Design of a floating offshore structure by a deep neural network.
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da USP |
Texto Completo: | https://www.teses.usp.br/teses/disponiveis/3/3135/tde-22052023-110138/ |
Resumo: | The Deep Neural Network (DNN) is a machine learning algorithm that principle is to concatenate nonlinear operations involving matrices. These artificial networks can achieve reasonable transformations of input to output data by updating a matrix of randomly initialized weights. It is necessary to provide a training dataset to minimize a loss function during the network training. Validation and test procedures guarantee the quality of the trained network. The offshore design requires complex modeling that reflects the nature of the ocean environment. To produce a mapping of the hydrodynamic response of the offshore system, an extensive volume of simulations is often necessary, which elevates the computational cost of the design process. At this point, the opportunity to converge the deep learning potentialities and the challenges of offshore design emerges. This work proposes a framework to assess deep neural networks used as response surfaces of the semi-submersible platform dynamic models in waves: a mass-spring-damper model and an analytical hydrodynamic model validated with reference data. The low computation cost of these models allowed the generation of large datasets. The N-dimensional response hypersurface in each case is a combination of input parameters. An appropriate study elucidated the correct parameters definition of the DNN: the number of layers and the number of neurons per layer, targeting the configuration that provides the minimum mean squared error. The response surface represented by the DNN can easily be coupled to an optimization algorithm that evaluates hundreds of viable solutions and finds the optimal design. Using neural networks as a response surface has excellent cost-benefit in preliminary design dynamic modeling, in cases where the available time before the optimization tasks is long enough to prepare a training dataset, and in cases subjected to requisites updates throughout the conceptual design phase. |
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Design of a floating offshore structure by a deep neural network.Projeto de uma estrutura oceânica flutuante por meio de uma rede neural profunda.Aprendizado computacionalDeep neural networksEstruturas offshore flutuantesInteligência artificialOffshore designRedes neuraisResponse surfaceSuperfícies de respostaThe Deep Neural Network (DNN) is a machine learning algorithm that principle is to concatenate nonlinear operations involving matrices. These artificial networks can achieve reasonable transformations of input to output data by updating a matrix of randomly initialized weights. It is necessary to provide a training dataset to minimize a loss function during the network training. Validation and test procedures guarantee the quality of the trained network. The offshore design requires complex modeling that reflects the nature of the ocean environment. To produce a mapping of the hydrodynamic response of the offshore system, an extensive volume of simulations is often necessary, which elevates the computational cost of the design process. At this point, the opportunity to converge the deep learning potentialities and the challenges of offshore design emerges. This work proposes a framework to assess deep neural networks used as response surfaces of the semi-submersible platform dynamic models in waves: a mass-spring-damper model and an analytical hydrodynamic model validated with reference data. The low computation cost of these models allowed the generation of large datasets. The N-dimensional response hypersurface in each case is a combination of input parameters. An appropriate study elucidated the correct parameters definition of the DNN: the number of layers and the number of neurons per layer, targeting the configuration that provides the minimum mean squared error. The response surface represented by the DNN can easily be coupled to an optimization algorithm that evaluates hundreds of viable solutions and finds the optimal design. Using neural networks as a response surface has excellent cost-benefit in preliminary design dynamic modeling, in cases where the available time before the optimization tasks is long enough to prepare a training dataset, and in cases subjected to requisites updates throughout the conceptual design phase.As Redes Neurais Profundas são um algoritmo de aprendizado de máquina que tem como princípio concatenar operações não lineares envolvendo matrizes. Essas redes artificiais podem realizar transformações entre dados de entrada e de saída atualizando uma matriz de pesos, inicializados aleatoriamente. É necessário fornecer um conjunto de dados de treinamento para minimizar uma função de perda, durante o treinamento da rede. Os procedimentos de validação e teste garantem a qualidade da rede treinada. O projeto offshore requer uma modelagem complexa que reflete a natureza do ambiente oceânico. Para produzir um mapeamento da resposta hidrodinâmica do sistema offshore, muitas vezes é necessário um grande volume de simulações, o que eleva o custo computacional do projeto. Neste ponto, surge a oportunidade de convergir as potencialidades do deep learning e os desafios do projeto offshore. Assim sendo, este trabalho propõe um método para avaliar redes neurais profundas, usadas como superfícies de resposta dos modelos dinâmicos de plataforma semissubmersível em ondas: um modelo massa-mola-amortecedor e um modelo hidrodinâmico analítico, validado com dados de referência. O baixo custo computacional desses modelos permitiu a geração de grandes conjuntos de dados. A hipersuperfície de resposta N-dimensional em cada caso é uma combinação de parâmetros de entrada. Um estudo adequado permitiu a correta definição dos parâmetros da rede neural: o número de camadas e o número de neurônios por camada, visando a configuração que fornecesse o mínimo erro quadrático médio. A superfície de resposta representada pela rede neural pode ser facilmente acoplada a um algoritmo de otimização que avalie centenas de soluções viáveis e encontre o projeto ótimo. O uso de redes neurais como superfície de resposta tem excelente custo-benefício na modelagem dinâmica durante o projeto preliminar de plataformas offshore, nos casos em que o tempo disponível antes das tarefas de otimização é longo o suficiente para preparar um conjunto de dados de treinamento e nos casos sujeitos a atualizações de requisitos ao longo da fase de projeto conceitual.Biblioteca Digitais de Teses e Dissertações da USPNishimoto, KazuoEsteves, Fillipe Rocha Leonel2022-06-13info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://www.teses.usp.br/teses/disponiveis/3/3135/tde-22052023-110138/reponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USPLiberar o conteúdo para acesso público.info:eu-repo/semantics/openAccesseng2023-05-23T18:21:49Zoai:teses.usp.br:tde-22052023-110138Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212023-05-23T18:21:49Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Design of a floating offshore structure by a deep neural network. Projeto de uma estrutura oceânica flutuante por meio de uma rede neural profunda. |
title |
Design of a floating offshore structure by a deep neural network. |
spellingShingle |
Design of a floating offshore structure by a deep neural network. Esteves, Fillipe Rocha Leonel Aprendizado computacional Deep neural networks Estruturas offshore flutuantes Inteligência artificial Offshore design Redes neurais Response surface Superfícies de resposta |
title_short |
Design of a floating offshore structure by a deep neural network. |
title_full |
Design of a floating offshore structure by a deep neural network. |
title_fullStr |
Design of a floating offshore structure by a deep neural network. |
title_full_unstemmed |
Design of a floating offshore structure by a deep neural network. |
title_sort |
Design of a floating offshore structure by a deep neural network. |
author |
Esteves, Fillipe Rocha Leonel |
author_facet |
Esteves, Fillipe Rocha Leonel |
author_role |
author |
dc.contributor.none.fl_str_mv |
Nishimoto, Kazuo |
dc.contributor.author.fl_str_mv |
Esteves, Fillipe Rocha Leonel |
dc.subject.por.fl_str_mv |
Aprendizado computacional Deep neural networks Estruturas offshore flutuantes Inteligência artificial Offshore design Redes neurais Response surface Superfícies de resposta |
topic |
Aprendizado computacional Deep neural networks Estruturas offshore flutuantes Inteligência artificial Offshore design Redes neurais Response surface Superfícies de resposta |
description |
The Deep Neural Network (DNN) is a machine learning algorithm that principle is to concatenate nonlinear operations involving matrices. These artificial networks can achieve reasonable transformations of input to output data by updating a matrix of randomly initialized weights. It is necessary to provide a training dataset to minimize a loss function during the network training. Validation and test procedures guarantee the quality of the trained network. The offshore design requires complex modeling that reflects the nature of the ocean environment. To produce a mapping of the hydrodynamic response of the offshore system, an extensive volume of simulations is often necessary, which elevates the computational cost of the design process. At this point, the opportunity to converge the deep learning potentialities and the challenges of offshore design emerges. This work proposes a framework to assess deep neural networks used as response surfaces of the semi-submersible platform dynamic models in waves: a mass-spring-damper model and an analytical hydrodynamic model validated with reference data. The low computation cost of these models allowed the generation of large datasets. The N-dimensional response hypersurface in each case is a combination of input parameters. An appropriate study elucidated the correct parameters definition of the DNN: the number of layers and the number of neurons per layer, targeting the configuration that provides the minimum mean squared error. The response surface represented by the DNN can easily be coupled to an optimization algorithm that evaluates hundreds of viable solutions and finds the optimal design. Using neural networks as a response surface has excellent cost-benefit in preliminary design dynamic modeling, in cases where the available time before the optimization tasks is long enough to prepare a training dataset, and in cases subjected to requisites updates throughout the conceptual design phase. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-06-13 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.teses.usp.br/teses/disponiveis/3/3135/tde-22052023-110138/ |
url |
https://www.teses.usp.br/teses/disponiveis/3/3135/tde-22052023-110138/ |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
|
dc.rights.driver.fl_str_mv |
Liberar o conteúdo para acesso público. info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Liberar o conteúdo para acesso público. |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.coverage.none.fl_str_mv |
|
dc.publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da USP instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Biblioteca Digital de Teses e Dissertações da USP |
collection |
Biblioteca Digital de Teses e Dissertações da USP |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br |
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1809090985297182720 |