidelity finite element model of the complete bogie system was developed to accurately reproduce the operational loads and the structural interactions between the motor support and its surrounding components. The proposed methodology integrates topology optimization within a manufacturability-oriented framework, enabling a systematic evaluation of the influence of material properties, draw direction, and minimum feature size on the optimized configuration. In this context, an adaptive stress coefficient, derived from the performance of the original component, was introduced and proved effective in improving both the material distribution and the resulting stress levels of the optimized design. The results demonstrate that the combined consideration of material selection, manufacturing constraints, and adaptive stress control leads to a structurally efficient and production-feasible design. Three different materials were tested, showing consistent stress distributions and mass savings across all cases. The innovative optimized configuration achieved over 16% mass reduction while maintaining admissible stress levels. The proposed approach provides a generalizable and standard-compliant framework for future applications of topology optimization in railway engineering.
Mechanical engineering and machinery, Machine design and drawing
Rodrigo Costa do Nascimento, Éder Alves de Moura, Thiago Rosado de Paula
et al.
This work proposes an analysis of the capability of three deep learning models—the feedforward neural network (FFNN), long short-term memory (LSTM) network, and physics-informed neural network (PINN)—to identify the parameters of a flexible fixed-wing aircraft using in-flight data. These neural networks, composed of multiple hidden layers, are evaluated for their ability to perform system identification and to capture the nonlinear and dynamic behavior of the aircraft. The FNN and LSTM models are compared to assess the impact of temporal dependency learning on parameter estimation, while the PINN integrates prior knowledge of the system’s governing of ordinary differential equations (ODEs) to enhance physical consistency in the identification process. The objective is to exploit the generalization capability of neural network-based models while preserving the accurate estimation of the physical parameters that characterize the analyzed system. The neural networks are evaluated for their ability to perform system identification and capture the nonlinear behavior of the aircraft. The results show that the FFNN achieved the best overall performance, with average Theil’s inequality coefficient (TIC) values of 0.162 during training and 0.386 during testing, efficiently modeling the input-output relationships but tending to fit high-frequency measurement noise. The LSTM network demonstrated superior noise robustness due to its temporal filtering capability, producing smoother predictions with average TIC values of 0.398 (training) and 0.408 (testing), albeit with some amplitude underestimation. The PINN, while successfully integrating physical constraints through pretraining with target aerodynamic derivatives, showed more complex convergence, with average TIC values of 0.243 (training) and 0.475 (testing), and its estimated aerodynamic coefficients differed significantly from the conventional values. All three architectures effectively captured the coupled rigid-body and flexible dynamics when trained with distributed wing sensor data, demonstrating that neural network-based approaches can model aeroelastic phenomena without requiring explicit high-fidelity flexible-body models. This study provides a comparative framework for selecting appropriate neural network architectures based on the specific requirements of aircraft system identification tasks.
Craig John MacDonell, Richard David Williams, Jon White
et al.
Quantifying riverscape topography is challenging because riverscapes comprise of both wet and dry surfaces. Advances have been made in demonstrating the capability of mounting topo-bathymetric LiDAR (Light Detection and Ranging) sensors on crewed, occupied aircraft to quantify riverscape topography. However, only recently has miniaturisation of electronic components enabled topo-bathymetric LiDAR to be mounted on consumer-grade Unoccupied Aerial Vehicles (UAVs). We evaluate the capability of a demonstration YellowScan Navigator topo-bathymetric, full waveform LiDAR sensor, mounted on a DJI Matrice 600 UAV, to survey a 1 km long reach of the braided River Feshie, Scotland. Ground-truth data, with centimetre accuracy, were collected across wet areas using an echo-sounder, and in wet and dry areas using RTK-GNSS (Real-Time Kinematic Global Navigation Satellite System). The processed point cloud had a density of 62 points/m<sup>2</sup>. Ground-truth mean errors (and standard deviation) across dry gravel bars were 0.06 ± 0.04 m, along shallow channel beds were −0.03 ± 0.12 m and for deep channels were −0.08 m ± 0.23 m. Geomorphic units with a concave three-dimensional shape (pools, troughs), associated with deeper water, had larger negative errors and wider ranges of residuals than planar or convex units. The case study demonstrates the potential of using UAV topo-bathymetric LiDAR to enhance survey efficiency but a need to evaluate spatial error distribution.
In the traditional finite control set model predictive current control(FCS-MPCC) for a permanent magnetic synchronous motor (PMSM), periodic delay caused by computational latency and hardware register update mechanisms leads to control commands lagging behind actual motor states, thereby impairing dynamic response and control stability of the PMSM. To address the issue, this paper introduces a two-step finite control set model predictive current control(FCS-MPCC) method. By predicting two-step current states simultaneously and generating control commands for the current time step in the previous cycle, the introduced method effectively reduces the impact of control delay and improves prediction accuracy. However, while the two-step FCS-MPCC method enhances control performance, the more complex computational logic increases the computational burden, limiting its real-time applicability. To overcome the limitation, the paper proposes a method based on the multilayer perceptron(MLP) neural network, which replaces traditional model predictive control strategies with a data-driven method. By learning the optimization rules of the two-step FCS-MPCC, the MLP neural network can replicate its control performance without requiring online computational efforts. The simulation results demonstrate that the proposed method exhibits strong robustness under secondary load disturbances, further validating its potentials for application in motor control.
Orbital maneuver detection is critical for space situational awareness, yet it remains challenging due to the complex and dynamic nature of satellite behaviors. This paper proposes a novel Multi-Level Firing Spiking Neural Network (MLF-SNN) for detecting orbital maneuvers based on changes in satellite orbital parameters. The MLF-SNN incorporates multiple firing thresholds and a leaky integrate-and-fire (LIF) neuron model to enhance temporal feature extraction and classification performance. The MLF-SNN encodes time-dependent input features, which include variations in orbital elements, and subsequently processes these features through a multi-layer spiking architecture. A surrogate gradient approach is adopted during training to enable end-to-end backpropagation through the spiking layers. Experimental results on real satellite data demonstrate that the proposed method achieves improved recall in maneuver detection compared to conventional approaches, effectively reducing false alarms and missed detections. The work highlights the potential of MLF-SNN in processing time-series spatial data and offers a robust solution for autonomous satellite behavior analysis.
Flow separation is a fundamental phenomenon in fluid mechanics governed by the Navier–Stokes equations, which are second-order partial differential equations (PDEs). This phenomenon significantly impacts aerodynamic performance in various applications across the aerospace sector, including micro air vehicles (MAVs), advanced air mobility, and the wind energy industry. Its complexity arises from its nonlinear, multidimensional nature, and is further influenced by operational and geometrical parameters beyond Reynolds number (Re), making accurate prediction a persistent challenge. Traditional models often struggle to capture the intricacies of separated flows, requiring advanced simulation and prediction techniques. This review provides a comprehensive overview of strategies for enhancing aerodynamic design by improving the understanding and prediction of flow separation. It highlights recent advancements in simulation and machine learning (ML) methods, which utilize flow field databases and data assimilation techniques. Future directions, including physics-informed neural networks (PINNs) and hybrid frameworks, are also discussed to improve flow separation prediction and control further.
The genomic DNA of most bacteria is significantly underwound, which constrains the DNA molecule to adopt a branched plectoneme geometry. Moreover, biological functions like replication and transcription require that the two DNA strands be transiently opened, which generates waves of positive (respectively, negative) supercoiling downstream (respectively, upstream) of the molecular motor, a feature known as Twin Supercoiled Domain (TSD). In this work, we used coarse-grained modeling and Brownian Dynamics simulations to investigate the interactions between a TSD and the plectonemes of bacterial DNA. Simulations indicate that the slithering dynamics of short plasmids is not significantly affected by a TSD. In contrast, the TSD potently stimulates the spontaneous displacement modes (diffusion and growth/shrinkage) of the plectonemes of longer DNA molecules. This results in the motor trailing a growing plectoneme behind itself if it translocates more slowly than the maximum slithering speed of plectonemes. In contrast, if the motor translocates more rapidly than this limit, then quasi immobile plectonemes nucleate almost periodically upstream of the motor, grow up to several kbp, detach from the motor, shrink and disappear. The effect of an eventual static bend imposed by the motor and of topological barriers was also investigated.
To achieve real-time phase detection, this paper presents a fast and precise spatial carrier phase-shifting interferometry based on the dynamic mode decomposition strategy. The algorithm initially produces a series of phase-shifted sub-interferograms with the aid of a spatial carrier interferogram. Subsequently, the measured phases are derived with great accuracy from these sub-interferograms through the use of the dynamic mode decomposition strategy, an outstanding non-iterative algorithm. Numerical simulation and experimental comparison show that this method is an efficient and accurate single-frame phase demodulation algorithm. The paper also analyzes the performance of the proposed method based on influencing factors such as random noise level, carrier frequency size, and carrier frequency direction. The results indicate that this method is a fast and accurate phase solution method, offering another effective solution for dynamic real-time phase measurement.
Aiming at the cooperative localization problem for the dynamic UAV swarm in an anchor-limited environment, an adaptive Wasserstein filter (AWF) with distance-constrained bare bones self-recovery particles (CBBP) is proposed. Firstly, to suppress the cumulative error from the inertial navigation system (INS), a position-prediction strategy based on transition particles is designed instead of using inertial measurements directly, which ensures that the generated prior particles can better cover the ground truth and provide the uncertainties of nonlinear estimation. Then, to effectively quantify the difference between the observed and the prior data, the Wasserstein measure based on slice segmentation is introduced to update the posterior weights of the particles, which makes the proposed algorithm robust against distance-measurement noise variance under the strongly nonlinear model. In addition, to solve the problem of particle impoverishment caused by traditional resampling, a diversity threshold based on Gini purity is designed, and a fast bare bones particle self-recovery algorithm with distance constraint is proposed to guide the outlier particles to the high-likelihood region, which effectively improves the accuracy and stability of the estimation. Finally, the simulation results show that the proposed algorithm is robust against cumulative error in an anchor-limited environment and achieves more competitive accuracy with fewer particles.
As the application of UAVs becomes more and more widespread, accidents such as accidental injuries to personnel, property damage, and loss and destruction of UAVs due to accidental UAV crashes also occur in daily use scenarios. To reduce the occurrence of such accidents, UAVs need to have the ability to autonomously choose a safe area to land in an accidental situation, and the key lies in realizing on-board real-time semantic segmentation processing. In this paper, we propose an efficient semantic segmentation method called KDP-Net for characteristics such as large feature scale changes and high real-time processing requirements during the emergency landing process. The proposed KDP module can effectively improve the accuracy and performance of the semantic segmentation backbone network; the proposed Bilateral Segmentation Network improves the extraction accuracy and processing speed of important feature categories in the training phase; and the proposed edge extraction module improves the classification accuracy of fine features. The experimental results on the UDD6 and SDD show that the processing speed of this method reaches 85.25 fps and 108.11 fps while the mIoU reaches 76.9% and 67.14%, respectively. The processing speed reaches 53.72 fps and 38.79 fps when measured on Jetson Orin, which can meet the requirements of airborne real-time segmentation for emergency landing.
Accurate semantic segmentation of high-resolution images captured by unmanned aerial vehicles (UAVs) is crucial for applications in environmental monitoring, urban planning, and precision agriculture. However, challenges such as class imbalance, small-object detection, and intricate boundary details complicate the analysis of UAV imagery. To address these issues, we propose Mamba-UAV-SegNet, a novel real-time semantic segmentation network specifically designed for UAV images. The network integrates a Multi-Head Mamba Block (MH-Mamba Block) for enhanced multi-scale feature representation, an Adaptive Boundary Enhancement Fusion Module (ABEFM) for improved boundary-aware feature fusion, and an edge-detail auxiliary training branch to capture fine-grained details. The practical utility of our method is demonstrated through its application to farmland segmentation. Extensive experiments on the UAV-City, VDD, and UAVid datasets show that our model outperforms state-of-the-art methods, achieving mean Intersection over Union (mIoU) scores of 71.2%, 77.5%, and 69.3%, respectively. Ablation studies confirm the effectiveness of each component and their combined contributions to overall performance. The proposed method balances segmentation accuracy and computational efficiency, maintaining real-time inference speeds suitable for practical UAV applications.
Mahmoud A. Hayajnh, Umberto Saetti, J. V. R. Prasad
This paper presents a novel step in the extension of subspace identification toward the direct identification of harmonic decomposition linear time-invariant models from nonlinear time-periodic system responses. The proposed methodology is demonstrated through examples involving the nonlinear time-periodic dynamics of a flapping-wing micro aerial vehicle. These examples focus on the identification of the vertical dynamics from various types of input–output data, including linear time-invariant, linear time-periodic, and nonlinear time-periodic input–output data. A harmonic analyzer is used to decompose the linear time-periodic and nonlinear time-periodic responses into harmonic components and introduce spurious dynamics into the identification, which make the identified model order selection challenging. A similar effect is introduced by measurement noise. The use of model order reduction and model-matching methods in the identification process is studied to recover the harmonic decomposition structure of the known system. The identified models are validated in the frequency and time domains.
Florian Saling, Andrea Emanuele Maria Casini, Andreas Treuer
et al.
The use of eXtended Reality (XR) technologies in the space domain has increased significantly over the past few years as it can offer many advantages when simulating complex and challenging environments. Space agencies are currently using these disruptive tools to train astronauts for Extravehicular Activities (EVAs), to test equipment and procedures, and to assess spacecraft and hardware designs. With the Moon being the current focus of the next generation of space exploration missions, simulating its harsh environment is one of the key areas where XR can be applied, particularly for astronaut training. Peculiar lunar lighting conditions in combination with reduced gravity levels will highly impact human locomotion especially for movements such as walking, jumping, and running. In order to execute operations on the lunar surface and to safely live on the Moon for an extended period of time, innovative training methodologies and tools such as XR are becoming paramount to perform pre-mission validation and certification. This research work presents the findings of the experiments aimed at exploring the integration of XR technology and parabolic flight activities for astronaut training. In addition, the study aims to consolidate these findings into a set of guidelines that can assist future researchers who wish to incorporate XR technology into lunar training and preparation activities, including the use of such XR tools during long duration missions.
The kinesin superfamily of motor proteins is a major driver of anterograde transport of vesicles and organelles within eukaryotic cells via microtubules. Numerous studies have elucidated the step-size, velocities, forces, and navigation ability of kinesins both in reconstituted systems and in live cells. Outside of cells, the kinesin-based transport is physically regulated and can be controlled by obstacles or defects in the path, or the interaction between several motors on the same cargo. To explore the physical control parameters on kinesin-driven transport, we created complex microtubule networks in vitro to test how kinesin cargoes made from quantum dots with one to 10 kinesin motors attached are able to navigate the network. We find that many motors on the quantum dot significantly alter distance walked, time spent bound, the average speed, and the tortuosity of the cargo. We also find that the average mesh size of the microtubule network affects the end-to-end distance of the motion, the run time, average speed and tortuosity of cargoes. Thus, both motor number and network density are physical aspects that regulate where cargoes traverse in space and time.
Eugenio Frias-Miranda, Hong-Anh Nguyen, Jeremy Hampton
et al.
The integration of technology into exercise regimens has emerged as a strategy to enhance normal human capabilities and return human motor function after injury or illness by enhancing motor learning and retention. Much research has focused on how active devices, whether confined to a lab or made into a wearable format, can apply forces at set times and conditions to optimize the process of learning. However, the focus on active force production often forces devices to either be confined to simple movements or interventions. As such, in this paper, we investigate how passive device behaviors can contribute to the process of motor learning by themselves. Our approach involves using a wearable resistance (WR) device, which is outfitted with elastic bands, to apply a force field that changes in response to a person's movements while performing exercises. We develop a method to measure the produced forces from the device without impeding the function and we characterize the device's force generation abilities. We then present a study assessing the impact of the WR device on motor learning of proper squat form compared to visual or no feedback. Biometrics such as knee and hip angles were used to monitor and assess subject performance. Our findings indicate that the force fields produced while training with the WR device can improve performance in full-body exercises similarly to a more direct visual feedback mechanism, though the improvement is not consistent across all performance metrics. Through our research, we contribute important insights into the application of passive wearable resistance technology in practical exercise settings.
In this innovative work, Inconel 718 alloy turning simulation models under dry and cryogenic machining (Cryo) conditions are developed. The machinability characteristics of the aforementioned alloy were assessed with relation to cutting temperature (Tct) and cutting force (Fcf). The comparison of the Tct and Fcf results from simulation with those obtained under the identical experimental conditions served as additional evidence of the effectiveness of the suggested simulation model. By varying the cutting speed, the reduction in Tct under Cryo conditions was 9.36% to 11.98% compared to dry cutting. Regarding the force comparison under experiment and simulation, the average difference between the simulation and experimental values for the main cutting force (Fc) was 13.73%, whereas the average deviation for the feed force (Ff) was 14.63%. Response surface methodology (RSM) was employed to build the forecasting models for Tct and Fcf in cryogenic settings. These mathematical models showed excellent predictive performance and were able to estimate the Tct and Fcf under machining operations settings, according to the present research. When compared to dry cutting, Cryo reduced the cutting temperature, which had a positive impact on the alloy’s machinability.
This paper proposes a control strategy for active lateral secondary suspension that uses preview data. Based on a derived analytical model, a model predictive controller (MPC) is implemented. The influence of the track irregularities upon carbody lateral dynamics is considered explicitly. The controller developed is applied to a full-scale rail vehicle model. Ride comfort is evaluated according to EN 12299. Multibody simulations show that there is a significant increase in continuous ride comfort on poor-quality tracks.
Mechanical engineering and machinery, Machine design and drawing
This study presents the numerical results for the aerodynamic and electromagnetic performances of an S-duct caret intake. Using the multilevel fast multipole method (MLFMM) to solve Maxwell equations, the current on the intake surface is calculated, and the radar cross-section (RCS) is analyzed. Moreover, the intake flow field is numerically investigated using the SST k–ω turbulence model to solve the Reynolds-averaged Navier–Stokes equations. Compared to a straight intake, for an S-duct caret intake, the average RCS is lower by 7.65 dB, and the maximum RCS difference value caused by the blade rotation is lower by 6.75 dB. However, the flow capacity deteriorates when the total pressure recovery coefficient decreases by 0.004. Based on the analysis of the aerodynamic and electromagnetic characteristics of different intakes, a double S-duct intake is designed. Compared to a traditional S-duct intake, for the novel intake after model parameter modification, the average RCS is lower by 0.05 dB, and the total pressure distortion (TPD) is lower by 0.18. The analysis of the effects of different boundary-layer bleed systems shows that the symmetrical layout adversely affects the aerodynamic and electromagnetic performances of the S-duct intake, but the unilateral partial layout is beneficial, whose TPD is lower by 0.04 and average RCS is higher by −2.17 dB compared to a straight intake.
Fish locomotion emerges from a diversity of interactions among deformable structures, surrounding fluids and neuromuscular activations, i.e., fluid-structure interactions (FSI) controlled by fish's motor systems. Previous studies suggested that such motor-controlled FSI may possess embodied traits. However, their implications in motor learning, neuromuscular control, gait generation, and swimming performance remain to be uncovered. Using robot models, we studied how swimming behaviours emerged from the FSI and the embodied traits. We developed modular robots with various designs and used Central Pattern Generators (CPGs) to control the torque acting on robot body. We used reinforcement learning to learn CPG parameters to maximize the swimming speed. The results showed that motor frequency converged faster than other parameters, and the emergent swimming gaits were robust against disruptions applied to motor control. For all robots and frequencies tested, swimming speed was proportional to the mean undulation velocity of body and caudal-fin combined, yielding an invariant, undulation-based Strouhal number. The Strouhal number also revealed two fundamental classes of undulatory swimming in both biological and robotic fishes. The robot actuators also demonstrated diverse functions as motors, virtual springs, and virtual masses. These results provide novel insights into the embodied traits of motor-controlled FSI for fish-inspired locomotion.