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AIAA Aviation Forum

作者： Jones, M. G. Nark, D. M. Howerton, B. M. Watson, W. R. NASA Langley Res Ctr Struct Acoust Branch Hampton VA 23681 USA NASA Langley Res Ctr Computat AeroSci Branch Hampton VA 23681 USA

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

This paper provides detailed results achieved by NASA in response to two liner research challenges supporting the International Forum for Aviation Research (IFAR) collaboration. The IFAR consists of representatives from a number of national research laboratories and is established to enable information exchange on aviation research activities. Over the past three years, members of IFAR have investigated three liner research topics of mutual interest. The first is intended to compare experimental methods and impedance eduction methodologies for evaluation of uniform liners. The second is to evaluate the ability of 3D duct propagation codes to predict the effects of spanwise variable-impedance liners on the acoustic pressure field. The third is to explore different approaches to simultaneously educe the impedances for each zone of a three-zone axially segmented liner. Since each challenge was conducted as a blind test, the NASA paper presented at the 2019 AIAA/CEAS Aeroacoustics Conference was limited to preliminary data for the first and third challenges (detailed NASA results for the second challenge were presented in an earlier paper). The purpose of this paper is to provide more extensive results for these two challenges to enable more thorough comparison with the results from the IFAR partners.

关键词： Aviation Sound Pressure Level Data Acquisition Finite Element Method Flow Conditions Mach Numbers Laser Doppler Velocimetry Boundary Value Problems Acoustic Properties optimization algorithm

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Linear-Gaussian Analysis of Information-based Decentralized Data Fusion

Linear-Gaussian Analysis of Information-based Decentralized ...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Dagan, Ofer Ahmed, Nisar R. Univ Colorado Boulder Ann & HJ Smead Aerosp Engn Sci 429 UCB Boulder CO 80309 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

In multi-platform decentralized systems, the problem of data fusion under unknown correlations is of high importance, as it can lead to double counting of common information and over-confident estimates. There are several algorithms that avoid this problem by discounting the information gathered from the local platform estimates being fused based on a predetermined objective function, such that the unknown common information is guaranteed to only be counted once, but this is at the expense of removing new information. Previous work suggested an objective function for covariance intersection (CI) which provides an upper bound on the maximum information loss for non-Gaussian distributions, but naturally accounts for differences in mean as well as covariances between state estimates. This work extends to analyze the special case and properties for linear-Gaussian applications. We derive a closed form solution for an information-based cost function for 1-dimension linear-Gaussian systems and show that a closed form solution for systems with dimensions higher than 2 does not exist. We further analyse an n-dimensional case (n = 16) based on a realistic multi-platform cooperative tracking Monte-Carlo simulation, results indicate that the approach performs similarly to existing CI techniques based on trace of fused covariance matrix, but tends to spread information throughout network more evenly and reduce information losses at each node.

关键词： Data Fusion Covariance Intersection Monte Carlo Simulation Kalman Filter Probability Density Functions Kullback Leibler Divergence optimization algorithm Numerical optimization Aerospace System Onboard Sensors

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The Structural Suitability of Tensegrity Aircraft Wings

The Structural Suitability of Tensegrity Aircraft Wings

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Mills, Austin S. Myszka, David H. Woods, Daniel C. Joo, James J. Murray, Andrew P. Univ Dayton Dept Mech & Aerosp Engn Dayton OH 45469 USA Air Force Res Lab Wright Patterson AFB OH 45433 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

This paper investigates the suitability of tensegrity aircraft wing concepts and compares their simulated structural performance to a baseline conventional wing structure. Tensegrity systems, which consist of arrangements of struts and cables, are appealing for their structural efficiency, enabling lightweight structures with each member loaded in tension or compression. Of specific interest, tensegrity systems may provide a pathway to morphing aircraft structures through the actuation of cables. With an eye towards this application, the present study compares two tensegrity-based wing designs, generated through designer insights and structural topology optimization methods, to the aluminum Van's RV-4 aircraft rib/spar wing structure, chosen as the baseline performance case. Aerodynamic loading conditions are derived which simulate a 2g pullup maneuver and a 1g pushover, intended to interrogate the structures under characteristic positive and negative loading. The first tensegrity concept, developed with design judgment, is configured by merging known unit cells and is shown to yield deflections and strain energies comparable to the conventional wing at a fraction of its weight. The second tensegrity design, in contrast, is developed by application of a topologya* optimization algorithm, intended to minimize the weight with maximum stress and strain energy constraints. The topology-optimized wing has similar structural performance at slightly less weight than the designer-developed tensegrity wing. This investigation is intended to further the understanding of tensegrity-based designs in the context of aerospace structures, potentially enabling future tensegrity aircraft wings that permit morphing capabilities.

关键词： Aircraft Wing Design Tensegrity Structures Mixed Integer Linear Programming Aerodynamic Force optimization algorithm Aircraft Structures Finite Element Analysis Structural Analysis NACA 23015 Abaqus

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The influence of shape and density of exemplary city areas on the possible benefit of optimized noise abatement A-RNP and RNP-AR procedures

The influence of shape and density of exemplary city areas o...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Morscheck, Fabian Deutsch Zentrum Luft & Raumfahrt D-38108 Braunschweig Niedersachsen Germany

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Precision approaches with curved segments approaches could be used to mitigate noise pollution on the population. The process has been automated to create optimized approaches with noise pollution reduction varying from 0% to up to 88%. Possible benefits depend on population density and the shape of populated areas. To low density reduces possible savings as no population is there to avoid. High density is unpropitious as well as there is no space to divert the flightpath to. Furthermore most noise cost is generated during the final approach segments. Population close to the runway is promising for possible benefits if not too close to be avoided. At this point RNP-AR approaches could avoid closer populations as late turns during the final approach are allowed. As most cost reduction is generated during the final approach phases promising candidates could be found by only optimizing the final and intermediate approach and comparing the results.

关键词： Required Navigation Performance Runways Intermediate Approach Segment Cost Reduction Aircraft Noise Sound Pressure Level Bank Angle Aircraft Trajectory optimization algorithm Model Aircraft

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Multiscale Analysis of Composites Using Surrogate Modeling and Information Optimal Designs

Multiscale Analysis of Composites Using Surrogate Modeling a...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Piekenbrock, Matthew Stucknert, Joshua Arnold, Steven M. Ricks, Trenton NASA Mat & Struct Div Glenn Res Ctr 21000 Brookpk Rd Cleveland OH 44135 USA NASA Mat & Struct Div Glenn Res Ctr Multiscale & Multiphys Modeling Branch 21000 Brookpk Rd Cleveland OH 44135 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

In this effort, a general workflow which leverages machine learning and recent advances in optimal experimental design theory is introduced to infer hidden properties from experimental observations. Optimal design provides the most efficient and informative tests and is especially useful when resources only permit a limited number of tests. The potential applicability of this field herein is broadened by utilizing machine learning models as efficient, accurate, and generalizable surrogate models to optimize over the experimental design space. This workflow is demonstrated by inferring the hidden lower length scale constituent fiber and matrix properties of composite materials from macroscale mechanical properties derived from the response of "virtual" laminated composite experiments. This method requires a model to map the relationship between the hidden and observable properties and an efficient surrogate model to make the optimization tractable. The virtual data, produced by multiple applications of the Generalized Method of Cells (GMC), consists of 'point-wise' properties extracted from composite laminate tensile response curves under uniaxial loading. The surrogate model is an artificial neural network trained to predict these pointwise properties (e.g., modulus, proportional limit stress, ultimate tensile stress) to a sufficiently high accuracy. The optimal experimental design framework proposed is a statistically principled approach designed to uncover information about microstructure-properties-performance relationships. A priori knowledge of the explicit connections between the virtual data and the microstructure properties (e.g., laminate lay-up sequence, fiber volume fraction, etc.) are exploited to demonstrate the fidelity of our method. In doing so, a verifiable information theoretic explanation of the relationships between composite response, microstructure, and constitutive properties for an arbitrarily diverse set of laminate composites is established

关键词： Surrogate Model Artificial Neural Network Fiber Volume Fraction Ultimate Tensile Strength Microstructure Hyperparameter optimization Stress Strain Curve Micromechanics optimization algorithm Polymer Matrix Composites

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Adaptive Retransmission Time Out in Flying Ad-Hoc Network By LSTM Machine Learning: Round Trip Time Prediction

Adaptive Retransmission Time Out in Flying Ad-Hoc Network By...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Mirzaeinial, Amir Mirzaeinia, Mehdi Shekaramiz, Mohammad Hassanalian, Mostafa New Mexico Inst Min & Technol Dept Comp Sci & Engn Socorro NM 87801 USA Amirkabir Univ Technol Dept Elect Engn Tehran Iran Utah Valley Univ Elect & Comp Engn Orem UT 84058 USA New Mexico Inst Min & Technol Dept Mech Engn Socorro NM 87801 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Swarming drones deploy flying Ad-hoc networks to communicate with each other and also with ground stations. In some applications, each flying unit has to forward a large amount of traffic data, such as recorded video, images, and collected data. Besides, in this type of application, data traffic loss is very probable in wireless radio communications. Therefore, a more efficient technique is required to detect traffic loss and traffic retransmission. In this study, advanced time series prediction machine learning technique such as LSTM is applied to a Round Trip Time (RTT) series prediction. Result of different LSTM configuration and some tuning reached to the prediction accuracy of 95.6%.

关键词： Machine Learning Recurrent Neural Network Drone Ground Station Radio Communication Wireless Communications optimization algorithm Autonomous Systems Data Transmission

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Matrix Factorization Based on BatchNorm and Preference Bias 13

Matrix Factorization Based on BatchNorm and Preference Bias

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13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Wang, Bing Ma, Wenming Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China

ISBN: (纸本)9780738105451

With the rapid development of science and technology, huge amounts of information fill people's lives, and the accompanying information overload phenomenon has become an urgent problem to be solved. Because the recommendation system can quickly find the products users want in the massive item information, to a certain extent It has attracted much attention to solve the problem of information overload. Matrix factorization is a commonly used technique in recommendation systems. It can effectively improve the recommendation effect when the scoring matrix is sparse. However, due to its own reasons, matrix factorization has many problems such as sparseness, cold start, and low interpretability. In the field of deep learning, because the normalization technology BatchNorm can optimize the training process, accelerate the training speed and make the training results more stable, it has been studied by a large number of scholars. In this paper, Matrix Factorization Based on BatchNorm and Preference Bias is proposed. BatchNorm is combined with matrix factorization, user and item preferences are added, and Adam algorithm is used for optimization. Experiments show that the algorithm in this paper has a good recommendation effect on sparse matrix.

关键词： matrix factorization batchnorm bias adam algorithm optimization algorithm

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Towards an effective deep learning-based intrusion detection system in the internet of things

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TELEMATICS AND INFORMATICS REPORTS 2022年 7卷

作者： Pampapathi, B. M. Guptha, M. Nageswara Hema, M. S. Sri Venkateshwara Coll Engn Dept Comp Sci & Engn Bengaluru Karnataka India Anurag Univ Dept Informat Technol Hyderabad Telangana India

Distributed Sensor Networks play a vital role in the day-to-day world of computing applications, from the cloud to the Internet of Things (IoT). These computing applications devices are normally attached with the microcontrollers like Sensors, actuators, and Adriano network connectivity. Defensive network with an Intrusion Detection System thus serves as the need of modern networks. Despite decades of inevitable development, the Intrusion Detection System is still a challenging research area as the existing Intrusion Detection System operates using signature-based techniques rather than anomaly detection. The existing Intrusion Detection System are thus facing challenges for improvement in Intrusion Detection, Handling heterogeneous data sources is hard for discovering zero-day attacks in IoT networks. This paper presents Filtered Deep Learning Model for Intrusion Detection with a Data Communication approach. The proposed model is composed of five phases: Initialization of Sensor Networks, Cluster Formation in addition to Cluster Head Selection, Connectivity, Attack Detection, and Data Broker. The proposed Model for Intrusion Detection was found to outperform the existing Deep Learning Neural Network and Artificial Neural Network. Experimental results showed a better result of 96.12% accuracy than the dominant algorithms.

关键词： Filtered deep learning neural network Modified K-means algorithm Advanced message queuing protocol Levy flight based Elephant herding optimization algorithm Message queuing telemetry transport

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Fairness in Decentralized Strategic Deconfliction in UTM

Fairness in Decentralized Strategic Deconfliction in UTM

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Evans, Antony Egorov, Maxim Munn, Steven Airbus UTM Sunnyvale CA 94086 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Decentralized strategic deconfliction is a critical function in Unmanned Aircraft System (UAS) Traffic Management (UTM), that serves as the enabler of safe operations for cooperative traffic. In this paper we explore the implications of different approaches to strategic decentralized trajectory deconfliction in UTM on fairness. First-come, first-served (FCFS) allocation of resources has long-standing acceptance in the aviation community and is applied both tactically and strategically in air traffic management. However, it is unclear what the fairness implications are for a FCFS allocation of access to airspace in a decentralized UTM architecture. In this paper, simulation is used to explore how a FCFS approach to strategic deconfliction in UTM - based on when operators file their flight plans - performs in terms of fairness. Fairness is quantified by comparing average ground delay across operators and by calculating a normalized fairness metric that accounts for operator cost of delay. Two scenario types are simulated: (1) two package delivery operators serving a common region from separate warehouses;and (2) two air taxi operators serving the same network of 7 vertiports. Results are generated for a range of traffic densities and differences in file-ahead time between operators. Results indicate that, for a decentralized FCFS approach to strategic deconfliction based on when operators file their flight plans, there may be a significant imbalance in delays between operators based on how far in advance they are able to file ahead, and on traffic demand levels. To ensure fairness at envisioned traffic densities it may therefore be necessary to constrain how early flight plan requests can be prioritized over later requests - similar in concept to a freeze horizon in TBFM.

关键词： Estimated Time of Arrival Controlled Airspace Air Traffic Flow Management Unmanned Aircraft System Air Transportation Collaborative Decision Making Federal Aviation Administration Air Navigation Service Provider Urban Air Mobility optimization algorithm

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Supervisory Energy Management in Hybrid AC-DC Microgrids Based on a Hybrid Distributed algorithm

Supervisory Energy Management in Hybrid AC-DC Microgrids Bas...

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Clemson-University Power Systems Conference (PSC)

作者： Papari, Behnaz Cox, Robert Sockeel, Nicolas Hoang, Phuong H. Ozkan, Gokhan Univ North Carolina Charlotte Energy Prod & Infrastruct Ctr EPIC 8700 Phillips Rd Charlotte NC 28223 USA Clemson Univ Holcombe Dept Elect & Comp Engn 105 Sikes Hall Clemson SC 29634 USA

ISBN: (纸本)9781728193847

As awareness of human global footprint grows, solution is investigated to reduce greenhouse gas emission (GHG). To reduce the carbon intensity of electricity production, a large deployment of renewable energy sources (RESs), such as solar panels and wind turbine, has become a mandatory goal. Therefore, more and more microgrids (MGs) appear. In this context, this paper proposes an effective framework for optimal operation management of hybrid AC-DC microgrids (MGs) incorporating both dispatchable and non-dispatchable energy sources as well as battery energy storage system (BESS). The proposed method is constructed based on mixture of an alternating direction method of multipliers (ADMM) and firefly algorithm (FA) as a non-lineara* optimization algorithm. The performance of the ADMM-MFA is assessed using a typical hybrid AC-DC microgrid. The simulation results show the high efficacy and accuracy of the proposed method in comparison with other well-known methods.

关键词： Alternating Direction Method of Multipliers Distributed Control Hybrid Microgrid Multi-Agent optimization algorithm

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