Energy is a matter of economic security and national security. Research into fusion-fission hybrid reactors began in the 1950s with the original idea of using fusion neutrons to multiply fissionable nuclides (239 Pu, 233 U) from fissionable nuclides (238 U, 232 Th) and to amplify the fusion energy output in the process. The Z-FFR (Z-Pinch driven fusion fission hybrid reactor) contains two physical processes, fusion and fission, and is inherently complex in structure. The Z-FFR requires a larger number of disciplines and software platforms for its physical design. The original overall physical design was split between modules and confusing software choices, thus making it difficult to couple the Z-FFR in its overall design. The architecture design platform for multi-user massively parallel development conducts research on product integration and parallel development of architecture design software platforms to solve the integration of products from different software platforms and collaborative design of architecture design software. This will solve the coupling and linking between different physical processes and different design software in the design process of Z-FFR, and achieve the overall design optimal solution.
Inertial confinement fusion (ICF) is an approach to fusion that relies on the inertial of the fuel mass to provide confinement. Conditions under which inertial confinement is sufficient for efficient thermonuclear burn, a capsule (generally a spherical shell) containing different materials and thermonuclear fuel is compressed in an implosion process to conditions of high density and temperature. Another important process is the energy transport, in which the hohlraum coupling effect and hohlraum radiation uniform are the important physical parameters that can limit the energy transport. It is described the ignition condition by different physical parameters. Because the physical processes in fusion ignition are complex, and more physical quantities in the existence of multiple correlations and strong correlations, a single model often can not cope with fusion physics, this paper uses artificial intelligence, combined with complex physical processes, repeated model combination and iteration, to obtain the fusion materials model combination method, to provide an optimal parameter library for experimental physics. In this paper, we obtain the neutron yield of the main fuel DT can reach 1020, which indicates that the aim of achieving fusion can be achieved.
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.
KEYWORDS: Data modeling, Modeling, Design and modelling, Machine learning, Feature extraction, Data analysis, Analytic models, Mathematical modeling, Systems modeling
The Z-FFR (Z-Pinch Driven Fusion Fission Hybrid Reactor) is an important innovative design concept. The high uncertainty of the operating process of the pulsed power unit and the physical process of fusion and the absence of some theoretical and experimental conditions make it difficult to establish a high-precision mechanistic model, and it is difficult to obtain an accurate mathematical model of a complex, dynamic system. A data-driven physical modelling approach is urgently needed to replace the mechanistic models obtained with the aid of extensive simulations and experiments. The approach includes the creation of functional modules, the packaging of sub-modules, the configuration of module interfaces and the configuration of analytical models. Based on the actual needs of Z-FFR design and operation monitoring, the online analysis can be autonomously configured to accommodate different experimental data through machine learning, enabling anomaly detection, trend prediction, model design evaluation and operation assessment during the experimental process.
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