Browse Topic: Optimization

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Deep Reinforcement Learning for Downlink User Scheduling in LEO Satellite Massive MIMO Systems2026-99-299010/19/2026
Low earth orbit (LEO) satellite communication system is expected to play a key role in the 6G wireless networks. Due to the limited communication resource and inter-user interference (IUI), the user equipments (UEs) must be well scheduled to maximize spectral efficiency. However, rapid channel variations and high mobility in LEO system make the conventional user scheduling strategies relying on instantaneous channel state information impractical. To address this challenge, this paper proposes a deep reinforcement learning (DRL) framework based on batch normalization in CrossQ algorithm to optimize the downlink user scheduling in a LEO satellite massive multi-input multi-output (MIMO) system. The scheduling task is modeled as a Markov decision process (MDP), where the observation space integrates high-dimensional features including large-scale fading (LSF) coefficients, channel matrix, and temporal features capturing the orbital progress of the satellite. Unlike traditional DRL methods, the CrossQ-based algorithm applies batch normalization (BN) within the critic networks to eliminate the need for slowly-moving target networks, fostering training stability and sample efficiency. A continuous priority score vector is mapped to discrete scheduling decisions through a top-z selection logic, allowing the agent to effectively identify spatially orthogonal user subsets to suppress the IUI. To guide the agent toward the true optimization direction, a differential reward mechanism is designed. Simulation results demonstrate that the proposed CrossQ-based scheduler consistently achieves nearly optimal performance at different signal-to-noise ratios (SNR), reaching at least 91% and 94% of the Monte Carlo search ceiling under different user densities. Moreover, the trained model provides scheduling strategy in millisecond inference time, making it a practical and robust solution for future 6G non-terrestrial networks.
Deng, Chaojun, Zhong, Yun, Meng, Xiangxu, Li, Wenfeng, Zhao, Kanglian
A Deep Learning Approach for Space Target Pose Estimation Based on Multi-Task Joint Training2026-99-298110/19/2026
The growing number of space objects raises collision risks and demand for on-orbit maintenance. Since most servicing tasks require observing the target, relative pose estimation is key for debris removal and satellite servicing. The complex orbital environment poses significant challenges for conventional feature extraction networks.. To address these challenges, this paper presents a multi-task convolutional neural network for space target pose estimation. The network uses a MobileNetV3-based encoder to extract image features and an Adaptively Spatial Feature Fusion (ASFF) module for adaptive multi-scale feature fusion. Although MobileNetV3 and ASFF have rarely been combined in the aerospace field, their combination offers significant advantages in low parameter count and reduced computational load, showing strong potential for spacecraft pose estimation. Two parallel prediction heads are employed: an EfficientPose head consisting of classification, bounding box regression, rotation matrix regression, and translation vector regression, together with a Heatmap head. This design leads to more robust and accurate pose estimation. The loss function follows a multi-prediction-head joint approach, where each component is weighted to form the final loss for multi-task optimization. The network is trained, validated, and tested on a simulation dataset of the DAWN satellite built with Unity. The dataset includes illumination variations, large distance changes, and multi-view target appearances, thus capturing the complex scenes typical of vision-based recognition tasks. Experimental results show that after 40 training epochs, the model achieves an average position error of about 0.05 m and a rotation error of about 0.8° on the test set. These results demonstrate the effectiveness and accuracy of the proposed network for space target pose estimation.
Wang, Wenzhi, Zhang, Zexu, Zhang, Fan, Song, Zhuo
An Electromagnetic-Thermal-Mechanical Coupling Model of the Three-Coil Wear Debris Sensors for Wide-Temperature and Kinematic Analysis2026-99-298510/19/2026
Real-time monitoring of metal wear debris in lubricating oil is crucial for accurately assessing aerospace equipment health and preventing catastrophic mechanical failures. Although three-coil differential inductive sensors are widely used due to their high sensitivity and robust structure, their detection performance inevitably degrades under the influence of multi-physics coupling effects inherent in complex industrial environments. Existing analytical models were primarily limited to single electromagnetic field simulations, failing to capture the intricate electromagnetic-thermal-mechanical interactions, which reduced analytical accuracy and hindered the optimization of sensor designs for extreme conditions. To overcome these challenges, this study established a comprehensive parametric simulation model to conduct a rigorous electromagnetic-thermal-mechanical coupled analysis of the differential sensor. Specifically, the research analyzed the underlying mechanisms by which wide temperature variations from -55 °C to 200 °C and specific debris kinematics exert varying degrees of influence on the induced voltage. Quantitative results indicated that both the isolated thermal and mechanical fields altered the voltage by a maximum of 0.649%, demonstrating that the sensor maintains a relatively stable response across extreme thermal gradients. Furthermore, the analysis revealed that increased debris velocity decreased the induced voltage amplitude due to the intensified motion-induced eddy current effects. Notably, in complex multiple debris scenarios, the strong dominant signals generated by ferromagnetic wear debris severely masked the weaker responses from non-ferromagnetic ones, indicating a substantial missed detection risk under mixed contamination conditions. Ultimately, this research successfully filled the critical simulation gap for evaluating these sensors under multi-physics coupling, providing robust theoretical guidance for their reliable deployment in complex industrial applications.
Shi, Xinfa, Duan, Chenhao, Zhen, Huanyi, Zhao, Yihe, Li, Min, Wang, Xiaozhang, Zhang, Qing, Li, Zhikang, Zhao, Libo
Low-Cost Ground Station Network Siting for Geostationary Space-to-Ground Laser Communication2026-99-295110/19/2026
The deployment of multi-link ground station networks for geostationary space-to-ground laser communication is constrained not only by local weather conditions but also by infrastructure accessibility and engineering cost. Existing ground station siting studies have mainly emphasized cloud-related meteorological indicators and site diversity, while giving insufficient attention to deployment-oriented cost factors. As a result, meteorologically favorable solutions may still be expensive to implement in practice. To address this issue, this paper investigates the low-cost siting problem of a ground station network under a target network availability constraint and proposes a two-stage method for a multi-link, single-telescope scenario. Under the adopted assumption, each ground station can provide at most one available link at any given time, so the number of available network links is equal to the number of clear-sky available stations. The network availability is therefore defined as the probability that at least a required number of links are simultaneously available. Using half-hourly cloud mask data from the Himawari 8 geostationary meteorological satellite for the year 2023, site-neighborhood cloud metrics and inter-site weather correlation metrics are constructed. A data-center clustering and distance-decay model is then introduced to build an infrastructure impact cost field. On this basis, nationwide optimization is carried out through candidate construction and final subset selection. A baseline method without data-center factors is also evaluated under the same total-cost definition. The results show that, under the same target network availability requirement, the proposed method can effectively reduce total deployment cost. This indicates that incorporating data-center factors during siting improves the engineering tradeoff of multi-link geostationary laser communication ground station networks and provides a reproducible route for low-cost planning under service availability constraints.
Zhang, Chi, Zhao, Juntong, Li, Wenfeng, Zhao, Kanglian
ARET-ALSTM: Intelligent Beam Prediction and Dynamic Compensation Network for Aerial Base Stations under Non-Stationary Turbulent Conditions2026-99-295410/19/2026
With the unprecedented surge in demand for flexible deployment and ultra-high throughput in next-generation wireless networks, airborne base stations (ABS), particularly unmanned aerial vehicles (UAVs), have become emerged as indispensable mobile communication infrastructure. To facilitate high-gain directional transmission within the millimeter-wave band, ABS frequently employ massive MIMO antenna arrays. However, under random disturbances caused by non-stationary turbulence, the system faces a severe physical bottleneck: weak anti-interference capability due to narrow beamwidth, which can easily lead to a sharp drop in link capacity. Addressing the two major shortcomings of existing deep learning prediction schemes—susceptibility to sensor noise interference and negative capacity performance shifts during stable periods, and response lag due to memory inertia during sudden turbulence periods—this paper proposes an ARET-ALSTM active compensation network based on SNR-aware multimodal attention, agile reset and adaptive event-triggered mechanisms. This network incorporates an event-triggered mechanism governed by a dynamic physical variance threshold, adaptively executing compensation during stable intervals to prevent excessive overshoot and redundant computational overhead. Simultaneously, it employs an agile feature reset mechanism based on an adaptive sliding window for concept drift detection, forcibly discarding old memories and reconstructing attention weights during sudden wind shear, achieving millisecond-level ultra-fast adaptation and directly driving network optimization with the goal of maximizing communication capacity. Simulation experiments show that, under different array configurations ranging from 2×2 to 16×16 and complex flow conditions, ARET-ALSTM successfully overcomes the physical capacity collapse limitation under large antenna scales, and its tracking agility and robustness are significantly better than benchmark algorithms such as Transformer, RNN and traditional Kalman filtering.
Tu, Jiajun, Zhou, Jihua, Li, Yanhang, Zhao, Tao, Huang, Hua, Wen, Yuanbin, Guo, Ziwen
Multi-Objective Optimization Design Method for MEO Constellations Oriented towards LEO Constellation Coverage2026-99-293110/19/2026
Aiming at the challenges faced by Low Earth Orbit (LEO) constellations, such as difficulties in cross-shell communication, complexity in global management and control, and highly dynamic network topologies, this paper evaluates the introduction of a Medium Earth Orbit (MEO) constellation as a management layer to optimize the coordination and servicing of the underlying LEO constellation. The core contributions of this work are twofold. First, a coverage performance analysis method based on Voronoi diagrams and Delaunay triangulation is adopted to evaluate the coverage of MEO constellations over LEO constellations. Second, a four-dimensional multi-objective fitness function is constructed, comprehensively considering launch cost, communication delay, coverage performance, and space security. On this basis, a multi-objective genetic algorithm is employed to optimize the key parameters of a Walker-type MEO constellation, including orbital altitude, orbital plane count, and satellites per plane. Simulation results show that, under the condition of a LEO constellation altitude of 600 km, highly fit MEO configuration parameters are obtained. STK simulations verify that this configuration can achieve complete and dynamic coverage of the underlying LEO constellation. This study provides a systematic theoretical framework and an efficient algorithmic tool for solving the configuration optimization problem of the MEO layer in the "LEO-MEO" architecture.
Li, Ruiyao, Cui, Hutao
Research on Temperature Hysteresis Error Suppression Technology for Capacitive MEMS Accelerometers2026-99-296810/19/2026
The performance of MEMS accelerometers is highly sensitive to temperature variations, manifesting as zero drift, sensitivity drift, and hysteresis error. This paper presents a low-temperature-drift, low-noise interface ASIC for capacitive MEMS accelerometers. The circuit employs an analog closed-loop architecture comprising a small-capacitance front-end detection circuit, a variable gain amplifier (VGA), a correlated double sampling (CDS) circuit, a PID controller, and a feedback module. To suppress temperature-induced errors, key optimizations are implemented: the charge amplifier employs active and passive charge compensation techniques to reduce common-mode offset drift; the VGA uses switched feedback resistors for gain adjustment; a Class-AB unity-gain buffer enhances driving capability while isolating load interference; and a high-order compensated bandgap reference with pre-regulation achieves low temperature drift and high power supply rejection. Post-layout simulations demonstrate a system sensitivity of approximately 2.27 V/g, a sensitivity temperature coefficient of 74.55 ppm/°C, and a 1σ zero-bias temperature drift of 1.757 mg. Experimental results over the full temperature range from -45°C to 85°C show an output hysteresis error of only 0.6 mg. These results validate the effectiveness of the proposed temperature hysteresis error suppression technique, providing a valuable reference for the design of high-precision MEMS accelerometer interface circuits.
Zhao, Weiqiao, Liu, Guowen, Hao, Zhiwen, Li, Zhaohan, Gao, Naikun, Zhang, Wenbo, Fu, Qiang, Yin, Liang
Pose Tracking and Formation Keeping Control of Spacecraft Formations under High-Order Control Barrier Functions2026-99-297610/19/2026
Aiming at the constraint issues of adjacent spacecraft solar panel collision avoidance and formation keeping in the pose tracking control of spacecraft formation, this paper proposes a distributed relative position and attitude cooperative control strategy on the basis of high-order control barrier functions. First, nominal control strategies for relative position and attitude tracking using the sliding mode control method to achieve high-precision tracking of relative motion are de-signed. Then, high-order control barrier functions suitable for solar panel collision avoidance and spacecraft formation keeping constraints are constructed. The control strategy constraints satisfying safety specifications are derived based on the forward invariant set theory. Finally, a quadratic programming optimization framework is established by combining the nominal control strategies and constraint conditions and then we obtain the optimal control strategy by solving this framework. Numerical simulations verify that the designed control scheme achieves precise tracking of relative position and attitude for spacecraft formation, avoids collisions between solar panels of adjacent spacecraft and prevents excessive loosening of the formation configuration.
Zhu, Ziqi, Qin, Wenyu, Lyu, Yueyong
Adaptive Optimization of Fault Diagnosis Models Based on Reinforcement Learning and Robustness Quantification Feedback2026-99-299510/19/2026
Spacecraft operate in a highly complex in-orbit environment. Telemetry data from power systems is often plagued by multi-source disturbances such as plasma noise, electromagnetic interference, and communication packet loss, which places very strict demands on fault diagnosis model reliability. Using dynamic disturbance test manifolds and the comprehensive robustness quantification index (RQI) for evaluation, we found a critical vulnerability: baseline models such as the joint loss enhanced global sparse attention transformer (JLE-GSA-Transformer) perform well in resisting continuous signal disturbances, but their performance drops sharply when temporal structures are disrupted due to data missingness. This finding highlights that static diagnostic models have fragile robustness boundaries when operating under extreme non-stationary conditions. To solve this problem, this paper puts forward an adaptive optimization method that combines deep reinforcement learning (RL) with quantitative robustness feedback. By making use of the dynamic disturbance manifold, this method embeds the RQI into the reward function of the RL algorithm. Through continuous updates to the agent’s policy, it drives the adaptive adjustment of key parameters in the JLE-GSA-Transformer, mainly including the coding structure of global sparse attention (GSA) and the discriminative boundaries of temporal cosine-center joint loss. This closed-loop mechanism can significantly improve the stability, adaptability, and long-term reliability of the model when it works in complex environments with multi-source disturbances.
Liu, He, Wei, Cheng, Zhang, Zexu
Analysis of MIMO Channel Characteristics under Large Spatio-Temporal Scales Based on Distribution Formula2026-99-296210/19/2026
In recent years, the global demand for high-rate, high-reliability, and wide-coverage wireless communications has continued to grow. Traditional satellite communications are faced with bottlenecks such as spectrum scarcity, insufficient single-satellite capacity, and poor adaptability to highly dynamic channels. Relying on the advantages of wide-area coverage and multi-satellite cooperation, low Earth orbit (LEO) satellite constellations can construct distributed satellite MIMO systems and form virtual large-scale antenna arrays, significantly improving system capacity and reliability. However, such systems are characterized by large spatiotemporal scales, and their channels exhibit strong heterogeneity in dimensions such as delay, frequency, and polarization, which are difficult to accurately evaluate using existing methods. To this end, this paper proposes a large-spatiotemporal-scale channel characteristic analysis method for distributed LEO satellite MIMO systems. A satellite-terrestrial full-link channel model is established, and quantitative characterization of multi-dimensional heterogeneity is realized based on statistical boundary analysis. Combined with the H3 global grid system, large-scale simulations are carried out to statistically analyze the distribution laws of delay differences, frequency offsets, and polarization angle deviations under different user configurations. The unique value of this analysis method lies in that its output results can be used to determine the upper limit of buffer capacity required for the receiver to complete coherent combining of multi-channel signals and the frequency offset correction range required for signal acquisition, provide a quantitative basis for optimizing satellite set selection, and offer a solid theoretical model and data support for performance evaluation and parameter design of multi-satellite cooperative systems.
Lu, Yi, Hou, Yucheng, Su, Xingyu, Yu, Xinyue, Jiang, Yanzi, Zhang, Jie, He, Wenxi
Move Blocking Event Triggered Model Predictive Control of Spacecraft Formation2026-99-295610/19/2026
This paper addresses the problem of formation reconfiguration for deputy spacecraft flying in a spatial circular formation around a chief spacecraft. To improve the real-time applicability of formation control under onboard computational limitations, a reconfiguration method is proposed by integrating Tschauner–Hempel (T-H) relative dynamics, Event-Triggered Model Predictive Control (ET-MPC), and a Move Blocking strategy, while explicitly considering collision-avoidance constraints. Based on the T-H relative dynamic model, an MPC framework is established to generate control inputs for the reconfiguration process. To reduce the computational burden of conventional MPC, an event-triggering mechanism is introduced so that the optimization problem is solved only when necessary, rather than at every sampling instant. Meanwhile, a Move Blocking strategy is employed to reduce the number of online decision variables by constraining successive control moves over predefined intervals. In addition, to simultaneously address inter-spacecraft collision avoidance and limited-thrust constraints, an optimization model with binary variables is formulated. To evaluate the proposed approach, four simulation cases, namely MPC, ET-MPC, MB-MPC, and MBET-MPC, are conducted under the same reconfiguration scenario. Simulation results show that all four methods can successfully accomplish the formation reconfiguration task while satisfying the minimum safe-distance requirement. Moreover, the methods incorporating Move Blocking reduce the computation time required for solving each individual optimization problem, while the event-triggered schemes significantly reduce the number of optimization updates compared with the corresponding time-triggered methods, thereby further lowering the overall computational cost. The results demonstrate that the proposed method provides an effective and feasible solution for real-time formation reconfiguration and control on resource-constrained spacecraft platforms.
Zhang, Yuning, Zhang, Zexu, Liang, Jintang
Rapid Trajectory Optimization for Mars Aerobraking Campaigns2026-99-294010/19/2026
Aerobraking is a key technology for reducing propellant consumption in deep space exploration missions. Targeting the Mars aerobraking trajectory optimization problem, this paper proposes a rapid trajectory optimization method based on a high-precision analytical approximate solution. First, a Mars aerobraking dynamics model is established. Based on the assumptions of a linear flight path angle and a linear altitude rate, a high-precision analytical approximate solution for Mars aerobraking trajectories is derived. Simulation results verify that the proposed analytical solution satisfies the accuracy requirements for Mars aerobraking trajectory optimization. Based on this analytical solution, the state transition function of the aerobraking trajectory optimization problem is reconstructed, and the Sequential Quadratic Programming (SQP) algorithm is employed for the optimization. Numerical simulation results demonstrate that the proposed method requires less than one minute to optimize an aerobraking campaign consisting of 80 atmospheric passes. Compared with traditional optimization methods, it exhibits significant advantages in computational efficiency when dealing with aerobraking trajectory optimization problems characterized by long durations and multiple velocity pulses.
Ding, Yuancheng, Dong, Jie, Cui, Hutao, Chen, Zheng
A Genetic Algorithm-Based Autonomous Mission Planning Method for Limited-Thrust Modes2026-99-294810/19/2026
To meet the requirements of rapidity, economy and efficiency for space debris removal missions and space debris governance missions, this study designs an autonomous mission planning scheme under finite thrust conditions, to achieve the disposal of multiple target space debris within a single orbital period. The scheme decomposes the planning problem into two sub-problems: mission object sequence optimization and transfer orbit design, and establishes mathematical models from the perspectives of global mission scheduling and local trajectory optimization respectively. An improved genetic algorithm is adopted for solution, to ensure the optimization of resource utilization under fuel constraints. This study systematically elaborates the theoretical basis, optimization indicators and constraint modes of the planning method, constructs a complete technical framework, and verifies the correctness and effectiveness of the method through numerical simulations, providing methodological guidance for the autonomous implementation of in-orbit rendezvous missions.
Yu, Dateng, Guo, Peng, Li, Ziyi
Trajectory Planning for Heterogeneous Multi-Satellite Cooperative Proximity Operations2026-99-291410/19/2026
This study addresses the challenges in heterogeneous multi-satellite cooperative proximity operations, including functional heterogeneity, strong temporal coupling, and difficulties in unified planning. A functionally layered framework composed of observation, support, and servicing satellites is constructed. Considering the distinct mission characteristics of different satellite types, differentiated decision variables, objective functions, and constraints are designed, and a particle swarm optimization algorithm is employed for solution. Simulation results demonstrate that the proposed method can generate feasible cooperative trajectories satisfying constraints such as safety distance, temporal coordination, communication links, and mission windows, while maintaining system stability and operational safety. Parametric analysis indicates that mission duration, minimum access distance, and support effectiveness significantly affect system performance. Through functional decomposition and cooperative optimization, the system achieves an effective trade-off among different preferences, providing theoretical support for the mission design and control of heterogeneous multi-satellite proximity operations.
Xiao, Jinzhao, Zhang, Jin
Friction Dynamics Modeling and System Simulation of Large-Angular-Momentum Flywheels for Satellites2026-99-296410/19/2026
In response of the stringent requirements of high-resolution optical remote sensing satellites for attitude control stability and micro-vibration environment, this study focuses on the friction dynamics modeling and simulation analysis of large-angular-momentum flywheels across a wide speed range. To quantify the impact of flywheel friction disturbances on satellite attitude pointing accuracy, friction torque characteristic data were first acquired through ground testing over the full speed range, and a high-fidelity static fitting mathematical model was constructed using the least squares method. To further elucidate the nonlinear friction mechanisms in the low-speed regime—particularly during speed zero-crossings—the LuGre dynamic friction model was introduced, and its core parameters were identified and optimized. Comparative results demonstrate that the model exhibits high-precision characterization capabilities, with a deviation of less than 0.0015 Nm from measured data within the critical low-speed range of ±50 rpm. Subsequently, the established friction model was integrated into a satellite attitude control closed-loop system for digital simulation to quantitatively evaluate the disturbance of friction torque on system steady-state accuracy. The results indicate that under the closed-loop control compensation mechanism, the impact of flywheel friction on satellite attitude pointing accuracy is effectively suppressed, with the attitude angle deviation maintained within 0.002°, thereby meeting the engineering requirements for high-precision remote sensing imaging. This research provides a theoretical basis and data support for the optimization design of flywheel systems and the refined design of satellite attitude controllers.
Hu, Yuewei, Liang, Zhulin, Lu, Ming, Zhang, Pengbo, Hu, Xiaozhen
Design and Performance Analysis of a Hilbert Fractal Antenna for Corona Discharge Detection2026-99-290310/19/2026
Fractal antennas have attracted considerable attention due to their compact geometry and favorable electromagnetic characteristics, particularly in broadband and multiband applications. In this paper, a fourth-order Hilbert fractal antenna is designed and analyzed for radio frequency signal reception in the 200-300 MHz band. The design is motivated by the spectral characteristics of electromagnetic radiation generated during corona discharge, whose primary energy is concentrated within this frequency range. The proposed antenna is optimized in terms of return loss and bandwidth to ensure effective signal reception. Simulation results demonstrate that the antenna exhibits satisfactory impedance matching and achieves stable performance within the target frequency band. Based on the optimized design, a prototype is implemented and evaluated through laboratory experiments. Experimental results indicate that the proposed antenna can effectively capture radio frequency signals associated with corona discharge, demonstrating reliable non-contact detection capability. The study provides a feasible antenna design approach for corona discharge monitoring and offers potential applications in avionics system health management and electromagnetic sensing.
Hei, Guoyu, Li, Shidong, Wu, Yang, Su, Zhenxia, Ye, Hongyan
NGO-Based Orbits Optimization for Impulsive Transfers between DRO and Halo Orbits2026-99-293910/19/2026
This research investigates the design of fuel-saving orbital transfer trajectories for spacecraft departing from distant retrograde orbits (DRO) toward Halo orbits around the Earth-Moon L2 libration point. Relevant trajectory designs hold practical value for future lunar space station projects and deep space exploration undertakings. Previous research commonly adopted indirect modeling methods or classic evolutionary algorithms to acquire transfer solutions, yet these techniques show limited performance in multi-body dynamic systems, particularly unsatisfactory convergence efficiency. To address this defect, this paper proposes an improved direct optimization strategy based on the Northern Goshawk Optimization (NGO) algorithm. All numerical analyses are conducted within the circular restricted three-body problem (CR3BP) framework, which accurately reflects gravitational features in cislunar space while avoiding overly complicated calculation processes. The departing and target orbits are discretized mathematically, and the whole transfer mission is defined as a constrained nonlinear optimization problem aiming to minimize total velocity variation. The adopted algorithm balances global exploration and local exploitation rather than prioritizing a single search mode. This balanced mechanism effectively lowers the risk of falling into local optimal solutions, a prevalent flaw of conventional optimization methods. Precise dynamic modeling is also implemented: canonical transformation is applied to reorganize dynamic governing equations for simpler computation, and differential correction technology is utilized to optimize preliminary trajectory results generated by NGO iteration. Multiple tests with diverse initial values and mission constraints are carried out to verify the method’s stability. Simulation results prove that the proposed strategy maintains steady performance under external interference and stably yields qualified feasible trajectories. Overall, the Northern Goshawk Optimization algorithm proves applicable for high-precision orbital transfer optimization. This study also provides valuable references for low-energy trajectory planning in cislunar space.
Huang, Jiaxin, Zhang, Zexu, Liang, Jintang, Wang, Yiyu
Power Allocation for Uplink RSMA in LEO Satellite Systems2026-99-292110/19/2026
This paper focuses on power allocation for RSMA-enabled uplink transmission in LEO satellite systems. A representative cluster is selected for analysis, where the terminal with the best channel quality serves as the target user and transmits its information by means of two split streams. The remaining users are treated as weak users sharing the uplink access structure. Since satellite-ground links are affected by channel estimation errors and propagation uncertainty, the available channel state information (CSI) may deviate from the actual channel. To improve the reliability of uplink transmission, a bounded CSI error model is adopted, and the decoding conditions for different successive interference cancellation (SIC) stages are formulated in a robust form. On this basis, the target-user rate is optimized under transmit-power and decoding-reliability constraints. The formulated problem is difficult to solve directly because the power variables are coupled with the SINR and rate expressions. Therefore, a successive convex approximation (SCA) method is employed to transform the original problem into a series of solvable convex subproblems. The obtained results confirm that the proposed scheme brings a clear target-user rate gain over the NOMA benchmark under the same simulation setup. The results show the effectiveness of robust RSMA power allocation for uplink interference management in LEO satellite systems.
Yu, Xiaodi, Jin, Hang, Song, Yu, Hong, Tao, Ding, Xiaojin, Zhang, Gengxin
Noise Analysis and Circuit Optimization for the Drive Loop of Resonant Accelerometers2026-99-296710/19/2026
Resonant MEMS accelerometers are core inertial devices in precision measurement fields such as inertial navigation, aerospace, and seismic monitoring, characterized by frequency signal output, strong anti-interference capability, and high-precision potential. With the growing demand for sub-microg-level measurement accuracy in high-end industrial and scientific applications, noise suppression and detection sensitivity optimization have become key research topics for resonant accelerometers. This paper conducts a systematic modeling study on resonant accelerometers, analyzes their mechanical structure and acceleration sensing mechanism based on the force-frequency effect of double-ended tuning forks, and establishes corresponding mathematical models. Non-ideal noise models of the mechanical structure and drive loop are constructed separately, and key noise source analysis identifies mechanical thermal noise and 1/f noise of the front-end charge amplifier as the main contributors to system noise. To solve these problems, a dedicated low-noise charge amplifier is designed for the drive loop. Simulation results show that the input-referred noise of the low-noise operational amplifier used in the charge amplifier reaches 18.804 nV/√Hz @ 50 kHz. When applied to the drive loop and verified through tape-out testing, the system achieves a phase noise better than -119.904 dBc/Hz @ 20 Hz offset from the resonant frequency, with a corresponding acceleration noise density of approximately 0.477 μg/√Hz @ 20 Hz.
Cai, Hebin, Li, Zhaohan, Zhang, Qiukun, Liu, Guowen, Zhang, Wenbo, Yin, Liang, Gao, Naikun, Fu, Qiang
Optimal Low-Thrust GEO-to-DRO Transfers via a Multi-Layer Homotopy Strategy for Cislunar Applications2026-99-293710/19/2026
Low-thrust transfers from Geostationary Earth Orbit (GEO) to Distant Retrograde Orbit (DRO) are numerically difficult to compute because the underlying two-point boundary value problem is strongly affected by nonlinear cislunar dynamics, discontinuous control behavior, and the high sensitivity of indirect methods to the initial costates. Within the framework of the Earth-Moon Circular Restricted Three-Body Problem (CR3BP), this paper formulates an indirect optimal control model for GEO-to-DRO low-thrust trajectory design and develops a multi-layer homotopy strategy to improve the solvability of the problem. In the minimum-fuel formulation, the optimal throttle generally follows a bang-bang structure, which makes the associated shooting equations highly sensitive and often causes convergence failure. To reduce this difficulty, a smoothing homotopy is first introduced into the performance index, allowing the solution to evolve continuously from a minimum-energy problem with smoother control characteristics toward the target minimum-fuel problem. On this basis, a thrust continuation procedure is adopted to transfer the converged solution from an auxiliary high-thrust case to the desired low-thrust regime. However, when the conventional discrete continuation scheme becomes ineffective in a local thrust interval, a nonlinear intra-interval thrust homotopy combined with pseudo-arclength continuation is further employed to pass through the problematic region and maintain continuous tracking of the solution branch. Numerical results show that the proposed method can successfully overcome the local breakdown encountered in classical continuation and eventually obtain the minimum-fuel optimal transfer trajectory from GEO to DRO. The study indicates that the proposed framework enhances the robustness and continuity of indirect optimization for low-thrust transfers in the CR3BP, and it provides an effective computational approach for the design of cislunar transfer trajectories.
Liang, Jintang, Zhang, Zexu, Wang, Yiyu, Mao, Yicheng, Li, Yingshuo
Adaptive Parameter Optimization for BP/LTP in Deep Space Communication Based on a Deep Q-Network2026-99-299610/19/2026
This study addresses the limited adaptability of Bundle Protocol/Licklider Transmission Protocol (BP/LTP) parameter configuration in deep-space communication environments through a cooperative optimization mechanism that operates under dynamic link conditions and diverse service demands. Conventional BP/LTP parameter settings typically rely on fixed thresholds or empirical rules, assuming relatively stable link states. However, in practical deep-space missions, the bit error rate, propagation delay, and available transmission rate all exhibit significant temporal variability. Moreover, variations in data size and reliability requirements further exacerbate the performance degradation caused by parameter mismatches. Owing to the multi-layer encapsulation and resource coupling of bundle size, block size, segment size, and the number of sessions, inappropriate parameter configurations can result in increased acknowledgment and retransmission overhead, underutilized link capacity, and prolonged end-to-end delivery latency. Traditional approaches are thus inadequate for achieving holistic, real-time, and joint optimization in such dynamic environments, necessitating adaptive parameter tuning mechanisms with link-state awareness. This paper formulates BP/LTP parameter optimization as a sequential decision process and proposes a method based on a Deep Q-Network (DQN) for adaptive parameter configuration. The state space includes transmitted data size, bit error rate, one-way propagation delay, and available transmission rate. Based on this state information, the DQN selects the bundle size and LTP block size from predefined candidate sets. The LTP segment size is derived from a mapping rule related to link conditions, and the number of concurrent sessions is calculated online using an analytical model. The method is evaluated on a satellite–ground simulation platform that consists of a BP/LTP sender, a BP/LTP receiver, and a computing unit for DQN parameter optimization. It is compared with three fixed parameter configurations in 36 experimental scenarios. The results show that, compared with the fixed configurations, the proposed method reduces end-to-end delivery latency, improves link utilization efficiency, and maintains more stable transmission performance under different link conditions and data scales.
Han, Yingxiang, Bian, Mingyue, Chang, Xiang, Cao, Suzhi, Yan, Lei
Trust-Aware Deep Reinforcement Learning for Access Control in Satellite Communication Systems2026-99-293610/19/2026
The rapid evolution of satellite communication systems enables direct network access for terrestrial users via portable devices, but also introduces critical security challenges in access control. In particular, identity hijacking attacks allow malicious users to exploit legitimate access channels, leading to inefficient occupation of scarce satellite resources. Existing approaches mainly depend on trust evaluation or heuristic-based decision strategies, which are inadequate for adapting to dynamic environments. To address this issue, this paper proposes a trust-aware deep reinforcement learning (DRL) framework for satellite access control, which jointly considers user behavior evaluation and decision optimization. Specifically, a Bayesian trust evaluation model is first developed to quantify the credibility of user behavior by incorporating historical interaction records as prior information. Unlike conventional static trust schemes, an adaptive mechanism is introduced to enhance sensitivity to behavioral dynamics. Building upon the trust assessment, the satellite access process is formulated as a Markov Decision Process (MDP), where system states integrate channel conditions, user trust levels, and service demands. A Deep Q-Network (DQN) is then employed to learn optimal admission and scheduling policies that maximize long-term system utility under resource constraints. Simulation results demonstrate that the proposed method significantly outperforms conventional greedy and threshold-based strategies. In particular, it effectively mitigates the impact of malicious behaviors and improves system utility by over 80%, while achieving more efficient resource utilization in dynamic environments.
Zhou, Qihao, Liu, Wei, Zhang, Hongyuan, Chen, Te, Wang, Yuqi
PPO-Based HAP Deployment for Throughput Maximization in SAGIN2026-99-292810/19/2026
Space-Air-Ground Integrated Networks (SAGINs) have attracted extensive attention as an effective solution for providing worldwide communication services and supporting diverse transmission requirements. Nevertheless, the highly dynamic wireless environment and fluctuating user distribution introduce considerable difficulties for efficient deployment of High-Altitude Platforms (HAPs). This work studies a throughput enhancement problem in a SAGIN architecture composed of a Low Earth Orbit (LEO) satellite, multiple HAPs, and terrestrial users. Considering practical communication constraints such as beam scheduling and subchannel assignment, the objective is to improve the overall data transmission capability through optimized HAP positioning. By interacting with varying network states, the proposed learning framework agent continuously learns suitable deployment policies for HAPs, thereby improving transmission performance and spectrum utilization efficiency. Numerical evaluations indicate that the policy optimization strategy achieves superior throughput performance and communication efficiency compared with traditional fixed deployment approaches.
Wang, Yin, Zhou, Keyan, Shao, Shuai, Zhang, Bin, Chen, Peifu, Wang, Jianfeng
Optimization of Low-Altitude Logistics Scheduling in Plateau Mountainous Areas: A Case Study of A.tsaoko Transportation in Nujiang Prefecture2026-99-293810/19/2026
Addressing the difficulties in logistics and transportation in Nujiang Prefecture’s rugged topography,where is primarily composed of high mountains and valleys. this study tackles the high expenses, the low transportation efficiency, and substantial cargo losses associated with conventional transport modes. The research adopts a dual-track approach: first, it develops integrated infrastructure frameworks and intelligent monitoring and dispatch systems; second, by conducting field research, performing cost factor analysis, constructing models, and carrying out on-site transportation verification, the research aims to optimize the end-to-end logistics efficiency of low-altitude delivery for A.tsaoko in the region. Through on-site verification, it is estimated that the low-altitude logistics of A.tsaoko in Nujiang Prefecture can save more than 30%, reduce labor hours by more than 80%, and decrease transportation losses by more than 10%.
Chen, Xiaoguang, Sun, Xiaoguang, Shu, Dan, Shi, Long, Sun, Liang, Feng, Chen, Liu, Haitao
Fine-tuned Large Language Model Enhanced Nonlinear Model Predictive Control for Spacecraft Attitude Stabilization2026-99-296610/19/2026
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Guo, Zili, Gao, Sihong, Liu, Yuhan, Wang, Pengyu, Guo, Yanning
Simulation Study on the Influence of Acceleration on Mass Flow Distribution of Supercritical Carbon Dioxide inside Complex Capillary Pipeline Structures2026-99-296110/19/2026
Supercritical carbon dioxide (sCO2) power cycles present promising application prospects in spacecraft and mobile energy systems owing to their outstanding advantages including high thermal efficiency and compact system structure. As a key component of sCO2 heat exchange systems, the complex capillary pipeline structure significantly influences heat transfer performance and system stability through its internal flow distribution uniformity. In this study, a multi-physics numerical simulation approach is employed to establish a complex capillary pipeline model with four compartments labeled A, B, C and D, aiming to explore the effects of different acceleration (overload) conditions on internal flow distribution. Various acceleration cases are considered, such as zero gravity, horizontal placement (0, 1g, 0), vertical placement (-1g, 0, 0), (0.15g, -0.7g, 0), (5g, -3g, 0) and (8g, -3g, 0). Meanwhile, the regulation effect of orifice plates on flow distribution is analyzed. The results demonstrate that both the magnitude and direction of acceleration exert remarkable impacts on the flow distribution ratio within the pipeline. Under vertical acceleration, flow distribution among the different branches becomes more uneven. Under high acceleration conditions, compartment A is prone to obvious flow deviation, which deteriorates overall flow uniformity. Orifice structures adjust flow distribution under acceleration pretty well—they work by changing the flow resistance characteristics. The present research provides reliable theoretical foundation and data support for the optimal design of complex capillary pipeline structures under variable acceleration environments.
Chen, Weihua, Wei, Xiaorui, Yang, Tianyu, Wang, Cong, Qin, Jiang
Acceleration Factor Derivation Using Test Bed and Field Failure Data of Automotive Engines2026-01-507710/19/2026
Reliability testing and validation are essential parts of any automobile development project. These tests mainly depend on two things: how much of the product’s target life is covered and whether enough samples are tested to capture variations. Today, most projects face tight timelines and cost constraints, which makes it important to design an optimized reliability test plan. Accelerated testing is widely used to reduce testing time, but finding the correct acceleration factor for a complex system like an automotive engine is difficult. Existing studies describe methods such as simple stress tests, step-stress tests, and degradation tests to shorten test duration. However, these methods need more samples at different stress levels to establish a clear relationship between stress and life and then calculate the acceleration factor. While such models work well at the component level, they become much more complicated at the system level because different components can have different acceleration behaviors. In this paper, a method is proposed to derive the acceleration factor by correlating engine test bed data with field failure and usage data. Test engineers often use standard accelerated test cycles for engines used in similar applications. As a result, organizations usually have historical test data and field data from earlier, similar products. The proposed approach models accelerated test data and field failure data separately and then links them to determine the acceleration factor. This derived factor can later be used in future engine development projects to convert test cycles into equivalent field mileage coverage. Weibull analysis and regression techniques are used to support the case studies presented in this work.
Koulage, Dasharath Baliram, Mondal, Kanchan
Response Surface Optimization of Alkylphenolate Additive AKI-152 for High-Performance Motor Oils in Oil and Gas Applications04-19-03-00129/22/2026
The behavior of detergent–dispersant additives in lubricating oils depends upon their chemical composition and synthesis, particularly for lubricants used in rotary equipment under harsh conditions, in the upstream and downstream oil and gas industries. Alkylphenolate-based additives are extensively used, among others, due to their high alkalinity and good dispersion stability. However, their performance is extremely sensitive to synthesis parameters, making experimental optimization time-consuming and resource-intensive. In this study, an alkylphenolate-based additive (AKI-152) was synthesized and optimized systematically via a response surface methodology (RSM), derived from a central composite design (CCD). The influence of three important process variables, namely the alkylphenol/amino mass ratio, reaction temperature, and Ca(OH)2/CO2 mass ratio, was assessed with respect to the total base number (TBN), kinematic viscosity, and corrosivity of the final product. A quadratic regression model was developed and validated, showing good agreement between predicted and experimental results. Analysis of variance (ANOVA) indicated that the alkylphenol/amino ratio and Ca(OH)2/CO2 ratio exert the strongest influence on TBN, while temperature also contributes significantly within the investigated operating range. Numerical optimization identified a stable optimum region corresponding to TBN values of approximately 160 mgKOH/g, viscosity values close to 54 mm2/s, and corrosivity values below 0.76 g/m2 under the selected optimization criteria. No separate experimental run was performed to verify the selected numerical optimum, as the optimization was conducted within the experimentally investigated CCD space using the developed and statistically evaluated response surface models. These results demonstrate that the applied modeling approach can be effectively used to define suitable synthesis conditions for high-performance lubricant additives.
Gasimov, Rahib, Naghiyeva, Elmira, Sujayev, Afsun, Gadirov, Ali, Nasibova, Nigar
Beyond Simulation: AI’s Role in Future Crash Studies2026-01-75109/22/2026
Artificial Intelligence (AI) and Machine Learning (ML) are increasingly transforming Computer-Aided Engineering (CAE) workflows by enabling faster design iterations and reducing computational costs. This paper presents the application of Ansys SimAI and Ansys GeomAI in modelling an automotive side impact scenario using high-fidelity data from LS-DYNA simulations. Two AI models are trained on datasets with systematically varied parameters: one encompassing pole impact position and door beam configurations, and another focusing on rocker panel reinforcements. Both models exhibit strong predictive performance, reliably capturing deformation patterns and force-time histories for previously unseen configurations. The datasets are subsequently merged to train a comprehensive surrogate model capable of simultaneously representing variations in pole position, door beam geometry, and rocker reinforcement design, demonstrating robust generalization across a multidimensional design space. To address the emerging bottleneck of geometry creation, GeomAI’s geometry exploration functionality is employed to generate new rocker reinforcement geometries from existing ones, which are then rapidly validated using the pre-trained surrogate model. The results confirm that LS-DYNA simulations can be leveraged effectively to build AI models that dramatically reduce design exploration time. With SimAI and GeomAI in the loop, CAE workflows can evolve from simulation-driven design toward AI-augmented autonomous engineering, where geometry generation, simulation, validation, and optimization converge into an intelligent closed loop.
Adya, Srikanth, Karadogan, Celalettin, Vasu, Shyam S., Lazarov, Nikolay, Husek, Martin, Haufe, André
Integrating MBSE with High-Fidelity Crash Simulation2026-01-75429/22/2026
Modern electrified ground vehicles introduce complex, multi-domain safety requirements, such as post-crash thermal runaway prevention, that expose the traceability limitations of Document-Based Systems Engineering (DBSE). This paper proposes a four-layer, bidirectional digital thread architecture that integrates Model-Based Systems Engineering (MBSE) with high-fidelity, non-linear Computer-Aided Engineering (CAE) crash simulations. Leveraging SysML, System-Theoretic Process Analysis (STPA), and Python-based orchestration middleware, the framework automates the translation of descriptive safety requirements into explicit finite element boundary conditions. The architecture programmatically extracts key performance indicators from massive binary solver outputs and injects them back into the SysML environment for automated compliance verification. Demonstrated through a simplified electric vehicle side-pole impact case study utilizing LS-DYNA and a 1D thermal model, the framework successfully eliminates manual data handoffs, accelerates multidisciplinary design optimization, and ensures robust, risk-driven requirement traceability across the engineering lifecycle.
Rye, Patrick J.
Optimizing Data Delivery for Ground Vehicle Teams2026-01-75399/22/2026
The radio-based wide area network (WAN) that forms the command-and-control backbone for deployments of multiple ground vehicles is a classic DIL (disconnected, intermittent, limited) communications infrastructure for bandwidth-intensive services like video streams, situational awareness feeds, and command-and-control messages. This paper describes an inter-vehicle network architecture that leverages the radio-aware routing features of an existing vehicle Ethernet switch/router to provide mission-optimized, fault-tolerant data delivery while minimizing both network overhead and vehicle SWaP requirements.
Al-Gharaibeh, Jafar, Bonney, Jordan
CI/CD for Mission-Critical Systems: A Framework for Any Level of Rigor2026-01-75299/22/2026
Modern mission-critical ground vehicle systems must adapt to rapidly evolving threats, deploying changes in months or days while maintaining reliable and safe operation. Historic manual development and testing methods cannot keep pace without compromising safety assurances. Continuous Integration and Continuous Deployment (CI/CD) pipelines offer proven approaches to accelerating development, but implementing them for mission-critical systems requires careful attention to verification rigor. This paper presents a practical framework for implementing CI/CD pipelines across any level of rigor, from rapid prototyping to DO-178C and ISO 26262 certified systems. Drawing on experience from aviation, medical device, and ground vehicle development, the framework provides guidance for each pipeline stage based on the system’s desired level of rigor. This framework includes an examination of the value of Software-in-the-Loop vs Hardware-in-the-Loop testing to optimize development timelines while maintaining software quality.
Lingg, Michael, Paul, Howard, Schulte, Brian, Wilkinson, Robert
Intelligent Optimization Simulation Framework for Test and Evaluation of Autonomous Systems2026-01-75519/22/2026
This paper describes ongoing research and development of an efficient optimization/search–based modeling and simulation framework for rapidly identifying low-performance scenarios in advanced autonomous systems. Ensuring predictable, safe behavior across complex, integrated systems remains a core operational test-and-evaluation challenge. Our goal is to balance rigorous validation with timely deployment. We are developing TEAAS (Test & Evaluation of Advanced Autonomous Systems), a scalable, faster-than-real-time framework designed to uncover critical failure scenarios efficiently. Key features include GPU-accelerated parallel simulation and learning, computational intelligence–based search of optimal parameters, uncertainty quantification for reproducibility, and real-time physics-accurate sensor models. We conducted simulation experiments to evaluate and demonstrate the framework performance for two black-box ground-vehicle autonomous systems. Key results were that adequate uncertainty quantification can be achieved with as few as 10 repeated runs per simulation scenario, sensor realism has a significant effect on failure rate, distinct differences between the two autonomies failure modes were identified, and our efficient optimization/search methods identify critical performance regions in a small fraction of the number of simulations required by a naïve Monte Carlo search.
Snarski, S., Menozzi, A., Persons, B., Lazar, D., Khan, N.
Thermal Management of Pre-Chamber Spark Plugs for the Mitigation of Abnormal Ignition in Hydrogen-Fueled Engines – An Experimental and Numerical Study2026-24-00079/21/2026
The internal combustion engine will continue to contribute to global mobility, particularly when operated with carbon dioxide low-carbon fuels. Pre-chamber ignition systems are increasingly investigated to improve efficiency, emissions, and combustion stability. In combination with hydrogen as a carbon-free fuel, they extend the lean operating limit while ensuring reliable ignition under demanding conditions. A key challenge is the thermal management of pre-chamber spark plugs. While the thermal behaviour of conventional spark plugs is well understood, limited knowledge exists for pre-chamber systems. Chamber geometry, material selection, manufacturing, and installation strongly influence thermal loading, where elevated local temperatures may contribute to knock, pre-ignition, and material degradation. The objective of this study is to establish a system-level understanding of pre-chamber thermal behaviour. Experiments are conducted on a single-cylinder research engine using hydrogen and research octane number 95 (RON 95) as a reference fuel. Dedicated temperature measurements identify thermal hotspots and assess parameter sensitivities. For the investigated configuration (14:1 compression ratio (CR), 1500 revolutions per minute (rpm), 12 bar indicated mean effective pressure (IMEP)), measurements and conjugate heat transfer (CHT) simulations suggest wall temperatures are not the primary contributor to pre-ignition. Reduced pre-ignition is observed with increasing scavenging bore diameter, indicating a strong influence of mixture preparation and residual gas effects. A coupled CHT model is integrated into a computational fluid dynamics (CFD) simulation with moving boundaries. The model includes realistic wall thicknesses, temperature-dependent material properties, and calibrated boundary conditions, enabling cycle-resolved analysis of heat fluxes and temperature fields for pre-chamber optimization.
Nenzel, Markus, Alkezbari, Ahmad Anas, Rottenkolber, Gregor
Multi-Level CFD and System Simulation Approach for Optimization of Cooling Circuits in High Power Density Powertrains2026-24-00099/21/2026
This paper presents a CFD-based optimization workflow for the simulation and development of automotive cooling circuits, integrating three-dimensional steady-state analyses with one-dimensional transient modeling. The objective of the activity is to establish a robust methodology that links detailed component-level thermal characterization to system-level dynamic simulations, enabling the assessment of cooling performance under both driving and charging operating conditions. The thermal behavior of the cooling circuit components was first investigated using three-dimensional steady-state simulations performed with Ansys Fluent. For each relevant operating point, the fluid flow and heat transfer were resolved in full 3D, and temperatures were monitored at multiple locations within the components and along the circuit. The steady-state analyses provided spatially resolved temperature fields and heat transfer characteristics for a range of boundary conditions representative of real operating scenarios. From these results, temperature and performance maps were generated, describing the relationship between operating conditions, heat loads, and thermal responses of the components. These maps were then used for the calibration of one-dimensional models implemented in GT-Suite. The calibrated 1D models reproduce the thermal behavior observed in the 3D CFD simulations while allowing efficient simulation of the entire cooling system under transient conditions. This multi-level approach enables the combination of detailed local physics from CFD with the computational efficiency required for system-level dynamic analyses. Transient simulations were carried out in GT-Suite to evaluate the thermal response of the cooling circuit during both driving and charging phases. The driving phase accounts for variable thermal loads and flow conditions associated with vehicle operation, while the charging phase represents operating conditions specific to battery recharging scenarios. The calibrated 1D models were used to simulate the evolution of temperatures throughout the system over time, considering the interactions between components and the overall thermal inertia of the circuit. The results show that the designed cooling system is capable of maintaining component temperatures within the targeted limits across the analyzed operating conditions. The thermal containment is achieved for all components included in the cooling circuit under both dynamic driving and charging scenarios. The electric motor is oil-cooled and therefore is not part of the water-based cooling circuit addressed in this study. The proposed CFD-to-1D workflow provides a consistent and transferable methodology for the thermal development of cooling systems of high power density electrified powertrains. The novel contribution lies in (i) the application of the multi-level framework to a heavy-duty platform with SiC-based power modules and dedicated on-board charger developed within the Horizon Europe POWERDRIVE project, (ii) a DOE-based map generation strategy that preserves the conjugate heat transfer interactions between actively cooled components (power modules, OBC) and passively cooled neighbors (busbar, capacitors) within the reduced-order representation, and (iii) the integration of the reduced-order maps within a single transient system-level model covering both vehicle-at-rest charging and dynamic driving operating modes. This activity is carried out within the framework of the Horizon Europe project POWERDRIVE.
Chiappini, Daniele, Tribioli, Laura, Rodionov, Artem
Short-Term Load Forecasting of Sustainable Power Systems Based on Newton–Raphson-Based Optimizer–Extreme Gradient Boosting under Extreme Weather Events13-07-02-00089/17/2026
With continuous advancements in load-side resources such as distributed photovoltaic systems, electric vehicles, and virtual power plants, the low-carbon and sustainable development attributes of power systems have been significantly enhanced. Meanwhile, the coupling intensity between sustainable power systems and meteorological conditions has been further consolidated. Considerable impacts are exerted by weather variations, particularly extreme weather events, on the dispatching and operation of sustainable power systems. Accurate load forecasting is critical for enabling sustainable power systems operators to optimize power generation strategy, ensuring supply stability and resilience against extreme weather-induced disruptions. However, the intrinsic non-stationarity and volatility of extreme weather events present significant challenges to conventional forecasting approaches. Herein, we introduce a hybrid algorithm integrating Newton–Raphson-based optimizer (NRBO) with extreme gradient boosting (XGBoost) to enhance short-term load predictions under such conditions. The model uses optimally selected meteorological and load features as inputs, while NRBO systematically tunes XGBoost’s hyper-parameters to maximize performance. Evaluated on an Irish dataset, the proposed framework is quantitatively compared against five baseline models, including traditional decision trees and neural networks. The case studies show that the mean absolute percentage error (MAPE) of the proposed model is 2.57%, which is the lowest among these decision tree and neural network algorithms.
Wang, Yi, Zhou, Jian, Wu, Gang, Ma, Tiannan, Ma, Ruiguang, He, Chuan, Zhu, Huixian
Predicting Wear of NAO Brake Pad Using Scaled-Down FMVSS 135 Testing and Machine Learning2026-01-08089/14/2026
Brake pad wear is a major and growing source of non-exhaust particulate emissions, projected to reach 1.3 million tons annually by 2030 and contributing up to roughly 55% by mass of non-exhaust traffic-related PM10 in urban environments, underscoring the need for improved durability and material optimization. This study investigates a three-stage eXtreme Gradient Boosting (XGBoost) ensemble paired with a residual Fully Connected Neural Network (FCNN) corrector to predict brake pad wear rate and support formulation optimization. Experiments used a simplified FMVSS 135 protocol on a Universal Mechanical Tester (UMT) simulating realistic braking across eight friction regimes. Wear rate was the sole machine-learning prediction target, while coefficient of friction (CoF) was retained as an input feature rather than a target. Despite a limited but high-quality 280-cycle dataset, regime-aware stratified splitting, sample reweighting, and hyperparameter optimization enabled robust generalization. The three-stage XGBoost ensemble with residual FCNN correction achieved a global held-out test R2 of 0.976 for wear rate prediction. A Taguchi L8 design of experiments defined the brake pad compositions, reducing experimental time and material consumption compared to conventional approaches. The framework demonstrated strong agreement between measurements and predictions for the dominant low-severity regime, while per-regime analysis identified the high-severity minority regimes as the priority for additional data collection, since within-regime R2 remains negative for every regime given current sample sizes. A sequence-aware mean absolute scaled error (MASE) analysis further shows that, despite the high global R2, none of the four pipeline stages currently outperforms a naive one-cycle persistence forecast on absolute error, a distinction reported here for transparency. The scalable architecture enables straightforward integration of additional material and process parameters, supporting iterative brake formulation development in industrial settings and, by reducing empirical testing requirements, sustainable brake material development with reduced replacement frequency and associated emissions.
Katakam, Abhishek, Eslamiat, Hossein, Kancharla, Sai Krishna, Filip, Peter
Evaluation Method for Driving Safety on Highway Successive Curves2026-99-15689/11/2026
To evaluate the driving safety performance of continuous curves, this study developed a safety assessment model using a human-computer interaction simulation platform. First, three indicators are selected, including the driver’s heart rate variability, the rate of change in steering wheel angle, and trajectory lateral deviation, which together form a driving safety evaluation indicator system. Secondly, through significance testing and range analysis. Through analysis, four key curve-related elements are identified as having a notable influence on the overall evaluation indicators. A global optimization algorithm using multivariate nonlinear regression is then applied to establish the driving safety model. Finally, taking a dual four-lane highway in Sichuan province as an example, the safety of the successive curve in the project is evaluated. Empirical results show that when the intermediate straight line section H ≤ 4.23, driving is hazardous; 4.23 < H ≤ 4.46, driving is relatively hazardous; 4.46 < H ≤ 4.78, driving is relatively safe; H > 4.78, driving is safe. For oval-shaped curve segments, when H ≤ 4.53, driving is hazardous; 4.53 < H ≤ 5.32, driving is relatively hazardous; 5.32 < H ≤ 5.86, driving is relatively safe; H > 5.86, driving is safe. Through this method, the driving safety of successive curves can be effectively evaluated, particularly with a focus on driver comfort and safety. This provides valuable references for assessing driving risks associated with different combinations of curve elements.
Huang, Yongheng, Zhang, Ruizheng, Sun, Chao, Zeng, Xinjie, Zheng, Liwen
Optimizing Dispatch Frequency of Feeder Buses for Integration2026-99-15749/11/2026
In the context of urban multi-modal transportation systems, the optimization of the integration between urban rail transit and feeder bus services remains a critical challenge for improving service quality and operational efficiency. The present study investigates the frequency optimization of a dedicated feeder bus line during the morning peak period, considering heterogeneous passenger arrival patterns from both random street arrivals and scheduled rail-to-bus transfers. A bi-objective non-linear programming model is proposed to minimize total passenger travel costs and operating costs for the bus system. The model incorporates heterogeneous passenger arrivals at stops, ensuring a realistic representation of feeder line usage. It also distinguishes between transfer and non-transfer passengers, who have different perceived waiting costs derived from queuing and scheduling principles. To evaluate the model, numerical experiments based on simulations are conducted under varying metro transfer intensities. These scenarios are created by applying scaling factors to the original station-level arrival data to approximate different levels of rail-to-bus demand propagation. Results demonstrate that Higher transfer intensity leads to shorter optimal dispatch intervals and a marginal increase in total operating cost, reflecting the additional service pressure from metro-induced demand. The framework provides flexible control through weighting parameters and can guide transit agencies in balancing service quality with cost-efficiency under different demand profiles.
Guo, Xiao, Zhang, Jing
Real-time Vehicle Speed Detection Based on YOLOv11 and DeepSORT2026-99-15959/11/2026
The technology of real-time and effective vehicle speed detection is considered a key technology to improve traffic monitoring efficiency and traffic safety management grade. To address the limitations of traditional speed detection schemes—including reliance on dedicated hardware, poor environmental adaptability, and high construction and maintenance costs—this paper proposes a r10eal-time vehicle speed detection system based on YOLOv11 and the DeepSORT algorithm. The proposed system uses the YOLOv11 target detection algorithm as its primary model. DeepSORT multi-target tracking technology is integrated to enhance tracking performance. Speed measurement is implemented using a virtual detection line. This approach enables accurate vehicle detection, continuous tracking, and real-time speed measurement within video frames. Through the experiments, the result shows that the improved YOLOv11n model reaches mAP@0.5 of 0.982 and a recall rate of 0.956 in the test set, higher than the YOLOv8n and YOLOv5s models. The speed detection error can be restricted to 3 km/h, satisfying the real-time detection need. There is no need for road surface modification, and the detection system has a flexible layout, providing a dynamic basis for traffic law enforcement and traffic data support for road construction and traffic control optimization.
Jin, Guowei, Ma, Wenlong, Jiang, Dali, Li, Nan
Research on Torque Allocation Control of Multi-Articulated Vehicle2026-99-11239/11/2026
The multi-articulated vehicle uses distributed drive mode. Due to its large degree of freedom of movement and the large number of driving shafts, different torque distribution methods affect the operational stability of the vehicle, how to coordinate and distribute the torque of each driving motor has become an urgent problem to be solved. To improve drive stability of the multi-articulated vehicles, propose a layered torque allocation control strategy. The upper-layer sliding mode controller determines the required additional yaw moments of each car body based on the linear reference model, the controller is characterized by swift response and a strong ability to resist interference. The lower-level allocation module comprehensively considers the torque output limitations of the electric hub motors, the prevailing road adhesion state, and the corrective yaw moment constraints given by the upper layer, and constructs an optimization objective function centered on the uniformity and stability of tire load. The optimal distribution of driving forces for each wheel is completed by solving this function dynamically. To validate the strategy's effectiveness, a vehicle dynamics model is built in the multi-body dynamics software ADAMS/View. Using a joint simulation framework integrating ADAMS/View and MATLAB®/Simulink, the effect of the layered control strategy is evaluated in comparative simulation with uncontrolled situation under U-turn and single lane change conditions. The simulation outcomes demonstrate that, compared to uncontrolled situation, the yaw rate deviation of each car body under the torque layered control are significantly reduced, and the adhesion utilization rate of tire is also effectively controlled, thereby the driving stability is improved.
An, Guanbo, Zhang, Liwei
Study on Rapid Formation Generation Method for Multi-UAV Swarms2026-99-14219/11/2026
This paper presents a generalizable geometric framework for rapid on-demand generation of multi-UAV formations with arbitrary 2D geometries and user-specified scalable scales. First, vertices, edge intersections and edges are extracted from a user-defined formation template to enable parametric description of both simple and composite formation geometries. Second, boundary interpolation, edge expansion and recursive internal expansion are integrated to synthesize hierarchical multi-layer UAV deployment point sets under a controllable expansion ratio. Third, a geometric distortion metric is proposed to optimize UAV node indexing and formation reconstruction while preserving inter-node topological consistency. Algorithmic derivations, complexity analysis and simulation assumptions are further elaborated. Simulation results verify that the proposed method preserves geometric fidelity of target formations while delivering superior scalability and spatial coverage, rendering it well-suited for emergency transport, aerial surveying and low-altitude cooperative missions in dense urban environments.
Fu, Mingyi, Zeng, Guoqi, Gu, XinZhu, Wang, Jia
Research on Heavy-Haul Train Operation Adjustment Considering the Degree of Chaos in Train Timetable2026-99-15229/11/2026
Heavy-haul railway development is as important a strategic direction for China’s railway sector as high-speed railway development. With the continuous expansion of heavy-haul railway operational mileage and the growing transportation demand, the complexity of operational adjustment in heavy-haul railways continues to escalate. The operational adjustment for heavy-haul trains subjected to speed restrictions following maintenance windows, aimed at maximizing the restoration of timetable regularity and guaranteeing freight volume, constitutes a critical and urgent research problem. Grounding on the operational principle of heavy-haul railways that prioritizes freight volume preservation over strict punctuality, this paper proposes a rescheduling model that incorporates the timetable disruption level. By adopting the order entropy theory, the model is formulated to minimize the total system disruption level and freight time cost, thereby framing the rescheduling problem as an order entropy optimization problem. The train disruption level is a metric that quantifies the perturbation intensity of a rescheduled timetable relative to the original operational plan within a given railway section. By initializing the entropy value of the pristine operational order to zero, any subsequent entropy variation directly facilitates a systematic investigation into the evolution of train operational order. Effective timetable adjustment strategies are subsequently derived, contingent upon the available buffer time and freight volume constraints. Guided by actual operational timetables and sectional line characteristics, this research designs a case study. The Gurobi solver is employed to obtain a rescheduled heavy-haul train timetable that successfully restores the original freight volume. The proposed methodology contributes to the enrichment of railway transportation organization theory and provides robust decision-making support for the strategic deployment of heavy-haul train services.
Fu, Lu, Lin, Li, Xu, Shichao, Ji, Guanggang, Li, Zheng
Experimental Study on Handling Stability of an 8×4 Heavy-Duty Truck2026-99-11229/11/2026
To analyze the handling stability of an 8×4 heavy-duty truck, a multi- body dynamics model of the heavy truck was established in ADAMS. Simulation tests for minimum turning radius, double lane change, steering wheel step input, and steady-state returnability were conducted on this model. Analysis of the simulation and experimental results revealed that, except for the significant discrepancy between the rigid-flex coupling model simulation results and the actual values in the returnability experiment, other experimental results were relatively close to the simulation data, indicating that the established vehicle model has high accuracy. It can provide a basis for the subsequent optimization design of this vehicle type.
He, Wenjian, Dong, Fulong
Impact of Transient Driving Characteristics on Bench-to-Road Emission Consistency of Light-Duty Vehicles2026-99-15739/11/2026
The thalweg at the outlet of the Yuxikou Waterway transitions from right to left, forming a 90-degree bend. It then merges with the Xihua Waterway after passing Xiliang Mountain, creating a main-branch confluence water area. Taking a typical main-branch confluence water area in the lower reaches of the Yangtze River as the research object, this paper reflects the current navigation status and existing problems of ships in the area through the analysis of ship traffic flow. It classifies the risk levels of passing ships, proposes suggestions for route reform and optimization, and uses a model to verify the probability of collision accidents in the area after the implementation of the round-island navigation method, providing a reference for the navigation safety of passing ships.
Qiao, Jiajun, Jin, Zhenhua, Huang, Qi, Li, Guohui, Zhang, Xinguo
Research on a Brake Energy Recovery Strategy Based on Improved ANFIS2026-99-11349/11/2026
This paper addresses the issue of regenerative braking energy recovery in new energy vehicles and designs and optimizes a braking force distribution strategy. The strategy uses an ANFIS controller to dynamically optimize the proportion of front-axle regenerative braking force. The introduction of a pruning algorithm reduces computational complexity, thereby enabling a significant increase in mileage while maintaining stable driving performance. Co- simulations integrating Simulink and AVL Cruise, alongside Hardware-in-the-Loop (HiL) tests, the proof is that this strategy can still maintain excellent stability under different braking intensities. Moreover, it exhibits significantly higher energy recovery efficiency compared to benchmark strategies, while its effectiveness and real- time performance are successfully validated.
Lin, Hui, Zhao, Xuezhan, Tian, Jiahao
Research on Interference Management of Shared Collection and Transportation Vehicle Scheduling of Bulky Garbage with Order Changes2026-99-15199/11/2026
This study presents a shared vehicle scheduling model designed to tackle scheduling conflicts arising from changes in orders for bulky waste collection and transportation. The model accounts for five types of interference events: alterations in the original order location, modifications to the time window, changes in waste size, the inclusion of new orders, and order cancellations. The goal of the model is to reduce variations in the frequency of collection and transportation, minimize costs, and meet the three requirements of three-dimensional loading for shared trucks, as well as satisfy user time windows. To solve this complex combinatorial optimization problem, the sparrow search algorithm is employed. Numerical studies indicate that the proposed method outperforms the genetic simulated annealing algorithm in objective function value and computational efficiency under the five interference scenarios. These results also confirm the model’s efficacy.
Xu, Chen, Ma, Huimin
Analysis of Traffic Capacity at the Intersection of Xiyou Road and Qianshan Road in Hefei City2026-99-15519/11/2026
With the advancement of urbanization and the popularization of automobiles, the traffic load on urban roads is becoming increasingly heavy, resulting in many traffic problems. Road intersections serve as crucial linchpins in the urban transportation grid, wielding considerable influence over the overall traffic capacity of a city’s road network. Enhancing intersection efficiency and cutting down on delays stand at the heart of tackling urban congestion challenges. This study zeroes in on the crossroads where Xiyou Road intersects with Qianshan Road in Hefei City. Employing hands-on observation and photographic documentation, the research examines traffic flow and signal configurations during the peak demand period (7:30-8:30). The analysis evaluates traffic capacity and utilization rates for through, left-turn, and right-turn lanes at this intersection. Findings reveal that the right-turn lane at the southern entrance and the left-turn lanes at both northern and eastern entries show relatively low saturation levels, while the saturation of other lanes is greater than or close to 1. Therefore, this intersection does not have sufficient capacity. The actual traffic operation at the intersection, particularly during peak traffic times, is analyzed to identify the reasons for congestion Finally, improvement plans for optimizing traffic organization at intersections are proposed, such as optimizing signal timing schemes and transforming traffic channelization. Simulation analysis using VISSIM shows a 9.34% reduction in total intersection parking time, a 34.26% decrease in average queue length, and an 8.12% reduction in average vehicle delay. These results provide a reference for future optimization work, including intersection signal timing and channelization.
Wang, Yanmei, Wang, Chen, Fu, Ziyue, Meng, Xianglong
Safety Assessment Model for Airborne Time-Sensitive Networking Based on Analytic Hierarchy Process2026-99-14149/11/2026
With the growing demand for high real-time performance and high reliability in airborne networks, Time-Sensitive Networking (TSN) has been widely adopted as a core technical basis for deterministic Ethernet for next-generation avionics systems. This paper proposes an AHP-based safety assessment model for airborne TSN, introducing a hybrid evaluation strategy that integrates both subjective and objective factors. By constructing a comprehensive evaluation index system, the model quantifies the weights of traffic attributes—including time sensitivity, priority level, and bandwidth guarantee requirements—and combines them with the degree centrality of network nodes to achieve a holistic assessment of TSN safety. The proposed model not only provides theoretical support for the safety-oriented design optimization of avionics systems but also offers practical guidance for the airworthiness verification of airborne networks. Feasibility and effectiveness are verified by applying the proposed method to a representative case scenario. Moreover, the model’s scalability supports its application in more complex network environments, meeting the broader assessment needs of airborne TSN safety.
Wang, Penghui, Mei, Yanan, Fu, Jinhua
Study on Shaking Table Model Test of Seismic Dynamic Response of Loess Multi-Arch Tunnels2026-99-15849/11/2026
Taking the Nieye Multi-Arch Tunnel in Zhuoni County as the engineering background, this study systematically explores the seismic dynamic response characteristics of loess multi-arch tunnels through shaking table model tests. The test results show that: (1) The strain distribution of the surrounding rock is significantly different. Under a peak acceleration of 0.6 g, the maximum strain in the tunnel portal section is concentrated on the right side, which is related to the incident direction of seismic waves and the stress concentration at the bottom of the central wall; the maximum strain in the tunnel body section is located on the left side, affected by the propagation characteristics of seismic waves, burial depth, and unsymmetrical pressure. (2) The acceleration amplification factors in the Z and ZX directions show nonlinear changes. Under bidirectional excitation, the Wenchuan wave-ZX combination exhibits the strongest response. The variation trend of acceleration at the soil-rock interface varies with wave types, and the slope damage undergoes three stages: elastic stage, elastoplastic stage, and plastic damage stage. (3) The ratio ω of tunnel burial depth to central wall thickness is positively correlated with the strains at key positions. For the seismic design of loess multi-arch tunnels, special attention should be paid to sensitive areas such as the bottom of the central wall and the left side of the tunnel body. It is suggested to improve the structural seismic performance by optimizing the lining reinforcement and adapting to regional seismic wave types. The research conclusions provide a reference for the seismic design of such tunnels under complex geological conditions.
Han, Tao, Cao, Xiaoping, Zhang, Shulin, Yang, Zibin
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