Hybrid modeling and design review of throttled surge tanks

The 2050 Swiss Energy Strategy aims to increase the annual production of hydroelectricity by 4% while 90% of the technically feasible potential is already used. The flexibility of high-head storage power plants (HPP), the main pillar of Swiss peak electricity production, may be improved by either increasing the installed capacity or heightening existing dams in order to concentrate the electricity production during peak demand periods and for the critical winter supply. This paper focuses on Gondo HPP that has been recently subjected to a moderate increase of installed capacity. As the upgrades of existing hydropower plants can lead to more critical mass oscillation between the upstream reservoir and the surge tank, the refurbishment of this plant required a modification of the existing surge tank by installing a throttle at its entrance. The throttle introduces asymmetrical head losses, which coefficients were identified in a previous study through a transient 1D numerical analysis involving critical load cases, followed by an optimization of the geometry by physical modeling. Hybrid modeling using a combination of 1D, 3D numerical and physical modeling is highly recommended to confirm the optimum solution, hence the need for this study to complement previous analyses throughout CFD modeling. The latter has the advantage of modeling the system in real dimensions in addition to visualizing flow patterns in internal sections, which were difficult to examine in the scaled physical model. The developed CFD model confirms the findings of the physical model regarding the head loss coefficients of the throttle. The conducted investigations provide an insight on the real behavior of a hydraulic system equipped with a throttle and are likely to improve the future design of throttled surge tanks. Additionally, they enhance the confidence in 3D numerical modeling which can provide preliminary convenient conclusions prior to physical modeling, decreasing therefore the number of likely costly modifications with the latter.

Gondo Throttled Surge Tank Numerical Modeling and Design Review

Mona Seyfeddine

In order to increase the installed capacity, the refurbishment of Gondo high-head power plant required a modification of the existing surge tank by installing a throttle at its entrance. In a previous study, the geometry of this throttle was optimized by physical modeling to achieve the target loss coefficients as identified by a transient 1D numerical analysis. This study complements previous analyses through 3D numerical modeling using the commercial software ANSYS-CFX 19 R1. Results show that: (i) a 3D CFD model predicts sufficiently accurate local head loss coefficients that agree closely with the findings of the physical model; (ii) in contrast to a standard surge tank, the presence of a connecting gallery proved to be of insignificant effect on a surge tank equipped with a throttle, as the variations in the section of the tank cause negligible local losses compared to the ones induced by the throttle; (iii) CFD investigations of transient flow regimes revealed that the head loss coefficient of the throttle only varies for flow ratios below 20% of the total flow in the system, without significantly affecting the conclusions of the 1D transient analysis concerning minimum and maximum water level in the surge tank as well as pressure peaks below the surge tank. This study highlights the importance of examining the characteristics of a hydraulic system from a holistic approach involving hybrid modeling (1D, 3D numerical and physical) backed by calibration as well as validation with in-situ measurements. This results in a more comprehensive design of throttled surge tanks that makes full use of the advantages associated with each modeling strategy.

2020

Non-Linear Stability Analysis of a Reduced Scale Model Pump-Turbine at Off-Design Operation

Sébastien Alligné, François Avellan, Vlad Hasmatuchi, Christian Landry, Andres Müller, Steven Roth

Nowadays, the pump-storage power plants are a proven solution for storing electricity at large scale and offering flexibility to the power management. Therefore, the hydraulic machines are increasingly subject to off-design operation, start-up and shutdown sequences. However, the fast and frequent switching between pumping mode and generating mode presents technical challenges. In the present study, the reduced scale model of a low specific speed pump-turbine is investigated in generating mode at off-design conditions. The operation in the typical “S-shaped” curve of pump-turbine may become unstable and the machine may switch back and forth from generating mode to reverse pumping mode preventing the correct experimental survey of this part during the model testing. The instability has been solved by a testing procedure imposing a restriction of section and a control valve for being able to increase the energy losses. This procedure, commonly used in model testing of pump-turbines, significantly improves the stability of the machine and allows for the survey of the entire “S-curve”. The aim of the present investigation is to understand and explain the origin of the switch to reverse pumping mode. Thus, a hydro-acoustic test rig model was developed with the In-house EPFL SIMSEN software and a comparison between the systems with and without a restriction of section was studied. A numerical analysis indicates that the operating points of a pump-turbine system are defined by the solution of the equation relating the test rig characteristic and the energy-discharge characteristic of the hydraulic machine for a given rotational speed and a constant guide vanes opening. Furthermore, the addition of a restriction alters the curvature of the test rig characteristic and creates a new degree of freedom to achieve stable operating points in the “S-curve”. Finally, to ensure the stability of each operating points described by the numerical model, an eigenvalue study of the non-linear hydraulic system is necessary.

2011

Design, Modeling and Optimization of Stochastic Reactive Distributed Robotic Systems

Grégory Mermoud

This dissertation describes a complete methodological framework for designing, modeling and optimizing a specific class of distributed systems whose dynamics result from the multiple, stochastic interactions of their constitutive components. These components can be robots endowed with very minimal capabilities, or even simpler entities such as insects, bacteria, particles, or molecules. We refer to such components as Smart Minimal Particles (SMPs). One of the main difficulties facing the modeling of SMPs is the potential complexity and richness of their dynamics. On the one hand, one needs detailed models that account for the physico-chemical properties of the lower-level components (e.g., shape, material, surface chemistry, charge, etc.), which, in turn, determine the nature and the magnitude of their interactions. On the other hand, one is also interested in models that can yield accurate numerical predictions of macroscopic quantities, and investigate formally their dependence on the system’s design and control parameters. These competing requirements motivate a combination of models at multiple levels of abstraction, as advocated by the Multi-Level Modeling Methodology (MLMM), which was introduced in prior works. The MLMM enables the fulfillment of both requirements in a very efficient way by incrementally building up models at increasing levels of abstraction in order to capture the relevant features of the system. This thesis extends and consolidates the MLMM along several axes. In a first step, we provide a theoretical consolidation of the MLMM. We propose a thorough classification of the different models of SMPs, and we discuss their underlying assumptions and simplifications. We shed light on the fundamental impact of embodiment and spatiality on models’ accuracy, and we define the conditions under which the macro-deterministic approximation is valid. These theoretical considerations are experimentally supported by five case studies of aggregation and Self-Assembly (SA) at different scales. The five case studies utilize three types of components: (i) miniature wheeled robots (Alice, 2 cm in size) endowed with limited computation, sensing, actuation, and communication capabilities, (ii) water-floating passive modules (Lily, 3 cm in size) endowed with four permanent magnets for mutual latching, and (iii) micro-fabricated cylinders (about 100 μm in diameter, studied in realistic simulation only) that can achieve SA in liquids. In a second step, we introduce the core contribution of this thesis, that is, a systematic and generic methodology for bridging the gap between real, physical systems and computationally efficient models at multiple abstraction levels. In particular, we describe the M3 computational framework, which enables the automated construction of models of SMPs. Our approach consists in observing (or simulating realistically) a system of interest, and building a hierarchical suite of models based on the observations (i.e., trajectories) collected during these experiments (or simulations). Internally, the framework first builds up a microscopic representation of the system based on these observations and on a list of interactions of interest specified by the user. This representation, called the Canonical Microscopic Model (CMM), is a formal and generic description of SMPs, and it serves as a blueprint for the construction of a macroscopic model, specified using the Chemical Reaction Network (CRN) formalism. The rates of the CRN are finally calibrated using a Maximum Likelihood Estimation (MLE) scheme. We validate the M3 framework on each of the three platforms discussed earlier, thereby illustrating its relevance both as a modeling and as an analysis tool. Finally, we discuss the role of multi-level modeling when designing and optimizing SMPs. In particular, we show that top-down model-based design of multi-robot systems is generally not amenable to efficient implementations when dealing with very resource-constrained robots. Instead, faithful and computationally efficient models built incrementally from the bottom up prove to be an essential tool for designing such systems. We further corroborate this claim by applying our automated modeling framework to the real-time control of the stochastic SA of Lily modules. Our scientific contribution is therefore three-fold. First, we provide a solid experimental and theoretical consolidation of the MLMM, which has been the subject of intense research efforts for the last decade. Second, we propose, for the first time, an approach to generate models at high abstraction level in a completely automated fashion, based solely on observations of the system of interest. Third, we provide deep insights into the modeling and the design of SMPs, with a specific emphasis on self-assembling systems ranging from the centimeter scale down to the micrometer scale.