This paper presents the flow physics of condensing steam flow across a straight through labyrinth seal from numerical simulations performed using ANSYS CFX. Homogeneous nucleation model and droplet growth model, which are critical in predicting condensation, are validated with good agreement against a well-known experimental data set from convergent-divergent nozzle. Validation data includes static pressure drop, condensation location, condensate mass fraction and Sauter mean radius. CFD study is performed on a five teeth labyrinth geometry to predict leakage flow rate, location of condensate accumulation and condensation rate. Impact of subcooled and condensed steam on leakage flow, pressure and temperature field are also discussed. For condensing steam, the condensate accumulation trend is identified. Some of the key findings and physical insights of interest to the designer are listed including: the effect of cooling on the leakage flow (with and without condensation) and the minimum seal wall temperature to avoid condensation based on subcooling needed for droplet formation (at location condition). Also investigated is whether steam condensation continues or if existing condensate evaporates in the downstream pockets, and the effect of heat release from condensation on number of droplets formed and the Sauter mean radius.
{"title":"Numerical Model for Condensing Steam Through Labyrinth Seal","authors":"R. Devi, S. Seshadri, V. Michelassi","doi":"10.1115/gt2021-58736","DOIUrl":"https://doi.org/10.1115/gt2021-58736","url":null,"abstract":"\u0000 This paper presents the flow physics of condensing steam flow across a straight through labyrinth seal from numerical simulations performed using ANSYS CFX. Homogeneous nucleation model and droplet growth model, which are critical in predicting condensation, are validated with good agreement against a well-known experimental data set from convergent-divergent nozzle. Validation data includes static pressure drop, condensation location, condensate mass fraction and Sauter mean radius. CFD study is performed on a five teeth labyrinth geometry to predict leakage flow rate, location of condensate accumulation and condensation rate. Impact of subcooled and condensed steam on leakage flow, pressure and temperature field are also discussed. For condensing steam, the condensate accumulation trend is identified. Some of the key findings and physical insights of interest to the designer are listed including: the effect of cooling on the leakage flow (with and without condensation) and the minimum seal wall temperature to avoid condensation based on subcooling needed for droplet formation (at location condition). Also investigated is whether steam condensation continues or if existing condensate evaporates in the downstream pockets, and the effect of heat release from condensation on number of droplets formed and the Sauter mean radius.","PeriodicalId":252904,"journal":{"name":"Volume 8: Oil and Gas Applications; Steam Turbine","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128855152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bernhard Valerian Weigel, S. Odenbach, W. Uffrecht, Thomas Polklas
Modern steam turbines must increasingly be designed for flexible operation. However an increasing amount of cold starts and load changes have a massive impact on fatigue resistance of the material. So the monitoring of thermal parameters of the casing is significant for checking thermally induced stresses and furthermore lifetime calculation. Additionally the measurement data is helpful for CFD validation reasons. This paper presents a new proprietary developed sensor setup and measurement results. The sensors are flush mounted into a steam turbine at different axial and circumferential locations in the recirculation area between the intermediate and the lower pressure turbine. Hence it is possible to detect temperatures, temperature gradients and heat flux in the part of the wall near the fluid. Moreover the field of temperature within the sensor can be modulated by powering an installed heater. So the adiabatic wall temperature can be identified. For measuring the temperature gradient, seven equidistant spaced thermocouples were used in difference circuit. Therefore two different types of thermocouples were applied. Both types have better transfer characteristics compared to a thermocouple of type K. High amplification enables monitoring of small differences in temperature. The temperature measures an integrated resistor thermometer. The sensors are applied on a real 12 MW industrial steam turbine with maximal live steam parameters of 400 °C and 30 bar. The measurements show various operation points and load changes.
{"title":"Thermal Characterization of a Steam Turbine Casing Including Measuring of Adiabatic Wall Temperatures Using Proprietary Sensors","authors":"Bernhard Valerian Weigel, S. Odenbach, W. Uffrecht, Thomas Polklas","doi":"10.1115/gt2021-59252","DOIUrl":"https://doi.org/10.1115/gt2021-59252","url":null,"abstract":"\u0000 Modern steam turbines must increasingly be designed for flexible operation. However an increasing amount of cold starts and load changes have a massive impact on fatigue resistance of the material. So the monitoring of thermal parameters of the casing is significant for checking thermally induced stresses and furthermore lifetime calculation. Additionally the measurement data is helpful for CFD validation reasons.\u0000 This paper presents a new proprietary developed sensor setup and measurement results. The sensors are flush mounted into a steam turbine at different axial and circumferential locations in the recirculation area between the intermediate and the lower pressure turbine. Hence it is possible to detect temperatures, temperature gradients and heat flux in the part of the wall near the fluid. Moreover the field of temperature within the sensor can be modulated by powering an installed heater. So the adiabatic wall temperature can be identified. For measuring the temperature gradient, seven equidistant spaced thermocouples were used in difference circuit. Therefore two different types of thermocouples were applied. Both types have better transfer characteristics compared to a thermocouple of type K. High amplification enables monitoring of small differences in temperature. The temperature measures an integrated resistor thermometer.\u0000 The sensors are applied on a real 12 MW industrial steam turbine with maximal live steam parameters of 400 °C and 30 bar. The measurements show various operation points and load changes.","PeriodicalId":252904,"journal":{"name":"Volume 8: Oil and Gas Applications; Steam Turbine","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127033589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate and real-time parameters forecasting is of great importance to the turbine control and predictive maintenance which can help the improvement of power system. In this study, deep-learning models including recurrent neural network (RNN) and convolutional neural network (CNN) for multi-parameter prediction are proposed, and are applied to predict real-time parameters of steam turbine based on data from a power plant. Firstly, the prediction results of RNN and CNN models are compared by the overall performance. The two models show good performance on forecasting of six state parameters while RNN performs better. Moreover, the detailed performance on a certain day show that the relative error of two models are both less than 2%. Finally, the influence of model designs including loss function, training size and input time-steps on the performance of RNN model are also explored. The effects of the above parameters on the prediction performance, training and prediction time of the models are studied. The results can provide a reference for model deployment in the power plant. It is convinced that the proposed method has a high potential for dynamic process prediction in actual industrial scenarios through the above research.
{"title":"Multi-Parameter Prediction for Steam Turbine Based on Real-Time Data Using Deep Learning Approaches","authors":"Lei Sun, Tianyuan Liu, Yonghui Xie, Xinlei Xia","doi":"10.1115/gt2021-60049","DOIUrl":"https://doi.org/10.1115/gt2021-60049","url":null,"abstract":"\u0000 Accurate and real-time parameters forecasting is of great importance to the turbine control and predictive maintenance which can help the improvement of power system. In this study, deep-learning models including recurrent neural network (RNN) and convolutional neural network (CNN) for multi-parameter prediction are proposed, and are applied to predict real-time parameters of steam turbine based on data from a power plant. Firstly, the prediction results of RNN and CNN models are compared by the overall performance. The two models show good performance on forecasting of six state parameters while RNN performs better. Moreover, the detailed performance on a certain day show that the relative error of two models are both less than 2%. Finally, the influence of model designs including loss function, training size and input time-steps on the performance of RNN model are also explored. The effects of the above parameters on the prediction performance, training and prediction time of the models are studied. The results can provide a reference for model deployment in the power plant. It is convinced that the proposed method has a high potential for dynamic process prediction in actual industrial scenarios through the above research.","PeriodicalId":252904,"journal":{"name":"Volume 8: Oil and Gas Applications; Steam Turbine","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115250204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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