Physical design for driven device of Z-FFR based on Machine Learning

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

et al. 2023

The Z-FFR (Z-Pinch driven fusion fission hybrid reactor) contains two physical processes, nuclear fusion and nuclear fission, and has a complex structure itself. Using artificial intelligence and big data technology to construct the digital Z-FFR, a decision decomposition method for writing the source code of the digital Z-FFR (Z-Pinch driven fusion fission hybrid reactor) by an artificial intelligence programmer is also proposed, including the following steps: using big data to build a normalized description of the digital Z-FFR; based on the normalized description of the digital Z-FFR, a dimensional decomposition method is used to split the digital Z-FFR modeling, digital Z-FFR simulation and digital Z-FFR writing structure to obtain the decision splitting set of digital Z-FFR. The decision selection method is determined according to the digital Z-FFR decision splitting set and the digital Z-FFR source code writing is completed. The decision splitting method for writing digital Z-FFR source code with artificial intelligence and big data proposed in this paper decomposes the writing logic of digital Z-FFR, and uses different artificial intelligence decision methods to complete the writing of digital Z-FFR source code according to different writing logics, which overcomes the disadvantages of long development cycle, repetitive development workload and high learning cost of various existing simulation systems.

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The new approach to writing source code for high-performance computing of Z-FFR models based on artificial intelligence and big data

Liu

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

et al. 2023

“…In order to solve these problems, foreign scholars have conducted a lot of research on reliability data acquisition theory and its application [1]. With the continuous development and depth of artificial intelligence and the extension of computer technology to reliability data acquisition, both reliability data acquisition theory and technology have been greatly developed [2][3][4][5][6]. At present, many researchers at home and abroad have conducted in-depth research on the integration, intelligence and networking of reliability data acquisition technology [1].…”

Section: Introductionmentioning

confidence: 99%

Z-FFR reliability analysis by big data in collaboration with artificial intelligence and industrial software

Liu

Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023)

et al. 2023

Z-FFR (Z-Pinch drive fusion fission hybrid reactor) is an important base-load energy route with high research significance for early global carbon neutrality. Z-FFR is a nuclear device and its reliability is a central and critical part. This paper attempts to use big data in collaboration with artificial intelligence and industrial software for Z-FFR reliability analysis. Reliability data analysis mining is carried out by artificial intelligence, which is divided into three main levels: foundation platform layer, data management layer and data application layer. The foundation platform is the server layer, including application server, database server, file server and integration interface. The data management layer is the database layer, which provides data management functions for the basic information of Z-FFR, fusion reliability data, blast chamber reliability data, sub-critical reactor reliability data, and reliability data of the whole Z-FFR machine and its direct components; the data application layer is the user layer for data query, reliability data analysis, two-dimensional graph display of reliability data, and three-dimensional graph display of reliability data, which is mainly supported by mainstream This part is mainly supported by mainstream industrial software. The work in this paper provides data support for quality prediction of Z-Pinch and hybrid stack devices, thus improving the quality stability and consistency of the devices, and laying the foundation for the improvement of Z-FFR's refinement management, precision manufacturing, quality assurance and information management.

“…A multilayer perceptron with numerous concealed layers is a type of deep learning configuration. Deep learning discovers distributed aspect representations of data by combining lower-level aspects to form more theoretical higher-level representations of attribute forms or aspects [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Deep learning to design Z-FFR device models

Yuan

et al. 2023

J. Phys.: Conf. Ser.

Z-Pinch fusion centre, encased by a fission envelope, serves as an individual neutron source. It can expeditiously catalyze fission reactions in 238U and 232Th nuclear materials, which are hard to use in current commercial nuclear reactors. This is the essence of the Z-Pinch Driven Fusion-Fission Hybrid Reactor (Z-FFR). The fusion core acts as a stand-alone neutron source, efficiently driving fission reactions in nuclear energy materials that are difficult to use in existing commercial nuclear reactors, such as 238U and 232Th. Then it can deliver enormous amounts of energy in a stable and controlled manner. This new type of reactor uses the fact that the fission discharges energy (∼200 Megaelectronvolts) and the neutrons’ number released is much greater than that released when the fusion discharge energy (∼17 Megaelectronvolts). Moreover, the neutrons’ number is released to achieve energy amplification and neutron amplification, significantly makes it less difficult in implementing fusion technology applications, and increases the utilisation of nuclear energy resources by more than one order of magnitude. The Z-FFR has a complex design and covers a wide range of physical processes. The use of deep learning to design the device model allows for a more closely engineered model. Deep learning allows the model design to be decomposed, the Z-FFR design data flow to be analysed and optimised, and the tedious physical process to be turned into a deep learning network layering so that we can obtain an accurate physical model. The deuterium-tritium combustion depth parameters obtained by deep learning reach around 30%, demonstrating the ability to achieve fusion self-sustained combustion.