Two Phase Flow CFD Modeling to Enhance Steam Turbines LP Stages Performance Predictability: Comparison With Data and Correlations

The whole energy market, from production plants to end-users, is marked by a strong impulse towards a sustainable use of raw materials and resources, and a reduction of its carbon foot-print. Increasing the split of energy produced with renewables, improving the efficiency of the power plants and reducing the waste of energy appear to be mandatory steps to reach the goal of sustainability. The steam turbines are present in the power generation market with different roles: they are used in fossil, combined cycles, geothermal and concentrated solar plants, but also in waste-to-energy and heat recovery applications. Therefore, they still play a primary role in the energy production market. There are many chances for efficiency improvement in steam turbines, and from a rational point of view, it is important to consider that the LP section contributes to the overall power delivered by the turbine typically by around 40% in industrial power generation. Therefore, the industry is more than ever interested in developing methodologies capable of providing a reliable estimate of the LP stages efficiency, while reducing development costs and time. This paper presents the results obtained using a CFD commercial code with a set of user defined subroutines to model the effects of non-equilibrium steam evolution, droplets nucleation and growth. The numerical results have been compared to well-known test cases available in literature, to show the effects of different modeling hypotheses. The paper then focuses on a test case relevant to a cascade configuration, to show the code capability in terms of bladerow efficiency prediction. Finally, a comprehensive view of the obtained results is done through comparison with existing correlations.

Competitive Bidding by Surrogate Modeling of Steam Parameter Influence on the Attainable Start Numbers of Turbine Casings

The continued expansion of fluctuating energy sources such as wind turbines and solar systems will increase the demand for more flexible operation modes of power plants. Especially steam turbines with all their components will have to sustain a higher amount of start-stop cycles in order to compensate for variations in wind and solar radiation. Besides the rotor, inner casings are an example for main steam turbine components which are strongly loaded by thermal cycles at each start and shut down procedure. A precise prediction of the attainable number of start-stop cycles enables a more flexible operation within the guaranteed lifetime. However, this would require time-consuming FE calculations for each power plant due to their specific steam parameters. In this paper, a physics based surrogate model is discussed for a fast prediction of permissible start-stop cycles at plant specific steam parameters. The correlation between the physical properties from the surrogate model (wall temperature difference and the resulting stresses) and the attainable number of start-stop cycles from the FE model is determined. A validation with a different inner casing design within a usual wall temperature range confirms the high accuracy level of the surrogate model compared to uncertainties like material scatter or casting tolerances. With the provided approach typically a higher number of starts can be efficiently calculated in the bidding phase compared to assuming only one conservative value for each turbine type or size. Furthermore, the steam parameters can be optimized for increasing the number of starts to the required value without additional and time-consuming FE calculations.

Meanline Analysis and CFD Study of a Radial Inflow Turbine With Vaneless Distributor for Low Temperature Organic Rankine Cycle

The Organic Rankine Cycle (ORC) allows the conversion of low-grade heat sources into electricity. Although this technology is not new, the increase in energy demand and the need to reduce CO2 emissions create new opportunities to harvest low grade heat sources such as waste heat. Radial turbines have a simple construction, they are robust and they are not very sensitive to geometry inaccuracies. Most of the radial inflow turbines used for ORC application feature a vaned nozzle ensuring the appropriate distribution angle at the rotor inlet. In this work, no nozzle is considered but only the vaneless gap (distributor). This configuration, without any vaned nozzle, is supposed to be more flexible under varying operating conditions with respect to fixed vanes and to maintain a good efficiency at off-design. This paper presents a performance analysis carried out by means of two approaches: a combination of meanline loss models enhanced with real gas fluid properties and 3D CFD computations, taking into account the entire turbomachine including the scroll housing, the vaneless gap, the turbine wheel and the axial discharge pipe. A detailed analysis of the flow field through the turbomachine is carried out, both under design and off design conditions, with a particular focus on the entropy field in order to evaluate the loss distribution between the scroll housing, the vaneless gap and the turbine wheel.

LM9000 Passive Clearance Control (PCC)

This paper describes the design of the Free Power Turbine (FPT) of the LM9000, in particularly the design of its Passive Clearance Control (PCC) system. The LM9000 is the aero-derivative version of the GE90-115B jet engine. Its core engine has many common parts with the GE90; what differs is the booster (low pressure compressor) and the lower pressure turbine (LPT). The booster of the LM9000 is without fan because the engine is not used to provide thrust but torque only, subsequently it has a new flow path [5]. The LPT has instead been replaced by an intermediate pressure turbine (IPT) and by the FPT. The IPT drives the booster, while the FPT is a free low-pressure turbine designed for both power generation and mechanical drive industrial applications, including LNG production plants. Due to its different application, the LM9000 FPT flow path differs sensibly from the GE90 LPT, however as the GE90 it is provided of a clearance control system that cools the casing in order to reduce its radial deflection. It is not the first time that a clearance control system has been used in industrial applications; in GE aero-derivative power turbines is already present in the LM6000 and LMS100. Design constraints, system complexity, high environment variability because the PCC is located outside the GT, harsh environments and long periods of usage still make the design of this component challenging. The design of the PCC has been supported by extensive heat transfer and mechanical simulations. Each PCC component has been addressed with a dedicated life calculation and all the blade and seal clearances have been estimated for all the operating conditions of the engine. Simulations have been validated by an extensive test campaign performed on the first engine.

Numerical Simulation of a Particle in Air Flow Around a Turbine Blade

The Steam turbine is widely used for generating electricity, in the thermal, nuclear and geothermal power generation systems. A wet loss is known as one of the degrading factors of the performance. To reduce the amount of liquid phase generated by condensation and atomization from nozzles, the prediction of the distribution of liquid mass flow rate inside the turbine is important. However, the quantitative understanding and the prediction method of the liquid flow inside the turbine remain unclear because physics inside a turbine is consisting of complex multiscale and multiphase events. In the present study, we proposed a theoretical model predicting the motion of droplet particles in gas flow based on Stokes number whose model does not require numerical simulation. We also conducted the numerical validation test using three-dimensional Eulerian-Lagrangian simulation for the problem with turbine blade T106. The numerical simulation shows that the particle motion is characterized by the Stokes number, that is consistent with the assumption of the theoretical model and previous studies. When Stokes number is smaller than one, the particle trajectory just follows the gas flow streamline and avoids the impacts on the surface of T106. With increasing Stokes number, the particles begin to deviate from the gas flow. As a result, many particles collide with the surface of T106 when the Stokes number is approximately one. When the Stokes number is extremely larger than one, particles move straight regardless of the background gas flow. The good agreements between the theoretical predictions and numerical experiment results justify the use of our proposed theoretical model for the prediction of the particle flow around the turbine blade.

Autoregressive Bayesian Hierarchical Model to Predict Gas Turbine Degradation

Gas turbine industry currently implements prognostic and health management systems as a fundamental task to predict the deteriorated characteristics of a gas turbine at future states and in turn plan maintenance actions. Thus, economic losses caused by system breakdowns and unnecessary repair actions can be reduced. In this work, a data-driven Bayesian Hierarchical Model (BHM) is implemented by means of an innovative autoregressive structure to predict gas turbine progressive deterioration. The novel autoregressive model provides an estimate of the output variable which depends on time and its previous values. In such a model, lagged values of the output are used as predictor variables. The autoregressive BHM, called ARBHM in this paper, is applied to highly heterogeneous field data taken from the literature, characterized by different degradation rates and referred to the power output of a large-size heavy duty gas turbine. The ARBHM tested in this paper includes up to a third-order lag and is compared to a BHM that only uses time as the regression variable. The comparison is carried out by performing both single-step prediction and multi-step prediction of power output. The results demonstrate that, in the considered degradation scenarios, the innovative ARBHM is usually preferable to BHM, since prediction errors decrease up to 2.0 % in the best case.

A Nitrogen Oxides Emission Prediction Model for Gas Turbines Based on Interpretable Multilayer Perceptron Neural Networks

Gas turbines, an important energy conversion equipment, produce Nitrogen Oxides (NOx) emissions, endangering human health and forming air pollution. With the increasingly stringent NOx emission standards, it is more significant to ascertain NOx emission characteristics to reduce pollutant emissions. Establishing an emission prediction model is an effective way for real-time and accurate monitoring of the NOx discharge amount. Based on the multi-layer perceptron neural networks, an interpretable emission prediction model with a monitorable middle layer is designed to monitor NOx emission by taking the ambient parameters and boundary parameters as the network inputs. The outlet temperature of the compressor is selected as the monitorable measuring parameters of the middle layer. The emission prediction model is trained by historical operation data under different working conditions. According to the errors between the predicted values and measured values of the middle layer and output layer, the weights of the emission prediction model are optimized by the back-propagation algorithm, and the optimal NOx emission prediction model is established for gas turbines under the various working conditions. Furthermore, the mechanism of predicting NOx emission value is explained based on known parameter influence laws between the input layer, middle layer and output layer, which helps to reveal the main measurement parameters affecting NOx emission value, adjust the model parameters and obtain more accurate prediction results. Compared with the traditional emission monitoring methods, the emission prediction model has higher accuracy and faster calculation efficiency and can obtain believable NOx emission prediction results for various operating conditions of gas turbines.

Methane Emissions Reduction Solutions: Product Development and Standardization

The Oil & Gas industry and environmental agencies around the world are working to find solutions to reduce greenhouse gas (GHG) emissions. A comprehensive study by the US EPA found that emissions from compressor stations, blow down and purge, accounted for 97.7 Bscf or just over 31% of the total methane emissions attributed to the Natural Gas industry. [1] With methane (CH4) having 25 times the impact on global warming compared to carbon dioxide (CO2), and global legislation like the Regulations Respecting Reduction in the Release of Methane and Certain Volatile Organic Compounds Upstream Oil and Gas Sector (or also called Canadian Methane Rule) and regional methane reduction regulations, developing solutions to further mitigate methane emissions from process gas vents and centrifugal gas compressor seals becomes necessary as the industry moves towards near-zero targets. This paper addresses the design requirements and selection of a process gas vent recapture system and primary dry seal vent recapture system. In addition, this paper will review the design consideration during the design phase to the data collected during site operation.

Numerical Investigation of a Partially Loaded Supersonic ORC Turbine Stage

The Organic Rankine Cycle (ORC) represents an emerging technology aimed at exploiting lower temperature heat sources, like waste heat in industrial processes or exhaust heat in combustion engines. One key aspect of this technology is an efficient and economical operation at part load, typically realized by a partial admission control, which is challenging to predict numerically. Full annulus computation can only be avoided applying empirical partial admission loss models to conventional full-admission computations. This article aims at assessing the reliability of such a loss model under real-gas and supersonic conditions as a first step towards knowledge-based improved loss models. Three different operating points of an 18.3 kW ORC turbine working with an ethanol-water mixture with two open stator passages (2 × 36°) are considered. Full annulus CFD computations are compared to experimental data and results of simulations in a conventional, full admission, periodic 72°-sector model with application of a 1D partial admission loss model. The experimentally obtained mass flow rate and efficiency are matched overall within their measurements accuracy. By highest inlet total pressure, the computed efficiency deviates about 4 % from the experiments. Predictions of efficiency based on the full admission and loss model correction deviate from full annulus computations less than 1 %. These findings suggest that the used empirical correlations for partial admission losses can provide acceptable results in the configuration under investigation.

Experimental and Numerical Study on Pressure Losses and Flow Fluctuations in a High-Pressure Valve Assembly of Steam Turbine Governing System

Aerodynamic measurements and numerical simulations carried out on a model of a high-pressure valve assembly used for nozzle governing of a turbine with 135MW output are described in this paper. Aim of the study is to investigate effects of control valve’s strainers on pressure losses and unsteadiness in the flow field. It is an important task since undesirable flow fluctuations can lead to operational reliability issues. Measurements were carried out in the Aerodynamic laboratory of the Institute of Thermomechanics of the Czech Academy of Sciences (IT) where an aerodynamic tunnel is installed. Numerical simulations were carried out in the Doosan Skoda Power (DSP) Company using ANSYS software tools. The experimental model consists of one of two identical parts of the real valve assembly. It means it consists of an inlet pipeline, a stop valve, a valve chamber with two independent control valves, its diffusers and outlet pipelines. The numerical model consists of both assembly parts and includes also an A-wheel control stage in order to simulate the real turbine operating points. The different lifts of the main cone in each control valve for its useful combinations were investigated. Results were evaluated on the model with control valve’s strainers, which were historically used in order to stabilize the flow, and without them. The results of the experimental measurement were compared with the numerical results in the form of pressure losses prediction. From measured pressure fluctuations, it was found out where and for which conditions a danger of flow instabilities occurs. It can be concluded that there is a border, in terms of operating conditions, where the flow field starts to be unstable and this border is different dependent of the fact whether the control valve’s strainers are used or not. Therefore, the areas of safe and danger operational reliability can be predicted. The influence of the control valve’s strainers on the maximal amplitude of periodic fluctuations appears only for the cases when valves are highly overloaded. For normal operating conditions, there is no difference. As a result, the control valve’s strainers do not have to be used in standard applications of valve assemblies. Furthermore, a loss model for valve pressure loss estimation could be updated. Therefore, a pressure loss should be predicted with a sufficient accuracy for each new turbine bid with similar valve assemblies.