Data-driven modelling ofAC losses in high-temperature superconducting coils
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/155437 |
Resumo: | Predicting the loss in superconductive power devices is of utmost importance when designing such devices. This is because the cooling system needs to be designed accordingly. The current methods for predicting AC Loss are either inaccurate or very time consuming. These conventional methods for predicting loss are of two types in which one is faster but inaccurate, while the other is very accurate but also very time consuming. While currently they are both employed in different stages of the design process, there is an interest in a faster, but still accurate, form of predicting AC Loss. Studies have time and time again shown that Artificial Neural Networks are capable of taking on complex tasks and handling them faster than regular computing. Because of this, in this work, an Artificial Neural Network based approach is proposed as to predict AC Loss in various configurations of HTS coils. This approach aims to replicate the accuracy of standard numerical models while being much faster than said models. This results in a final framework comprised of two distinct sequential Neural Networks that are capable of predicting the AC Loss for different configurations of HTS coils nearly instantaneously while still being very accurate and reliable in their predictions. |
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Data-driven modelling ofAC losses in high-temperature superconducting coilsSuperconductorHTSPower DevicesAC LossArtificial Neural NetworkDomínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaPredicting the loss in superconductive power devices is of utmost importance when designing such devices. This is because the cooling system needs to be designed accordingly. The current methods for predicting AC Loss are either inaccurate or very time consuming. These conventional methods for predicting loss are of two types in which one is faster but inaccurate, while the other is very accurate but also very time consuming. While currently they are both employed in different stages of the design process, there is an interest in a faster, but still accurate, form of predicting AC Loss. Studies have time and time again shown that Artificial Neural Networks are capable of taking on complex tasks and handling them faster than regular computing. Because of this, in this work, an Artificial Neural Network based approach is proposed as to predict AC Loss in various configurations of HTS coils. This approach aims to replicate the accuracy of standard numerical models while being much faster than said models. This results in a final framework comprised of two distinct sequential Neural Networks that are capable of predicting the AC Loss for different configurations of HTS coils nearly instantaneously while still being very accurate and reliable in their predictions.A capacidade de previsão de perdas em dispositivos de potência supercondutores é um assunto de alta importância aquando do desenho dos mesmos. Isto deve-se ao facto de o sistema de arrefecimento necissitar de ser desenhado de acordo com as mesmas. Os métodos atuais de previsão de perdas AC são ou pouco fiávies, ou bastante demorados. Estes métodos atuais de previsão de perdas são de dois tipos em que um é mais rápido mas pouco preciso, enquanto o outro é bastante preciso mas, no entanto,muito demorado. Embora atualmente sejam ambos empregados em fases diferentes do processo de desenho, continua a existir interesse numa forma rápida e precisa de prever perdas AC. Estudos têm vindo a provar que as Redes Neuronais são capazes de enfrentar tarefas complexas e lidar com elas de forma mais rápida que a computação tradicional. Dado isto, neste trabalho propõe-se uma abordagem baseada em Redes Neuronais para prever perdas AC em várias configurações de bobinas HTS. Esta abordagem visa a replicar a fiabilidade de métodos numéricos sendo, no entanto, bastante mais rápida. Isto resulta numa framework final composta por duas Redes Neuronais distintas sequenciais que é capaz the prever perdas AC em diversas configurações de bobinas de forma quase instantânea sendo, no entanto, bastante correta e confiável nas suas previsões.Pina, JoãoRUNTeixeira, Miguel Alexandre Amaral2023-07-18T12:01:47Z2022-072022-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/155437enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:38:00Zoai:run.unl.pt:10362/155437Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:56:04.161385Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Data-driven modelling ofAC losses in high-temperature superconducting coils |
title |
Data-driven modelling ofAC losses in high-temperature superconducting coils |
spellingShingle |
Data-driven modelling ofAC losses in high-temperature superconducting coils Teixeira, Miguel Alexandre Amaral Superconductor HTS Power Devices AC Loss Artificial Neural Network Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
title_short |
Data-driven modelling ofAC losses in high-temperature superconducting coils |
title_full |
Data-driven modelling ofAC losses in high-temperature superconducting coils |
title_fullStr |
Data-driven modelling ofAC losses in high-temperature superconducting coils |
title_full_unstemmed |
Data-driven modelling ofAC losses in high-temperature superconducting coils |
title_sort |
Data-driven modelling ofAC losses in high-temperature superconducting coils |
author |
Teixeira, Miguel Alexandre Amaral |
author_facet |
Teixeira, Miguel Alexandre Amaral |
author_role |
author |
dc.contributor.none.fl_str_mv |
Pina, João RUN |
dc.contributor.author.fl_str_mv |
Teixeira, Miguel Alexandre Amaral |
dc.subject.por.fl_str_mv |
Superconductor HTS Power Devices AC Loss Artificial Neural Network Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
topic |
Superconductor HTS Power Devices AC Loss Artificial Neural Network Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
description |
Predicting the loss in superconductive power devices is of utmost importance when designing such devices. This is because the cooling system needs to be designed accordingly. The current methods for predicting AC Loss are either inaccurate or very time consuming. These conventional methods for predicting loss are of two types in which one is faster but inaccurate, while the other is very accurate but also very time consuming. While currently they are both employed in different stages of the design process, there is an interest in a faster, but still accurate, form of predicting AC Loss. Studies have time and time again shown that Artificial Neural Networks are capable of taking on complex tasks and handling them faster than regular computing. Because of this, in this work, an Artificial Neural Network based approach is proposed as to predict AC Loss in various configurations of HTS coils. This approach aims to replicate the accuracy of standard numerical models while being much faster than said models. This results in a final framework comprised of two distinct sequential Neural Networks that are capable of predicting the AC Loss for different configurations of HTS coils nearly instantaneously while still being very accurate and reliable in their predictions. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-07 2022-07-01T00:00:00Z 2023-07-18T12:01:47Z |
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 |
http://hdl.handle.net/10362/155437 |
url |
http://hdl.handle.net/10362/155437 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799138146691055616 |