Browse Topic: Measurements
Graphene materials have recently been shown to produce measurable propulsion when exposed to light, raising the possibility of using them to enhance solar sail performance for satellite deorbiting. The pendulum-based experiments on graphene samples under controlled laser illumination inside a vacuum chamber and a quantitative model that describes the propulsion force as a function of laser power, illuminated area, incident angle, and ambient pressure were carried out. The measured pressure dependence and its shift with sample characteristic size point to Knudsen forces as the dominant mechanism. Orbital dynamics simulations incorporating this model show that a graphene-based sail can deorbit a 100 kg satellite from 660 km altitude roughly 21% faster than a conventional membrane sail of the same area, because the graphene configuration combines atmospheric drag with light-induced propulsion in a hybrid decay process.
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.
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 demand for high-precision, real-time, and continuous localization in aerospace and low-altitude unmanned systems is steadily increasing. However, under weak GNSS availability, obstruction, and degraded ranging geometry, conventional 3D localization methods often suffer from insufficient observation constraints and severe accuracy degradation. To address observability degradation and localization error divergence in single-auxiliary-node systems, this paper proposes a 3D cooperative localization method based on maneuver reconfiguration of a single auxiliary node. Within a particle filtering framework, the method fuses target motion increments with single-shot range measurements to recursively estimate the target’s 3D position and heading. A geometric degradation metric and a radial-consistency metric are further constructed from the line-of-sight distribution and the alignment between target velocity and line of sight within a sliding window, enabling online detection of degraded observability. Once degradation is detected, the auxiliary node performs either lateral maneuvering or circular maneuvering to actively reshape the target–auxiliary relative geometry and restore observation diversity; meanwhile, the two metrics can also serve as feedback variables for adaptive maneuver adjustment. Comparative analyses are conducted under three modes, namely no reconfiguration, lateral maneuvering, and circular maneuvering, from the perspectives of 3D trajectory evolution, error time series, degradation-metric time series, and statistical error performance. Parameter sensitivity analyses and an additional nonlinear velocity-varying scenario are also included. Experimental results show that the no-reconfiguration mode exhibits clear error divergence under degraded conditions, whereas both maneuver-based strategies effectively reduce localization errors. Lateral maneuvering is more suitable for rapid recovery in the early degradation stage, while circular maneuvering achieves better overall accuracy and stronger post-degradation recovery capability. These results demonstrate the effectiveness and engineering feasibility of degradation-triggered active geometric reconfiguration for cooperative localization in low-configuration unmanned systems.
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.
Teardown evaluation of chassis system components plays a critical role in benchmarking, failure analysis, and competitive product assessment. These inspections rely heavily on experienced engineers who interpret visual defect patterns, material conditions, wear signatures, and manufacturing variations. However, expert driven evaluation processes are often subjective, difficult to standardize, and challenging to scale across global engineering teams. This paper presents a structured AI-assisted expert evaluation framework developed to enhance consistency, preserve institutional knowledge, and enable continuous improvement in chassis component teardown analysis. The proposed system integrates convolutional neural network architectures, including ResNet18 and its variants, into a human in loop inspection workflow. AI models perform initial classification of component images (e.g., OK/not OK and defect subclasses) and provide associated confidence scores. These predictions are presented as decision support, while final authority remains with the evaluating expert. Experts can confirm or override AI outputs, annotate defect regions using bounding boxes, assign subclass categories, and provide structured technical comments. All expert interactions, including AI disagreements, are systematically recorded. Correction instances are analyzed to identify model limitations, ambiguous defect conditions, and data gaps. Expert validated evaluations are incorporated into the training dataset to enable iterative model refinement. This closed loop process supports progressive improvements in model robustness and classification accuracy across varying teardown conditions and component types. A centralized cloud-based repository maintains full traceability of inspections, including timestamps, AI confidence levels, expert modifications, and annotation metadata. This structured knowledge capture converts tacit engineering judgment into a persistent digital asset, supporting auditability, cross-site alignment, and accelerated onboarding of new engineers. The framework demonstrates how AI can be effectively deployed as an assistive technology in chassis teardown evaluation, improving repeatability, enhancing data driven benchmarking, and enabling scalable knowledge preservation without displacing expert authority.
To accurately assess the navigation safety status of LNG vessels in port waters and balance safety control with waterway capacity efficiency, this study constructs a 3D dynamic safety domain model for port LNG vessels, integrating human–ship–environment multi-factors. The model introduces the Weibull function to quantify the impact of drivers’ knowledge, skills, and physiological-psychological states on safety boundaries, combines a ship motion mathematical model to establish a 2D safety domain boundary equation, and incorporates hull subsidence to build a vertical dimension, forming a complete 3D model. Longitudinally, the safety distance is calculated using the car-following braking theory, while laterally, boundaries are determined by controlling the ratio of inter-vessel interference force to navigation resistance. Through static scenario analysis and dynamic simulation verification, results show that the safety domain scale is dominated by ship speed and environmental conditions, and its shape tends to shrink as the driver’s state improves, making it more suitable for actual port scenarios than traditional models. Verified with a specific LNG hub port as a case, the safety distance calculated by the model is significantly reduced compared with current specifications, while the delay impact rate and average delay time on other vessels are decreased. The research results establish a quantifiable framework for dynamic safety assessment, providing maritime administrations and on-board pilots with a scientifically-grounded tool to determine real-time safe navigation boundaries in complex port environments, balancing safety control with operational efficiency.
Impact testing utilizing instrumented hammers and accelerometers is a widely adopted technique in dynamic testing. The mass loading effect of the accelerometer alters the dynamic response of the test structure, leading to deviations between the measured frequency response functions (FRFs) and their true values. Furthermore, the effects on the FRFs are contingent upon the positioning of the accelerometer, thereby causing the measured FRFs between two points to fail to meet the principle of reciprocity. This paper investigates the compensation method for the mass of a single accelerometer in impact testing. Compensation formulas for both origin–FRF and cross–FRF are derived using the frequency domain substructure decoupling method. Numerical simulations on a cantilever beam and experimental tests with milling tools validate the proposed methodology. The compensation formulas for FRFs presented in this paper are expected to enhance the measurement accuracy of FRFs in modal testing of small structures, particularly relevant for lightweight components in aerospace, aircraft, and transportation systems, where precise dynamic characterization is critical.
Conventional measurement instruments such as scales, thermocouples, and laser-based technologies present challenges when used on lengthy and winding underground pipelines. These methods are often not feasible because of physical constraints, the challenge of light traveling in curves, and the need for large, energy-intensive sensors. Ultrasonic and microwave techniques both face challenges in making long-distance measurements because of rapid signal weakening and high energy requirements, which make them impractical for small pipes. This study introduces an original technique for Time-of-Flight (ToF) estimation using the Discrete Logarithmic Frequency (DLF) method to address these limitations. By analyzing the time–frequency correlations of signals transmitted through channels, the proposed technique enhances the precision and dependability of ToF measurements. By employing the DLF method, we are able to effectively gather and assess the signal’s performance as conduit lengths vary.
Penn Engineers have developed a novel design for solar-powered data centers that will orbit the Earth and could realistically scale to meet the growing demand for AI computing while reducing the environmental impact of data centers.
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