Application of Advanced Probabilistic Fracture Mechanics to Life Evaluation of Turbine Rotor Blade Attachments

1995 ◽  
Author(s):  
H. R. Millwater ◽  
Y.-T. Wu ◽  
J. W. Cardinal ◽  
G. G. Chell
Keyword(s):  
Fracture Mechanics ◽  
Computational Algorithm ◽  
Operating Time ◽  
Turbine Rotor ◽  
Life Assessment ◽  
Inspection Planning ◽  
Element Analysis ◽  
Turbine Rotor Blade ◽  
Probabilistic Fracture Mechanics ◽  
Life Evaluation

This paper describes the application of an advanced probabilistic fracture mechanics computational algorithm with inspection simulation to the probabilistic life assessment of a turbine blade attachment, sometimes referred to as a steeple or fir tree. The life of the steeple is limited by high cycle fatigue. The methodology utilized combines structural finite element analysis, stochastic fatigue crack growth, and crack inspection and repair. The resulting information provides the engineer with an assessment of the probability of failure of the structure as a function of operating time and the effect of the inspection procedure. This information can form the basis of inspection planning and retirement-for-cause decisions.

Application of Advanced Probabilistic Fracture Mechanics to Life Evaluation of Turbine Rotor Blade Attachments

1996 ◽  
Vol 118 (2) ◽  
pp. 394-398 ◽  
Author(s):  
H. R. Millwater ◽  
Y.-T. Wu ◽  
J. W. Cardinal ◽  
G. G. Chell
Keyword(s):  
Fracture Mechanics ◽  
Computational Algorithm ◽  
Operating Time ◽  
Turbine Rotor ◽  
Life Assessment ◽  
Inspection Planning ◽  
Element Analysis ◽  
Turbine Rotor Blade ◽  
Probabilistic Fracture Mechanics ◽  
Life Evaluation

This paper describes the application of an advanced probabilistic fracture mechanics computational algorithm with inspection simulation to the probabilistic life assessment of a turbine blade attachment, sometimes referred to as a steeple or fir tree. The life of the steeple is limited by high cycle fatigue. The methodology utilized combines structural finite element analysis, stochastic fatigue crack growth, and crack inspection and repair. The resulting information provides the engineer with an assessment of the probability of failure of the structure as a function of operating time and the effect of the inspection procedure. This information can form the basis of inspection planning and retirement-for-cause decisions.

Probabilistic Fracture Mechanics for Heavy Duty Gas Turbine Rotor Forgings

2017 ◽  
Author(s):  
Kai Kadau ◽  
Phillip W. Gravett ◽  
Christian Amann
Keyword(s):  
Fracture Mechanics ◽  
High Performance ◽  
Computing Methodology ◽  
Direct Simulation Monte Carlo ◽  
Turbine Rotor ◽  
Heavy Duty ◽  
Element Analysis ◽  
Probabilistic Fracture Mechanics ◽  
Risk Of Failure ◽  
Heavy Duty Gas Turbine

We developed and successfully applied a direct simulation Monte-Carlo scheme to quantify the risk of fracture for heavy duty rotors commonly used in the energy sector. The developed Probabilistic Fracture Mechanics high-performance computing methodology and code ProbFM routinely assesses relevant modes of operation for a component by performing billions of individual fracture mechanics simulations. The methodology can be used for new design and life-optimization of components, as well as for the risk of failure quantification of in service rotors and their re-qualifications in conjunction with non-destructive examination techniques, such as ultrasonic testing. The developed probabilistic scheme integrates material data, ultra-sonic testing information, duty-cycle data, and finite element analysis in order to determine the risk of failure. The methodology provides an integrative and robust measure of the fitness for service and allows for a save and reliable operation management of heavy duty rotating equipment.

Probabilistic Fracture Mechanics for Heavy-Duty Gas Turbine Rotor Forgings

2018 ◽  
Vol 140 (6) ◽  
Author(s):  
Kai Kadau ◽  
Phillip W. Gravett ◽  
Christian Amann
Keyword(s):  
Fracture Mechanics ◽  
High Performance ◽  
Computing Methodology ◽  
Direct Simulation Monte Carlo ◽  
Turbine Rotor ◽  
Heavy Duty ◽  
Element Analysis ◽  
Nondestructive Examination ◽  
Probabilistic Fracture Mechanics ◽  
Heavy Duty Gas Turbine

We developed and successfully applied a direct simulation Monte Carlo (MC) scheme to quantify the risk of fracture for heavy-duty rotors commonly used in the energy sector. The developed probabilistic fracture mechanics (FM), high-performance computing methodology, and code ProbFM routinely assess relevant modes of operation for a component by performing billions of individual FM simulations. The methodology can be used for new design and life optimization of components, as well as for the risk of failure RoF quantification of in service rotors and their requalifications in conjunction with nondestructive examination techniques, such as ultrasonic testing (UT). The developed probabilistic scheme integrates material data, UT information, duty-cycle data, and finite element analysis (FEA) in order to determine the RoF. The methodology provides an integrative and robust measure of the fitness for service and allows for a save and reliable operation management of heavy-duty rotating equipment.

Comparative Evaluation of Fatigue Assessment Techniques on a Forged Steel Crankshaft of a Single Cylinder Diesel Engine

2012 ◽  
Author(s):  
Rajesh M. Metkar ◽  
Vivek K. Sunnapwar ◽  
Subhash Deo Hiwase
Keyword(s):  
Diesel Engine ◽  
Fracture Mechanics ◽  
Early Stage ◽  
Life Assessment ◽  
Growth Time ◽  
Connecting Rod ◽  
Rotating System ◽  
Element Analysis ◽  
Ic Engine ◽  
Single Cylinder

Crankshaft is one of the critical components of an IC engine, failure of which may result in disaster and makes engine useless unless costly repair performed. It possesses intricate geometry and while operation experiences complex loading pattern. In IC engines, the transient load of cylinder gas pressure is transmitted to crankshaft through connecting rod, which is dynamic in nature with respect to magnitude and direction. However, the piston along with connecting rod and crankshaft illustrate respective reciprocating and rotating system of components. the dynamic load and rotating system exerts repeated bending and shear stress due to torsion, which are common stresses acting on crankshaft and mostly responsible for crankshaft fatigue failure. Hence, fatigue strength and life assessment plays an important role in crankshaft development considering its safety and reliable operation. The present paper is based on comparative studies of two methods of fatigue life assessment of a single cylinder diesel engine crankshaft by using fracture mechanics approach viz. linear elastic fracture mechanics (LEFM) and recently developed critical distance approach (CDA). These methods predict crack growth, time required for failure and other parameters essential in life assessment. LEFM is an analytical method based on stress intensity factor which characteristics the stress distribution in the vicinity of crack tip, where as CDA is a group of methods predicts failure using stress distance plot. The maximum stress value required for both the methods are obtained using finite element analysis. The present paper provides an insight of LEFM and CDA methods along with its benefits to the designers to correctly assess the life of crankshaft at early stage of design. This paper also gives a detailed overview of failure analysis process including theoretical methods and result integration for predicting life of components as compared to life estimation by means of software.

Parametric Study on Pressure-Temperature Limit Curve

2016 ◽  
Author(s):  
Shin-Beom Choi ◽  
Han-Bum Surh ◽  
Jong-Wook Kim
Keyword(s):  
Sensitivity Analysis ◽  
Fracture Mechanics ◽  
Crack Depth ◽  
Temperature Limit ◽  
Round Robin ◽  
Robin Problem ◽  
Limit Curve ◽  
Element Analysis ◽  
Probabilistic Fracture Mechanics ◽  
Key Parameter

The final goal of this study is to solve the round-robin problem for the safety of a reactor pressure vessel by adopting a finite element analysis and probabilistic fracture mechanics. To do so, a sensitivity analysis and a deterministic analysis should be conducted. This paper contains the results of the sensitivity analysis as intermediate results of a round-robin problem. Key parameters such as the initial Reference Temperature for Nil Ductility Transition, Ni contents, Cu contents, fluence, and input transient were chosen to conduct the sensitivity analysis. In addition, different values of crack depth to the thickness ratio are considered to develop FE models. Moreover, a series of FE analyses are carried out. As a result, each key parameter has an influence on RTNDT and KIc. This means that the P-T limit curve is shifted. If the value of each key parameter is increased, the P-T limit curve is moved to the right side. Therefore, the operating area of the P-T limit curve should be reduced. The results of this paper will be very helpful in enhancing our understanding of the P-T limit curve. In addition, it will be used to adjust the probabilistic fracture mechanics and solve the round-robin problem.

High-Performance Computing Probabilistic Fracture Mechanics Implementation for Gas Turbine Rotor Disks on Distributed Architectures Including Graphics Processing Units (GPUs)

2021 ◽  
Author(s):  
Mrugesh Gajjar ◽  
Christian Amann ◽  
Kai Kadau
Keyword(s):  
Monte Carlo ◽  
Fracture Mechanics ◽  
Gas Turbine ◽  
Graphics Processing Units ◽  
High Performance ◽  
Message Passing Interface ◽  
Turbine Rotor ◽  
Integration Step ◽  
Probabilistic Fracture Mechanics ◽  
Runge Kutta

Abstract We present an efficient Monte Carlo based probabilistic fracture mechanics simulation implementation for heterogeneous high-performance (HPC) architectures including CPUs and GPUs. The specific application focuses on large heavy-duty gas turbine rotor components for the energy sector. A reliable probabilistic risk quantification requires the simulation of millions to billions of Monte Carlo (MC) samples. We apply a modified Runge-Kutta algorithm in order to solve numerically the fatigue crack growth for this large number of cracks for varying initial crack sizes, locations, material and service conditions. This compute intensive simulation has already been demonstrated to perform efficiently and scalable on parallel and distributed HPC architectures including hundreds of CPUs utilizing the Message Passing Interface (MPI) paradigm. In this work, we go a step further and include GPUs in our parallelization strategy. We develop a load distribution scheme to share one or more GPUs on compute nodes distributed over a network. We detail technical challenges and solution strategies in performing the simulations on GPUs efficiently. We show that the key computation of the modified Runge-Kutta integration step speeds up over two orders of magnitude on a typical GPU compared to a single threaded CPU. This is supported by our use of GPU textures for efficient interpolation of multi-dimensional tables utilized in the implementation. We demonstrate weak and strong scaling of our GPU implementation, i.e., that we can efficiently utilize a large number of GPUs/CPUs in order to solve for more MC samples, or reduce the computational turn-around time, respectively. On seven different GPUs spanning four generations, the presented probabilistic fracture mechanics simulation tool ProbFM achieves a speed-up ranging from 16.4x to 47.4x compared to single threaded CPU implementation.

High-Performance Computing Probabilistic Fracture Mechanics Implementation for Gas Turbine Rotor Disks On Distributed Architectures Including Graphics Processing Units (GPUS)

2021 ◽  
Author(s):  
Mrugesh Gajjar ◽  
Christian Amann ◽  
Kai Kadau
Keyword(s):  
Fracture Mechanics ◽  
Gas Turbine ◽  
Graphics Processing Units ◽  
High Performance ◽  
Message Passing Interface ◽  
Turbine Rotor ◽  
Integration Step ◽  
Distributed Architectures ◽  
Probabilistic Fracture Mechanics ◽  
Runge Kutta

Abstract We present an efficient Monte Carlo (MC) based probabilistic fracture mechanics simulation implementation on heterogeneous high-performance (HPC) architectures including CPUs and GPUs for large heavy-duty gas turbine rotor components for the energy sector. A reliable probabilistic risk quantification requires simulating millions to billions of MC samples. We apply a modified Runge-Kutta algorithm to solve numerically the fatigue crack growth for this large number of cracks for varying initial crack sizes, locations, material and service conditions. This compute intensive simulation was demonstrated to perform efficiently and scalable on parallel and distributed architectures with hundreds of CPUs utilizing Message Passing Interface (MPI). In this work, we include GPUs in parallelization strategy. We develop a load distribution scheme to share one or more GPUs on compute nodes distributed over network. We detail technical challenges and strategies in performing the simulations on GPUs efficiently. We show that the key computation of the modified Runge-Kutta integration step speeds up over two orders of magnitude on a typical GPU compared to a single threaded CPU supported by use of GPU textures for efficient interpolation of multi-dimensional tables. We demonstrate weak and strong scaling of our GPU implementation, i.e., that we can efficiently utilize large number of GPUs/CPUs to solve for more MC samples, or reduce the computational turnaround time, respectively. On seven different GPUs spanning four generations, our probabilistic fracture mechanics simulation tool ProbFM achieves speedups ranging from 16.4x to 47.4x compared to single threaded CPU implementation.

Probabilistic Prediction of Crack Depth Distributions Observed in Structures Subjected to Thermal Fatigue

2006 ◽  
Author(s):  
Tai Asayama ◽  
Hideki Takasho ◽  
Takehiko Kato
Keyword(s):  
Fracture Mechanics ◽  
Thermal Fatigue ◽  
Structural Reliability ◽  
Evaluation Method ◽  
Crack Depth ◽  
Inspection Planning ◽  
Probabilistic Fracture Mechanics ◽  
Nuclear Plants ◽  
Depth Distributions ◽  
Failure Probabilities

The application of risk-based technologies not only to inservice inspections but also to the design of components and systems, encompassing a plant life-cycle, is the way to be pursued for the improvement of design of new reactors such as fast breeder reactors. When doing so it is necessary to develop an analytical method that is capable of estimating failure probabilities without a failure database that can only be established on the long-time accumulation of operational experiences. The prediction method should estimate failure probabilities based on actual mechanisms that cause failure. For this purpose, this study developed a probabilistic structural reliability evaluation method for fatigue which is a representative failure mode to be prevented in components of nuclear plants. This method is an extension of probabilistic fracture mechanics approach but is capable of modeling crack initiation, crack propagation, as well as crack depth density distribution at a given cycle. To verify the methodology, crack depth distribution observed in thermal fatigue test specimens were evaluated, and it was shown that the method could reproduce the observed crack depth distributions fairly well. This is considered to explore the possibility that probabilistic fracture mechanics approach can be verified by experiments, which was deemed impossible so far. Further improvement such as explicit implementation of interaction mechanisms between adjacent cracks will allow this methodology to be applied to the procedure of optimization of inservice inspection planning, as well as to the optimization of safety factors in component design of nuclear plants.

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