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 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.

scholarly journals A lightweight approach to performance portability with targetDP

2016 ◽  
Vol 32 (2) ◽  
pp. 288-301
Author(s):  
Alan Gray ◽  
Kevin Stratford
Keyword(s):  
Particle Physics ◽  
Message Passing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Large Scale ◽  
Message Passing Interface ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Performance Portability ◽  
Graphics Processing

Leading high performance computing systems achieve their status through use of highly parallel devices such as NVIDIA graphics processing units or Intel Xeon Phi many-core CPUs. The concept of performance portability across such architectures, as well as traditional CPUs, is vital for the application programmer. In this paper we describe targetDP, a lightweight abstraction layer which allows grid-based applications to target data parallel hardware in a platform agnostic manner. We demonstrate the effectiveness of our pragmatic approach by presenting performance results for a complex fluid application (with which the model was co-designed), plus separate lattice quantum chromodynamics particle physics code. For each application, a single source code base is seen to achieve portable performance, as assessed within the context of the Roofline model. TargetDP can be combined with Message Passing Interface (MPI) to allow use on systems containing multiple nodes: we demonstrate this through provision of scaling results on traditional and graphics processing unit-accelerated large scale supercomputers.

Custom Built of Smart Computing Platform for Supporting Optimization Methods and Artificial Intelligence Research

2021 ◽  
Vol 58 (S) ◽  
pp. 59-64
Author(s):  
Indar Sugiarto ◽  
Doddy Prayogo ◽  
Henry Palit ◽  
Felix Pasila ◽  
Resmana Lim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Message Passing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Message Passing Interface ◽  
Optimization Methods ◽  
Computer Hardware ◽  
Production Environment ◽  
Computing Platform ◽  
Commercial Off The Shelf

This paper describes a prototype of a computing platform dedicated to artificial intelligence explorations. The platform, dubbed as PakCarik, is essentially a high throughput computing platform with GPU (graphics processing units) acceleration. PakCarik is an Indonesian acronym for Platform Komputasi Cerdas Ramah Industri Kreatif, which can be translated as “Creative Industry friendly Intelligence Computing Platform”. This platform aims to provide complete development and production environment for AI-based projects, especially to those that rely on machine learning and multiobjective optimization paradigms. The method for constructing PakCarik was based on a computer hardware assembling technique that uses commercial off-the-shelf hardware and was tested on several AI-related application scenarios. The testing methods in this experiment include: high-performance lapack (HPL) benchmarking, message passing interface (MPI) benchmarking, and TensorFlow (TF) benchmarking. From the experiment, the authors can observe that PakCarik's performance is quite similar to the commonly used cloud computing services such as Google Compute Engine and Amazon EC2, even though falls a bit behind the dedicated AI platform such as Nvidia DGX-1 used in the benchmarking experiment. Its maximum computing performance was measured at 326 Gflops. The authors conclude that PakCarik is ready to be deployed in real-world applications and it can be made even more powerful by adding more GPU cards in it.

Advanced Probabilistic Fracture Mechanics Using the R6 Procedure

2010 ◽  
Author(s):  
David W. Beardsmore ◽  
Karen Stone ◽  
Huaguo Teng
Keyword(s):  
Fracture Toughness ◽  
Monte Carlo ◽  
Fracture Mechanics ◽  
Monte Carlo Methods ◽  
Probability Of Failure ◽  
Defect Size ◽  
Probabilistic Fracture Mechanics ◽  
Safety Margins ◽  
Reliability Method ◽  
Failure Assessment

Deterministic Fracture Mechanics (DFM) assessments of structural components (e.g. pressure vessels and piping used in the nuclear industry) containing defects can usually be carried out using the R6 procedure. The aim of such an assessment is to demonstrate that there are sufficient safety margins on the applied loads, defect size and fracture toughness for the safe continual operation of the component. To ensure a conservative assessment is made, a lower-bound fracture toughness, and upper-bound defect sizes and applied loads are used. In some cases, this approach will be too conservative and will provide insufficient safety margins. Probabilistic Fracture Mechanics (PFM) allow a way forward in such cases by allowing for the inherent scatter in material properties, defect size and applied loads explicitly. Basic Monte Carlo Methods (MCM) allow an estimate of the probability of failure to be calculated by carrying out a large number of fracture mechanics assessments, each using a random sample of the different random variables (loads, defect size, fracture toughness etc). The probability of failure is obtained by counting the proportion of simulations which lead to assessment points that lie outside the R6 failure assessment curve. This approach can give good results for probabilities greater than 10−5. However, for smaller probabilities, the calculation may be inefficient and a very large number of assessments may be necessary to obtain an accurate result, which may be prohibitive. Engineering Reliability Methods (ERM), such as the First Order Reliability method (FORM) and the Second Order Reliability Method (SORM), can be used to estimate the probability of failure in such cases, but these methods can be difficult to implement, do not always give the correct result, and are not always robust enough for general use. Advanced Monte Carlo Methods (AMCM) combine the two approaches to provide an accurate and efficient calculation of probability of failure in all cases. These methods aim to carry out Importance Sampling so that only assessment points that lie close to or outside the failure assessment curve are calculated. Two methods are described in this paper: (1) orthogonal sampling, and (2) spherical sampling. The power behind these methods is demonstrated by carrying out calculations of probability of failure for semi-elliptical, surface breaking, circumferential cracks in the inside of a pressure vessel. The results are compared with the results of Basic Monte Carlo and Engineering Reliability calculations. The calculations use the R6 assessment procedure.

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.

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