Redes neurais artificiais na predição de respostas e estimação de derivadas aerodinâmicas de aeronaves
Data
2017-11-15
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Biblioteca Digital de Teses e Dissertações da USP
Universidade de São Paulo
Escola de Engenharia de São Carlos
Universidade de São Paulo
Escola de Engenharia de São Carlos
Resumo
Descrição
A área de dinâmica de aeronaves atingiu um alto nível de desenvolvimento e devido à crescente disponibilidade de computadores cada vez mais rápidos e com maior capacidade de processamento; a aplicação de técnicas numéricas de identificação nesta área também teve grande avanço. Este trabalho apresenta uma metodologia para predição de respostas de aeronaves dentro de envelopes de vôo pré-estabelecidos usando redes neurais recorrentes e uma metodologia para estimação das suas derivadas aerodinâmicas usando redes neurais feedforward. Para obter os conjuntos de dados para treinar as redes neurais, foi implementado um modelo não linear de dinâmica de vôo e simulado o comportamento de uma aeronave de combate em nove pontos de um envelope de vôo. Foram usadas as respostas simuladas correspondentes a quatro pontos para treinar a rede neural e depois disto, esta capturou satisfatoriamente a dinâmica da aeronave, identificando com grande sucesso as respostas do movimento longitudinal da aeronave por todo o envelope de vôo considerado. Após a simulação e identificação das respostas da aeronave dentro do envelope de vôo, é apresentada a resolução do problema inverso, ou seja, usando velocidades escalares e angulares da aeronave juntamente com seus dados geométricos como entradas para a rede neural feedforward, é obtido um modelo neural estimador de derivadas aerodinâmicas. Para mostrar a capacidade deste modelo neural estimador, este é aplicado na estimação das derivadas da aeronave simulada e também aplicado na estimação das derivadas aerodinâmicas da aeronave militar a jato Xavante AT-26 da Força Aérea Brasileira. Estas metodologias propostas reduzem custo de obtenção das derivadas aerodinâmicas e mostram a eficácia das redes neurais em estimar as respostas de aeronaves dentre de um envelope de vôo pré-definido.
The area of aircraft dynamics has reached a high level of development and due to the increasing availability of computers continuously faster and with bigger processing capacity, the application of numerical identification techniques in this area also had great advance. This work presents two methodologies, one for prediction of aircraft responses within a pre-established flight envelope using recurrent neural networks and another one for estimation of its aerodynamic derivatives using feedforward neural networks. To get data sets to train the neural networks, a combat aircraft flight dynamics non-linear model was implemented and simulated in nine points of the flight envelope to obtain its behavior. The simulated responses corresponding to a four points of the flight envelope were used to train the neural network and after that, it was possible to verify that this net satisfactorily captured the dynamics of the aircraft, identifying with great success the longitudinal motion responses of the aircraft at all the considered flight envelope positions. After the simulation and identification of the aircraft responses inside the flight envelope, the solution of the inverse problem is presented, i.e., using scalar and angular aircraft velocities together with its geometric data as input to the feedforward neural network, a neural estimator model of aerodynamic derivatives is obtained. In order to show the capacity of this neural estimator model, this model is applied to the estimation of the derivatives of the simulated aircraft as well as to the estimation of the aerodynamic derivatives of a brazilian air force military jet aircraft, the Xavante AT-26. These proposed methodologies reduce the cost of obtaining the aerodynamic derivatives and show the estimation effectiveness of the neural networks to estimate the responses of an aircraft inside a pre-defined flight envelope.
The area of aircraft dynamics has reached a high level of development and due to the increasing availability of computers continuously faster and with bigger processing capacity, the application of numerical identification techniques in this area also had great advance. This work presents two methodologies, one for prediction of aircraft responses within a pre-established flight envelope using recurrent neural networks and another one for estimation of its aerodynamic derivatives using feedforward neural networks. To get data sets to train the neural networks, a combat aircraft flight dynamics non-linear model was implemented and simulated in nine points of the flight envelope to obtain its behavior. The simulated responses corresponding to a four points of the flight envelope were used to train the neural network and after that, it was possible to verify that this net satisfactorily captured the dynamics of the aircraft, identifying with great success the longitudinal motion responses of the aircraft at all the considered flight envelope positions. After the simulation and identification of the aircraft responses inside the flight envelope, the solution of the inverse problem is presented, i.e., using scalar and angular aircraft velocities together with its geometric data as input to the feedforward neural network, a neural estimator model of aerodynamic derivatives is obtained. In order to show the capacity of this neural estimator model, this model is applied to the estimation of the derivatives of the simulated aircraft as well as to the estimation of the aerodynamic derivatives of a brazilian air force military jet aircraft, the Xavante AT-26. These proposed methodologies reduce the cost of obtaining the aerodynamic derivatives and show the estimation effectiveness of the neural networks to estimate the responses of an aircraft inside a pre-defined flight envelope.
Palavras-chave
Dinâmica de vôo, Envelope de vôo, Predição, Redes neurais artificiais, Artificial neural networks, Flight dynamics, Flight envelope, Prediction