GPU-Accelerated Cellular Automaton Model for Grain Growth during Directional Solidification of Nickel-Based Superalloy

Zhang, Yongjia; Zhou, Jianxin; Yin, Yajun; Shen, Xu; Shehabeldeen, Taher A.; Ji, Xiaoyuan

doi:10.3390/met11020298

Cited by 5 publications

(3 citation statements)

References 40 publications

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“…The last mode used in this work is GPU-mode. In this case, the possibilities of GPU acceleration were used: CA evolution parallelizes well on GPUs [23,24]. For GPU computations, WS with NVidia RTX 2080ti and Jetson Nano with 128 CUDA cores were used.…”

Section: Gpu Modementioning

confidence: 99%

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Moving Multiscale Modelling to the Edge: Benchmarking and Load Optimization for Cellular Automata on Low Power Microcomputers

Hajder

Rauch

2021

Processes

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Numerical computations are usually associated with the High Performance Computing. Nevertheless, both industry and science tend to involve devices with lower power in computations. This is especially true when the data collecting devices are able to partially process them at place, thus increasing the system reliability. This paradigm is known as Edge Computing. In this paper, we propose the use of devices at the edge, with lower computing power, for multi-scale modelling calculations. A system was created, consisting of a high-power device—a two-processor workstation, 8 RaspberryPi 4B microcomputers and 8 NVidia Jetson Nano units, equipped with GPU processor. As a part of this research, benchmarking was performed, on the basis of which the computational capabilities of the devices were classified. Two parameters were considered: the number and performance of computing units (CPUs and GPUs) and the energy consumption of the loaded machines. Then, using the calculated weak scalability and energy consumption, a min–max-based load optimization algorithm was proposed. The system was tested in laboratory conditions, giving similar computation time with same power consumption for 24 physical workstation cores vs. 8x RaspberryPi 4B and 8x Jetson Nano. The work ends with a proposal to use this solution in industrial processes on example of hot rolling of flat products.

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Section: Gpu Modementioning

confidence: 99%

“…In this work, particular attention is paid to grain growth process using Cellular Automata (CA). According to the current approaches to CA, it parallelizes very well on GPU streaming processors [23,24]. Using this approach the execution time can be improved by several times.…”

mentioning

confidence: 99%

Moving Multiscale Modelling to the Edge: Benchmarking and Load Optimization for Cellular Automata on Low Power Microcomputers

Hajder

Rauch

2021

Processes

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“…Cellular automaton (CA) has an intrinsic advantage over the description and visualization of microstructural evolution in metals and alloys [19,20]. It has been successfully applied to describe the evolution of the grain structure in alloy steels [21,22], titanium alloys [23][24][25], and Ni-based superalloys [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Study of Grain Growth in a Ni-Based Superalloy by Experiments and Cellular Automaton Model

Liu

et al. 2021

Materials

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The grain growth behavior in a typical Ni-based superalloy was investigated using isothermal heat treatment experiments over a holding temperature range of 1353–1473 K. The experimental results showed that the grain structure continuously coarsened as the holding time and holding temperature increased during heat treatment. A classical parabolic grain growth model was used to explore the mechanism of grain growth under experimental conditions. The grain growth exponent was found to be slightly above 2. This indicates that the current grain growth in the studied superalloy is mainly governed by grain boundary migration with a minor pinning effect from the precipitates. Then, the grain growth in the studied superalloy during isothermal heat treatment was modelled by a cellular automaton (CA) with deterministic state switch rules. The microscale kinetics of grain growth is described by the correlation between the moving velocity and curvature of the grain boundary. The local grain boundary curvature is well evaluated by a template disk method. The grain boundary mobility was found to increase with increasing temperature. The relationship between the grain boundary mobility and temperature has been established. The developed CA model is capable of capturing the dependence of the grain size on the holding time under different holding temperatures.

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Prediction of grain-size transition during solidification of hypoeutectic Al-Si alloys by an improved three-dimensional sharp-interface model

Ren

Liu

2022

Computational Materials Science

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GPU-Accelerated Cellular Automaton Model for Grain Growth during Directional Solidification of Nickel-Based Superalloy

Cited by 5 publications

References 40 publications

Moving Multiscale Modelling to the Edge: Benchmarking and Load Optimization for Cellular Automata on Low Power Microcomputers

Moving Multiscale Modelling to the Edge: Benchmarking and Load Optimization for Cellular Automata on Low Power Microcomputers

Study of Grain Growth in a Ni-Based Superalloy by Experiments and Cellular Automaton Model

Prediction of grain-size transition during solidification of hypoeutectic Al-Si alloys by an improved three-dimensional sharp-interface model

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