N. Casari, M. Pinelli, A. Suman, M. Manganelli, Mirko Morini, K. Brun, L. Larosiliere, V. Jariwala
The operability region of a centrifugal compressor is bounded by the low-flow (or high-pressure ratio) limit, commonly referred to as surge. The exact location of the surge line on the map can vary depending on the operating condition and, as a result, a typical Surge Avoidance Line is established at 10% to 15% above the stated flow for the theoretical surge line. The current state of the art of centrifugal compressor surge control is to utilize a global recycle valve to return flow from the discharge side of a centrifugal compressor to the suction side to increase the flow through the compressor and, thus, avoid entering the surge region. This is conventionally handled by defining a compressor surge control line that conservatively assumes that all stages must be kept out of surge at all the time. In compressors with multiple stages, the amount of energy loss is disproportion-ally large since the energy that was added in each stage is lost during system level (or global) recycling. This work proposes an internal stage-wise recycling that provides a much more controlled flow recycling to affect only those stages that may be on the verge of surge. The amount of flow needed for such a scheme will be much smaller than highly conservative global recycling approach. Also, the flow does not leave the compressor casing and therefore does not cross the pressure boundary. Compared to global recycling this inherently has less loss depending upon application and specific of control design.
{"title":"Dynamic Model of Multistage Centrifugal Compressor With a Stage-by-Stage Anti-Surge Recirculating System","authors":"N. Casari, M. Pinelli, A. Suman, M. Manganelli, Mirko Morini, K. Brun, L. Larosiliere, V. Jariwala","doi":"10.1115/gt2021-04273","DOIUrl":"https://doi.org/10.1115/gt2021-04273","url":null,"abstract":"\u0000 The operability region of a centrifugal compressor is bounded by the low-flow (or high-pressure ratio) limit, commonly referred to as surge. The exact location of the surge line on the map can vary depending on the operating condition and, as a result, a typical Surge Avoidance Line is established at 10% to 15% above the stated flow for the theoretical surge line. The current state of the art of centrifugal compressor surge control is to utilize a global recycle valve to return flow from the discharge side of a centrifugal compressor to the suction side to increase the flow through the compressor and, thus, avoid entering the surge region. This is conventionally handled by defining a compressor surge control line that conservatively assumes that all stages must be kept out of surge at all the time. In compressors with multiple stages, the amount of energy loss is disproportion-ally large since the energy that was added in each stage is lost during system level (or global) recycling.\u0000 This work proposes an internal stage-wise recycling that provides a much more controlled flow recycling to affect only those stages that may be on the verge of surge. The amount of flow needed for such a scheme will be much smaller than highly conservative global recycling approach. Also, the flow does not leave the compressor casing and therefore does not cross the pressure boundary. Compared to global recycling this inherently has less loss depending upon application and specific of control design.","PeriodicalId":252904,"journal":{"name":"Volume 8: Oil and Gas Applications; Steam Turbine","volume":"33 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":"125164183","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}
Griffin C. Beck, N. Andrews, A. G. Berry, A. McCleney
In gas processing, boosting, and gathering applications, gas-liquid separator equipment (typically referred to as a scrubber) is placed upstream of each reciprocating compressor stage to remove water and hydrocarbon condensates. However, field experience indicates that liquids are often still present downstream of the separation equipment. When liquids are ingested into the reciprocating compressor, machinery failures, some of which are severe, can result. While it is generally understood that liquid carryover and condensation can occur, it is less clear how the multiphase fluid moves through equipment downstream of the scrubber. In this paper, mechanisms responsible for liquid formation and carryover into reciprocating compressors are explored. First, the effects of liquid ingestion on reciprocating compressors reported in the open literature are reviewed. Then, the role of heat and pressure loss along the gas flow path is investigated to determine whether liquid formation (i.e., condensation) is likely to occur for two identical compressors with different pulsation bottle configurations. For this investigation, conjugate heat transfer (CHT) models of the suction pulsation bottles are used to identify regions where liquid dropout is likely to occur. Results of these investigations are presented. Next, liquid carryover from the upstream scrubber is considered. Multiphase models are developed to determine how the multiphase fluid flows through the complex flow path within the pulsation bottle. Two liquid droplet size distributions are employed in these models. Descriptions of the modeling techniques, assumptions, and boundary conditions are provided.
{"title":"Wet Gas Formation and Carryover in Compressor Suction Equipment","authors":"Griffin C. Beck, N. Andrews, A. G. Berry, A. McCleney","doi":"10.1115/gt2021-59353","DOIUrl":"https://doi.org/10.1115/gt2021-59353","url":null,"abstract":"\u0000 In gas processing, boosting, and gathering applications, gas-liquid separator equipment (typically referred to as a scrubber) is placed upstream of each reciprocating compressor stage to remove water and hydrocarbon condensates. However, field experience indicates that liquids are often still present downstream of the separation equipment. When liquids are ingested into the reciprocating compressor, machinery failures, some of which are severe, can result. While it is generally understood that liquid carryover and condensation can occur, it is less clear how the multiphase fluid moves through equipment downstream of the scrubber.\u0000 In this paper, mechanisms responsible for liquid formation and carryover into reciprocating compressors are explored. First, the effects of liquid ingestion on reciprocating compressors reported in the open literature are reviewed. Then, the role of heat and pressure loss along the gas flow path is investigated to determine whether liquid formation (i.e., condensation) is likely to occur for two identical compressors with different pulsation bottle configurations. For this investigation, conjugate heat transfer (CHT) models of the suction pulsation bottles are used to identify regions where liquid dropout is likely to occur. Results of these investigations are presented. Next, liquid carryover from the upstream scrubber is considered. Multiphase models are developed to determine how the multiphase fluid flows through the complex flow path within the pulsation bottle. Two liquid droplet size distributions are employed in these models. Descriptions of the modeling techniques, assumptions, and boundary conditions are provided.","PeriodicalId":252904,"journal":{"name":"Volume 8: Oil and Gas Applications; Steam Turbine","volume":"34 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":"122842943","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}
Rasidi Mohamed, Syafeq Moazari Sukeri, Robert Mendoza, R. Kurz
A key function for a control system in a gas turbine train is to keep the operation of all components within a range of parameters that keep the unit safe. If the operating parameters of components fall outside the desired range for safe operation, the control system will detect these and create an alarm. For critical parameters, the control system may initiate an alarm and a shutdown of the unit. In many instances, an alarm may precede the shutdown command. Frequent discussions evolve around situations that lead to a shutdown of the train, as shutdowns impact the availability of the turbomachinery equipment, but in a wider sense also the availability of the compressor station. Therefore, shutdowns impact the profitability of a system. On the other hand, shutdowns may prevent significant, costly damage to the equipment, with significant downtime, and financial implications. In this lecture, we will discuss different methodologies for shutdown requirements, in the effort to maximize availability of units. Particular emphasis will be given to aging machines as well as machines where the instrumentation, and the control algorithms may no longer be state of the art, or where unnecessary or spurious shutdowns plague an installation.
{"title":"Alarms, Shutdowns and Trip Rationalization","authors":"Rasidi Mohamed, Syafeq Moazari Sukeri, Robert Mendoza, R. Kurz","doi":"10.1115/gt2021-00646","DOIUrl":"https://doi.org/10.1115/gt2021-00646","url":null,"abstract":"\u0000 A key function for a control system in a gas turbine train is to keep the operation of all components within a range of parameters that keep the unit safe. If the operating parameters of components fall outside the desired range for safe operation, the control system will detect these and create an alarm. For critical parameters, the control system may initiate an alarm and a shutdown of the unit. In many instances, an alarm may precede the shutdown command.\u0000 Frequent discussions evolve around situations that lead to a shutdown of the train, as shutdowns impact the availability of the turbomachinery equipment, but in a wider sense also the availability of the compressor station. Therefore, shutdowns impact the profitability of a system. On the other hand, shutdowns may prevent significant, costly damage to the equipment, with significant downtime, and financial implications.\u0000 In this lecture, we will discuss different methodologies for shutdown requirements, in the effort to maximize availability of units. Particular emphasis will be given to aging machines as well as machines where the instrumentation, and the control algorithms may no longer be state of the art, or where unnecessary or spurious shutdowns plague an installation.","PeriodicalId":252904,"journal":{"name":"Volume 8: Oil and Gas Applications; Steam Turbine","volume":"3 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":"129791804","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}
E. Losi, M. Venturini, L. Manservigi, G. Ceschini, G. Bechini, Giuseppe Cota, Fabrizio Riguzzi
A gas turbine trip is an unplanned shutdown, of which the most relevant consequences are business interruption and a reduction of equipment remaining useful life. Thus, understanding the underlying causes of gas turbine trip would allow predicting its occurrence in order to maximize gas turbine profitability and improve its availability. In the ever competitive Oil & Gas sector, data mining and machine learning are increasingly being employed to support a deeper insight and improved operation of gas turbines. Among the various machine learning tools, Random Forests are an ensemble learning method consisting of an aggregation of decision tree classifiers. This paper presents a novel methodology aimed at exploiting information embedded in the data and develops Random Forest models, aimed at predicting gas turbine trip based on information gathered during a timeframe of historical data acquired from multiple sensors. The novel approach exploits time series segmentation to increase the amount of training data, thus reducing overfitting. First, data are transformed according to a feature engineering methodology developed in a separate work by the same authors. Then, Random Forest models are trained and tested on unseen observations to demonstrate the benefits of the novel approach. The superiority of the novel approach is proved by considering two real-word case-studies, involving filed data taken during three years of operation of two fleets of Siemens gas turbines located in different regions. The novel methodology allows values of Precision, Recall and Accuracy in the range 75–85 %, thus demonstrating the industrial feasibility of the predictive methodology.
{"title":"Prediction of Gas Turbine Trip: a Novel Methodology Based on Random Forest Models","authors":"E. Losi, M. Venturini, L. Manservigi, G. Ceschini, G. Bechini, Giuseppe Cota, Fabrizio Riguzzi","doi":"10.1115/gt2021-58916","DOIUrl":"https://doi.org/10.1115/gt2021-58916","url":null,"abstract":"\u0000 A gas turbine trip is an unplanned shutdown, of which the most relevant consequences are business interruption and a reduction of equipment remaining useful life. Thus, understanding the underlying causes of gas turbine trip would allow predicting its occurrence in order to maximize gas turbine profitability and improve its availability.\u0000 In the ever competitive Oil & Gas sector, data mining and machine learning are increasingly being employed to support a deeper insight and improved operation of gas turbines. Among the various machine learning tools, Random Forests are an ensemble learning method consisting of an aggregation of decision tree classifiers.\u0000 This paper presents a novel methodology aimed at exploiting information embedded in the data and develops Random Forest models, aimed at predicting gas turbine trip based on information gathered during a timeframe of historical data acquired from multiple sensors. The novel approach exploits time series segmentation to increase the amount of training data, thus reducing overfitting.\u0000 First, data are transformed according to a feature engineering methodology developed in a separate work by the same authors. Then, Random Forest models are trained and tested on unseen observations to demonstrate the benefits of the novel approach. The superiority of the novel approach is proved by considering two real-word case-studies, involving filed data taken during three years of operation of two fleets of Siemens gas turbines located in different regions.\u0000 The novel methodology allows values of Precision, Recall and Accuracy in the range 75–85 %, thus demonstrating the industrial feasibility of the predictive methodology.","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":"131302421","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}
E. Losi, M. Venturini, L. Manservigi, G. Ceschini, G. Bechini, Giuseppe Cota, Fabrizio Riguzzi
A gas turbine trip is an unplanned shutdown, of which the consequences are business interruption and a reduction of equipment remaining useful life. Therefore, detection and identification of symptoms of trips would allow predicting its occurrence, thus avoiding damages and costs. The development of machine learning models able to predict gas turbine trip requires the definition of a set of target data and a procedure of feature engineering that improves machine learning generalization and effectiveness. This paper presents a methodology that focuses on the steps that precede the development of a machine learning model, i.e., data selection and feature engineering, which are the key for a successful predictive model. Data selection is performed by partitioning units into homogeneous groups according to different criteria, e.g., type, region of installation, and operation. A subsequent matching algorithm is applied to rotational speed data of multiple gas turbine units to identify start-ups and shutdowns so that the considered units can be partitioned according to their operation, i.e., base load or peak load. Feature engineering aims at creating features that improve machine learning model accuracy and reliability. First, the Discrete Fourier Transform is used to identify and remove from the time series the seasonal components, i.e., patterns that repeat with a given periodicity. Then, new features are created based on gas turbine domain knowledge in order to capture the complex interactions among system variables and trip occurrence. The outcomes of this paper are the definition of a set of target examples and the identification of a meaningful set of features suitable to develop a machine learning model aimed at predicting gas turbine trip.
{"title":"Data Selection and Feature Engineering for the Application of Machine Learning to the Prediction of Gas Turbine Trip","authors":"E. Losi, M. Venturini, L. Manservigi, G. Ceschini, G. Bechini, Giuseppe Cota, Fabrizio Riguzzi","doi":"10.1115/gt2021-58914","DOIUrl":"https://doi.org/10.1115/gt2021-58914","url":null,"abstract":"\u0000 A gas turbine trip is an unplanned shutdown, of which the consequences are business interruption and a reduction of equipment remaining useful life. Therefore, detection and identification of symptoms of trips would allow predicting its occurrence, thus avoiding damages and costs.\u0000 The development of machine learning models able to predict gas turbine trip requires the definition of a set of target data and a procedure of feature engineering that improves machine learning generalization and effectiveness.\u0000 This paper presents a methodology that focuses on the steps that precede the development of a machine learning model, i.e., data selection and feature engineering, which are the key for a successful predictive model.\u0000 Data selection is performed by partitioning units into homogeneous groups according to different criteria, e.g., type, region of installation, and operation. A subsequent matching algorithm is applied to rotational speed data of multiple gas turbine units to identify start-ups and shutdowns so that the considered units can be partitioned according to their operation, i.e., base load or peak load.\u0000 Feature engineering aims at creating features that improve machine learning model accuracy and reliability. First, the Discrete Fourier Transform is used to identify and remove from the time series the seasonal components, i.e., patterns that repeat with a given periodicity. Then, new features are created based on gas turbine domain knowledge in order to capture the complex interactions among system variables and trip occurrence.\u0000 The outcomes of this paper are the definition of a set of target examples and the identification of a meaningful set of features suitable to develop a machine learning model aimed at predicting gas turbine trip.","PeriodicalId":252904,"journal":{"name":"Volume 8: Oil and Gas Applications; Steam Turbine","volume":"218 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":"114657163","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}
N. Casari, E. Fadiga, M. Pinelli, A. Suman, R. Kurz, Kevin C. Davis, Flavio Marin
Gas turbine driven centrifugal compressors often undergo a detailed performance analysis either for the acceptance by the user or to evaluate their existing condition. Often, the tests are conducted at site, where the compressor and its driver must prove their capability to fulfill the requirements of the project specifications. Typical field testing of gas turbine compressor packages requires the evaluation of head, efficiency, capacity, fuel consumption, as well as the available driver power. The process is not straightforward since the conditions at which the compressor package is tested are typically different from the originally agreed upon conditions. In particular, the ambient conditions, as well as the load conditions at the test are usually different. There are a number of standard procedures for carrying out a field test. In this work, one of them is considered for quantifying how the variation in some parameters can impact the result of the field test. Particular attention is given to the evaluation and data correction for the gas turbine. The results of the test can be affected by several factors. Some of them are related to the installation, such as the array of RTDs used. Further, the influence of the accuracy of the input data that are used for the calculations must be considered by determining the effect of test uncertainty. Other parameters that can affect the results are related to the modeling: The natural gas exhibits real gas behavior at the test conditions, and an equation of state has to be used for data conversion. The choice of an equation of state can translate into a differences in the test results. An assessment of the impact of these factors on the outcome of a field test is reported. From the results of this work, the expected error as consequence of deviation from the specification can be quantified.
{"title":"Assessment of Non-Standard Procedure in Field Testing of Gas Turbine Driven Centrifugal Compressors","authors":"N. Casari, E. Fadiga, M. Pinelli, A. Suman, R. Kurz, Kevin C. Davis, Flavio Marin","doi":"10.1115/gt2021-04249","DOIUrl":"https://doi.org/10.1115/gt2021-04249","url":null,"abstract":"\u0000 Gas turbine driven centrifugal compressors often undergo a detailed performance analysis either for the acceptance by the user or to evaluate their existing condition. Often, the tests are conducted at site, where the compressor and its driver must prove their capability to fulfill the requirements of the project specifications. Typical field testing of gas turbine compressor packages requires the evaluation of head, efficiency, capacity, fuel consumption, as well as the available driver power. The process is not straightforward since the conditions at which the compressor package is tested are typically different from the originally agreed upon conditions. In particular, the ambient conditions, as well as the load conditions at the test are usually different. There are a number of standard procedures for carrying out a field test. In this work, one of them is considered for quantifying how the variation in some parameters can impact the result of the field test. Particular attention is given to the evaluation and data correction for the gas turbine. The results of the test can be affected by several factors. Some of them are related to the installation, such as the array of RTDs used. Further, the influence of the accuracy of the input data that are used for the calculations must be considered by determining the effect of test uncertainty. Other parameters that can affect the results are related to the modeling: The natural gas exhibits real gas behavior at the test conditions, and an equation of state has to be used for data conversion. The choice of an equation of state can translate into a differences in the test results. An assessment of the impact of these factors on the outcome of a field test is reported. From the results of this work, the expected error as consequence of deviation from the specification can be quantified.","PeriodicalId":252904,"journal":{"name":"Volume 8: Oil and Gas Applications; Steam Turbine","volume":"216 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":"114982340","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}
This study presents the results of measurements in a scaled model turbine test rig operated at Mitsubishi Hitachi Power Systems, Ltd. In this paper, the flow pattern obtained by traverse measurements is compared with the results of CFD. In order to investigate the flow field in the low pressure steam turbine, the tests are carried out using a test turbine (4 stages) of × 0.33 scale. The velocity and pressure fields are evaluated by traverse measurements. The corresponding CFD are performed by ANSYS CFX. Generally, shroud and stub are used in last stage rotating blades of industrial steam turbine to provide high structural stability by increasing stiffness and damping. In this study, the shroud and stub are modeled in CFD to evaluate the effect on flow pattern. Besides, in order to evaluate the effects of super cooling in blade rows, non-equilibrium condensation is modeled in CFD by ANSYS CFX. The computation model is constructed as accurate reproduction of the scaled model test steam turbine including some steam pipes, supporting sheet metal and the measurement equipment such as traverse pipes and instruments. However, the simple computation model which consists of blade rows with cavities (multi stages) and short diffuser is applied for non-equilibrium condensation calculation due to convergence problems. Comparative evaluation of the test results with the corresponding CFD results showed that the flow patterns predicted by CFD are good. In order to capture the flow pattern characteristics by CFD, it is necessary to consider both real shape modeling and non-equilibrium condensation modeling.
{"title":"Experimental and Numerical Investigations of the Effects of Real Shape Modeling and Non-Equilibrium Condensation Modeling on the Flow Pattern in Steam Turbine","authors":"S. Tabata, Y. Sasao, K. Segawa","doi":"10.1115/gt2021-01754","DOIUrl":"https://doi.org/10.1115/gt2021-01754","url":null,"abstract":"\u0000 This study presents the results of measurements in a scaled model turbine test rig operated at Mitsubishi Hitachi Power Systems, Ltd. In this paper, the flow pattern obtained by traverse measurements is compared with the results of CFD.\u0000 In order to investigate the flow field in the low pressure steam turbine, the tests are carried out using a test turbine (4 stages) of × 0.33 scale. The velocity and pressure fields are evaluated by traverse measurements.\u0000 The corresponding CFD are performed by ANSYS CFX. Generally, shroud and stub are used in last stage rotating blades of industrial steam turbine to provide high structural stability by increasing stiffness and damping. In this study, the shroud and stub are modeled in CFD to evaluate the effect on flow pattern. Besides, in order to evaluate the effects of super cooling in blade rows, non-equilibrium condensation is modeled in CFD by ANSYS CFX. The computation model is constructed as accurate reproduction of the scaled model test steam turbine including some steam pipes, supporting sheet metal and the measurement equipment such as traverse pipes and instruments. However, the simple computation model which consists of blade rows with cavities (multi stages) and short diffuser is applied for non-equilibrium condensation calculation due to convergence problems.\u0000 Comparative evaluation of the test results with the corresponding CFD results showed that the flow patterns predicted by CFD are good. In order to capture the flow pattern characteristics by CFD, it is necessary to consider both real shape modeling and non-equilibrium condensation modeling.","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":"125504903","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}
Testing a sub-component or testing a scaled model are the approaches currently used to reduce the development cost of the new low-pressure (LP) section of a steam turbine. In any case, testing campaigns are run at a limited number of operating conditions. Therefore, some correlations are used to build a performance model of the LP module and expand the usage of a limited set of experimental data to cover the application range encountered in the steam turbine market. Another approach, which has become feasible during the last decade, is the usage of CFD calculations. These two approaches include a certain amount of uncertainty in the performance of the LP section, mainly related to the losses caused by the moisture content in the flow. In the present paper, the results of the analysis of a cutting-edge low-pressure section for small steam turbines are presented. The results are obtained by using a CFD commercial code with a set of user defined subroutines to model the effects of droplets nucleation and growth. Different operating conditions are considered, with different wetness at the exit and different pressure ratios, in order to clearly show the loss trend for different levels of exit moisture. The numerical results are compared with the experimental data, showing a significant improvement in the performance predictability for the considered case and demonstrating the benefit of using a CFD approach instead of using existing correlations.
{"title":"Two Phase Flow CFD Modeling of a Steam Turbine Low Pressure Section: Comparison With Data and Correlations","authors":"N. Maceli, Lorenzo Arcangeli, A. Arnone","doi":"10.1115/gt2021-59645","DOIUrl":"https://doi.org/10.1115/gt2021-59645","url":null,"abstract":"\u0000 Testing a sub-component or testing a scaled model are the approaches currently used to reduce the development cost of the new low-pressure (LP) section of a steam turbine. In any case, testing campaigns are run at a limited number of operating conditions. Therefore, some correlations are used to build a performance model of the LP module and expand the usage of a limited set of experimental data to cover the application range encountered in the steam turbine market. Another approach, which has become feasible during the last decade, is the usage of CFD calculations.\u0000 These two approaches include a certain amount of uncertainty in the performance of the LP section, mainly related to the losses caused by the moisture content in the flow.\u0000 In the present paper, the results of the analysis of a cutting-edge low-pressure section for small steam turbines are presented. The results are obtained by using a CFD commercial code with a set of user defined subroutines to model the effects of droplets nucleation and growth. Different operating conditions are considered, with different wetness at the exit and different pressure ratios, in order to clearly show the loss trend for different levels of exit moisture. The numerical results are compared with the experimental data, showing a significant improvement in the performance predictability for the considered case and demonstrating the benefit of using a CFD approach instead of using existing correlations.","PeriodicalId":252904,"journal":{"name":"Volume 8: Oil and Gas Applications; Steam Turbine","volume":"334 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":"134124126","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}
Rotating instability (RI) in steam turbines is a phenomenon occurring during operation at very low volume flow conditions. Whereas RI is well-known in compressors, it is rather uncommon in turbines, where it is limited to the last stages of low-pressure steam turbines. The phenomenon has been studied numerically by means of viscous 3D CFD simulations employing mainly URANS equations. Given the possible difficulties to accurately predict heavily separated flows using such methods, this paper deals with the question whether the more sophisticated Improved Delayed Detached Eddy Simulation (iDDES) model is applicable in an industrial environment and whether it is capable of capturing the complex unsteady flow physics in a more realistic manner. For this purpose, the commercial CFD solver STAR-CCM+ is employed. A three-stage low-pressure model steam turbine featuring a non-axisymmetric inlet and an axial-radial diffuser is used as a test object. In order to capture the asymmetry, the model spans the full annulus and comprises the inlet section, all three stages, the diffuser as well as the exhaust hood. URANS and iDDES simulations have been performed at various low-volume flow part-load operating points and compared to test data. Unsteady pressure fluctuations at the casing as well as time-resolved probe traverse data have been used to validate the simulations. It is found that both models capture the overall flow physics well and that the iDDES model is superior at the most extreme part-load operating condition. In addition to the model accuracy and applicability of the CFD tool used, the paper discusses the challenges encountered during simulation setup as well as during initialization.
{"title":"Detached Eddy Simulation of Rotating Instabilities in a Low-Pressure Model Steam Turbine Operating Under Low Volume Flow Conditions","authors":"Ilgit Ercan, D. Vogt","doi":"10.1115/gt2021-58704","DOIUrl":"https://doi.org/10.1115/gt2021-58704","url":null,"abstract":"\u0000 Rotating instability (RI) in steam turbines is a phenomenon occurring during operation at very low volume flow conditions. Whereas RI is well-known in compressors, it is rather uncommon in turbines, where it is limited to the last stages of low-pressure steam turbines. The phenomenon has been studied numerically by means of viscous 3D CFD simulations employing mainly URANS equations. Given the possible difficulties to accurately predict heavily separated flows using such methods, this paper deals with the question whether the more sophisticated Improved Delayed Detached Eddy Simulation (iDDES) model is applicable in an industrial environment and whether it is capable of capturing the complex unsteady flow physics in a more realistic manner. For this purpose, the commercial CFD solver STAR-CCM+ is employed.\u0000 A three-stage low-pressure model steam turbine featuring a non-axisymmetric inlet and an axial-radial diffuser is used as a test object. In order to capture the asymmetry, the model spans the full annulus and comprises the inlet section, all three stages, the diffuser as well as the exhaust hood. URANS and iDDES simulations have been performed at various low-volume flow part-load operating points and compared to test data. Unsteady pressure fluctuations at the casing as well as time-resolved probe traverse data have been used to validate the simulations. It is found that both models capture the overall flow physics well and that the iDDES model is superior at the most extreme part-load operating condition.\u0000 In addition to the model accuracy and applicability of the CFD tool used, the paper discusses the challenges encountered during simulation setup as well as during initialization.","PeriodicalId":252904,"journal":{"name":"Volume 8: Oil and Gas Applications; Steam Turbine","volume":"42 6 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":"131939598","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}
The steam turbine rotor is still the main power generation equipment. Affected by the impact of new energy on the power grid, the steam turbine needs to participate in peak load regulation, which will make turbine rotor components more prone to failure. The rotor is an important equipment of a steam turbine. Unbalance and misalignment are the normal state of rotor failure. In recent years, more and more attention has been paid to the fault detection method based on deep learning, which takes rotating machinery as the object. However, there is a lack of research on actual steam turbine rotors. In this paper, a method of rotor unbalance and parallel misalignment fault detection based on residual network is proposed, which realizes the end-to-end fault detection of rotor. Meanwhile, the method is evaluated with numerical simulation data, and the multi task detection of rotor unbalance, parallel misalignment, unbalanced parallel misalignment coupling faults (coupling fault called in this paper) is realized. The influence of signal-to-noise ratio and the number of training samples on the detection performance of neural network is discussed. The detection accuracy of unbalanced position is 93.5%, that of parallel misalignment is 99.1%. The detection accuracy for unbalance and parallel misalignment is 89.1% and 99.1%, respectively. The method can realize the direct mapping between the unbalanced, parallel misalignment, coupling fault vibration signals and the fault detection results. The method has the ability to automatically extract fault features. It overcomes the shortcoming of traditional methods that rely on signal processing experience, and has the characteristics of high precision and strong robustness.
{"title":"Research on Fault Diagnosis of Steam Turbine Rotor Unbalance and Parallel Misalignment Based on Numerical Simulation and Convolutional Neural Network","authors":"Chongyu Wang, Di Zhang, Yonghui Xie","doi":"10.1115/gt2021-60247","DOIUrl":"https://doi.org/10.1115/gt2021-60247","url":null,"abstract":"\u0000 The steam turbine rotor is still the main power generation equipment. Affected by the impact of new energy on the power grid, the steam turbine needs to participate in peak load regulation, which will make turbine rotor components more prone to failure. The rotor is an important equipment of a steam turbine. Unbalance and misalignment are the normal state of rotor failure. In recent years, more and more attention has been paid to the fault detection method based on deep learning, which takes rotating machinery as the object. However, there is a lack of research on actual steam turbine rotors. In this paper, a method of rotor unbalance and parallel misalignment fault detection based on residual network is proposed, which realizes the end-to-end fault detection of rotor. Meanwhile, the method is evaluated with numerical simulation data, and the multi task detection of rotor unbalance, parallel misalignment, unbalanced parallel misalignment coupling faults (coupling fault called in this paper) is realized. The influence of signal-to-noise ratio and the number of training samples on the detection performance of neural network is discussed. The detection accuracy of unbalanced position is 93.5%, that of parallel misalignment is 99.1%. The detection accuracy for unbalance and parallel misalignment is 89.1% and 99.1%, respectively. The method can realize the direct mapping between the unbalanced, parallel misalignment, coupling fault vibration signals and the fault detection results. The method has the ability to automatically extract fault features. It overcomes the shortcoming of traditional methods that rely on signal processing experience, and has the characteristics of high precision and strong robustness.","PeriodicalId":252904,"journal":{"name":"Volume 8: Oil and Gas Applications; Steam Turbine","volume":"59 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":"121823681","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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