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A Methodology To Predict The Empennage In Flight Loads Of A General Aviation Aircraft Using Backpropagation Neural Networks
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Book Synopsis A Methodology to Predict the Empennage In-flight Loads of a General Aviation Aircraft Using Backpropagation Neural Networks by : David Kim
Download or read book A Methodology to Predict the Empennage In-flight Loads of a General Aviation Aircraft Using Backpropagation Neural Networks written by David Kim and published by . This book was released on 2001 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Methodology for the Prediction of the Empennage In-flight Loads of a General Aviation Aircraft Using Backpropagation Neural Networks by : Maciej Marciniak
Download or read book A Methodology for the Prediction of the Empennage In-flight Loads of a General Aviation Aircraft Using Backpropagation Neural Networks written by Maciej Marciniak and published by . This book was released on 1996 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The purpose of this research was to develop a methodology for prediction of strain in the tail section of a general aviation aircraft and to determine the minimum set of sensors necessary to adequately train the neural networks."--Leaf v.
Book Synopsis Improved Methodology for the Prediction of the Empennage Maneuver In-flight Loads of a General Aviation Aircraft Using Neural Networks by : David Kim
Download or read book Improved Methodology for the Prediction of the Empennage Maneuver In-flight Loads of a General Aviation Aircraft Using Neural Networks written by David Kim and published by . This book was released on 2001 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis An Ameliorated Prediction of the Empennage In-flight Gust Loads for a General Aviation Aircraft by : Philippe Marchand
Download or read book An Ameliorated Prediction of the Empennage In-flight Gust Loads for a General Aviation Aircraft written by Philippe Marchand and published by . This book was released on 2000 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book SAE Technical Paper Series written by and published by . This book was released on 1999 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Prediction of Buffet Loads Using Artificial Neural Networks by : Oleg Paul Levinski
Download or read book Prediction of Buffet Loads Using Artificial Neural Networks written by Oleg Paul Levinski and published by . This book was released on 2001 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of artificial neural networks (ANN) for predicting the empennage buffet pressures as a function of aircraft state has been investigated. The buffet loads prediction method which is developed depends on experimental data to train the ANN alogorithm and is able to expand its knowledge base with additional data. The study confirmed that neural networks have a great potential as a method for modelling buffet data. The ability of neural networks to accurately predict magnitude and spectral content of unsteady buffet pressures was demonstrated. Bases on the ANN methodology investigated, a buffet prediction system can be developed to characterise the F/A-18 vertical tail buffet environment at different flight conditions. It will allow better understanding and more efficient alleviation of the empennage buffeting problem.
Book Synopsis Using an Artificial Neural Network to Predict Flight Loads for Maneuver Envelope Expansion of a Modern Aircraft by : Randolph Carlyle Thompson
Download or read book Using an Artificial Neural Network to Predict Flight Loads for Maneuver Envelope Expansion of a Modern Aircraft written by Randolph Carlyle Thompson and published by . This book was released on 2007 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Aircraft Aerodynamic Parameter Estimation from Flight Data Using Neural Partial Differentiation by : Majeed Mohamed
Download or read book Aircraft Aerodynamic Parameter Estimation from Flight Data Using Neural Partial Differentiation written by Majeed Mohamed and published by Springer Nature. This book was released on 2021-02-23 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis A Neural Network Approach to Aircraft Performance Model Forecasting by : Nicolas Vincent-Boulay
Download or read book A Neural Network Approach to Aircraft Performance Model Forecasting written by Nicolas Vincent-Boulay and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Predicting General Aviation Accidents Using Machine Learning Algorithms by : Bradley S. Baugh
Download or read book Predicting General Aviation Accidents Using Machine Learning Algorithms written by Bradley S. Baugh and published by . This book was released on 2020 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Aviation safety management is implemented through reactive, proactive, and predictive methodologies. Unlike reactive and proactive safety, predictive safety can predict the next accident and enable prevention before an actual occurrence. The study outlined here promotes predictive safety management through machine learning technologies using large amounts of data to facilitate predictive modeling. The study addresses efforts to reduce General Aviation accidents, an effort that was renewed in earnest with the Federal Aviation Administration’s 1998 Safer Skies Initiative. Over the past 22 years, the General Aviation fatality rate has decreased. However, accidents still happen, and there is some evidence showing the number of accidents, representing hazard exposure, is increasing. The accident data suggest that the aviation community still has more to learn about the variables involved in an accident sequence. The purpose of the study was to conduct an exploratory data-driven examination of General Aviation accidents in the United States from January 1, 1998, to December 31, 2018, using machine learning and data mining techniques. The goal was to determine what model best predicts fatal and severe injury aviation accidents and further, what variables were most important in the prediction model. The study sample comprised 26,387 fixed-wing general aviation accidents accessed through the publicly accessible National Transportation Safety Board Aviation Accident Database and Synopses archive. Using a mixed-methods approach, the study employed both unstructured narrative text and structured tabular data within the predictive modeling. First, the accident narratives were culled using text mining algorithms to develop text-based quantitative variables. Next, data mining algorithms were used to develop models based on both text- and data-based variables derived from the accident reports. Five types of machine learning models were created using SAS® Enterprise MinerTM, including the Decision Tree, Gradient Boosting, Logistic Regression, Neural Network, and Random Forest. Additionally, three broad sets of variables were used in modeling, including text-only, data-only, and a combination of text and data variables. Three models, Logistic Regression (text-only variables), Random Forest (text-only variables), and Gradient Boosting (text and data variables), emerged with a similar prediction capability. The top six variables within the models were all text-based covering Medical, Slow-flight and stalls, Flight control, IMC flight, Weather factors, and Flight hours topics. The Logistic Regression (Text) model was selected as the champion model: Misclassification Rate = 0.098, ROC Index = 0.945, and Cumulative Lift = 3.46. The results of the study provide insights to the entire General Aviation community, including government, industry, flight training, and the operational pilot. Specific recommendations include the following areas: 1) improve the quality and usefulness of accident reports for machine learning applications, 2) investigate ways to capture and publish more open-source flight data for use in safety modeling, 3) invest in additional medical education and find ways to address impairing medications and high risk medical conditions, 4) renew efforts on improving flight skills and combatting decision-based errors, 5) emphasize the importance of weather briefings, pre-flight planning, and weather-based risk management, and 6) create an aviation-specific corpus for text mining to improve text analysis and transformation."--Abstract.
Book Synopsis Aircraft Position Prediction Using Neural Networks by : Anuja Doshi
Download or read book Aircraft Position Prediction Using Neural Networks written by Anuja Doshi and published by . This book was released on 2005 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis A Unified Approach to Buffet Response of Fighter Aircraft Empennage by : M. A. Ferman
Download or read book A Unified Approach to Buffet Response of Fighter Aircraft Empennage written by M. A. Ferman and published by . This book was released on 1990 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unified approach has been derived from predicting buffet response of fighter aircraft empennage operating in high angle of attack maneuvering conditions. Since the advent of high angle of attack flight using controlled vortex flows, incidences of severe structural stress, and in some cases, damages have resulted. This has been pronounced on twin tailed aircraft, including McDonnell's F-15 and F/A-18 aircraft which require structural beef-ups to their empennage. Two concepts are shown for predicting buffet response of empennage. The first approach uses elastically scaled models in wind tunnel tests to provide full scale prediction. The second approach is based on calculations using measured pressure data from wind tunnel tests. The latter method is more versatile. Detailed applications are shown for the F/A-18 empennage, while other applications at McDonnell are noted.
Book Synopsis International Aerospace Abstracts by :
Download or read book International Aerospace Abstracts written by and published by . This book was released on 1999 with total page 974 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Health Monitoring of Aerospace Structures by : Wieslaw Staszewski
Download or read book Health Monitoring of Aerospace Structures written by Wieslaw Staszewski and published by John Wiley & Sons. This book was released on 2004-02-13 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing quality research for the reader, this title encompasses all the recent developments in smart sensor technology for health monitoring in aerospace structures, providing a valuable introduction to damage detection techniques. Focussing on engineering applications, all chapters are written by smart structures and materials experts from aerospace manufacturers and research/academic institutions. This key reference: Discusses the most important aspects related to smart technologies for damage detection; this includes not only monitoring techniques but also aspects related to specifications, design parameters, assessment and qualification routes. Presents real case studies and applications; this includes in-flight tests; the work presented goes far beyond academic research applications. Displays a balance between theoretical developments and engineering applications
Book Synopsis Proceedings of International Conference on Intelligent Manufacturing and Automation by : Hari Vasudevan
Download or read book Proceedings of International Conference on Intelligent Manufacturing and Automation written by Hari Vasudevan and published by Springer Nature. This book was released on 2020-06-30 with total page 831 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected papers presented at the Second International Conference on Intelligent Manufacturing and Automation (ICIMA 2020), which was jointly organized by the Departments of Mechanical Engineering and Production Engineering at Dwarkadas J. Sanghvi College of Engineering (DJSCE), Mumbai, and by the Indian Society of Manufacturing Engineers (ISME). Covering a range of topics in intelligent manufacturing, automation, advanced materials and design, it focuses on the latest advances in e.g. CAD/CAM/CAE/CIM/FMS in manufacturing, artificial intelligence in manufacturing, IoT in manufacturing, product design & development, DFM/DFA/FMEA, MEMS & nanotechnology, rapid prototyping, computational techniques, nano- & micro-machining, sustainable manufacturing, industrial engineering, manufacturing process management, modelling & optimization techniques, CRM, MRP & ERP, green, lean & agile manufacturing, logistics & supply chain management, quality assurance & environmental protection, advanced material processing & characterization of composite & smart materials. The book is intended as a reference guide for future researchers, and as a valuable resource for students in graduate and doctoral programmes.
Book Synopsis Composite Materials for Aircraft Structures by : Alan A. Baker
Download or read book Composite Materials for Aircraft Structures written by Alan A. Baker and published by AIAA. This book was released on 2004 with total page 626 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Neural Networks for Modelling and Control of Dynamic Systems by : M. Norgaard
Download or read book Neural Networks for Modelling and Control of Dynamic Systems written by M. Norgaard and published by . This book was released on 2003 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt:
