Methodology for physical modeling of reservoir sedimentation

Abstract

RESUMO Reservatórios construídos em cursos d’água estão sujeitos a algum grau de assoreamento. Por isso, é importante a estimativa do volume de sedimentos acumulados, visto que esse depósito pode interferir nas funções do reservatório. Uma forma de se fazer isso é utilizando modelos físicos. Entretanto, a partir da literatura existente na área não é possível responder a algumas questões metodológicas que surgem em uma simulação física do assoreamento, como: qual vazão e descarga sólida utilizar? Simular um hidrograma ou uma vazão constante? Assim, esse trabalho teve como objetivo propor uma metodologia de simulação do assoreamento em modelos físicos, de forma que ao se utilizar essa metodologia o assoreamento observado no modelo reduzido pudesse representar o do protótipo. A partir de simulações numéricas e experimentais, optou-se por simular no modelo físico as vazões médias das ondas que compreendiam as vazões máximas. O tempo de escoamento de cada vazão foi de 30 minutos e as descargas sólidas totais utilizadas foram às referentes às vazões médias anuais. Utilizando-se essa metodologia, promoveu-se a simulação do assoreamento no modelo reduzido para o período entre 2013 e 2017, sendo que o resultado obtido foi próximo ao do protótipo, o que indica que a metodologia proposta permitiu representar no modelo reduzido o fenômeno do assoreamento ocorrido no reservatório real.

Stochastic simulation in reservoir sedimentation estimation: application in a PCH

“…Aiming at validating the developed stochastic method, the statistics of the siltation results obtained via the stochastic method were compared with the siltation results observed in the SHP reduced model. It is noteworthy that in the physical model the result was available for two periods: 2008 to 2012, which was obtained by the authors of this article, and the period from 2013 to 2017, obtained by Teixeira et al (2020).…”

“…Since it was verified that the AR(1) model adjusted well to the data, each "Q" value of the 1000 series generated for the SHP was multiplied by the "K" factor, which was obtained randomly from a Normal distribution, as explained in the methodology . This was necessary to convert the annual flows (Q) to average flows of the maximum periods (Q MM ), since it is these flows that causes sediment transport in the SHP reservoir, as observed by Teixeira et al (2020). It should be noted that the statistical tests applied, at 5% significance, showed that the data set containing the "K" factor (Figure 4) is homogeneous, independent, random and stationary.…”

“…However, Teixeira et al ( 2020) observed, in the same reduced model used for this article, that annual flows (Q) did not move the sediment. Thus, Teixeira et al (2020) proposed that the flow responsible for causing siltation in the SHP, for the period from 2013 to 2017, were the average of the waves that contained the maximum flows (Q MM ). Thereby, it was necessary to generate synthetic series of "Q MM ".…”

“…The stochastically generated series of "Q", "Q MM " and "Q ST ", were introduced in a deterministic mathematical model, which has hydrodynamic and sediment transport equations. The HEC-RAS 5.0.3 software was used in this work due to its consolidated application in this area, as can be seen in the works of Teixeira et al (2020) and Gibson et al (2017).…”

“…In order to validate the developed stochastic method, the siltation period (2008 to 2012) was simulated in the reduced model. The entire simulation procedure was carried out according to Teixeira et al (2020). The silting of the period in question is shown in Figure 7, while the longitudinal profile of this silting is shown in Figure 8.…”