The Z-FFR(Z-pinch driven fusion fission hybrid reactor) is composed of a pulsed power driver, a fusion target and a subcritical reactor. The docking of the fusion load in the burst chamber is the most critical link to shorten the fusion cycle and ensure the quality of fusion. The electrical and mechanical properties of the load need to be ensured during the docking process, and the difficulty of docking quality assurance lies in the high precision invisible docking and dynamic operation. Therefore, in this paper, we first conduct docking experiments in the invisible case using a multi-degree-offreedom parallel platform to obtain the initial data set for docking. The training data set is used to train the neural network model and the prediction data set is optimised by the Wolf Pack algorithm for deep learning models to improve the accuracy of docking prediction points. The mathematical model of the soft control enables the docking of fusion loads without visual guidance and still with the assistance of multidimensional force sensors only, with high quality and efficiency. In general, the motion state and docking mode of the fusion load are planned by the artificial intelligence assisted training. The docking of the fusion load to the end of the drive is accomplished in a vision-free operation.
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.
Thermonuclear fusion is the use of nuclear fusion reactions to produce energy. With higher energy density and less nuclear waste production than nuclear fission, thermonuclear fusion is considered to be a safer and more sustainable source of energy. However, the extreme conditions required to achieve thermonuclear fusion, such as high temperatures and densities, pose significant challenges to the design, construction and operation of fusion reactions. To overcome these challenges, iterative integrated design with training in thermonuclear fusion modelling and simulation has emerged as an important approach. With the continuous development of the crossover between artificial intelligence and physical design, combined with the powerful fitting capabilities of deep neural networks, deep learning was born, which utilises the highly anthropomorphic features of deep learning (DL) to learn itself through constant interaction and trial and error with the controlled object. Deep reinforcement learning has received great attention for its highly anthropomorphic features, pointto-point design ideas, and low a priori dependence. In this paper, deep learning is used to train and iterate on thermonuclear fusion models and their visualisation simulations to quickly obtain parameters for fusion reactions, significantly shortening the development cycle and providing more options and possibilities for the design and optimisation of fusion reactions, thus avoiding unnecessary costs and waste. The integrated design of thermonuclear fusion modelling and simulation training and iteration provides strong support for the research and application of thermonuclear fusion technology.
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.
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.
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.
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