Simulação estocástica na estimativa de assoreamento em reservatórios

Detalhes bibliográficos
Ano de defesa: 2019
Autor(a) principal: Emmanuel Kennedy da Costa Teixeira
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Minas Gerais
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: https://hdl.handle.net/1843/RAOA-BELSHZ
Resumo: Reservoirs are built for various purposes such as power generation, water supply, etc. These structures are subject to some degree of sedimentation, since by altering the river's equilibrium, its capacity to transport sediments is altered. Such siltation, among other problems, may interfere with the use for which the reservoir was built. Thus, the height of deposited material must be estimated and when the accumulated sediments will begin to interfere with the functions of the reservoir. However, predicting the accumulation of sediments is difficult because the processes involved are complex, subject to temporal variability and uncertainties, which makes the study not only deterministic but also stochastic. Thus, the objective of this research was to develop a stochastic model and to evaluate its performance in the estimation of sedimentation in reservoirs. For the realization of this project, the hydrosedimentological and topographic data of the Salto do Paraopeba PCH was used, which was built in 1956, and the reservoir is intensely silted, which made it inoperative. In the CPH of the UFMG there is a reduced model of the reservoir of this PCH, and the result of silting observed in this model was used to validate the stochastic model. The discharge data and sediment concentrations in suspension of the PCH were obtained, which were converted to the reality of the reduced model according to the hydraulic similarity scales. From these data, thousands of synthetic series were stochastically generated, using statistical software R and model AR(1). The data generated were introduced in the HEC-RAS software to estimate the siltation in the reduced SHP model. For this, a computational code was developed that allows the automatic coupling of the stochastic model with the deterministic one. The result obtained by stochastic simulation was compared with the sedimentation measured in the physical model, observing that the actual siltation for the two periods analyzed (2008-2012 and 2013-2017) was always between the 1st and 3rd quartile of probability of the result stochastic, that is, the actual silting was always greater than 25% of the stochastically generated values and less than 75% of them. Thus, it is possible that the stochastic model can help in future projects of estimation of sedimentation in reservoirs, since it allows to obtain the probabilities of heights silted in sections of interest.
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spelling 2019-08-11T11:11:08Z2025-09-09T00:16:17Z2019-08-11T11:11:08Z2019-03-14https://hdl.handle.net/1843/RAOA-BELSHZReservoirs are built for various purposes such as power generation, water supply, etc. These structures are subject to some degree of sedimentation, since by altering the river's equilibrium, its capacity to transport sediments is altered. Such siltation, among other problems, may interfere with the use for which the reservoir was built. Thus, the height of deposited material must be estimated and when the accumulated sediments will begin to interfere with the functions of the reservoir. However, predicting the accumulation of sediments is difficult because the processes involved are complex, subject to temporal variability and uncertainties, which makes the study not only deterministic but also stochastic. Thus, the objective of this research was to develop a stochastic model and to evaluate its performance in the estimation of sedimentation in reservoirs. For the realization of this project, the hydrosedimentological and topographic data of the Salto do Paraopeba PCH was used, which was built in 1956, and the reservoir is intensely silted, which made it inoperative. In the CPH of the UFMG there is a reduced model of the reservoir of this PCH, and the result of silting observed in this model was used to validate the stochastic model. The discharge data and sediment concentrations in suspension of the PCH were obtained, which were converted to the reality of the reduced model according to the hydraulic similarity scales. From these data, thousands of synthetic series were stochastically generated, using statistical software R and model AR(1). The data generated were introduced in the HEC-RAS software to estimate the siltation in the reduced SHP model. For this, a computational code was developed that allows the automatic coupling of the stochastic model with the deterministic one. The result obtained by stochastic simulation was compared with the sedimentation measured in the physical model, observing that the actual siltation for the two periods analyzed (2008-2012 and 2013-2017) was always between the 1st and 3rd quartile of probability of the result stochastic, that is, the actual silting was always greater than 25% of the stochastically generated values and less than 75% of them. Thus, it is possible that the stochastic model can help in future projects of estimation of sedimentation in reservoirs, since it allows to obtain the probabilities of heights silted in sections of interest.Universidade Federal de Minas GeraisEstocásticaAssoreamentoModelo físicoModelagem físicaRecursos hídricos DesenvolvimentoEngenharia sanitáriaReservatóriosSimulação estocástica na estimativa de assoreamento em reservatóriosinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisEmmanuel Kennedy da Costa Teixeirainfo:eu-repo/semantics/openAccessporreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGMarcia Maria L Pinto CoelhoEber Jose de Andrade PintoAloysio Portugal Maia SalibaFrancisco Eustaquio Oliveira e SilvaMarcio Benedito BaptistaChristopher Freire SouzaAna Luiza de Oliveira BorgesReservatórios são construídos para diversas finalidades, como geração de energia, abastecimento de água, etc. Essas estruturas estão sujeitas a algum grau de assoreamento, visto que ao aumentar a área molhada do curso dágua, a velocidade de escoamento é diminuída, o que altera a capacidade do transporte de sedimentos. Esse assoreamento, dentre outros problemas, pode interferir no uso para o qual o reservatório foi construído. Assim, deve-se estimar a altura de material depositado e quando os sedimentos acumulados começarão a interferir nas funções do reservatório. Entretanto, prever o acúmulo de sedimentos é difícil porque os processos envolvidos são complexos, sujeitos à variabilidade temporal e a incertezas, o que torna o estudo não apenas determinístico, mas também estocástico. Assim, o objetivo desta pesquisa foi desenvolver um método estocástico e avaliar o seu desempenho na estimativa do assoreamento em reservatórios. Para a realização desse projeto foram utilizados os dados hidrossedimentológicos e topográficos da PCH Salto do Paraopeba, que foi construída em 1956, sendo que o reservatório se encontra intensamente assoreado, o que a tornou inoperante. No CPH da UFMG há um modelo reduzido do reservatório dessa PCH, sendo que o resultado de assoreamento observado nesse modelo foi utilizado para validar o método estocástico. Obtiveram-se os dados de vazões e concentrações de sedimentos em suspensão da PCH, os quais foram convertidos para a realidade do modelo reduzido, de acordo com as escalas de semelhanças hidráulicas. A partir desses dados, foram geradas estocasticamente milhares de séries sintéticas, utilizando o software estatístico R e o modelo AR(1). Os dados gerados foram introduzidos no HEC-RAS para estimar o assoreamento no modelo reduzido da PCH. Para isso, foi desenvolvido um código computacional que permite o acoplamento automático do modelo estocástico com o determinístico. O resultado obtido via simulação estocástica foi comparado com o assoreamento medido no modelo físico, observando-se que o assoreamento real, para os dois períodos analisados (2008-2012 e 2013-2017), sempre esteve entre o 1º e 3º quartil de probabilidade do resultado estocástico, ou seja, ao se ordenar de forma crescente os resultados gerados estocasticamente, tem-se que o assoreamento real sempre foi maior que 25% dos assoreamentos gerados e menor que 75% deles. Assim, tem-se que o método estocástico pode auxiliar em projetos futuros de estimativa de assoreamento em reservatórios, visto que ele permite a obtenção das probabilidades de alturas assoreadas em seções de interesse.UFMGORIGINALtese_simula__o_estoc_stica_na_estimativa_de_assoreamento_em_reservat_rios__emmanuel.pdfapplication/pdf6557415https://repositorio.ufmg.br//bitstreams/2c0b7e90-c58b-4758-a8c3-0ac08d79b555/download590eb94df16362b4ce2d345ffd625403MD51trueAnonymousREADTEXTtese_simula__o_estoc_stica_na_estimativa_de_assoreamento_em_reservat_rios__emmanuel.pdf.txttext/plain348577https://repositorio.ufmg.br//bitstreams/00873ee6-32c6-4d71-a137-689b8d929fa8/downloadaeca0af6bfff20db80f10b90443ab5a1MD52falseAnonymousREAD1843/RAOA-BELSHZ2025-09-08 21:16:17.853open.accessoai:repositorio.ufmg.br:1843/RAOA-BELSHZhttps://repositorio.ufmg.br/Repositório InstitucionalPUBhttps://repositorio.ufmg.br/oairepositorio@ufmg.bropendoar:2025-09-09T00:16:17Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
dc.title.none.fl_str_mv Simulação estocástica na estimativa de assoreamento em reservatórios
title Simulação estocástica na estimativa de assoreamento em reservatórios
spellingShingle Simulação estocástica na estimativa de assoreamento em reservatórios
Emmanuel Kennedy da Costa Teixeira
Modelagem física
Recursos hídricos Desenvolvimento
Engenharia sanitária
Reservatórios
Estocástica
Assoreamento
Modelo físico
title_short Simulação estocástica na estimativa de assoreamento em reservatórios
title_full Simulação estocástica na estimativa de assoreamento em reservatórios
title_fullStr Simulação estocástica na estimativa de assoreamento em reservatórios
title_full_unstemmed Simulação estocástica na estimativa de assoreamento em reservatórios
title_sort Simulação estocástica na estimativa de assoreamento em reservatórios
author Emmanuel Kennedy da Costa Teixeira
author_facet Emmanuel Kennedy da Costa Teixeira
author_role author
dc.contributor.author.fl_str_mv Emmanuel Kennedy da Costa Teixeira
dc.subject.por.fl_str_mv Modelagem física
Recursos hídricos Desenvolvimento
Engenharia sanitária
Reservatórios
topic Modelagem física
Recursos hídricos Desenvolvimento
Engenharia sanitária
Reservatórios
Estocástica
Assoreamento
Modelo físico
dc.subject.other.none.fl_str_mv Estocástica
Assoreamento
Modelo físico
description Reservoirs are built for various purposes such as power generation, water supply, etc. These structures are subject to some degree of sedimentation, since by altering the river's equilibrium, its capacity to transport sediments is altered. Such siltation, among other problems, may interfere with the use for which the reservoir was built. Thus, the height of deposited material must be estimated and when the accumulated sediments will begin to interfere with the functions of the reservoir. However, predicting the accumulation of sediments is difficult because the processes involved are complex, subject to temporal variability and uncertainties, which makes the study not only deterministic but also stochastic. Thus, the objective of this research was to develop a stochastic model and to evaluate its performance in the estimation of sedimentation in reservoirs. For the realization of this project, the hydrosedimentological and topographic data of the Salto do Paraopeba PCH was used, which was built in 1956, and the reservoir is intensely silted, which made it inoperative. In the CPH of the UFMG there is a reduced model of the reservoir of this PCH, and the result of silting observed in this model was used to validate the stochastic model. The discharge data and sediment concentrations in suspension of the PCH were obtained, which were converted to the reality of the reduced model according to the hydraulic similarity scales. From these data, thousands of synthetic series were stochastically generated, using statistical software R and model AR(1). The data generated were introduced in the HEC-RAS software to estimate the siltation in the reduced SHP model. For this, a computational code was developed that allows the automatic coupling of the stochastic model with the deterministic one. The result obtained by stochastic simulation was compared with the sedimentation measured in the physical model, observing that the actual siltation for the two periods analyzed (2008-2012 and 2013-2017) was always between the 1st and 3rd quartile of probability of the result stochastic, that is, the actual silting was always greater than 25% of the stochastically generated values and less than 75% of them. Thus, it is possible that the stochastic model can help in future projects of estimation of sedimentation in reservoirs, since it allows to obtain the probabilities of heights silted in sections of interest.
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