State of the Sayano-Shushenskoe data and its foundations during the first filling of the reservoir to the normal pool level

1992 ◽  
Vol 26 (1) ◽  
pp. 13-20 ◽  
Author(s):  
�. K. Aleksandrovskaya ◽  
V. P. Volkov ◽  
O. G. Margolina ◽  
L. N. Pavlovskaya ◽  
O. N. Nosova
Keyword(s):  
Normal Pool Level ◽  
Pool Level

State of the Sayano-Shushenskoe dam during fourfold rise of the upper pool level to the elevation of the normal pool level

1994 ◽  
Vol 28 (10) ◽  
pp. 630-634 ◽  
Author(s):  
�. K. Aleksandrovskaya
Keyword(s):  
Normal Pool Level ◽  
Pool Level ◽  
Upper Pool

scholarly journals Modelling of Seepage and Slope Stability Analysis of Ribb Embankment Dam, Ethiopia

2021 ◽  
Author(s):  
Amanuel Zewdu
Keyword(s):  
Stability Analysis ◽  
Slope Stability ◽  
Factor Of Safety ◽  
Slope Stability Analysis ◽  
The Body ◽  
Army Corps ◽  
Loading Conditions ◽  
Normal Pool Level ◽  
Embankment Dam ◽  
Pool Level

Abstract Safety against seepage is one in all the primary important steps for checking the possibility of failure of embankment dam and the stability of an embankment dam depends on its geometry, its components, materials, properties of every component, and therefore the forces to which it's subjected. This paper presented seepage and slope stability analysis against Ribb dam safety using finite element-based PLAXIS software, and so the result was compared with different standards. PLAXIS is alternative software that will be used for evaluating the protection of embankment dams due to seepage conditions. The simulated results showed the common rate of flow of seepage through the body of the dam at normal pool level was equal to 5.05*10−6 m3/s/m and through the foundation of the dam was 3.00*10−6 m3/s/m. According to Look (2014) recommendation, the seepage results within the tolerable limit. The results of the factor of safety were considered too different loading conditions. The factor of safety results during the end of construction for both static and dynamic stability analysis were 1.3063 and 1.2226, respectively. For steady-state conditions, the factor of safety obtained for static stability analysis was 1.2604, and also the dynamic analysis 1.1803. The rapid drawdown condition is analyzed with a normal pool level of 1940 m lowered to 1900 m or rapidly reduced 57% of the reservoir water. The analysis results showed that the factor of safety for the static, and dynamic analyses were 1.2021 and 1.0662, respectively. Using different recommended design standards: United States Army Corps of Engineers (USACE), British dam society (BDS), and Canadian dam association (CDA) the slope stability analysis of the Ribb embankment dam at all critical loading conditions is safe.

Characteristics of the behavior of the Sayano-Shushenskoe dam during filling of the reservoir to the normal pool level

1994 ◽  
Vol 28 (4) ◽  
pp. 216-221 ◽  
Author(s):  
V. A. Bulatov ◽  
E. Yu. Shakhmaeva
Keyword(s):  
Normal Pool Level ◽  
Pool Level

The Design of Computer Control System for Grinding-Classification Process

2011 ◽  
Vol 130-134 ◽  
pp. 1894-1897
Author(s):  
Zhong An Yu ◽  
Wei Qin Tan ◽  
Zheng Hua Xie
Keyword(s):  
Control System ◽  
Automatic Control ◽  
Computer Control ◽  
Variable Ratio ◽  
Computer Control System ◽  
Paper Briefly ◽  
Classification Procedure ◽  
Pool Level ◽  
Average Control

Grinding-classification procedure is the key link of dressing production. This paper briefly introduced the technology of grinding and classification, and designed the computer control system of grinding and classification process, the realization of the grinder concentration control is by variable ratio control and the control of pump pool level is realized by average control, which realized automatic control for the concentration and flow of the grinding and classification, and presented the design of hardware and software for the control system.

On Subcool Control in Steam-Assisted-Gravity-Drainage Producers— Part I: Stability Envelopes

SPE Journal ◽  
2017 ◽  
Vol 23 (03) ◽  
pp. 841-867 ◽  
Author(s):  
Mazda Irani
Keyword(s):  
Liquid Pool ◽  
Gravity Drainage ◽  
Steam Chamber ◽  
Steam Assisted Gravity Drainage ◽  
Pool Level ◽  
Steam Trap ◽  
Electrical Submersible Pumps ◽  
Long Time ◽  
Observation Wells ◽  
Gas Lift

Summary Steam-assisted-gravity-drainage (SAGD) industry experience indicates that the majority of producer workovers occur because of liners or electrical submersible pumps (ESPs), and both failures appear to result from inefficient “steam-trap control.” Thermodynamic steam-trap control, also termed “subcool control,” is a typical operation strategy for most SAGD wells. Simply, subcool (or reservoir subcool vs. pump subcool) is the temperature difference between the steam chamber (or injected steam) and the produced fluid. The main objective is to keep subcool higher than a set value that varies between 0 to 40° and even higher values. This study presents a method to calculate the liquid-pool level from the temperature profile in observation wells, and liquid-pool shrinkage as a function of time. Unfortunately, it is not practical to monitor the liquid level by having observation wells for every SAGD well pair. For this reason, the algebraic equation for liquid-pool depletion on the basis of wellbore-drawdown, subcool, and emulsion productivity is generated. By use of this equation, the envelopes are suggested to differentiate three different regimes: “stable production,” “liquid-pool depletion,” and “steam-breakthrough limit.” Gas lift operations such as the MacKay River thermal project suggested that envelopes for constant wellbore drawdown are not practical. Therefore, the steam-breakthrough limit is defined for constant rate, which is more consistent in gas lift operations. In this study, the steam-breakthrough limit is validated for operation data from the MacKay River. This study provides a new insight into how factors such as production rate and wellbore drawdown can compromise subcool control and cause steam breakthrough, and how liquid-pool depletion may result in uncontrolled steam coning at long time. As a part of this study, a minimum-subcool concept (or target reservoir subcool) is presented as a function of skin and pressure drawdown. It is shown that the minimum subcool is highly dependent on the maturity of steam-chamber and underburden heat loss especially for zero-skin producers. The results of this work emphasize that the target subcool on the producer should increase slightly with chamber maturity, considering that the skin is nonzero for most SAGD producers.

Thermal Flow-Solidification Simulation and Analysis of Strip Defects in Vertical Twin-Roll Casting of Magnesium Alloy

2015 ◽  
Vol 833 ◽  
pp. 15-18 ◽  
Author(s):  
Zhi Pu Pei ◽  
Dong Ying Ju ◽  
Hong Yang Zhao ◽  
Xiao Dong Hu
Keyword(s):  
Temperature Distribution ◽  
Magnesium Alloy ◽  
Casting Process ◽  
Thermal Flow ◽  
Twin Roll Casting ◽  
Solidification Simulation ◽  
Roll Casting ◽  
Pool Level ◽  
Cast Magnesium ◽  
Twin Roll

A quantitative understanding of the twin-roll casting process is required to get high quality as-cast magnesium alloy strips. In this paper, a thermal flow-solidification simulation was carried out to study the behavior of casting zone and its effects on defects generation deeply. Results show that a lower pouring temperature is not suitable for producing defect-free magnesium alloy strips. With increasing of the casting speed, the tendency of cracks formation will getting smaller because of the more uniform temperature distribution. A low pool level leads to a small metal-roll contact area, and a sharp temperature distribution will generates under this situation, which is not good for strips quality.

Agency MBS Prepayment Model Using Neural Networks

2019 ◽  
Keyword(s):  
Risk Factors ◽  
Neural Networks ◽  
Regime Shift ◽  
Deep Neural Networks ◽  
Black Box ◽  
Trial And Error ◽  
Fixed Rate ◽  
Pool Level ◽  
Highly Nonlinear ◽  
The Neural Networks

The authors apply deep neural networks, a type of machine learning method, to model agency mortgage-backed security (MBS) 30-year, fixed-rate pool prepayment behaviors. The neural networks model (NNM) is able to produce highly accurate model fits to the historical prepayment patterns as well as accurate sensitivities to economic and pool-level risk factors. These results are comparable with model results and intuitions obtained from a traditional agency pool-level prepayment model that is in production and was built via many iterations of trial and error over many months and years. This example shows NNM can process large datasets efficiently, capture very complex prepayment patterns, and model large group of risk factors that are highly nonlinear and interactive. The authors also examine various potential shortcomings of this approach, including nontransparency/“black-box” issues, model overfitting, and regime shift issues.

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