Search Results (2,626)

Achieving climate-neutral aviation requires propulsion systems capable of reducing emissions and noise while maintaining high aerodynamic efficiency. Distributed Electric Propulsion (DEP) represents a promising solution; however, accurately predicting the unsteady aerodynamic interactions between multiple propellers and lifting surfaces remains challenging. This work investigates the aerodynamic performance of two Distributed Propulsion (DP) configurations using FLOWUnsteady, a meshless Computational Fluid Dynamics (CFD) solver based on the reformulated Vortex Particle Method (rVPM) within a Large-Eddy Simulation (LES) framework. The Lagrangian particle formulation eliminates mesh generation and limits numerical dissipation. Two layouts—a twin wingtip-mounted arrangement and a four-propeller configuration including inboard units are analyzed and compared with a clean wing baseline as functions of propeller position, inflow speed (20 and 33 m/s), and angle of attack. Beyond global aerodynamic performance metrics, the rVPM–LES framework provides a time-resolved and spatially resolved characterization of local propeller–wing interference in multi-propulsor configurations, highlighting differences in loading and torque demand between inboard and wingtip propellers that are not typically captured by low- to mid-fidelity modeling approaches. The results show that distributed propulsion increases lift and reduces drag relative to the clean wing by accelerating the local flow, delaying separation, and enhancing wing circulation. Thrust and torque coefficients exhibit a clear dependence on rotational speed and angle of attack: inboard propellers experience stronger aerodynamic interference and higher torque demand, whereas wingtip propellers maintain more uniform loading. These findings confirm the capability of the meshless rVPM approach to accurately and efficiently capture unsteady interactions in distributed propulsion systems, supporting its application to the analysis and design of future DEP aircraft. Full article

This exploratory study assessed the vehicle- and route-dependent response of five multipoint injection passenger vehicles to two commercial fuel additives marketed as octane-related gasoline additives under real-world high-altitude driving conditions in Quito, Ecuador. The tests were conducted on one urban route and one rural/peripheral route using base gasoline with a nominal octane index of RON 85, RON 85 gasoline with Additive A, and RON 85 gasoline with Additive B. Fuel economy and CO₂-related indicators were obtained through the OBD-II port using the Torque Pro application; therefore, the reported values were interpreted as electronic control unit-based estimates rather than direct gravimetric fuel consumption or laboratory emissions measurements. The revised analysis used OBD-derived trip-average fuel economy as the primary response variable. The mixed-effects model showed a significant effect of route on fuel economy (p < 0.001) and a significant fuel condition × route interaction (p = 0.0089), while the main effect of fuel condition was not statistically significant (p = 0.0699). Additive B increased the mean OBD-derived trip-average fuel economy on the urban route from 11.56 to 12.60 km·L⁻¹, but reduced it on the rural route from 13.46 to 12.65 km·L⁻¹. At the vehicle level, the previously extreme Vehicle 3 response was revised to a more plausible increase from 11.03 to 13.64 km·L⁻¹ (+23.68%) when trip-average fuel economy was used. Since the actual RON/MON values and physicochemical properties of the final fuel blends were not experimentally measured, the observed responses cannot be attributed exclusively to octane number enhancement. Overall, the findings indicate that commercial additive performance was vehicle- and route-dependent rather than universally beneficial. This field-based assessment supports evidence-informed decision-making for sustainable mobility and aligns with SDG 16 and SDG 17 through transparent technical evaluation and academic collaboration. Full article

Non-exhaust emissions represent a growing share of traffic-related particulate matter and are increasingly addressed by regulatory frameworks. This study presents a comparison of two brake emission test cycles, the California Brake Dynamometer Cycle (CBDC) and Worldwide Harmonized Light Vehicles Test Procedure Braking Cycle (WLTP-BC), the latter being formally embedded in current regulations. Firstly, we present a detailed comparison of WLTP-BC and CBDC in terms of parameters that are shown to affect or may affect braking control and particle emissions (braking torque, vehicle speed, acceleration, friction work, disc temperature, etc.). Secondly, we present a way to evaluate test system torque control quality, supplementing the friction work-based method present in United Nations Regulation (UNR) No. 179, and quantitatively assess the control quality between the cycles. CBDC was found to be the more challenging cycle to execute. However, the testbench control architecture was found to be sufficient to execute it with high fidelity. Thirdly, we present the emission results obtained from the cycles, both per kilometre driven, as well as per friction work done. We argue that the latter is better for comparing the emission results obtained from cycles with different braking profiles. Driving the CBDC resulted in higher particle mass (PM) emissions but similar particle number (PN) emissions. In light of this dataset, friction work seems to be a better predictor for PN than for PM. While this study encompasses only a single friction pair, and more tests with different brakes would be required to generalize the findings, the results highlight the importance of cycle selection in emission research, both in terms of quantifying the emissions and demands for the test system. Full article

Benthic AUVs (underwater quadruped robots) merge the cruising efficiency of submersibles with the bottom-crawling stability of legged robots for unstructured deep-sea exploration. However, the deliberate separation of the center of gravity and buoyancy—essential for static stability—generates a significant righting moment. When climbing steep slopes, this moment resists hull alignment. If the slope exceeds the robot’s maximum hydrostatic pitch limit, conventional inverse kinematics algorithms fail: the hind legs lose ground contact and propulsion is lost. To overcome this, this paper proposes a framework integrating optimal force distribution, adaptive trajectory probing, and admittance control. An analytical multi-point moment balance model derives the terrain-adaptive pitch boundaries. A Quadratic Program (QP) then distributes contact forces, tasking front legs with stabilizing the righting moment while hind legs provide thrust. During the swing phase, adaptive Bezier sequences prevent anterior slope collisions and ensure posterior ground contact. Furthermore, a Cartesian admittance controller provides active compliance to manage the nonlinear friction of dynamic waterproof seals. Validated via a high-fidelity physics-based simulation model calibrated against physical pool trials, the robot achieved robust traversal of $15°$ and $33°$ steep slopes. Statistical robustness is substantiated via a 30-trial Monte Carlo study, where postural stability remained remarkably consistent with a mean Pitch RMSE of $2.88°$ across a $\pm 10\%$ parameter uncertainty envelope. Compared to traditional baseline algorithms, the proposed method successfully suppressed torque chattering by $54.1\%$ in the high-frequency band (2–$50\mathrm{Hz}$) and improved energetic efficiency by up to $43\%$ on steep gradients. These findings offer a validated control architecture for heavy-duty deep-sea platforms navigating complex benthic topographies. Full article

Distributed Electric Propulsion aircraft have gained significant attention for advancing green aviation. However, their application is constrained by the limited energy density of batteries, resulting in weight compensation and flight range limitation. Current research on DEP energy management predominantly focuses on thrust allocation during the cruise phase while largely neglecting the energy regeneration potential during the descent phase. Conventional all-motors active energy recovery strategies force the multi-motor array to operate within a low-efficiency region, since the required drag torque is small under low aerodynamic drag conditions. To solve this issue, this paper proposes an energy recovery strategy that dynamically adjusts the number of activated motors during the descent phase of aircraft. The proposed N-Active strategy can adaptively regulate the number of operating motors, shifting motor operating points from the low-efficiency region to the high-efficiency region, which effectively decouples energy regulation within the longitudinal symmetry plane and maximizes energy recovery benefits. In this study, a high-fidelity simulation platform is established, including nonlinear aerodynamic characteristics and propeller windmilling motor efficiency models. Moreover, the optimal performance of the N-Active multi-motor cooperative energy recovery optimization strategy is verified based on the constructed platform. Simulation results demonstrate that compared with the traditional all motors active strategy, the proposed method improves battery state of charge by 11.96% and reduces virtual weight of battery. This method can effectively alleviate the weight compensation effect of distributed electric propulsion aircraft without additional physical weight increment, thereby enhancing the loading capacity of aircraft. Full article

Traditional optimization methods for interior permanent magnet synchronous motors (IPMSMs) often treat the stator and rotor as independent design domains, which limits the potential for suppressing torque fluctuations due to the neglected electromagnetic coupling between these components. This paper proposes a synergistic optimization strategy for a 120 kW IPMSM, aiming to overcome the inherent limitations of conventional unilateral optimization in design space exploration and achieve global performance enhancement through cross-domain collaboration. By establishing a unified surrogate model incorporating both stator slot geometries and rotor pole topologies, the collaborative effect of seven high-sensitivity design variables is systematically analyzed. The NSGA-II algorithm, coupled with a Kriging surrogate model, is employed to navigate the complex trade-offs among average torque, torque ripple, and cogging torque. Results demonstrate that the synergistic approach achieves a 28.1% reduction in torque ripple while maintaining high average torque, demonstrating superior improvement over conventional stator-only or rotor-only optimization schemes. Analysis based on Maxwell stress tensors and air-gap permeance functions reveals that the proposed method achieves simultaneous suppression of cogging torque and torque ripple by effectively harmonizing the 24th and 48th spatial harmonics. This study provides an efficient synergistic design methodology for the comprehensive performance enhancement of traction motors, offering practical reference value for the engineering development of high-performance electric vehicles. Full article

This article addresses the challenge of positioning accuracy in robotic manipulators applied to automated tape placement (ATL). A hybrid control strategy is proposed that integrates a Proportional-Integral-Derivative (PID) controller with a Backpropagation Neural Network (BP-NN). The proposed approach, called PID + NN, acts as a robust control scheme designed to compensate for parametric uncertainties and unmodeled perturbations arising from the integration of high-inertia tools in the end effector, dynamic mass variation due to tape consumption, and external reaction forces during the compaction process. Within this framework, the PID controller manages the nominal dynamics of the system, while the neural network operates as an adaptive compensator that adjusts the control signal in real time to minimize trajectory tracking errors. A rigorous stability analysis based on Lyapunov theory is presented, and the results are validated through numerical simulations on a six-degree-of-freedom manipulator. In addition, experimental tests are performed in a real operating environment to verify the practical performance of the strategy. The experimental results indicate that the proposed PID + NN controller significantly improves trajectory tracking accuracy, achieving a substantial reduction in tracking error and smoother control torque profiles compared to the conventional PID controller. These findings validate the effectiveness and robustness of the method for advanced manufacturing applications that demand high precision. Full article

Herringbone gear pairs are critical in high-speed aerospace transmissions, where windage power loss significantly impacts efficiency and thermal management. This study proposes a prediction method that decomposes the total windage loss into five components based on structural features: the tooth, end, circumferential, and relief groove surface losses for both gears, and the meshing extrusion loss. Theoretical models for each component are established to form a complete prediction method using fluid–structure interaction principles. CFD simulations analyze the velocity, pressure, and energy fields around the gear pair, with windage loss integrated via fluid torque on gear surfaces. Results indicate that windage loss escalates rapidly and becomes non-negligible when the driving gear speed exceeds 7000 rpm. The prediction model demonstrates strong agreement with CFD simulations, with a maximum relative error of 13.6%. Analysis reveals that the driving gear contributes the largest share of the total gear pair loss, with meshing extrusion accounting for 20.1–23.6%. For a single herringbone gear, the tooth surface is the primary source of loss (~83%), followed by the end surface (~8%), while relief groove and circumferential losses remain below 10%. This research provides a validated theoretical foundation for optimizing efficiency and thermal control in high-speed aerospace gear systems. Full article

Near-bit multi-parameter MWD (measurement while drilling) is a key technology for achieving precise and efficient directional drilling in underground and tunnel engineering. The near-bit multi-parameter MWD method was studied, and a “center + side wall” distributed measurement scheme was proposed, based on an analysis of special application scenarios in underground and tunnel engineering. The transmission characteristics of Bluetooth wireless signals in water were investigated. An analysis of the underwater Bluetooth signal link was conducted. When the transmission distance is 100 mm, the received signal strength is −17.5 dBm, and the link margin is 69.5 dB. Wireless Bluetooth was used to transmit the near-bit data. A Bluetooth wireless communication simulation model was established using ANSYS software, and the influence of transmission power, transmission medium, and transmission distance on the Bluetooth signal strength was analyzed. The results indicate that: (1) the received signal strength increases with transmission power, and appropriately increasing the transmission power can improve the effect of Bluetooth wireless communication and extend the communication distance. (2) When the transmission medium is water, the received signal is unstable, and the echo loss curve shows a high and low oscillation form, presenting a frequency shift feature; when the transmission medium is air, the received signal is relatively stable, and the echo loss curve shows a parabolic form. The echo loss of Bluetooth wireless signal in water transmission is significantly higher than that in air transmission, indicating that the Bluetooth signal attenuates more rapidly when transmitted in water. (3) When the transmission distance increases near the optimal transmission frequency of 2.4 GHz, the echo loss increases accordingly, and the received signal strength of the wireless receiving module gradually decreases. The theoretical analysis, simulation, and indoor test results are in good agreement. The reasonable Bluetooth transmission power is 1 mW, and the transmission distance is 100 mm. After completing the overall scheme design and simulation analysis optimization, the structure, circuit, and program development were carried out, and the near-bit multi-parameter MWD device was developed. A laboratory water supply test was conducted, and the power supply, collection, and wireless transmission were all normal. A drilling test was carried out at an underground engineering of a coal mine in Wuhai City, achieving a drilling depth of 2328 m. A continuous and stable collection of various parameters such as WOB (weight on bit), torque, rotation speed, vibration, and gamma was carried out. A wireless transmission channel for near-bit data was established across the screw drilling tool. It can provide key technical support for the research and development of near-bit MWD in underground and tunnel engineering. Full article

The purpose of this study is to develop a computationally efficient deep learning-based control framework for high-degree-of-freedom exoskeleton robots to address the real-time computational limitations associated with conventional model-based control. A parallel-structured deep neural network was designed for a seven-degree-of-freedom human lower extremity exoskeleton robot. The network consists of four layers with 49 densely connected neurons and was trained using physics-based data generated from the analytical dynamic model. During real-time implementation, the trained neural network predicts joint torque commands required for trajectory tracking, while a proportional derivative controller compensates for residual prediction errors. Stability of the proposed control scheme was analytically established, and robustness to parameter variations was evaluated using analysis of variance. Comparative simulations were conducted against computed torque, model reference computed torque, sliding mode, adaptive, and linear quadratic controllers under identical robot dynamics. Results demonstrate accurate trajectory tracking with torque profiles comparable to conventional nonlinear controllers while reducing computational burden. These findings suggest that the proposed deep learning-based hybrid controller offers an efficient and robust alternative for controlling multi-degree-of-freedom exoskeleton robots. Full article

To address the longitudinal vibration issues in high-speed elevators induced by external excitations, this study constructs a high-precision multi-degree-of-freedom (MDOF) dynamic model to systematically analyze vertical dynamic response characteristics. Utilizing the substructure method, the complex traction system is decomposed into several subsystems, including the traction device, tensioning device, car and car frame, counterweight system, and segmented wire ropes. By integrating Lagrange’s equations with Newton’s second law, the governing differential equations of motion for each component are derived, establishing an adaptable global dynamic model. The forced vibration analysis focuses on the impacts of periodic excitation from traction sheave eccentricity, piecewise reverse braking torque, and vertical impacts from guide rail joints on car vibration response and wire rope dynamic stress. The results indicate that: traction sheave eccentricity leads to periodic fluctuations in car acceleration, with vibration peaks decreasing as the payload increases; reverse braking torque triggers impulsive acceleration overshoots, where the peak value under full-load conditions increases by approximately 15% compared to the no-load condition, accompanied by a longer duration of low-frequency vibrations; guide rail joint impacts produce instantaneous acceleration spikes, which increase by about 18% under high-speed operating conditions; and the wire rope stress exhibits significantly higher sensitivity to load variations within the low-load range of 0–0.2. Full article

Direct Torque Control (DTC) for induction motors (IMs) is an advanced method derived from Field-Oriented Control (FOC). In DTC, a voltage source inverter (VSI) is employed to directly regulate the stator flux linkage and electromagnetic torque through space vector modulation (VSM), where the optimal switching vector is selected for the VSI. Similarly to FOC, the stator flux and electromagnetic torque are independently controlled to deliver enhanced dynamic performance. However, DTC still suffers from certain drawbacks, such as slow transient response, limited dynamic performance, and high ripples in torque and flux. In this paper, an improved DTC method is proposed for a three-phase squirrel-cage induction motor. Specifically, a Type-2 fuzzy logic controller is employed to regulate both the stator flux and electromagnetic torque (T2FLC). The proposed method (FLCDTC) combines a three-level VSI with dual-band hysteresis (DBHW) switching to generate the gating signals for the insulated gate bipolar transistors (IGBTs). This approach effectively reduces the total harmonic distortion (THD) in torque and stator current, lowers the common-mode voltage (CMV), and enhances the overall motor performance. Simulation results under random noise distribution demonstrate the robustness of the proposed controller, even at low operating speeds. Finally, the effectiveness of the algorithm is validated in real-time through hardware-in-the-loop (HIL) implementation. Full article

To improve the speed regulation performance of permanent magnet synchronous motor (PMSM) drive systems, a composite control strategy consisting of an improved sliding mode controller (ISMC) and an adaptive Luenberger load torque observer (ALLTO) is proposed. The ISMC is constructed based on a novel sliding mode reaching law (NSMRL). The proposed NSMRL overcomes the slow convergence and chattering problems of conventional reaching laws by introducing system state variables and a nonlinear adaptive function, ensuring rapid convergence with reduced chattering. In parallel, the ALLTO is developed to estimate and compensate load disturbances in real time, where its bandwidth is adaptively adjusted according to the speed error to achieve fast response and high estimation accuracy without degrading steady-state performance. Experimental results demonstrate that the proposed control scheme significantly improves the dynamic response and disturbance rejection capability of PMSM drive systems. Full article

Lithium-ion pouch cells exhibit significant irreversible expansion during long-term cycling, which determines overall performance and induces degradation failure without an appropriate mechanical fixture. However, the synergistic mechanism of mechanical pre-torque and battery state on battery electrochemical performance is unclear. To address this issue, this study reveals the electrochemical characteristic evolution of commercial lithium-ion pouch cells during cycling degradation, under varying mechanical pre-torques (0 N·m, 0.5 N·m, 1 N·m, and 1.5 N·m) and at different states of charge (SOCs, 0%, 25%, 50%, 75%, and 100%). Results indicate that moderate pressure (0.5 N·m) optimizes the electrode–electrolyte contact, reducing solid–electrolyte interphase resistance (R_SEI), ohmic resistance (R_O), charge transfer resistance (R_ct), and Warburg coefficient (W) by over 55%, 60%, 30% and 20%, respectively, compared with the free state. High pressure (1.5 N·m) induces impedance rebound due to pore compression, with the increment ranging from 20% to 40%. Furthermore, synergistic impact analysis proves that pressure alters impedance sensitivity to SOC, with changing rates amplifying from <5% per SOC unit under low pressure to 10–15% under high pressure, particularly exacerbating interface passivation at low SOC and side reactions at high SOC. Moreover, a Gaussian process regression (GPR) based adaptive SOC estimation model is developed, incorporating impedance features and pressure paths, achieving a root mean square error of 2.1% and enhancing accuracy by 10–15% over conventional methods in high-pressure scenarios. This study provides guidance for the next-generation pouch cell module design and management. Full article

Conventional memory machines often suffer from magnetic interference between high-coercive-force (HCF) and low-coercive-force (LCF) permanent magnets, which unintentionally alters the magnetization state and limits overload capability. To address this challenge, this paper proposes a novel axial parallel memory machine (DCB-AXMM) featuring a DC-bias-controlled variable-flux capability. Instead of a conventional structure, the proposed machine employs an axially segmented topology to spatially isolate the excitation sources, effectively shielding the LCF PMs from HCF PM interference and armature reaction. Furthermore, integrated windings are utilized to perform both armature excitation and pulse magnetization, thereby enhancing the overall space utilization. The flux-regulating mechanism is theoretically elucidated using a piecewise linear hysteresis model. To maximize electromagnetic performance, a two-step optimization framework based on a genetic algorithm (GA) is implemented. Comprehensive non-linear finite element analysis (FEA) is conducted to validate the proposed design. Quantitative results demonstrate that the DCB-AXMM achieves a wide flux regulation range, characterized by a 21.8% average torque reduction from 2.2 Nm at full magnetization to 1.72 Nm at zero magnetization, while maintaining a robust 1.5-times overload capability. These measurable outcomes confirm the topology’s effectiveness and reliability for high-performance variable-flux applications. Full article