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Adaptive Kriging Model for Fuzzy Safety Degree Analysis to Time-Dependent Structure

AIAA JOURNAL 2021年第5期59卷 1528-1538页

作者： Feng, Kaixuan Lu, Zhenzhou Wei, Ning Li, Guijie Northwestern Polytech Univ Sch Aeronaut POB 120 Xian 710072 Shaanxi Peoples R China China Acad Engn Phys Inst Syst Engn POB 64 Mianyang 621999 Sichuan Peoples R China

Time-dependent failure possibility (TDFP) has the capability to measure the safety degree of a structure under fuzzy uncertainty over a service time interval of interest. To improve the computational efficiency in estimating TDFP, an adaptive kriging model is developed in this paper. In the proposed method, TDFP is first represented as a bilevel problem, where the outer level is a one-dimensional rooting problem with respect to the membership level of fuzzy variables and the inner level is a minimum optimization of the model response function where the interval parameters and time are regarded as the same level. Second, the dichotomy instead of the interpolation technique is employed to solve the one-dimensional rooting problem. Third, the numerical simulation replacing the optimization technique is used to settle the optimization problem. Finally, to greatly reduce the computational cost in estimating the TDFP especially involved in numerical simulations, an adaptive kriging model is proposed for TDFP analysis. Additionally, the proposed kriging model can be used to estimate the safety life based on the TDFP constraint. The results of a mathematical example and two aerospace engineering examples show that the proposed method significantly improves the efficiency in estimating the TDFP and the TDFP-based safety life with acceptable precision.

关键词： probability density functions Numerical Simulation Aerospace Engineering Elevator Aircraft Wings Surrogate Model Finite Element Modeling Aerodynamic Force Aeronautical Engineering Allowable Stress

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Uncooperative Spacecraft Pose Estimation Using Monocular Monochromatic Images

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JOURNAL OF SPACECRAFT AND ROCKETS 2021年第2期58卷 284-301页

作者： Shi, Jian-Feng Ulrich, Steve MDA 9445 Airport Rd Brampton ON L6S 4J3 Canada Carleton Univ Dept Mech & Aerosp Engn 1125 Colonel Dr Ottawa ON K1S 5B6 Canada

Imaging cameras are cost-effective sensors for spacecraft navigation. Image-driven techniques to extract the target spacecraft from its background are efficient and do not require pretraining. In this paper, we introduce several image-driven foreground extraction methods, including combining the difference of Gaussian-based scene detection and graph manifold ranking-based foreground saliency generation. We successfully apply our foreground extraction method on infrared images from the STS-135 flight mission captured by the space shuttle's Triangulation and LIDAR Automated Rendezvous and Docking System (TriDAR) thermal camera. Our saliency approach demonstrates state of-the-art performance and provides an order of magnitude reduction in processing speed from the traditional methods. Furthermore, we develop a new uncooperative spacecraft pose estimation method by combining our foreground extraction technique with the level-set region-based pose estimation with novel initialization and gradient descent enhancements. Our method is validated using synthetically generated Envisat, Radarsat model, and International Space Station motion sequences. The proposed process is also validated with real rendezvous flight images of the International Space Station.

关键词： Spacecrafts ISS RADARSAT Sensor Fusion Target Spacecraft Spacecraft Proximity Operations Space Shuttle LIDAR probability density functions Earth

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Fully Bayesian Human-Machine Data Fusion for Robust Online Dynamic Target Characterization

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2021年第2期18卷 26-49页

作者： Muesing, Jeremy Ahmed, Nisar Burks, Luke Iuzzolino, Michael Szafir, Danielle Albers Univ Colorado Smead Aerosp Engn Sci 429 UCB Boulder CO 80309 USA Univ Colorado Dept Comp Sci 430 UCB Boulder CO 80309 USA Univ Colorado Dept Informat Sci 478 UCB Boulder CO 80309 USA

This work examines the problem of fusing human operator observations with probabilistic information extracted by an automated data fusion system, in the context of dynamic multitarget track characterization for large-scale surveillance. This soft data fusion problem is challenging because human operator observation errors are difficult to calibrate a priori and in general exhibit subtle conditional dependencies. Furthermore, the efficacy of combining inputs from operators depends heavily on operator error characteristics as well as track characterization uncertainty. A hierarchical fully Bayesian probabilistic model is developed to explicitly account for human sensor quality and Markovian dependencies in human reports. This model is used to perform online Bayesian inference via Gibbs sampling to simultaneously update the data fusion system's knowledge of human sensor characteristics and target type probabilities. A strategy is also developed for value of information assessments to automatically query human operators for inputs as needed. Efficient approximations of high-dimensional human sensor parameter posterior distributions via Dirichlet probability density function (pdf) moment matching and parameter tying are also developed to provide significant computational speedups with little information loss. Results for different simulated operator profiles and automated target characterization baselines show that fully Bayesian fusion improves track characterization performance, while intelligently accounting for uncertain operator characteristics.

关键词： Data Visualization Multisensor Fusion probability density functions Automated Machine Learning False Positive Rate Computing Automatic Target Recognition Maximum Likelihood Estimation Long Short Term Memory Kalman Filter

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Transfer Function to Predict Warhead Fragmentation In-Flight Behavior from Static Data

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AIAA JOURNAL 2021年第11期59卷 4777-4793页

作者： Mulekar, Omkar S. Bevilacqua, Riccardo Jerome, Elisabetta L. Hatch-Aguilar, Thomas J. Univ Florida Dept Mech & Aerosp Engn 939 Sweetwater DrMAE A 211 Gainesville FL 32611 USA Air Force Test Ctr Weap T&E 101West D AveBldg 1Suite 115 Eglin Air Force Base FL 32542 USA US Naval Air Warfare Ctr Weap Div 1 Admin Circle China Lake CA 93555 USA

In recent years, static warhead arena tests have used stereoscopic cameras to count fragments in real time and correlate their individual velocity and mass. This new technique provides the ability to generate higher confidence dynamic fragmentation data that in turn can be used for code validation and much more realistic lethality and collateral damage calculations. Arena tests involve detonating static warheads;whereas, in reality, warheads arrive and detonate at high speeds. State-of-the-art simulations for high-speed warhead detonations can be challenging and time consuming while potentially missing the relevant physics of real-world detonations. In this investigation, a framework to predict warhead fragment track characteristics from real-world static arena experimental data and dynamic simulation data is explored. A model is trained on simulation and experimental data to predict the number of fragments that pass through a defined surface of interest given warhead in-flight terminal conditions. Distributions of fragment-surface intersections are modeled by Gaussian mixture models (GMMs), and random forest regressors are trained to predict these GMMs. Monte Carlo methods are used to show that random forests trained by both simulation and experimental data can predict fragment-surface intersection distributions of both static and dynamic high-speed warhead configurations.

关键词： Warheads Gaussian Mixture Models Detonation Monte Carlo Simulation probability density functions Yaw Ball Bearings Numerical Simulation Sandia National Laboratories Naval Air Warfare Center

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Tunnel Gaussian Process Model for Learning Interpretable Flight's Landing Parameters

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2021年第12期44卷 2263-2275页

作者： Goh, Sim Kuan Jun Lim, Zhi Alam, Sameer Pratap Singh, Narendra Nanyang Technol Univ Air Traff Management Res Inst Sch Mech & Aerosp Engn Singapore 639798 Singapore Airports Author India Lucknow Airport 226009 India

Approach and landing accidents have resulted in a significant number of hull losses worldwide. Technologies (e.g., instrument landing system) and procedures (e.g., stabilized approach criteria) have been developed to reduce the risks. This paper proposes a data-driven method to learn and interpret flight's approach and landing parameters to facilitate comprehensible and actionable insights into flight dynamics. Specifically, two variants of tunnel Gaussian process (TGP) models are developed to elucidate aircraft's approach and landing dynamics using advanced surface movement guidance and control system (A-SMGCS) data, which then indicates the stability of flight. TGP hybridizes the strengths of sparse variational Gaussian process and polar Gaussian process to learn from a large amount of data in cylindrical coordinates. This paper examines TGP qualitatively and quantitatively by synthesizing three complex trajectory datasets and compared TGP against existing methods on trajectory learning. Empirically, TGP demonstrates superior modeling performance. When applied to operational A-SMGCS data, TGP provides the generative probabilistic description of landing dynamics and interpretable tunnel views of approach and landing parameters. These probabilistic tunnel models can facilitate the analysis of procedure adherence and augment existing aircrew and air traffic controller' displays during the approach and landing procedures, enabling necessary corrective actions.

关键词： probability density functions Advanced Surface Movement Guidance and Control System Instrument Landing System Cylindrical Coordinates Aircrew Air Traffic Controller Flight Dynamics Missed Approach Point Gaussian Mixture Models Air Traffic Management

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Combining distribution-based neural networks to predict weather forecast probabilities

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QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 2021年第741期147卷 4337-4357页

作者： Clare, Mariana C. A. Jamil, Omar Morcrette, Cyril J. Imperial Coll London Dept Earth Sci & Engn London SW7 2AZ England Met Off Exeter Devon England Univ Exeter Exeter Devon England

The success of deep learning techniques over the last decades has opened up a new avenue of research for weather forecasting. Here, we take the novel approach of using a neural network to predict full probability density functions at each point in space and time rather than a single output value, thus producing a probabilistic weather forecast. This enables the calculation of both uncertainty and skill metrics for the neural network predictions, and overcomes the common difficulty of inferring uncertainty from these predictions. This approach is data-driven and the neural network is trained on the WeatherBench dataset (processed ERA5 data) to forecast geopotential and temperature 3 and 5 days ahead. Data exploration leads to the identification of the most important input variables. In order to increase computational efficiency, several neural networks are trained on small subsets of these variables. The outputs are then combined through a stacked neural network, the first time such a technique has been applied to weather data. Our approach is found to be more accurate than some coarse numerical weather prediction models and as accurate as more complex alternative neural networks, with the added benefit of providing key probabilistic information necessary for making informed weather forecasts.

关键词： data exploration deep learning ensemble dropout probabilistic weather forecasting probability density functions ResNet stacked neural network

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Transient Energy Response Analysis of Vibro-Acoustic Systems with Fuzzy Uncertainty

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JOURNAL OF AIRCRAFT 2021年第1期58卷 210-215页

作者： Chen, Qiang Fei, Qingguo Wu, Shaoqing Li, Yanbin Southeast Univ Jiangsu Engn Res Ctr Aerosp Machinery Sch Mech Engn Nanjing 211189 Peoples R China Southeast Univ Jiangsu Engn Res Ctr Aerosp Machinery Dept Engn Mech Nanjing 211189 Peoples R China

Transient energy response analysis of vibro-acoustic systems with fuzzy uncertainty is investigated by researchers. The first test model is a plate–cavity system consisting of a simply supported plate and a cavity with rigid walls. The analysis frequency band is set to [1778, 2239] Hz. The fuzzy uncertainties in statistical energy analysis (SEA) parameters are considered. The SEA parameters of systems are regarded as fuzzy parameters with triangular membership functions. The membership function of each fuzzy parameter is divided into four sublevels. The Monte Carlo method (MCM) with 10⁶ samples is used to validate the accuracy of reduced transformation method (RTM).

关键词： Sound Pressure Level Launch Vehicle Fairing Transient Response Frequency Domain Modal Analysis Statistical Energy Analysis Industrial Applications Finite Element Method Numerical Simulation probability density functions

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Methods to Extract Underlying Boundary Conditions from Transient Metal Effectiveness Measurements

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JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER 2021年第4期35卷 657-668页

作者： Michaud, Mathias Povey, Thomas Univ Oxford Oxford Thermofluids Inst Dept Engn Oxford OX1 3PJ England

Metal effectiveness measurements (or overall cooling effectiveness measurements) are becoming increasingly used to understand complex coupled systems in gas turbine experimental research. Unlike traditional techniques in which individual boundary conditions are measured in isolation and superposed using a thermal model, metal effectiveness measurements give the final result of a complex coupled system. In correctly scaled experiments this allows aerothermal performance at near-engine conditions to be evaluated directly, and is thus powerful both as a research technique and for derisking engine development programs. The technique is particularly useful for evaluating the thermal performance of internally cooled turbine components, because of the complexity and degree of interaction of the underlying boundary conditions. An intrinsic limitation of metal effectiveness measurement data is that the individual boundary conditions (e.g., the internal and external heat transfer coefficients) cannot be directly obtained from the final measurement. Decoupling of these boundary conditions would allow deeper understanding of the systems that are the subject of experiments. The objective of this paper is to present methods to extract the individual underlying boundary conditions from data available in typical metal effectiveness experimental measurements, and to assess the uncertainty associated with decoupling techniques. Although we reference experimental data from advanced facilities for metal effectiveness research throughout, much of the analysis is performed using a low-order heat transfer model to allow the impact of experiment design and measurement errors to be clearly separated at each stage of the analysis.

关键词： Thermal Diffusivity Heat Transfer Coefficients Thermal Modeling Gas Turbine External Flows Thermal Response High Aspect Ratio High Pressure Turbine probability density functions Monte Carlo Simulation

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Effects of Atmospheric Profiles on Sonic Boom Perceived Level from Supersonic Vehicles

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AIAA JOURNAL 2021年第12期59卷 5020-5028页

作者： Leal, Pedro B. C. Schrass, Jacob A. Giblette, Ted N. Hunsaker, Doug F. Shen, Hao Logan, Timothy S. Hartl, Darren J. Texas A&M Univ Dept Aerosp Engn College Stn TX 77843 USA Utah State Univ Mech & Aerosp Engn Dept Logan UT 84322 USA Boeing Co Boeing Res & Technol Hazelwood MO 63042 USA

U.S. national research priorities include reducing the disruption caused by sonic booms, which hindered the economic viability of supersonic aircraft in decades past. However, shock waves are unavoidable during supersonic flight;and exposure of people, animals, and structures to these shock waves on the ground cannot be eliminated entirely. It is herein shown that changing atmospheric conditions, gusts, and heights above ground can result in any supersonic aircraft that is acceptably quiet at specific atmospheric conditions becoming uncomfortably loud in other flight conditions. These performance variations are noticeable for periods ranging from 12 h to one year across the continental United States of America. Therefore, strategies to actively mitigate sonic booms may become necessary as researchers work toward resumption of overland commercial supersonic flight.

关键词： Conical Shock Wave Commercial Aircraft Atmospheric Conditions Radiosondes National Oceanic and Atmospheric Administration Los Angeles International Airport Federal Aviation Administration Winter Solstice Atmospheric Turbulence probability density functions

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Performance of Mixture-Ratio-Controlled Hybrid Rockets Under Uncertainties in Fuel Regression

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JOURNAL OF PROPULSION AND POWER 2021年第1期37卷 86-99页

作者： Ozawa, Kohei Shimada, Toru Kyushu Inst Technol Dept Mech & Control Engn Fac Engn Tobata Ku 1-1 Senshi Cho Kitakyushu Fukuoka 8048550 Japan Japan Aerosp Explorat Agcy JAXA Inst Space & Astronaut Sci Dept Space Flight Syst Chuo Ku 3-1-1 Yoshinodai Sagamihara Kanagawa 2525210 Japan

This paper evaluates various sources of oxidizer to fuel mass ratio (O/F) shifts in hybrid rockets and paths (physical phenomena) through which these O/F shifts affect flight performance. Moreover, the performance increase of O/F control in hybrid rockets is evaluated. Vertical launches of O/F uncontrolled and O/F controlled of hybrid sounding rockets were simulated under two uncertainty models of fuel regression behavior based on experimental data: a) systematic errors with a constant deviation within +/- 3 sigma and 2) random errors subject to a probability distribution. These simulations included all sources of O/F shifts that originated in the fuel regression behavior and all paths through which the O/F shifts affect flight performance. Residual propellant mass and decreases in specific impulse are found to be the dominant causes of performance loss under both uncertainty models. For both cases 1 and 2, the O/F-controlled hybrid rockets maintained the performance expected under nominal fuel regression behavior, whereas the O/F-uncontrolled hybrid rockets had a lower performance by upwards of 6.69 and 4.06% in Delta V for cases 1 and 2, respectively. For case 2, 3008 flight simulations revealed that the worst case of the O/F-controlled hybrid rocket had a 4.06 to 4.49% larger Delta V and 10.5 to 13.3% higher apogee than that of the O/F-uncontrolled hybrid rocket, and that the O/F-uncontrolled hybrid rocket had a 6.61 times larger standard deviation in Delta V. These results mean that the elimination O/F shift in hybrid rockets significantly improves performance, as well as the accuracy and reliability of performance predictions.

关键词： Fuel Regression Hybrid Rocket Propellant Mass Fraction Sounding Rockets Flight Performance Flight Simulation probability density functions Apogee Mass Flow Rate Precombustion Chamber

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