WO2019135747A1 - Algorithme d'évaluation de durée de vie probabiliste pour éléments de moteur à turbine à gaz - Google Patents

Algorithme d'évaluation de durée de vie probabiliste pour éléments de moteur à turbine à gaz Download PDF

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Publication number
WO2019135747A1
WO2019135747A1 PCT/US2018/012289 US2018012289W WO2019135747A1 WO 2019135747 A1 WO2019135747 A1 WO 2019135747A1 US 2018012289 W US2018012289 W US 2018012289W WO 2019135747 A1 WO2019135747 A1 WO 2019135747A1
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WO
WIPO (PCT)
Prior art keywords
gas turbine
life
component
data
deterministic
Prior art date
Application number
PCT/US2018/012289
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English (en)
Inventor
Dipankar DUA
Björn SJÖDIN
Original Assignee
Siemens Aktiengesellschaft
Siemens Energy, Inc.
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Filing date
Publication date
Application filed by Siemens Aktiengesellschaft, Siemens Energy, Inc. filed Critical Siemens Aktiengesellschaft
Priority to PCT/US2018/012289 priority Critical patent/WO2019135747A1/fr
Publication of WO2019135747A1 publication Critical patent/WO2019135747A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01D—NON-POSITIVE DISPLACEMENT MACHINES OR ENGINES, e.g. STEAM TURBINES
    • F01D21/00—Shutting-down of machines or engines, e.g. in emergency; Regulating, controlling, or safety means not otherwise provided for
    • F01D21/003—Arrangements for testing or measuring
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
    • G05B23/0235—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions based on a comparison with predetermined threshold or range, e.g. "classical methods", carried out during normal operation; threshold adaptation or choice; when or how to compare with the threshold
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
    • F05D2220/00—Application
    • F05D2220/70—Application in combination with
    • F05D2220/74—Application in combination with a gas turbine
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
    • F05D2230/00—Manufacture
    • F05D2230/72—Maintenance
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
    • F05D2260/00—Function
    • F05D2260/80—Diagnostics
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
    • F05D2260/00—Function
    • F05D2260/82—Forecasts
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
    • F05D2260/00—Function
    • F05D2260/94—Functionality given by mechanical stress related aspects such as low cycle fatigue [LCF] of high cycle fatigue [HCF]
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
    • F05D2270/00—Control
    • F05D2270/01—Purpose of the control system
    • F05D2270/11—Purpose of the control system to prolong engine life
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • Disclosed embodiments are generally related to turbine engines, and in particular to determining and applying a life evaluation for the gas turbine components.
  • aspects of the present disclosure relate to a system and method for establishing the probabilistic life evaluation of a gas turbine component.
  • An aspect of the present disclosure may be a method of determining the probabilistic life evaluation of a gas turbine component.
  • the method may comprise receiving deterministic life data of the gas turbine component at a deterministic life data database, wherein the deterministic life data comprises physical properties of the gas turbine component; storing the deterministic life data in the deterministic life data database, receiving component life response data at a component life response database; wherein component life response data comprises operational and environmental data affecting the gas turbine component; storing the component life response data in the component life response database; and combining the component life response data and the deterministic life data in order to obtain a probabilistic life evaluation result for the gas turbine component.
  • Another aspect of the present invention may be a system for the determination of the life cycle of a gas turbine component.
  • the system may comprise a processor adapted to process data; a compiled data database for storing data related to components of a gas turbine engine; sensors connected to the gas turbine component for determining properties of the gas turbine component, deterministic life data stored in a deterministic life database, wherein the deterministic life data is based on strength properties of the gas turbine engine; component life response data obtained from the sensors, wherein the component life response data is transmitted to a component life response data database; and wherein the processor combines the deterministic life data and component life data to establish a probabilistic life evaluation result of the gas turbine component.
  • FIG. 1 is a diagram of the system for performing the method for evaluating the life cycle of a component.
  • Fig. 2 is a flow chart of the steps for evaluating the life cycle of a component.
  • Fig. 3 shows two graphs that illustrate the determination of the predictive life result.
  • the system and method of the present invention employs a probabilistic life evaluation algorithm that performs predictive analytics to estimate the risk of failure of gas turbine components.
  • the system and method employs data from sensors and data from analysis of the physical properties of the gas turbine component. The data is defined as the deterministic life data and the component life response data.
  • Deterministic life data is data that is predicted deterministic life of the gas turbine components assuming several pre-set operating conditions, materials data scatter and inspection data plotted and stored as a response surface. Additionally the deterministic life database includes data on the materials properties of the gas turbine component as manufactured. This is the data pertaining to the strength properties of the gas turbine component and how that component will likely perform based upon its implementation in a system. This data may be supplied by the manufacturer of the gas turbine component. This data includes physical parameters of the component, such as creep, fatigue, tensile properties, pre-installation testing data, such as dimensional and weight variations.
  • Component life response data is data that is obtained from the gas turbine component while it is installed or located on the gas turbine engine.
  • Component life response data may be ascertained from sensors located on the gas turbine component.
  • the component life response data may also be compiled from inspection data. This data may include engine operational parameters, gas turbine component entry temperature, speeds, time spent at baseload conditions, time to ramp up and time to shutdown, location of damage on the component, the manner in which the damage occurred to the component, environmental conditions at the site where the component is located, such as ambient temperatures, conditions related to the unit operating in corrosive marine environments etc.
  • the method and system comprise receiving predicted deterministic life data of the gas turbine components assuming several pre- set several operating conditions, material data scatter, and inspection data plotted and stored as a response surface in a deterministic life database. Additionally, the variation in components material properties to the specification requirements for a new component manufacture may be stored in a materials quality management database, wherein the materials quality data comprises of strength properties of the as manufactured gas turbine component taken from the in situ inspections and from inspections performed during overhaul stored in the component inspection database.
  • the operational and environmental data may be maintained in a component health monitoring database; wherein component life response data comprises operational and environmental data affecting the gas turbine component; storing the component life response data in the component life response database; and combining the component life response data with the materials quality database,, inspection database and the deterministic life data base in order to obtain a probabilistic life evaluation result for the gas turbine component.
  • power turbine disks are discussed herein. It should be understood that while power turbine disks are discussed herein, the system and method may be applicable to other gas turbine components.
  • the system and method discussed herein will provide the probability of failure of a gas turbine component, such as a power turbine disk, through the operational life cycle.
  • the deterministic life data and the component life response data are combined. Once the sets of data are combined the probability of failure will be determined by comparing the results of the combined data to a threshold value.
  • the threshold value will be based on allowable risk levels designated for the gas turbine engine component. This information can be used to determine when a product should be serviced or inspected. This can avoid unnecessary shut downs of the gas turbine engine, which enables more continuous energy output.
  • the determination of the potential failure can also be used to order replacement gas turbine components prior to the anticipated loss of or serving of the component. This can also be used to prevent or reduce the unnecessary stoppage of power production.
  • FIG. 1 is a diagram of a probabilistic life evaluation system 100 in which the method is employed.
  • Fig. 2 is a flow chart of the method employed in the system. The following discussion is made in reference to Fig. 1 and Fig. 2. It should be understood that while the diagram of the system shown in Fig. 2 is directed to a particular gas turbine components, it is contemplated that other gas turbine components may benefit from the predictive analysis.
  • Fig. 1 is a diagram of the probabilistic life evaluation (PLE) system 100.
  • the gas turbine component 10 is part of a gas turbine engine.
  • the gas turbine component 10 may be any part of a gas turbine engine, such as an airfoil, blade, combustor, power turbine disk, etc. In the embodiments discussed herein the gas turbine component 10 is a power turbine disk.
  • Attached to the gas turbine component 10 may be a sensor 12.
  • the sensor 12 may be adapted to transmit data regarding conditions related to the gas turbine engine.
  • the sensor 12 may sense temperatures, pressures, material stress, operational parameters such as shaft speed, time spent at baseload, rate of ramp-up and down from baseload power, etc.
  • Data detected by the sensor 12 may be transmitted to the component life response data (CLRD) database 14.
  • CLRD database 14 may be located at a location proximate to the gas turbine engine, may be part of a cloud storage system or may be located in the same location as the other modules and components that make up the PLE system 100.
  • the CLRD database 14 maintains and stores the component life response data such as discussed above.
  • the CLRD database 14 may include environmental information, operations parameters and data determined from the sensor 12 and other material inspections.
  • the PLE system 100 also comprises the deterministic life data (DLD) database 16.
  • the DLD database 16 may be located at a location proximate to the gas turbine engine, may be part of a cloud storage system or may be located in the same location as the other modules and components that make up the PLE system 100.
  • the DLD database 16 maintains and stores the deterministic life data response such as discussed above. This may include data provided by suppliers directed to the material properties of the gas turbine component 10.
  • Both the CLRD database 14 and the DLD database 16 may transmit the data to or have the respective data accessed by the PLE processor 20 and the compiled data database 18.
  • the PLE processor 20 is operably connected to the compiled data database 18 and accesses and compiles the data from the DLD database 16 and the CLRD database 14. The data is then stored and processed by the PLE processor 20 at the compiled data database 18.
  • the compiled data database 18 may be located at a location proximate to the gas turbine engine, may be part of a cloud storage system or may be located in the same location as the other modules and components that make up the PLE system 100.
  • deterministic life data of the gas turbine component is received by the PLE system 100.
  • the deterministic life data may be from a supplier of the gas turbine component 10. Alternatively, the deterministic life data may be independently determined.
  • the deterministic life data sets forth the material properties of the gas turbine component 10 and the predicted life span of the product when placed within a gas turbine engine.
  • the deterministic life data is stored in a DLD database 16.
  • the DLD database 16 may be located at a location proximate to the gas turbine engine, may be part of a cloud storage system or may be located in the same location as the other modules and components that make up the PLE system 100.
  • the DLD database 16 maintains and stores the deterministic life data response such as discussed above.
  • the deterministic life data may be an estimation of variation in the mechanical properties of a gas turbine component based on supplier quality information for a given manufactured gas turbine component 10 being installed into the engine.
  • step 106 CLRD data from sensors 12 located on the gas turbine component 10 is received at the CLRD database 16.
  • step 108 the CLRD data is stored in the CLRD database 14.
  • the CLRD database 14 may be located at a location proximate to the gas turbine engine, may be part of a cloud storage system or may be located in the same location as the other modules and components that make up the PLE system 100.
  • Other component life response data may be received from operator or field service personnel identifying the location and size of damage as observed during inspection and entering the information into the PLE system 100.
  • step 110 the component life response data and the deterministic life data are combined by the PLE processor 20 with the probabilistic life evaluation result stored in the compiled data database 18.
  • the PLE result may then be compared to a threshold value.
  • the comparison to the threshold value can be used by the PLE system 100 to institute a number of steps. For example, if a certain threshold value is exceeded, then the PLE system 100 may order a new gas turbine component 10. In yet another example, if the threshold value is exceeded then the PLE system 100 may institute an order to repair the gas turbine component 10. In another example, if the threshold value is exceeded the PLE system 100 may institute an inspection of the gas turbine component 10. Furthermore, the PLE system 100 may use different threshold values in order to institute different tasks.
  • the probabilistic life evaluation algorithm used by the PLE system 100 is a statistical response surface failure function that can map the impact of variation of random variables taken from the DLD database 14 and the CLRD database 16. These random variables may be things such as operating PT entry temperature, operating speed, disk cavity temperatures as a function of bleed air, material properties, field inspection data that inputs to the material models for deterministic lifing models that quantify life at several predetermined operating conditions based on thermochemical fatigue and creep life damage.
  • the PLE system 100 uses the data on actual operating history using the engine parameters and provides real time risk levels to disk burst, cyclic and creep lives of the damaged disks.
  • the PLE system 100 provides guidance on acceptance/rejection of observed rotor damage as well as for the subsequent inspection and overhaul of the power turbine rotor.
  • the PLE system 100 can provide field service/fleet management personnel a capability to track the risk profile of the operating gas turbine components such as disks in real time utilizing the PLE system 100 rather than stopping an engine, stripping a PT rotor and scrapping the parts each time damage is observed.
  • the PLE system 100 improves reliability, availability and extended mean time between the next overhaul while reducing the overall scrap rate of the components.
  • the PLE system will enable overall reduction in the currently defined inspection intervals thus increasing the engine availability and reducing costs.
  • FIGS. 3 and 4 show two graphs that illustrate the determination of the threshold value.
  • Ni stands for the cycles to crack initiation.
  • Pf stands for product failure.
  • Fig. 3 shows the frequency vs. crack initiation. The frequency is the number of times a given range of Ni is achieved. It is to show the variation in the range of life cycle fatigue life due to variations in parameters such as temperature, speed, power, inspection data, material properties etc.
  • Fig. 4 shows the probability of failure. Equation 1 is shown below.
  • Ni is the cycles to crack initiation.
  • the Ni is a function of different variables which represent operating conditions such as temperature, speed, power, inspection data, material properties etc.
  • the subscript i in equation 1 represents the life cycle fatigue computed for one set of combination of variables“a”.
  • the deterministic life data feeds into the modeled response surface that is computed for multiple conditions using equation 2 shown below.
  • Lg (Ni) creates a linear regression model of Ni on logarithmic scale and forms the mathematical basis for the response surface of deterministic lives.
  • Variable“b” represents the fitting constants that are used to fit Ni vs ai data as shown in equation (1).
  • Various physical properties of the gas turbine component 10 are utilized in order to ascertain a probability of failure, which is the probabilistic life expectancy result. The probability of failure will increase over time as the parameters that impact the probability of failure increase. The determination of the probability of failure and thus the PLE result is discussed below. The determined PLE result is then compared to a threshold value in order to take action on the gas turbine engine.
  • the PLE result it determined for a gas turbine component 10, which in this instance is a power turbine disk. It should be understood that for other gas turbine components different variables may be used.
  • the deterministic life data used in this example is the result of the following evaluations; crushing stress evaluation, unzipping assessment and disk burst and yield assessment. These various evaluations are the application of algorithms and calculations know to one of ordinary skill in the art for determining the properties. While the individual combinations are known the combination and application within the manner set forth herein is not known.
  • the component life response data used in this example is the result of life cycle fatigue, creep life evaluation, half cycle evaluation, tie bolt mechanical integrity and life assessment evaluation. This data can be taken from properties measured in the environment by sensors 12 located on or near the gas turbine component 10.
  • Life cycle fatigue may be performed by taking the strain vs component life curves from strain controlled smooth specimen data for estimating initiation life. Additionally fracture mechanics may be based on the estimation for propagation life. The typical lives of components may be converted to minimum lives by using scaling factors.
  • a stress analysis of the gas turbine component 10 provides the strain range and maximum von-Mises stress required for the calculation of the LCF damage.
  • Some of the variables used for evaluation can be LCF -Data, strain range to number of cycles to crack initiation. Stress-strain curve, Fracture toughness, Fatigue crack growth rate curves, PT entry temperature, centrifugal load, rated PT speed, blade mass, constraint and contact boundary conditions etc. These calculations are used to determine the PLE result for the power turbine disc.
  • the threshold value to which the PLE results may be compared can be established by having a life assessment performed for the worst engine condition operating at max continuous speed (i.e. 105% of the rated speed).
  • max continuous speed i.e. 105% of the rated speed.

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Abstract

Un procédé et un système d'évaluation prédictive de la durée de vie du moteur à turbine à gaz sont utilisés pour programmer des éléments de maintenance, de réparation et de remplacement. Le système et le procédé utilisent des données basées sur des propriétés de l'élément de turbine à gaz (10) et des données prises pendant le cycle de vie de l'élément. Les données sont ensuite analysées et utilisées pour prolonger le cycle de vie des éléments de turbine à gaz (10) et réduire le temps nécessaire pour remplacer des éléments de turbine à gaz (10) endommagés.
PCT/US2018/012289 2018-01-04 2018-01-04 Algorithme d'évaluation de durée de vie probabiliste pour éléments de moteur à turbine à gaz WO2019135747A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112380656A (zh) * 2020-11-20 2021-02-19 西安热工研究院有限公司 一种燃气轮机燃烧室部件裂纹扩展寿命评估方法
CN112906281A (zh) * 2021-03-15 2021-06-04 中国航发湖南动力机械研究所 一种基于拟蒙特卡洛抽样的涡轮盘裂纹扩展可靠性分析方法
JP7458357B2 (ja) 2021-09-24 2024-03-29 三菱パワー株式会社 モデル学習装置、性能評価装置、モデル学習方法、及びプログラム

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3249200A1 (fr) * 2016-05-23 2017-11-29 United Technologies Corporation Moteur à turbine à gaz ayant des calculs de durée de vie basés sur l'utilisation réelle

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3249200A1 (fr) * 2016-05-23 2017-11-29 United Technologies Corporation Moteur à turbine à gaz ayant des calculs de durée de vie basés sur l'utilisation réelle

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112380656A (zh) * 2020-11-20 2021-02-19 西安热工研究院有限公司 一种燃气轮机燃烧室部件裂纹扩展寿命评估方法
CN112906281A (zh) * 2021-03-15 2021-06-04 中国航发湖南动力机械研究所 一种基于拟蒙特卡洛抽样的涡轮盘裂纹扩展可靠性分析方法
JP7458357B2 (ja) 2021-09-24 2024-03-29 三菱パワー株式会社 モデル学習装置、性能評価装置、モデル学習方法、及びプログラム

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