807 research outputs found

    On the creep fatigue and creep rupture behaviours of 9-12% Cr steam turbine rotor

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    This paper presents the study of creep-fatigue interaction damage and creep rupture limit for 9-12% Cr steam turbine rotor under coupled cyclic thermal-mechanical loadings that alter in phase and out of phase. The investigation is implemented using the Linear Matching Method (LMM) and based on the newly developed creep-fatigue and creep rupture evaluation procedures. Latest experimental creep data of FB2 which belongs to 9-12% Cr heat-resistant steel family is employed to calculate creep-related damage and creep rupture bearing capacity of steam turbine rotor. Various factors that affect creep and fatigue damage of steam turbine rotor are analyzed and discussed, including dwell period and rotating speed of rotor. From the parametric studies, the damage locations and cycles to failure induced by creep and fatigue mechanism are presented respectively. Moreover, through creep rupture analyses for varying desired fracture time, the novel creep rupture curves under multi-type double-cyclic-loading conditions are given and further compared with cyclic plasticity failure curve. These surrogate-model analyses offer a deep understanding of structural responses of steam turbine rotor under long-term high temperature operation and provide critical loading conditions to be referenced for smooth running

    Coupling crystal plasticity and continuum damage mechanics for creep assessment in Cr-based power-plant steel

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    To improve the design and safety of power plant components, long-term hightemperature creep behaviour of a power-plant material, such as Cr-based alloy, should be assessed. Prior studies indicate that power-plant components undergo material degradation and premature failure by nucleation, growth and coalescence of microvoids as a result of creep damage. In classical crystal-plasticity-based models, a flow rule and a hardening law do not account for global stiffness degradation of materials due to evolving microvoids, having a significant influence on material behaviour, especially under large deformations. In this study, a crystal-plasticity scheme coupled with an appropriate continuum damage model is developed to capture the anisotropic creep-damage effect on the overall deformation behaviour of Cr-based power-plant steel. Numerical simulations show that the developed approach can characterize creep deformation of the material exposed to a range of stress levels and temperatures under consideration of stiffness degradation under large deformation

    Numerical Analysis of a Steam Turbine Rotor subjected to Thermo-Mechanical Cyclic Loads

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    The contribution at hand discusses the thermo-mechanical analysis of a steam turbine rotor, made of a heat-resistant steel. Thereby, the analysis accounts for the complicated geometry of a real steam turbine rotor, subjected to practical and complex thermo-mechanical boundary conditions. Various thermo-mechanical loading cycles are taken into account, including different starting procedures (cold and warm starts). Within the thermal analysis using the FE code ABAQUS, instationary steam temperatures as well as heat transfer coefficients are prescribed, and the resulting temperature field serves as input for the subsequent structural analysis. In order to describe the mechanical behavior of the heat-resistant steel, which exhibits significant rate-dependent inelasticity combined with hardening and softening phenomena, a robust nonlinear constitutive approach, the binary mixture model, is employed and implemented in ABAQUS in two different ways, i.e. using explicit as well as implicit  methods for the time integration of the governing evolution equations. The numerical performance, the required computational effort, and the obtained accuracy of both integration methods are examined with reference to the thermo-mechanical analysis of a steam turbine rotor, as a typical practical example for the numerical analysis of a complex component. In addition, the obtained temperature, stress, and strain fields in the steam turbine rotor are discussed in detail, and the influence of the different starting procedures is examined closely

    A Framework for Remaining Useful Life Prediction of Steam Turbines Applicable to Various Data Types

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2018. 8. ์œค๋ณ‘๋™.์ตœ๊ทผ ๋ฐœ์ „์‚ฌ๊ฐ„ ๊ฒฝ์Ÿ์ด ์น˜์—ดํ•ด์ง์— ๋”ฐ๋ผ ๋ฐœ์ „ ์‚ฐ์—…์—์„œ๋Š” ์šด์ „ ๋น„์šฉ์„ ์ ˆ๊ฐํ•˜๊ณ  ํ•ต์‹ฌ ์„ค๋น„์˜ ์ˆ˜๋ช…์„ ์—ฐ์žฅํ•˜๋Š”๋ฐ ๋งŽ์€ ๋…ธ๋ ฅ์„ ๊ธฐ์šธ์ด๊ณ  ์žˆ๋‹ค. ํ•œํŽธ ์šด์ „ ์‹œ๊ฐ„์ด ์„ค๊ณ„ ์ˆ˜๋ช…์— ๊ทผ์ ‘ํ•จ์— ๋”ฐ๋ผ ์ฆ๊ธฐํ„ฐ๋นˆ๊ณผ ๊ฐ™์€ ํ•ต์‹ฌ ์„ค๋น„์˜ ์—ดํ™”๊ฐ€ ๊ฐ€์†๋˜๊ณ  ํฌ๊ณ  ์ž‘์€ ๊ณ ์žฅ์ด ๋งŽ์ด ๋ฐœ์ƒํ•˜๊ณ  ์žˆ๋‹ค. ๊ฐ€์†ํ™”๋œ ์—ดํ™”๋‚˜ ์˜ˆ๊ธฐ์น˜ ๋ชปํ•œ ์†์ƒ์œผ๋กœ ๋ฐœ์ „์†Œ๊ฐ€ ์ •์ง€๋˜๋ฉด ๋ง‰๋Œ€ํ•œ ๊ฒฝ์ œ์  ์†์‹ค๊ณผ ๊ตญ๊ฐ€์ ์ธ ์žฌํ•ด๋ฅผ ์•ผ๊ธฐํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด์— ๋”ฐ๋ผ ์•ˆ์ •์ ์ธ ์„ค๋น„์˜ ์šด์ „์„ ๊ฐ€๋Šฅ์ผ€ ํ•˜๋Š” ๋‹ค์–‘ํ•œ ๊ธฐ์ˆ ๋“ค์ด ๊ฐœ๋ฐœ๋˜๊ณ  ์žˆ์œผ๋ฉฐ ์ตœ๊ทผ ๋“ค์–ด ๋”์šฑ ๋งŽ์€ ๊ฐ๊ด‘์„ ๋ฐ›๊ณ  ์žˆ๋Š” ์‹œ์Šคํ…œ ๊ฑด์ „์„ฑ ๊ด€๋ฆฌ ๊ธฐ์ˆ ์€ ํšจ๊ณผ์ ์œผ๋กœ ์‹œ์Šคํ…œ์˜ ์ƒํƒœ๋ฅผ ๊ฐ์ง€, ์ง„๋‹จ, ๊ทธ๋ฆฌ๊ณ  ์˜ˆ์ง€ํ•˜์—ฌ ๊ด€๋ฆฌ์ž๊ฐ€ ์œ ์ง€ ๋ณด์ˆ˜์— ์žˆ์–ด ํ•„์š”ํ•œ ๊ฒฐ์ •์„ ๋‚ด๋ฆด ์ˆ˜ ์žˆ๋„๋ก ๋„์™€์ค€๋‹ค. ํŠนํžˆ ์ตœ์  ์œ ์ง€์ •๋น„ ๊ด€์ ์—์„œ ์ ํ•ฉํ•œ ๋ฐฉ๋ฒ•๋ก ์„ ํ†ตํ•ด ์˜ˆ์ธก๋œ ์ž”์กด์œ ํšจ์ˆ˜๋ช…์€ ์„ค๋น„ ์ˆ˜๋ช…์— ์ •ํ™•ํ•œ ์ •๋ณด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํšจ๊ณผ์ ์ธ ์œ ์ง€ ์ •๋น„๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•œ๋‹ค. ์ฆ๊ธฐ ํ„ฐ๋นˆ์€ ๋ฐœ์ „์†Œ ์ˆ˜๋ช…์„ ๊ฒฐ์ •ํ•˜๋Š” ํ•ต์‹ฌ ์„ค๋น„์ด๊ธฐ ๋•Œ๋ฌธ์— ๋ฐœ์ „์†Œ์˜ ์ตœ์  ์šด์˜์„ ์œ„ํ•ด ํ™œ์šฉ ๊ฐ€๋Šฅํ•œ ์ •๋ณด๋ฅผ ์ตœ๋Œ€ํ•œ ํ™œ์šฉํ•˜์—ฌ ์šด์ „ ์ค‘์ธ ์ฆ๊ธฐํ„ฐ๋นˆ์˜ ์ž”์กด์œ ํšจ์ˆ˜๋ช…์„ ์ •ํ™•ํ•˜๊ฒŒ ์˜ˆ์ธกํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์˜ ๊ฐœ๋ฐœ์ด ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ์ด์— ๋ณธ ๋ฐ•์‚ฌํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” (1) ์ฆ๊ธฐ ํ„ฐ๋นˆ์— ๋Œ€ํ•œ ๊ณ ์žฅ๋ชจ๋“œ์˜ํ–ฅ๋ถ„์„๊ณผ ์—ฐ๊ณ„ํ•œ ์ž”์กด์œ ํšจ์ˆ˜๋ช… ์˜ˆ์ธก ํ”„๋ ˆ์ž„์›Œํฌ, ๊ทธ๋ฆฌ๊ณ  ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•œ (2) ์†์ƒ ์„ฑ์žฅ ๋ชจ๋ธ (๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•๋ก ), (3) ํฌ๋ฆฌํ”„-ํ”ผ๋กœ ์†์ƒ ์ƒํ˜ธ์ž‘์šฉ์„ ๊ณ ๋ คํ•œ ๋ชจ๋“œ ์˜์กด ์†์ƒ ๋ชจ๋ธ (๋ชจ๋ธ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•๋ก ) ๋“ฑ์˜ ์—ฐ๊ตฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ณ ์žฅ๋ชจ๋“œ์˜ํ–ฅ๋ถ„์„์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์ฆ๊ธฐํ„ฐ๋นˆ์˜ ์ž”์กด์œ ํšจ์ˆ˜๋ช…์„ ์˜ˆ์ธกํ•˜๋Š” ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ์ธก์ •๋œ ๋ฐ์ดํ„ฐ์— ๊ธฐ๋ฐ˜ํ•œ ๋ฐฉ๋ฒ•๋ก ๊ณผ ์†์ƒ ๋ชจ๋ธ์— ๊ธฐ๋ฐ˜ํ•œ ๋ฐฉ๋ฒ•๋ก ์œผ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์˜คํ”„๋ผ์ธ์ด๋‚˜ ์˜จ๋ผ์ธ๊ณผ ๊ฐ™์ด ๋‹ค๋ฅธ ๋ชฉ์ ์œผ๋กœ ์ž”์กด์œ ํšจ์ˆ˜๋ช…์„ ์˜ˆ์ธกํ•  ๋•Œ ๋ถˆํ™•์‹ค๋„๋ฅผ ํ‰๊ฐ€ํ•˜๊ณ  ๊ฐ์†Œ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋„๋ก ๋ถˆํ™•์‹ค๋„๋ฅผ ์ •๋Ÿ‰ํ™”ํ•˜๋Š” ์ ˆ์ฐจ๋ฅผ ํฌํ•จํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•๋ก ์„ ์ด์šฉํ•ด ์ฆ๊ธฐํ„ฐ๋นˆ์˜ ์ž”์กด์œ ํšจ์ˆ˜๋ช…์„ ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ๋Š” ์†์ƒ ์„ฑ์žฅ ๋ชจ๋ธ์˜ ๊ฐœ๋ฐœ์„ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. ์ž”์กด์œ ํšจ์ˆ˜๋ช…์€ ์†์ƒ ์ธ์ž๋กœ๋ถ€ํ„ฐ ์†์ƒ ์„ฑ์žฅ ๋ชจ๋ธ์„ ์—ฐ๊ณ„ํ•˜์—ฌ ์˜ˆ์ธกํ•œ๋‹ค. ํ˜„์žฅ์—์„œ ์ธก์ •๋œ ๊ฒฝ๋„๊ฐ’์œผ๋กœ๋ถ€ํ„ฐ ์†์ƒ์ธ์ž์˜ ํ™•๋ฅ ๋ถ„ํฌ๋ฅผ ์ถ”์ •ํ•˜๊ณ  ์†์ƒ์˜ ์„ฑ์žฅ์„ ํ‰๊ฐ€ํ•  ๋•Œ ๋ถˆํ™•์‹ค๋„๋ฅผ ๊ณ ๋ คํ•˜๊ธฐ ์œ„ํ•ด ๋ฒ ์ด์ง€์•ˆ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ์†์ƒ ์„ฑ์žฅ ๋ชจ๋ธ์„ ํ†ตํ•ด ๊ธฐ์ €๋ถ€ํ•˜๋‚˜ ์ฒจ๋‘๋ถ€ํ•˜์— ์‚ฌ์šฉ๋˜๋Š” ์ฆ๊ธฐํ„ฐ๋นˆ์˜ ์ข…๋ฅ˜์— ์ƒ๊ด€์—†์ด ์ •ํ™•ํ•œ ์ž”์กด์œ ํšจ์ˆ˜๋ช… ์˜ˆ์ธก์ด ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ๊ฒƒ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•๋ก ์„ ์ด์šฉํ•ด ํฌ๋ฆฌํ”„์™€ ํ”ผ๋กœ ์ƒํ˜ธ์ž‘์šฉ์ด ๊ณ ๋ ค๋œ ๋ชจ๋“œ ๊ธฐ๋ฐ˜ ์†์ƒ๋ชจ๋ธ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์†์ƒ๊ธฐ๊ตฌ์— ๋”ฐ๋ฅธ ์žฌ๋ฃŒ ๋ฐ์ดํ„ฐ๋ฅผ ํ†ต๊ณ„์  ๊ธฐ๋ฒ•์œผ๋กœ ๋ถ„์„ํ•˜๊ณ  ์‹ค ์ฆ๊ธฐํ„ฐ๋นˆ์˜ ํ˜•์ƒ ์ •๋ณด์™€ ์šด์ „์ •๋ณด๋ฅผ ์ด์šฉํ•ด ๊ธฐ์ €๋ถ€ํ•˜์™€ ์ฒจ๋‘๋ถ€ํ•˜ ํ„ฐ๋นˆ์„ ๋Œ€์ƒ์œผ๋กœ ํฌ๋ฆฌํ”„ ๋ฐ ํ”ผ๋กœ ์†์ƒ์œจ์„ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ๊ฐ๊ฐ ๊ณ„์‚ฐ๋œ ์†์ƒ์œจ ๊ฒฐ๊ณผ์™€ ํฌ๋ฆฌํ”„-ํ”ผ๋กœ ์ƒํ˜ธ์ž‘์šฉ ๋ชจ๋ธ์„ ํ†ตํ•ด ์šด์ „๋ชจ๋“œ ๋˜๋Š” ์†์ƒ๋ชจ๋“œ์— ๋”ฐ๋ฅธ ์ฆ๊ธฐํ„ฐ๋นˆ์—์„œ์˜ ํฌ๋ฆฌํ”„์™€ ํ”ผ๋กœ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. Abstract i List of Tables viii List of Figures x Nomenclatures xiv Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Research Scope and Overview 4 1.3 Dissertation Layout 7 Chapter 2 Literature Review 8 2.1 Life Prediction Methodologies of Steam Turbine 8 2.1.1 Destructive Method 11 2.1.2 Non-destructive Method 11 2.1.3 Analytical Method 13 2.1.4 Summary and Discussion 13 2.2 Data-driven and Model-based Life Prediction 15 2.2.1 Data-driven Approach 21 2.2.2 Model-based Approach 21 2.3 Empirical Model-based Life Prediction 15 2.3.1 On-site Data Measurement 18 2.3.2 Bayesian Inference 19 2.3.3 Summary and Discussion 20 2.4 Damage Model-based Life Prediction 21 2.4.1 Creep or Fatigue Damage Model Analysis 22 2.4.2 Creep-Fatigue Damage Summation Model analysis 23 2.4.3 Summary and Discussion 27 Chapter 3 A Practical RUL Prediction Framework of Steam Turbine with FMEA Analysis 28 3.1 Overview of Steam Turbines 28 3.2 FMEA for Steam Turbines 31 3.3 A Framework for RUL Prediction of Steam Turbine 34 3.4 Summay and Discussion 38 Chapter 4 A Bayesian Approach for RUL Prediction of Steam Turbines with Damage Growth Model 39 4.1 Characteristics of On-site Measurement Data 40 4.2 Measured Data based Damage Indices 46 4.3 Damage Growth Model using Sporadically Measured and Heterogeneous On-site Data 51 4.3.1 Proposed Damage Growth Model 51 4.3.2 Bayesian Updating Scheme of the Damage Growth Model 58 4.3.3 Damage Growth Model Updating 60 4.4 Predicting the Remaining Useful Life(RUL) of Steam Turbines 68 4.4.1 Damage Threshold 68 4.4.2 Validation of the Proposed Damage Growth Model 72 4.4.3 RUL Prediction 74 4.5 Summary and Discussion 78 Chapter 5 Mode-Dependent Damage Assessment for Steam Turbines with Creep-Fatigue Interaction Model 80 5.1 Dominant Damage Mechanisms of Steam Turbine 82 5.2 Typical Opeation Data of Steam Turbine 83 5.3 Dominant Damage Model of Steam Turbine 86 5.3.1 Creep Damage Model 86 5.3.2 Fatigue Damage Model 88 5.3.3 Creep-Fatigue Damage Model 90 5.4 Statiatical Damage Calculation for Steam Turbine 91 5.4.1 Statistical Characterization of Creep-Fatigue Damage Data 91 5.4.2 Creep Damage Calculation with Steady State Stress 94 5.4.3 Fatigue Damage Calculation with Transient Strain 95 5.5 Mode-Dependent Multiple Damage Interaction Model 100 5.5.1 Estimation of Damage Interaction Parameters 100 5.5.2 Validation of Mode-Dependent Model 101 5.5.3 Effect of Mode-dependence Effects on Multiple Damage 104 5.5.4 Case Study : Risk Assessment 108 5.6 Summary and Discussion 111 Chapter 6 Conclusions 113 6.1 Contributions and Impacts 113 6.2 Suggestions for Future Research 116 References 119 ๊ตญ๋ฌธ ์ดˆ๋ก 142Docto

    Computational Methods for Failure Analysis and Life Prediction

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    This conference publication contains the presentations and discussions from the joint UVA/NASA Workshop on Computational Methods for Failure Analysis and Life Prediction held at NASA Langley Research Center 14-15 Oct. 1992. The presentations focused on damage failure and life predictions of polymer-matrix composite structures. They covered some of the research activities at NASA Langley, NASA Lewis, Southwest Research Institute, industry, and universities. Both airframes and propulsion systems were considered

    Gas Turbines

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    This book is intended to provide valuable information for the analysis and design of various gas turbine engines for different applications. The target audience for this book is design, maintenance, materials, aerospace and mechanical engineers. The design and maintenance engineers in the gas turbine and aircraft industry will benefit immensely from the integration and system discussions in the book. The chapters are of high relevance and interest to manufacturers, researchers and academicians as well

    Hot section components life usage analyses for industrial gas turbines

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    Industrial gas turbines generally operate at a bit stable power levels and the hot section critical components, especially high pressure turbine blades are prone to failure due to creep. In some cases, plants are frequently shut down, thus, in addition to creep low cycle fatigue failure equally sets in. Avoiding failure calls for proper monitoring of how the lives of these components are being consumed. Efforts are thus being made to estimate the life of the critical components of the gas turbine, but, the accuracy of the life prediction methods employed has been an issue. In view of the above observations, in this research, a platform has been developed to simultaneously examine engine life consumption due to creep, fatigue and creep-fatigue interaction exploiting relative life analysis where the engine life calculated is compared to a reference life in each failure mode. The results obtained are life analysis factors which indicate how well the engine is being operated. The Larson-Miller Parameter method is used for the creep life consumption analysis, the modified universal slopes method is applied in the low cycle fatigue life estimation while Taira's linear accumulation method is adopted for creep-fatigue interaction life calculation. Fatigue cycles counting model is developed to estimate the fatigue cycles accumulated in any period of engine operation. Blade thermal and stress models are developed together with a data acquisition and pre-processing module to make the life calculations possible. The developed models and the life analysis algorithms are implemented in PYTHIA, Cranfield University's in-house gas turbine performance and diagnostics software to ensure that reliable simulation results are obtained for life analysis. The developed life analysis techniques are applied to several months of real engine operation data, using LM2500+ engine operated by Manx Utilities at the Isle of Man to test the applicability and the feasibility of the methods. The developed algorithms provide quick evaluation and tracking of engine life. The lifing algorithms developed in this research could be applied to different engines. The relative influences of different factors affecting engine life consumption were investigated by considering each effect on engine life consumtion at different engine operation conditions and it was observed that shaft power level has significant impact on engine life consumption while compressor degradation has more impact on engine life consumption than high pressure turbine degradation. The lifing methodologies developed in this work will help engine operators in their engine conditions monitoring and condition-based maintenance
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