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Performance Prediction in Nuclear Materials by Using a Collaborative Framework of Supercomputing, Big Data and Artificial Intelligence

  • Danning LiEmail author
  • Dandan Chen
  • Changjun Hu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 911)

Abstract

Irradiation effects of materials have a direct influence on the safety of nuclear reactors. Multiscale simulations are often used to understand the evolution of material irradiation damage. With the improvement of memory and computational power, the multiscale simulation puts forward high demands for supercomputing, big data, and artificial intelligence. This paper explores a collaborative framework of supercomputing, big data, and artificial intelligence to perform multiscale simulations for irradiation damage and to predict the macroscopic properties of materials. First, this paper proposes a general collaborative framework about model-calculation-analysis, based on the collaboration of SC, BD, and AI. Next, for multiscale simulation of materials, we design a specific collaborative framework inheriting the above general one. Furthermore, this paper presents a framework instance of microscopic simulations for material swelling under neutron irradiation. Finally, this paper gives two case studies. The first case study is a nuclear material simulation program by using the collaboration of supercomputing and big data. The second case study is a prediction for irradiation hardening by using the collaboration of big data and artificial intelligence. The proposed framework not only provides insights into the performance prediction of nuclear materials, but also can be applied to other application domains.

Keywords

Collaborative framework Supercomputing Big data Artificial intelligence Performance prediction of nuclear materials 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Computer and Communication EngineeringUniversity of Science and Technology BeijingBeijingChina
  2. 2.Beijing Key Laboratory of Knowledge Engineering for Material ScienceBeijingChina

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