Search Results (135)

Accurate diagnosis of micro short-circuits (MSCs) is essential for ensuring the safety of lithium-ion batteries used in electric vertical take-off and landing (eVTOL) aircraft. Unlike conventional electric vehicles, eVTOL batteries normally operate under high-rate discharge conditions, where strong polarization and rapid voltage variations are apt to mask the weak signatures of MSCs. To address this challenge, this study proposes an MSC diagnosis method based on multiscale voltage residual analysis. A battery model is first established to characterize the normal response under high-rate discharge, and the discrepancy between the measured and estimated terminal voltages is used to construct the model residual. Features describing the overall voltage evolution, residual statistical distribution, and multiscale residual fluctuations are then extracted. Specifically, the shadow region integral area and voltage–capacity slope are used to characterize the global voltage trajectory, while the residual mean and kurtosis quantify the systematic deviation and non-Gaussian fluctuation of the residual. Wavelet decomposition is further applied to capture the low- and high-frequency residual characteristics. After feature reduction, eight representative features are retained to establish the diagnostic model. Experimental validation under high-rate discharge conditions demonstrates that the proposed method can effectively identify MSCs despite interference from abnormal aging, thereby reducing the false alarms caused by feature similarity. This study provides a reliable approach for micro short-circuit diagnosis of eVTOL lithium-ion batteries under strong polarization and highly dynamic operating conditions. Full article

Electric vertical takeoff and landing (eVTOL) represents a novel mode of transportation emerging within the low-altitude economy framework, exhibiting extensive development prospects. As a widely adopted incentive policy, government subsidy constitutes a crucial method for supporting the development of this strategic emerging industry. This paper constructs a differential game model of a supply chain composed of a manufacturer and retailer capable of simultaneously producing and selling eVTOL, considering three scenarios: centralized decision-making and decentralized decision-making with or without cost-sharing. Based on optimal control and differential game theory, the decision-making processes of supply chain members are investigated, and equilibrium strategies under different scenarios are compared and analyzed, with the model’s validity confirmed through numerical simulations. The findings indicate that the subsidy policy exerts a positive impact on the eVTOL supply chain: as the subsidy rates increase, supply chain members are better resourced to invest in endurance and marketing, thereby fostering eVTOL development. Concurrently, a reduction in wholesale and retail prices is observed, rendering eVTOL more affordable and of higher quality for consumers. The competitive structure of the upstream market does not alter this fundamental conclusion. The optimal endurance effort, marketing effort, eVTOL brand goodwill, and demand under the centralized decision-making model are higher than those under the decentralized one. In a certain feasible region, the two-way cost-sharing contract enables the eVTOL supply chain to achieve Pareto improvement, but it does not reach the level of centralized decision-making. This research expands the application of differential game theory in the field of the low-altitude economy, providing a scientific basis for the government to formulate policies and enterprises to distribute products. Full article

Reliable pack-level battery systems capable of safely handling instantaneous high-C-rate discharge above 10C during take-off, climb, and hovering are required for the commercialization of urban air mobility (UAM) aircraft. However, pack-level studies on wide-range C-rate characteristics of battery systems for UAM applications remain very limited, and most previous studies have been restricted to single-cell experiments or battery-pack simulations. In this study, a 24S1P test battery pack using nickel–cobalt–manganese (NCM) pouch cells, with a nominal voltage of 88.8 V and a capacity of 22 Ah, was designed and fabricated. A two-level battery management system (BMS) based on the LTC6803G-4 was also developed. To evaluate the charge–discharge characteristics of the battery system, constant-current discharge tests were conducted under five conditions ranging from 0.2C (4.4 A) to 10.68C (235 A), and charging tests were performed over the range of 0.2C–2C. The discharge test results showed that the capacity retention remained within 97.5–100.0% in the 1C–5C range, confirming excellent power capability. Continuous discharge operation was confirmed at 10.68C, the maximum discharge condition considered for vertical take-off and climb. Under this condition, the capacity decreased to 16.26 Ah, corresponding to 74.2% of the rated capacity, owing to internal-resistance-induced voltage drop, electrochemical polarization, and early attainment of the cut-off voltage. The Peukert exponent was estimated to be 1.113. An apparent pack-level direct-current internal resistance (DCIR) of approximately 40.3 mΩ was estimated from the initial voltage-drop analysis under different discharge-current conditions. In addition, the maximum temperature during 10.68C discharge was measured as 55.1 °C, providing a thermal margin of 4.9 °C relative to the operational temperature limit of 60 °C adopted in this study. Finally, a 24S4P battery system with a capacity of 88 Ah, consisting of four 24S1P battery packs connected in parallel, was installed in the VS-210, a 210 kg-class maximum take-off weight (MTOW) eVTOL aircraft. An in-flight test was conducted by repeating six take-off–hovering–landing cycles during a total test session of 15 min 20 s, and a stable propulsion power supply was maintained throughout all flight cycles. This study provides experimental baseline data for the design and preliminary safety assessment of high-power battery systems for UAM applications by presenting both the electrical and thermal characteristics of a 24S NCM battery pack over a wide discharge-rate range of 0.2C–10.68C and in-flight eVTOL data. Full article

Reliable launch environment recognition is essential for assisted-takeoff unmanned aerial vehicles (UAVs) because it determines the timing of flight-control activation, motor startup, and mode switching. However, recognition parameters are commonly selected empirically, and their effects on failure rate are rarely quantified. This study proposes a failure-rate-oriented parameter optimization framework integrating Monte Carlo failure-rate modeling, Sobol global sensitivity analysis, and particle swarm optimization (PSO) for UAV launch environment recognition. Three parameters in the sliding-window logic, namely the acceleration threshold $\left(a_{th}\right)$, number of sampling points within the judgment window $\left(n_s\right)$, and threshold-reaching proportion $\left(r\right)$, are analyzed and optimized under low-overload catapult-launch and high-overload gun-launch conditions. Sobol analysis identifies both individual contributions and interaction effects, providing interpretable guidance for parameter design, while PSO searches for parameter combinations that minimize the recognition failure rate. An additional multi-objective optimization was conducted by incorporating recognition delay, false-trigger probability, and sampling frequency to evaluate the trade-off between recognition reliability and response speed. Results showed that $n_s$ dominated the low-overload case, contributing 54.88%, whereas $r$ dominated the high-overload case, contributing 37.06%. PSO reduced the upper limit of the 95% confidence interval of the failure rate by 88.47% and 90.22%, respectively. The multi-objective optimization further reduced the mean recognition delay from 55.8 ms to 45.5 ms under the low-overload condition and from 8.8 ms to 4.1 ms under the high-overload condition while maintaining low false-trigger probability and recognition failure rate. Experimental tests verified successful recognition in six low-overload and six high-overload launches. Runtime comparisons with genetic algorithm (GA), differential evolution (DE), and random search (RS) further demonstrated the offline computational efficiency of PSO. Full article

Vertical take-off and landing (VTOL) configurations incur a structural weight penalty that reduces payload fraction and endurance compared to conventional fixed-wing and multirotor aircraft of comparable gross weight. To extend the endurance of VTOL UAVs, this work presents the design, analysis and control of a novel unmanned tilt trirotor aircraft with tandem wings, named Gong-H, featuring VTOL capability and high aerodynamic efficiency. A prototype of this aircraft was built with the rotor system mounted between tandem wings with a high wing coverage rate, which can achieve a more compact structure than other VTOL aircraft. The control forces and torques are provided not only by the rotor system in VTOL flight mode and the two tandem wings in cruise mode, but also by both the rotor system and wings in transition mode. Additionally, Computational Fluid Dynamics (CFD) simulations are conducted to optimize the wing configuration to improve the efficiency of cruise mode. Moreover, an airspeed-scheduled hybrid control framework based on incremental nonlinear dynamic inversion (INDI) and PID is adopted for different flight modes to improve the robustness of control and the stability of flight mode switching. Hover experiments confirm improved power efficiency compared to tilt quadrotor configuration, which extends endurance time and increases range. Additionally, complete flight cycle field experiments were conducted to demonstrate the aerodynamic feasibility of the prototype, including VTOL flight, cruise flight, and transition flight modes. Control surface redundancy tests and comparative INDI-PID validation under asymmetric disturbances further verify the practical robustness of the control framework. This work provides a design concept of VTOL aircraft and a practical solution for VTOL applications. Full article

Turbofan engine performance is critically sensitive to operating conditions, yet comprehensive frameworks that simultaneously assess exergo-economic, exergo-environmental, and lifecycle cost performance across realistic flight envelopes remain limited, particularly for Gulf-region climates. In this study, we present a comprehensive analysis of the exergo-economic, exergo-environmental, and lifecycle costings of five different configurations of two-spool and triple-spool turbofan engines. The analysis was carried out for a wide range of four operating conditions, namely ambient temperature, flight altitude, Mach number, and % relative humidity, with emphasis on the climate conditions likely to be found in the Gulf region. The computational models developed were validated against published data to confirm their reliability. It was found that fuel consumption was the most significant contributor to total lifecycle ownership cost, between 60 and 75% of hourly operating cost over a 20-year service period. Ambient temperature, Mach number, and Cruise altitude represented the most significant drivers of long-term economic performance, with % relative humidity having little effect. Exergo-economic analysis showed that the major cost mechanisms changed dramatically with operating conditions. Exergy destruction and component inefficiencies determined the costs at Takeoff, with capital investment being the dominant factor when cruising. Increase in both or either ambient temperature and altitude was shown to reduce cost rates but simultaneously reduced thermo-economic efficiency via higher specific exergy costs. However, increase in Mach number enhances both exergy output and cost-effectiveness, confirming that specific exergy cost is a more reliable indicator of true system performance than cost rate alone. The two-spool configurations show superior specific CO₂ emissions, with Case 3 recording the lowest emissions at Takeoff and Case 2 at Cruise. For exergy-based environmental indicators, Case 3 performs best at both Takeoff and Cruise, achieving the lowest environmental destruction coefficient and index, as well as the highest environmental benign index among all five configurations. These findings provide actionable guidance for engine selection, operational optimization, and sustainable propulsion system design. Full article

Surface congestion and taxiing delays at high-density airports increasingly constrain aviation sustainability, as ground-phase fuel consumption and emissions constitute a significant share of total airport emissions. Existing studies typically decouple air traffic flow management from ground resource scheduling, hindering coordinated optimization of punctuality, environmental benefits, and resource utilization. This paper proposes a multi-objective optimization method for runway-taxiway systems oriented toward air–ground collaborative decision-making, integrating Calculated Take-Off Time (CTOT) compliance constraints. A tri-objective mixed-integer programming model is formulated to minimize CTOT deviation, total taxiing time, and runway workload imbalance. A hybrid intelligent algorithm, SSA-SCA-NSGA-II, is designed with a bidirectional elite feedback mechanism to address this NP-hard problem. Validation uses real operational data of 58 departure flights during a peak period at Beijing Daxing International Airport. The results demonstrate that the proposed method achieves effective trade-offs on the Pareto front: CTOT compliance rate increased from 77.6% to 89.7–96.6%; total taxiing time decreased from 692 min to 551–635 min; and dual-runway utilization imbalance declined from 5.2% to 1.7–3.8%. These improvements translate into quantifiable sustainability gains: fuel consumption is reduced by 1425–3525 kg and CO₂ emissions by 4503–11,139 kg per peak hour, alongside a 19-percentage point improvement in punctuality that lowers passenger delay costs and reduces controller coordination workload. By simultaneously advancing environmental sustainability (carbon footprint reduction), economic sustainability (fuel and operational cost savings), and social sustainability (service punctuality and labor efficiency), the framework provides a measurable, monitorable, and policy-relevant decision-support tool for green airport surface operations aligned with sustainable development goals (SDGs). Full article

In the context of growing attention to environmental sustainability, emission reduction efforts increasingly involve all sectors, including agriculture. European “Stage” regulations (from Stage I in 2002 to Stage V in 2019) have progressively reduced regulated pollutants such as hydrocarbons (HC), nitrogen oxides (NO_x), particulate matter (PM), and carbon monoxide (CO). However, carbon dioxide (CO₂) emissions from agricultural tractors are not currently subject to specific legislation. This study assesses CO₂ emissions through their direct relationship with fuel consumption. Hourly and specific CO₂ emissions (g/kWh) were estimated using power and fuel consumption data from 877 tractors tested under OECD Code 2 procedures from the 1960s to the present. The same tractors were analyzed under two operating conditions: power take-off (PTO) dynamometer bench tests and drawbar tests, considering maximum power and rated engine speed. The four testing conditions were compared to assess differences in delivered power, fuel consumption, and CO₂ emissions. Fuel consumption was modeled through linear regression using power as the independent variable, while specific fuel consumption and fuel productivity were estimated using a nonlinear regression approach. The comparison between test conditions shows a reduction in delivered power of 21.2% when moving from the PTO dynamometer test at maximum power to the drawbar test at rated engine speed, accompanied by an 18.9% increase in specific CO₂ emissions. These findings indicate that operating conditions significantly influence tractor carbon emissions and suggest that assessments accounting for traction-related losses provide a more realistic estimate of tractor environmental performance than PTO dynamometer tests alone. The proposed approach may support the development of carbon-oriented mitigation strategies and future greenhouse gas reduction policies for agricultural mechanization. Full article

Proton exchange membrane fuel cell–battery hybrid power systems provide an effective solution to overcome the limited endurance of battery-powered multirotor unmanned aerial vehicles. However, the highly transient power demands of quadcopter platforms, combined with balance-of-plant losses and operational constraints, create significant challenges for reliable energy management. This study proposes a degradation-aware stress-mitigation model predictive control-based energy management framework to maximize mission endurance under realistic conditions. A control-oriented, physics-consistent model is developed using manufacturer polarization data from a 500 W Aerostak proton exchange membrane fuel cell. The model captures polarization behavior, balance-of-plant loads, battery dynamics, and direct current-bus power balance. The model predictive control strategy optimally allocates power by maintaining direct current-bus stability, regulating battery state-of-charge within safe limits, and constraining fuel cell power ramp rates to mitigate degradation. High-fidelity simulations are conducted under stochastic wind disturbances and mission-dependent load profiles, including takeoff, climb, cruise, and maneuvering phases. The results show continuous power delivery without unmet load demand. The hybrid system achieves a flight endurance of 220–224 min, consuming a total of 89.99 g of hydrogen at an average rate of 0.398–0.412 g/min, indicating a notable reduction under the considered operating conditions. Additionally, long-term analysis indicates that over 97% of initial endurance is preserved after 100 cycles, demonstrating robustness against fuel cell aging. An analytical real-time feasibility assessment further indicates that the control-oriented formulation is compatible with the computational resources of typical unmanned aerial vehicle-class onboard processors, while the integration of adaptive and robust predictive control techniques is identified as a direction for future work. Full article

A biomechanical model of the flight phase in the long jump was constructed to analyze the factors influencing performance. Time-series coordinate data of key joints, obtained through AI-based human pose estimation, were incorporated into the model. For Problem 1, vertical velocity and acceleration of the joints were calculated using the dynamic parameter framework, and reference values with adaptive thresholds were applied to precisely identify take-off and landing moments. For Problem 2, joint information was used to derive arm swing amplitude, take-off angle, and joint rate of change as metrics to characterize athletes’ movement patterns and enable comparison before and after training. For Problem 3, physical and kinematic features were integrated, and Random Forest, multiple linear regression, and Recursive Feature Elimination were employed to evaluate key determinants of long jump performance and provide targeted training recommendations. The first two models achieved R² of 0.9772 and 0.9526, respectively, indicating excellent predictive accuracy. Finally, for Problem 4, the Random Forest and regression models developed in Problem 3 were applied to predict the performance of an athlete following posture optimization training. Full article

Objective: Rapid vertical manoeuvres and intermittent vibration in autonomous electric vertical take-off and landing (eVTOL) aircraft can provoke pronounced psychological anxiety in passengers. To address this, we propose a closed-loop adaptive system that integrates an in-ear wearable sensor with dynamic regulation of the cabin microenvironment, enabling real-time monitoring of each passenger’s autonomic state and delivering individualised mitigation through a continuous sense–analyse–intervene–feedback loop. Methods: The system is built around a pair of custom in-ear modules that integrate dual-wavelength photoplethysmography (PPG; 525 nm green and 940 nm infrared), galvanic skin response (GSR), and a six-axis inertial measurement unit (IMU) sampled at 200 Hz. To suppress the 20–80 Hz vibration generated by the distributed electric propulsion system, a compliant silicone damping sleeve attenuates high-frequency components at the hardware level, while a Kalman filter fuses the IMU and PPG streams and an adaptive notch filter removes residual rotor harmonics. The pipeline raises the heart-rate-variability (HRV) signal-to-noise ratio (SNR) to 24.1 dB, with a Pearson correlation of 0.96 against a medical-grade chest strap. A hybrid CNN–LSTM network—two convolutional layers (32 filters each) followed by two LSTM layers (128 hidden units)—predicts impending anxiety from HRV time-domain features (RMSSD, pNN50) and frequency-domain features (LF/HF ratio), triggering intervention 8.2 s in advance on average. According to the predicted anxiety level (mild/moderate/severe), a fuzzy controller modulates transcutaneous auricular vagus nerve stimulation (1–5 mA), the binaural-beat frequency (4–8 Hz, theta band), and the cabin lighting colour temperature (2700–6500 K) in real time. The intervention parameters are continuously refined by SPSA-based stochastic optimisation of the HRV recovery rate (step size 0.01; updated every 30 s). Results: In a randomised controlled experiment conducted in a simulated flight environment (N = 50; aged 22–45 years; 1:1 sex ratio), the active group reached physiological recovery in 52.3 s on average, compared with 98.6 s for the sham-controlled group—a 47% reduction (Cohen’s d = 1.24, p < 0.001). User acceptance reached 94%. Conclusions: The proposed in-ear platform enables closed-loop adaptive regulation of anxiety in the eVTOL cabin and overcomes the limitations of conventional passive mitigation strategies. By combining vibration-tolerant physiological sensing with multimodal environmental control, the work offers a practical pathway for improving passenger experience in urban air mobility and provides a useful reference for human-factors standards governing autonomous aircraft. Full article

Currently, improving runway utilization under operational safety constraints has become a critical concern for small and medium airports. Existing research focuses primarily on landing-phase runway occupation time, while predictive studies on the takeoff phase remain limited. Analysis of 1749 Quick Access Recorder (QAR) records from ten airports reveals that departure runway occupation time is strongly correlated with ground speed at liftoff (0.72) and airport elevation (0.67) but weakly correlated with aircraft weight and meteorological conditions, providing guidance for feature engineering. To address the prediction of departure runway occupation time, this study proposes a TCN-FEP hybrid model. The model employs an enhanced Temporal Convolutional Network (TCN) module with multi-scale convolutions (kernel sizes 3, 5, 7) and dilated convolutions (rates 2, 4, 8) to capture multi-scale feature interactions, alongside a Feature Enhancement Projection (FEP) module that maps local features into a high-dimensional latent space for implicit relationship mining and global information integration. Experimental results demonstrate that the proposed TCN-FEP model achieves an MSE of 90.20, RMSE of 9.49, MAE of 5.84 s, MAPE of 3.80%, and R² of 0.97, outperforming Informer (MSE 117.95), Longformer (MSE 132.11), XGBoost (MSE 92.30), and LightGBM (MSE 91.45). Under 5% outlier injection, MSE increases by 7.9%, compared to 24.3% for LSTM and 18.4% for Informer. With 94% of prediction errors within ±5 s, the model’s accuracy may offer a useful reference for runway resource optimization at small and medium airports. Full article

Autonomous trajectory planning for electric Vertical Takeoff and Landing (eVTOL) Unmanned Aerial Vehicles (UAVs) faces the dual challenges of low-altitude environmental interference and limited onboard energy, which affects the reliability and safety of unmanned missions. To address these challenges, this paper develops the EA-TD3 autonomous trajectory planning framework for eVTOL UAV systems. First, a stochastic urban wind field model is established to simulate low-altitude interference. Then, by integrating eVTOL UAV battery discharge data from Carnegie Mellon University (CMU), a mapping relationship between maneuvers and energy consumption is identified to construct a nonlinear energy consumption model. Finally, an energy boundary penalty function is introduced into the TD3 algorithm to ensure that trajectory planning remains within battery safety margins. Experiments based on the parameters of the EH216-S platform show that EA-TD3 achieves a near 100.00% success rate under ideal conditions and outperforms benchmark algorithms while reducing average energy consumption by 11.6%. Under an energy constraint of 120 J, its success rate remains at 87.80%, which exceeds the performance of the DDPG, SAC, and standard TD3 algorithms. This study optimizes the autonomous trajectory planning of eVTOL UAV platforms in urban air mobility (UAM) to improve the energy perception and power management of the autonomous system. Full article

Background: Soccer aerial duels require rapid take-off, repeated-performance maintenance, and effective head–neck control under physically demanding conditions. This study examined the effects of caffeine (CAF), Rhodiola rosea (RHO), and their combination on repeated aerial duel performance and neck neuromuscular function in male collegiate soccer players. Methods: Ninety-six players were randomly assigned, in a double-blind, placebo-controlled, parallel design, to placebo control (CTR), RHO, CAF, or RHO + CAF groups (n = 24 each) for 4 weeks. CAF was acutely administered at 3 mg·kg⁻¹ before testing, whereas RHO was chronically supplemented at 2.4 g·day⁻¹. Outcome measures included countermovement jump height, early take-off impulse, repeated heading contact height, ball exit velocity, heading duel success rate, neck maximal voluntary isometric contraction, and session rating of perceived exertion (session-RPE). Results: Significant group × time or group × repetition effects were observed for CMJ height (p = 0.0034), early take-off impulse (p = 0.0007), and post-intervention repeated heading contact height (p < 0.0001), with additional significant effects across heading-specific, neck strength, duel-success, and perceived-load outcomes. CAF was mainly associated with improved take-off-related explosive performance and duel success, whereas RHO was mainly associated with lower perceived exertion and better maintenance of heading contact height during the later repeated trials. Combined RHO + CAF supplementation produced the broadest pattern of benefits across explosive output, ball-contact performance, duel success, and multidirectional neck strength. Conclusions: These findings suggest that, in male collegiate soccer players, CAF and RHO may contribute differently to repeated aerial duel-related performance, and their combination may offer broader sport-specific benefits under repeated high-intensity demands. Full article

With the increasing complexity of wildfire emergency response, the aerial emergency response system is imposing increasing demands on both safety and decision rationality of takeoff and landing site selection. Site selection decisions are influenced by multi-dimensional factors, including geographical location, meteorological factors, and operational safety considerations, resulting in a pronounced coupling of multiple factors in the decision-making process. However, existing studies primarily focus on spatial suitability evaluation or technical implementation, often relying on predefined indicator systems and independence assumptions, while lacking a systematic characterization of the influencing factor system and its interrelationships in takeoff and landing site selection. To address this gap, this study proposes a novel structured decision-making framework to systematically analyze and optimize the selection of takeoff and landing sites for bucket firefighting helicopters in wildfire aerial emergency response scenarios. First, a procedural grounded theory approach is employed to systematically identify the influencing factors associated with site selection, thereby constructing a traceable decision-making factor system. Second, fuzzy DEMATEL is applied to model the causal relationships and structural interdependencies among these factors. Finally, a cumulative contribution rate based on centrality is introduced to screen and optimize the decision indicators, resulting in a refined set of key decision indicators. The results reveal the structural roles of different influencing factors in site selection, reduce the reliance on experience-driven judgment, and reconceptualize the problem from traditional indicator weighting and ranking into a structured decision-making process involving multi-factor coupling. This provides systematic decision support for takeoff and landing site selection in wildfire aerial emergency response and establishes a foundation for subsequent spatial suitability analysis and case-based validation. Furthermore, the results are consistent with expert experience and practical operational constraints, indicating the potential applicability of the proposed method in real-world decision-making. Full article