Browse Topic: Statistical analysis
This paper addresses non-sustained ignition in numerical simulations of hydrogen/air turbulent diffusion flames. A reproducible LES-PaSR case study was constructed on the OpenFOAM platform, and a box-shaped ignition core was applied using setFields. The work is positioned as a numerical diagnostic and benchmark workflow, not as an experimentally validated prediction of a stable flame. The evidence chain, consisting of three diagnostic indicators - the domain-wide maximum temperature, centerline statistics along the ignition axis, and slices at critical moments - tracks the complete ignition-peak decay process. Calculations show that the domain-wide maximum temperature (Tmax) reaches a peak of 2852.5 K at t = 3.13×10^–4 s and then rapidly drops to 956.4 K at t = 0.0015 s. The duration of Tmax ≥ 2000 K is 0.818 ms, while Tmax ≥ 1000 K lasts for 1.388 ms. Comparison between the centerline and cross-sections further indicates that the high-temperature OH reaction zone is mainly limited to the ignition core and its downstream neighboring region. Under the combined effects of convective transport and turbulent mixing, this region rapidly cools and dissipates, failing to form a stable self-sustained exothermic structure. Finally, three physically plausible explanatory pathways and corresponding sensitivity-case designs are proposed, providing a practical basis for subsequent parameter scanning, mechanism replacement, and future comparison with experimental or literature-reported data. A supplementary sensitivity case with the ignition-kernel temperature increased from 2200 K to 2400 K exhibits a higher peak temperature and delayed threshold crossings, yet the reacting structure remains non-sustained within the simulated time window. Tmax ≥ 2000KTmax ≥ 1000K
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.
Civil aircraft, as typical complex product systems, exhibit characteristics such as a high concentration of high-tech technologies, strong interdisciplinarity, a high level of system integration, long development cycles, substantial project investments, and complex management. During the R&D process of civil aircraft projects, there are often high risks in performance, cost, and schedule. Delays in the schedule can lead to losses in project manpower and material resources, as well as project failure. A mature objective criteria system for maturity assessment provides a reference basis for determining whether the project has reached its optimal state at a specific stage, thereby reducing project management risks and increasing the probability of project success. This research will adopt a research approach combining theoretical studies with practical case analysis. First, it will conduct extensive and in-depth investigations into various maturity models and their applications across the entire product lifecycle within relevant fields. A requirement maturity model and requirement maturity KPI (Key Performance Indicator) indicators will be established to clarify the maturity status of requirements at different development stages, enabling judgment of whether the project is ready to proceed to the next development phase. Concurrently, by developing a KPI statistical system platform integrating application servers and data processing tools, a scientific and quantitative inspection mechanism will be implemented to visualize project development progress, status, and risk data. This will provide actionable insights for project decision-making and achieve effective project management and control.
To explore the impact of guiding and warning visual combination factors at the entrance sections of highway tunnels on drivers’ visual characteristics and driving behavior, this study recruited 16 drivers to conduct on-road vehicle experiments at the entrance sections of the Yunling Tunnel’s left bore (with visual combination factors) and right bore (without visual combination factors). Seven visual characteristics and driving behavior indicators, including pupil diameter and vehicle speed, were collected and statistically analyzed. Representative indicators such as pupil diameter, standard deviation of fixation point position, and vehicle speed were selected to establish a trend surface model of visual characteristics and driving behavior. The results indicate that when driving at the entrance section of the left bore, drivers’ pupil diameter and fixation duration were significantly lower than those at the entrance section of the right bore. With the increase in the sweeping view angle, there was a more dispersed distribution of fixation points. Additionally, there were significant differences in the acceleration and lateral deviation of the driving vehicle, with the range of variation narrowing by 52.5% and 35.7%, respectively. The trend surface model results show that under the influence of visual combination factors, the reduction in drivers’ vehicle speed was smaller, and the impact of pupil diameter and standard deviation of fixation point position on vehicle speed was less pronounced. Overall, under the influence of visual combination factors, drivers’ visual characteristics showed significant changes, with improved speed control and manipulation levels, leading to more stable vehicle operation.
AMS6885/2 gives specific information about the qualification program for unidirectional carbon fiber tape epoxy repair prepreg capable of curing under vacuum for repair of carbon fiber reinforced epoxy structures. The prepreg system shall include an epoxy film adhesive to be applied in a co-bonding process with the prepreg for solid laminate and sandwich bonding.
This study presents a data-driven approach for strengthening aviation safety by integrating human factors assessment with modern predictive modeling techniques. The work focuses on understanding how human performance, operational conditions, and system-level interactions collectively influence safety risk, and how these interactions can be quantified to support improved design and decision-making. Unlike previous studies that address human factors or predictive modeling in isolation, this research offers a unified framework that links causal human factors indicators with statistical modeling, feature extraction, and machine learning based risk estimation. The novelty of this work lies in the structured pipeline that transforms raw categorical and narrative human factors information into measurable predictors that can be analyzed using structural modeling and machine learning. The methodology includes data preparation, dimensionality reduction, latent pattern discovery, dependence modeling, model training, and interpretability analysis. The study demonstrates how this pipeline uncovers hidden relationships among operational errors, environmental influences, maintenance actions, design considerations, and crew behavior. The findings show that the integrated approach improves the accuracy and stability of risk prediction and highlights specific human factors patterns that consistently contribute to elevated risk levels. These insights support targeted mitigation strategies, inform design improvements, and help prioritize safety interventions. The work concludes that a combined human factors and predictive modeling framework enhances the ability of organizations to identify vulnerabilities earlier, allocate resources more effectively, and strengthen system resilience. This approach is adaptable to diverse aviation contexts and offers a practical path for transforming human factors data into actionable safety intelligence.
This study develops an efficient framework coupling the Lattice Boltzmann Method with the Actuator Line Method to evaluate the unsteady downwash/outwash of eVTOL aircraft. By incorporating a modified Prandtl loss function with geometric smearing correction, the framework accurately predicts velocity profiles and preserves unsteady vortices at lower computational costs than conventional RANS-based simulations. Analyzing three distinct eVTOL configurations sized for identical payload missions reveals that higher disk loading and multi-rotor interactions generate highly asymmetric, localized jet-like outwash structures, contrasting with the symmetric ground-level footprint of single rotor designs. Utilizing a 95th percentile velocity metric, transient peak hazards breach the regulated vertiport Safety Area boundary, extending up to 1.65 times the prescribed baseline limit. These findings demonstrate that time-averaged metrics underestimate physical hazards, highlighting the necessity for future guidelines to mandate configuration specific evaluations utilizing high-resolution unsteady flow data and robust statistical processing.
A wind tunnel investigation to assess the impact of rotor-fuselage spacing on the development of the Vortex Ring State and flow topology is presented. Particle Image Velocimetry was utilised to investigate flow mechanisms across a range of rotor-fuselage spacings and descent ratios, which were compared to that of an isolated rotor configuration. Mean flow data was used to identify coherent flow structures, whilst flow unsteadiness was investigated through statistical analysis of the velocity fluctuations. It was found at cases of Vortex Ring State onset, the presence of the fuselage delays the development of the Vortex Ring State for all rotor-fuselage separation distances tested. Furthermore, certain cases of rotor-fuselage spacings display a rotor-fuselage aerodynamic interaction that results in an increased effective descent ratio.
For Urban Air Mobility taxis, passengers will experience different levels of heave motion during flight. Researchers at NASA Armstrong Flight Research Center conducted two studies in which passengers were exposed to varying levels of heave motion in the Armstrong Virtual Reality Passenger Ride Quality Laboratory. In the first study, twenty-three volunteers from the Armstrong workforce evaluated the motions on a five-point rating scale and a binary comfort scale; in the second study, fifty volunteers evaluated a flight experience with varying levels of stimuli using a five-point comfort scale and a five-point passenger acceptance scale. This paper combines the results of these two studies to observe the relationship between heave motion and passenger rider quality and acceptance. Both passenger comfort and acceptance were found to decrease with increasing heave acceleration The statistically significant relationship between the magnitude of heave acceleration and passenger comfort for individual studies and combined results are discussed.
Every year brings new targeted therapies, immunotherapies, and genomic tests, yet far too many patients still cycle through multiple lines of treatment that don’t work, or work only briefly. Behind those statistics is a simple reality: most therapy decisions are still driven by generalized protocols and population-level evidence, not by the unique biology of each patient’s tumor. 1
Vehicle interior noise is a crucial assessment criterion for automotive NVH. It has a significant effect on customer opinions about the quality of a vehicle. Articulation Index (AI) is one of the key sound metrics used to describe speech intelligibility and quantifies the middle and high frequency spectra associated to the internal noise of vehicle. In reality, Vehicle operating under dynamic condition experiences various air-borne noise sources such as tire rolling noise, powertrain noise, intake-exhaust noise & wind noise along with structure borne excitations such as powertrain vibrations, suspension vibrations. It is very challenging to predict cumulative effect of all these excitations to interior noise level and Articulation Index (AI) of vehicle over complete frequency range. The statistical energy analysis (SEA) is a well-known methodology being used to simulate & predict mid & high frequency noise. Objective of this paper is to present the process of development of a SEA simulation model designed to investigate vehicle interior noise & Articulation index and associated correlation against test measurements for various real- world operating scenarios. The SEA simulation model was meticulously developed with close attention given to structural representation which allowed to consider the structure borne excitations along with air borne noise sources during the analysis. The interior trims & sound insulation pack were also in detailed in the model. Both static & dynamic real-world operating scenarios of vehicle or load cases are demonstrated to validate the model against test measurements. The contribution study was performed to determine dominant noise sources and weaker transfer paths for improvement of Articulation index & interior noise quality of vehicle.
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