Browse Topic: Simulation and modeling
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.
To satisfy the real-time beam tracking requirement of low earth orbit (LEO) satellite ground terminals under highly dynamic pass conditions, this paper proposes a three-state hierarchical state-machine beam-control algorithm for liquid crystal phased array antennas (LCPAs). The tracking process is divided into three stages. S1 coarse nine-point acquisition is used to rapidly cover a wide pointing-uncertainty region. S2 fine nine-point refinement introduces quadratic-surface fitting to obtain local peak estimation at sub-step resolution. S3 predictive tracking adopts an interacting multiple model Kalman filter (IMM-KF) based on the constant-acceleration (CA) model and the Singer model, and designs an adaptive covariance regulation mechanism for both measurement and process noise. Combined with explicit delay compensation for liquid-crystal response time, the method suppresses pointing lag and error accumulation in high-dynamic intervals. A dual-threshold hysteretic loss-of-lock criterion and a filter-reset mechanism are also integrated to guarantee autonomous link recovery. Simulation results show that, in an LEO pass scenario with a maximum angular rate of about 2 °/s, the proposed method reduces the median 2D RMSE by about 52%, reduces the 90% quantile error by about 54%, and lowers the median average power loss from 0.14 dB to 0.03 dB compared with the traditional scheme.
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.
A switched adaptive flight control architecture is proposed for fixed-wing carrier-sub UAVs to handle abrupt mass and inertia variations, as well as parameter uncertainties induced by transient aerodynamic interference, during sequential deployment. The separation process is rigorously modeled as a switched linear system, and a combined direct and indirect adaptive control scheme featuring an online Recursive Least Squares (RLS) modification is designed to compensate for model uncertainties. Furthermore, a dwell-time admissible switching law is established by solving Linear Matrix Inequalities (LMIs), ensuring the stability of the switching process through theoretically guaranteed Lyapunov energy drops at switching instants. Numerical simulations verify that transient tracking error spikes are significantly suppressed, and high-frequency oscillations are effectively attenuated compared to baseline controllers.
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.
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.
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.
Space membrane structures, prized for their lightness, affordability, and ability to fold and unfold, hold immense promise for missions like deep space exploration, high-definition Earth observation, and advanced satellite communications. However, these structures are typically large-size, ultra-lightweight, highly flexible, and have very low damping. This leads to complex dynamic behavior featuring significant geometric nonlinearity, strong coupling between rigid and flexible motions, and uncertain parameters. Consequently, creating accurate yet simple mathematical models for designing controllers is exceptionally difficult, and traditional control methods that rely on such models often fall short. To tackle this problem, this study focuses on a membrane antenna spacecraft equipped with cable actuators, aiming to suppress its vibrations during attitude maneuvers. Using the Deep Deterministic Policy Gradient (DDPG) algorithm, an intelligent vibration controller based on the Actor-Critic architecture is designed. This approach is data-driven, meaning it learns effective control strategies directly through interaction with the system’s environment, bypassing the need for a precise pre-defined mathematical model. Simulation results show that the controller demonstrated superior vibration suppression and strong adaptability across several key scenarios.
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.
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.
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