The performance of wind tunnel balance, which largely depends on the calibration modeling, plays a decisive role in the wind tunnel test capabilities and force measurement accuracy. However, there are still challenges for the calibration modeling in extreme conditions, two of which are inaccurate model feature selection and parameter estimation. This paper proposes a novel wind tunnel balance modeling method based on multivariate orthogonal functions coupled with the predicted squared error criterion for selecting model terms so that the model feature selection and parameter estimation processes are decoupled. This innovative method has been rigorously validated by its application to a multi-piece welding balance. The measured high-precision Mx demonstrates that the regression model with better fitting applicability can be obtained using the orthogonal functions method, and the mean square error of the verification set can be reduced by 28.6% and 16.2%, respectively, when compared with the traditional total regression and stepwise regression methods. Moreover, for bidirectional welded balance, introducing the absolute value into the model can further reduce the mean squared error of the verification set, yielding a remarkably accurate model.
After the dual-arm space robot captures a noncooperative target, a closed-chain multibody system is formed, making dynamic modeling and detumbling trajectory planning particularly challenging. This paper proposes a novel trajectory planning strategy that guides the combined system into a uniaxial rotational state about its maximum principal inertia axis. Unlike prior work that focuses solely on eliminating the target’s relative motion, our approach additionally drives the closed-chain system into a dynamically favorable uniaxial rotation. This configuration avoids multiaxis coupling and simplifies subsequent stabilization, requiring only unidirectional thruster torque. By reducing the number of required control directions and eliminating complex angular momentum interactions, the subsequent attitude stabilization becomes more fuel-efficient. As a result, the fuel required for postdetumbling control can be significantly reduced. The detumbling process is executed solely by actuating the joints of the dual manipulators without consuming any base thruster fuel. Numerical simulations validate that the proposed strategy effectively establishes the desired rotational mode and that the joint-space tracking controller enables accurate execution of the planned trajectory.
In the presented work, the calculation of critical rotor speeds and determination of vibration characteristics of an advanced gas turbine engine are considered. The engine under study contains a low-pressure compressor with an elastic damper support of a new design that has non-linear characteristics of stiffness and damping coefficients. The problem of creating a model of elastic damper quasi-linear supports of gas turbine engine rotors is solved by adding a quasi-linear element to the dynamic calculation model created by the DYNAMICS A4 software system. The parameters of the element change their values depending on the rotor speed. The task of verifying the results of experimental data is investigated. The presented technique makes it possible to build a quasi-linear model of the rotor support and ensure the coincidence of calculated and experimental data.
The paper primarily focuses on the control strategy of an electric dump truck equipped with an M100 methanol range extender. In response to the significant adverse impact of the constant power control strategy on the lifespan of power batteries and the large rotational speed fluctuations of range extenders under the power-following control strategy, a constant-speed and variable-torque range extender control strategy based on the rule-based control strategy is proposed. This strategy enables power following within the range of 70 kW to 130 kW and fixed-point operation at 50 kW and 150 kW. Through co-simulation using AVL Cruise and MATLAB R2022b/Simulink, the results indicate that under the China Heavy-duty Commercial Vehicle Test Cycle-Dynamic (CHTC-D), with an average vehicle speed of 23.19 km/h, the constant-speed and variable-torque range extender control strategy achieves a higher methanol saving rate compared to both the constant power control strategy and the power-following control strategy, thereby demonstrating better fuel economy. The methanol consumption per 100 km for the dump truck using the constant power control strategy, the power-following control strategy, and the constant-speed and variable-torque control strategy are 62.89 L, 64.49 L, and 62.53 L, respectively. Compared with the same type of diesel range-extended electric dump truck, its fuel usage cost has a significant advantage.
Mechanical engineering and machinery, Machine design and drawing
The analysis of flight loads during symmetric aircraft maneuvers is an essential but computationally intensive task in aircraft design. The significant structural elastic deformation in modern aircraft further complicates this work, adding to the computational demands. Therefore, improving the analysis efficiency of flight loads during maneuvers is crucial for accelerating design interactions and shortening the development cycle. This study explores a method for analyzing flight loads in the time domain during maneuvers of elastic aircraft by introducing a database of high-precision rigid-body aerodynamic loads. Furthermore, it combines the gradient-enhanced Kriging model to efficiently predict elastic flight loads during longitudinal maneuvers. The results indicate that the proposed surrogate-based method has high fitting accuracy with significantly improved computational efficiency, providing a new approach for efficient analysis of flight loads during aircraft maneuvers.
The Xfinity Racing Series is an American stock car racing series organized by NASCAR. For the 2017 racing season, NASCAR introduced new regulations with the objective of creating a level playing field by reducing aerodynamic influence on vehicle performance. In this context, the primary objective of this work is to explore the differences in the aerodynamic performance between the 2016 and 2017 Toyota Camry Xfinity racecars using only open-source Computational Fluid Dynamics (CFD) and CAE tools. During the CFD validation process, it was observed that none of the standard turbulence models, with default turbulence model closure coefficients, were able to provide racecar aerodynamic characteristics predictions with acceptable accuracy compared to experiments. This necessitated a fine-tuning of the closure coefficient numeric values. This work also demonstrates that it is possible to generate CFD predictions that are highly correlated with experimental measurements by modifying the closure coefficients of the standard <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>k</mi><mo>−</mo><mi>ω</mi></mrow></semantics></math></inline-formula> SST turbulence model.
Mechanical engineering and machinery, Machine design and drawing
The Phase-Change Heat Exchanger Unit in Layered Porous Media (PCEU-LPM) is obtained through frozen pouring processing, and exhibits characteristics such as high thermal conductivity, high latent heat, and high permeability, making it suitable for dissipating heat in airborne electronic devices. This study numerically investigates the impact of aircraft speed acceleration conditions, which lead to weightlessness or overload, on the performance of the PCHEU-LPM, with a particular focus on the influence of natural convection in the liquid-phase region. Initially, a microscale thermal analysis model is established based on the Navier–Stokes equation scanning electron micrograph to calculate the effective thermal conductivity and permeability of the PCHEU-LPM under different porosities. Subsequently, these parameters are incorporated into a macroscale thermal analysis model based on Darcy’s law, employing an average parameter approach. Using the macroscale thermal analysis model, temperature and velocity fields are computed under various porosities, acceleration magnitudes, and directions. The calculation results indicate that as the acceleration increases from α = 0 to α = 10 g, the interface temperature of the PCHTU-LPM decreases by approximately 5.2 K, and the temperature fluctuation decreases by 2.4 K. If the porosity of the PCHTU-LPM is increased from ε = 70% to ε = 85%, the influence of acceleration change on natural convection will be further amplified, resulting in a decrease in the interface temperature of the PCHTU-LPM by approximately 10.2 K and a decrease in temperature fluctuation by 5.8 K. When the acceleration direction is +z, the interface temperature of the PCHTU-LPM is at its lowest, while it is highest when the acceleration direction is −z, with a maximum difference of 15.4 K between the two. When the acceleration direction is ±x and ±y, the interface temperature lies between the former two cases, with the interface temperature slightly higher for ±y compared to ±x, with a maximum difference of 3.9 K between them.
Vinícius Correa, Peter Funk, Nils Sundelius
et al.
Research on unmanned autonomous vehicles (UAVs) for search and rescue (SAR) missions is widespread due to its cost-effectiveness and enhancement of security and flexibility in operations. However, a significant challenge arises from the quality of sensors, terrain variability, noise, and the sizes of targets in the images and videos taken by them. Generative adversarial networks (GANs), introduced by Ian Goodfellow, among their variations, can offer excellent solutions for improving the quality of sensors, regarding super-resolution, noise removal, and other image processing issues. To identify new insights and guidance on how to apply GANs to detect living beings in SAR operations, a PRISMA-oriented systematic literature review was conducted to analyze primary studies that explore the usage of GANs for edge or object detection in images captured by drones. The results demonstrate the utilization of GAN algorithms in the realm of image enhancement for object detection, along with the metrics employed for tool validation. These findings provide insights on how to apply or modify them to aid in target identification during search stages.
Collaborative autonomous landing of a quadrotor Unmanned Aerial Vehicle (UAV) on a moving Unmanned Ground Vehicle (UGV) presents challenges due to the need for accurate real-time tracking of the UGV and the adjustment for the landing policy. To address this challenge, we propose a progressive learning framework for generating an optimal landing policy based on vision without the need of communication between the UAV and the UGV. First, we propose the Landing Vision System (LVS) to offer rapid localization and pose estimation of the UGV. Then, we design an Automatic Curriculum Learning (ACL) approach to learn the landing tasks under different conditions of UGV motions and wind interference. Specifically, we introduce a neural network-based difficulty discriminator to schedule the landing tasks according to their levels of difficulty. Our method achieves a higher landing success rate and accuracy compared with the state-of-the-art TD3 reinforcement learning algorithm.
The heterogeneity of unmanned aerial vehicle (UAV) nodes and the dynamic service demands make task scheduling particularly complex in the drone edge cluster (DEC) scenario. In this paper, we provide a universal intelligent collaborative task scheduling framework, named DECCo, which schedules dynamically changing task requests for the heterogeneous DEC. Benefiting from the latest advances in deep reinforcement learning (DRL), DECCo autonomously learns task scheduling strategies with high response rates and low communication latency through a collaborative Advantage Actor–Critic algorithm, which avoids the interference of resource overload and local downtime while ensuring load balancing. To better adapt to the real drone collaborative scheduling scenario, DECCo switches between heuristic and DRL-based scheduling solutions based on real-time scheduling performance, thus avoiding suboptimal decisions that severely affect Quality of Service (QoS) and Quality of Experience (QoE). With flexible parameter control, DECCo can adapt to various task requests on drone edge clusters. Google Cluster Usage Traces are used to verify the effectiveness of DECCo. Therefore, our work represents a state-of-the-art method for task scheduling in the heterogeneous DEC.
Karine Klippel, Elisa Valentim Goulart, Gilberto Fisch
et al.
The Alcantara Launch Center (ALC) is the main Brazilian access to space. It is positioned over a complex terrain, and it has some important buildings for assembling, integration and launching activities, such as the Mobile Integration Tower. Being in a region of prevalent trade winds, the flow interaction between the complex terrain and the buildings can affect the safety of operations on the platform, and the dispersion of toxic gases emitted during the launching. The main objective of this work was to study the influence of topography and buildings on the atmospheric flow of ALC using computational fluid dynamics (CFD) techniques. Three geometries were considered: simplified terrain (case 1), smooth complex terrain(case 2), and roughness complex terrain (case 3). The flow conditions over ALC were simulated using the ANSYS Fluent 19.0 CFD commercial code. The numerical simulations used a realizable κ-ε to model turbulence effects and the results presented a good agreement with the in-situ field measurements for the most complex geometry (case 3). The topography clearly influences the flow pattern at ALC, with the cliff influence over the wind being the major cause for establishing the flow patterns.
Technology, Motor vehicles. Aeronautics. Astronautics
The vast, open, and interconnected characteristics of the transportation system make it a prime target for terrorists and hackers. However, there are no standard measures of transport system vulnerability to physical or cyberattacks. The separation of governance over different modes of transport increases the difficulty of coordination in developing and enforcing a common security index. This paper contributes a perspective and roadmap toward developing multimodal security indices that can leverage a variety of existing and emerging connected vehicle, sensing, and computing technologies. The proposed technologies include positive train control (PTC), vehicle-to-everything (V2X), weight-in-motion (WIM), advanced air mobility (AAM), remote sensing, and machine learning with cloud intelligence.
Mechanical engineering and machinery, Machine design and drawing
In the presence of compound disturbances, a multi-spacecraft cooperative collision avoidance capture control method based on disturbance observer was proposed, which can solve the problem of low speed rolling non-cooperative target close-range capture in space. Firstly, a relative motion model of attitude and orbit coupling is established. Secondly, the disturbance observer is used to estimate and cancel the compound disturbance in the capture process. At the same time, the hyperquadric surfaces are used to describe the shape of space non-cooperative targets and capture spacecraft to establish a composite artificial potential field, and a robust control law with collision avoidance function is also designed. Finally, the stability of the controlled system is proved by using Lyapunov function, and the collision avoidance performance of the system is analyzed. Numerical simulations are carried out to evaluate the effectiveness of the proposed control scheme.