A Neural Network Approach to Aircraft Performance Model Forecasting
A Neural Network Approach to Aircraft Performance Model Forecasting

Author: Nicolas Vincent-Boulay

Publisher:

Published: 2020

Total Pages: 0

ISBN-13:

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Performance models used in the aircraft development process are dependent on the assumptions and approximations associated with the engineering equations used to produce them. The design and implementation of these highly complex engineering models are typically associated with a longer development process. This study proposes a non-deterministic approach where machine learning techniques using Artificial Neural Networks are used to predict specific aircraft parameters using available data. The approach yields results that are independent of the equations used in conventional aircraft performance modeling methods and rely on stochastic data and its distribution to extract useful patterns. To test the viability of the approach, a case study is performed comparing a conventional performance model describing the takeoff ground roll distance with the values generated from a neural network using readily-available flight data. The neural network receives as input, and is trained using, aircraft performance parameters including atmospheric conditions (air temperature, air pressure, air density), performance characteristics (flap configuration, thrust setting, MTOW, etc.) and runway conditions (wet, dry, slope angle, etc.). The proposed predictive modeling approach can be tailored for use with a wider range of flight mission profiles such as climb, cruise, descent and landing.

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics
A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

Author: Takahisa Kobayashi

Publisher:

Published: 2001

Total Pages: 18

ISBN-13:

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In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

Neural Network Prediction of New Aircraft Design Coefficients
Neural Network Prediction of New Aircraft Design Coefficients

Author: National Aeronautics and Space Adm Nasa

Publisher: Independently Published

Published: 2019

Total Pages: 36

ISBN-13: 9781792937910

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This paper discusses a neural network tool for more effective aircraft design evaluations during wind tunnel tests. Using a hybrid neural network optimization method, we have produced fast and reliable predictions of aerodynamical coefficients, found optimal flap settings, and flap schedules. For validation, the tool was tested on a 55% scale model of the USAF/NASA Subsonic High Alpha Research Concept aircraft (SHARC). Four different networks were trained to predict coefficients of lift, drag, moment of inertia, and lift drag ratio (C(sub L), C(sub D), C(sub M), and L/D) from angle of attack and flap settings. The latter network was then used to determine an overall optimal flap setting and for finding optimal flap schedules. Norgaard, Magnus and Jorgensen, Charles C. and Ross, James C. Ames Research Center NASA-TM-112197, A-976719, NAS 1.15:112197 RTOP 519-30-12...

Aircraft Position Prediction Using Neural Networks
Aircraft Position Prediction Using Neural Networks

Author: Anuja Doshi

Publisher:

Published: 2005

Total Pages: 144

ISBN-13:

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The Federal Aviation Administration (FAA) has been investigating early warning accident prevention systems in an effort to prevent runway collisions. One system in place is the Airport Movement Area Safety System (AMASS), developed under contract with the FAA. AMASS uses a linear prediction system to predict the position of an aircraft 5 to 30 seconds in the future. The system sounds an alarm to warn air traffic controllers if it foresees a potential accident. However, research done at MIT and Volpe National Transportation Systems Center has shown that neural networks more accurately predict the future position of aircraft. Neural networks are self-learning, and the time required for the optimization of safety logic will be minimized using neural networks. More accurate predictions of aircraft position will deliver earlier warnings to air traffic controllers while reducing the number of nuisance alerts. There are many factors to consider in designing an aircraft position prediction neural network, including history length, types of inputs and outputs, and applicable training data. This document chronicles the design, training, performance, and analysis of a position prediction neural network, and the presents the resulting optimal neural network for the AMASS System. Additionally, the neural network prediction model is then compared other prediction models, including a constant speed, linear regression, and an auto regression model. In this analysis, neural networks present themselves as a superior model for aircraft position prediction.

Aircraft Aerodynamic Parameter Estimation from Flight Data Using Neural Partial Differentiation
Aircraft Aerodynamic Parameter Estimation from Flight Data Using Neural Partial Differentiation

Author: Majeed Mohamed

Publisher: Springer Nature

Published: 2021-02-23

Total Pages: 66

ISBN-13: 9811601046

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This book presents neural partial differentiation as an estimation algorithm for extracting aerodynamic derivatives from flight data. It discusses neural modeling of the aircraft system. The neural partial differentiation approach discussed in the book helps estimate parameters with their statistical information from the noisy data. Moreover, this method avoids the need for prior information about the aircraft model parameters. The objective of the book is to extend the use of the neural partial differentiation method to the multi-input multi-output aircraft system for the online estimation of aircraft parameters from an established neural model. This approach will be relevant for the design of an adaptive flight control system. The book also discusses the estimation of aerodynamic derivatives of rigid and flexible aircraft which are treated separately. The longitudinal and lateral-directional derivatives of aircraft are estimated from flight data. Besides the aerodynamic derivatives, mode shape parameters of flexible aircraft are also identified in the book as part of identification for the state space aircraft model. Since the detailed description of the approach is illustrated through the block diagram and their results are presented in tabular form with figures of parameters converge to their estimates, the contents of this book are intended for readers who want to pursue a postgraduate and doctoral degree in science and engineering. This book is useful for practicing scientists, engineers, and teachers in the field of aerospace engineering.

Time Series Analysis Methods and Applications for Flight Data
Time Series Analysis Methods and Applications for Flight Data

Author: Jianye Zhang

Publisher: Springer

Published: 2016-12-22

Total Pages: 244

ISBN-13: 3662534304

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This book focuses on different facets of flight data analysis, including the basic goals, methods, and implementation techniques. As mass flight data possesses the typical characteristics of time series, the time series analysis methods and their application for flight data have been illustrated from several aspects, such as data filtering, data extension, feature optimization, similarity search, trend monitoring, fault diagnosis, and parameter prediction, etc. An intelligent information-processing platform for flight data has been established to assist in aircraft condition monitoring, training evaluation and scientific maintenance. The book will serve as a reference resource for people working in aviation management and maintenance, as well as researchers and engineers in the fields of data analysis and data mining.

A Reinforcement One-Shot Active Learning Approach for Aircraft Type Recognition
A Reinforcement One-Shot Active Learning Approach for Aircraft Type Recognition

Author: HONGLAN HUANG

Publisher: Infinite Study

Published:

Total Pages: 11

ISBN-13:

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Target recognition is an important aspect of air trafc management, but the study on automatic aircraft identication is still in the exploratory stage. Rapid aircraft processing and accurate aircraft type recognition remain challenging tasks due to the high-speed movement of the aircraft against complex backgrounds. Active learning, as a promising research topic of machine learning in recent decades, can use less labeled data to obtain the same model accuracy as supervised learning, which greatly reduces the cost of labeling a dataset.

A Subsonic Aircraft Design Optimization with Neural Network and Regression Approximators
A Subsonic Aircraft Design Optimization with Neural Network and Regression Approximators

Author: National Aeronautics and Space Administration (NASA)

Publisher: Createspace Independent Publishing Platform

Published: 2018-06-21

Total Pages: 26

ISBN-13: 9781721668342

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The Flight-Optimization-System (FLOPS) code encountered difficulty in analyzing a subsonic aircraft. The limitation made the design optimization problematic. The deficiencies have been alleviated through use of neural network and regression approximations. The insight gained from using the approximators is discussed in this paper. The FLOPS code is reviewed. Analysis models are developed and validated for each approximator. The regression method appears to hug the data points, while the neural network approximation follows a mean path. For an analysis cycle, the approximate model required milliseconds of central processing unit (CPU) time versus seconds by the FLOPS code. Performance of the approximators was satisfactory for aircraft analysis. A design optimization capability has been created by coupling the derived analyzers to the optimization test bed CometBoards. The approximators were efficient reanalysis tools in the aircraft design optimization. Instability encountered in the FLOPS analyzer was eliminated. The convergence characteristics were improved for the design optimization. The CPU time required to calculate the optimum solution, measured in hours with the FLOPS code was reduced to minutes with the neural network approximation and to seconds with the regression method. Generation of the approximators required the manipulation of a very large quantity of data. Design sensitivity with respect to the bounds of aircraft constraints is easily generated. Patnaik, Surya N. and Coroneos, Rula M. and Guptill, James D. and Hopkins, Dale A. and Haller, William J. Glenn Research Center NASA/TM-2004-213059, AIAA Paper 2004-4606, E-14504