Yuanzhe Zhao

Due to the specificity of neutral beam injection (NBI) system, the control of its actual physical system is achieved by the integrated control system (ICS). The current NBI ICS adopts a distributed design structure to balance the system load, which is also the mainstream system design and architecture model of ICS around the world. However, from a practical point of view, the distributed system architecture is not perfect. In the long run, upgrading the system intelligence and automation capabilities is an inevitable choice for the future, so it is necessary to explore ways to transform and upgrade the ICS. At present, Internet of Things (IoT), as an important part of the new generation information technology, can realize the control of everything through data exchange. This is because on the one hand, IoT has wide compatibility and powerful scenario-based capabilities, it not only has the advantages and characteristics of distributed design, but also can pull the NBI subsystems into the same level scenario and lay the foundation for the further construction of digital NBI; on the other hand, the intervention of artificial intelligence makes IoT have some new typical characteristics such as intelligent sensing, ubiquitous connectivity, precise control, digital modeling, real-time analysis and iterative optimization, which is enough to pull the current NBI ICS into a new intelligent control era. Finally, it is worth mentioning that due to its inherent design structure and functional characteristics, ICS tends to be broadly generic, so it is not exclusively used for NBI operation in nuclear fusion, and it can provide some insight into other application areas.

The mission of negative ion-based neutral beam injection (NNBI) is to conduct experiments with pulses lasting thousands of seconds. It is crucial to develop a simplified physical calculation model for the long-pulse negative ion source in the current NNBI device. This model will be used to evaluate the advantages and disadvantages of the selected parameters prior to the experiment, and to assist in adjusting and establishing the experimental parameters for the long-pulse ion source experiment. This paper presents the development of a static performance prediction model using a back propagation neural network. The model assesses the yield of negative hydrogen ions and the quantity of electrons in the ion source under specific parameter conditions, utilizing various experimental parameters as input. The experimental data used for this model are derived from historical data generated during the operation of the 2022 NNBI experiment. The test results indicate that under the current optimal hyperparameter condition, the prediction accuracy of H⁻ ion current (I_H⁻) is 80.84%, and the prediction accuracy of extraction grid electronic current (I_EG) is 77.57%. This can effectively prevent invalid shots, accurately assess the advantages and disadvantages of the input parameters, and enhance the performance of the long-pulse NNBI device.

The Experimental Advanced Superconducting Tokamak (EAST) was constructed to explore the potential for steady-state nuclear fusion operation under long-pulse, high-performance conditions. Neutral Beam Injection (NBI) has been implemented to assist in heating the magnetically confined plasma during fusion reactions. The NBI Control System (NBICS) is a distributed control system used for controlling the operation of NBI experiments. As more and more achievements are made, experiments based on EAST continue to evolve towards higher goals, which also impose stricter control requirements on NBI. To further ensure the efficiency of NBI control and injection, a new Operation Control System (OCS) has been developed. The new system incorporates the latest features of Experimental Physics and Industrial Control System (EPICS), significantly enhancing the robustness and safety of the experimental operation process compared to the past. It possesses strong scalability, fully meeting the requirements of EAST experiments. OCS consists of several components. It integrates technologies such as the PV Access (PVA) protocol and the Phoebus framework in EPICS, achieving functionalities like parameter processing, experimental flow control, exception monitoring, and cooperative control. In this paper, the architectural design and development of the OCS are introduced.

The RF-driven negative ion source has a steady state characteristic and will be a promising ion source for neutral beam injection (NBI) in the future. Cesium (Cs) injection is an efficient method to enhance the yield of negative ions during plasma discharge. In order to support the engineering and physical research in this field, the Cs oven prototype has been developed for negative ion source based neutral beam injector at ASIPP. This article presents the design details of a Cs oven system, including the constant temperature control system, the mechanical structure of Cs oven, and the surface ionization detector (SID). SID is a measurement method for the cesium flux in the nozzle. The experiment results of constant temperature control system show that the control accuracy and function meet the requirements of device operation. Meanwhile, the simulation analysis of Cs vapor concentration has been carried out in this paper. According to the simulation results, the graph of total Cs flux is given in the article, which presents the reference for the subsequent device testing.

The timing synchronization system (TSS) in a radio-frequency–driven negative ion–based neutral beam injection system (NNBIS) is an important part of a negative ion–based neutral beam injection control system. In order to ensure the orderly conduct of experiments and the integrity of experimental data, the TSS needs to complete the corresponding timing synchronization function. There are two aspects to control of the NNBIS: controlling the synchronization of NNBIS subsystems within a certain precision and ensuring correct timing and amplitude output during the experiment. This paper presents the design and development of the TSS in the NNBIS, aiming at the characteristics of the IEEE 1588-2019, “IEEE Standard for a Precision Clock Synchronization Protocol for Networked Measurement and Control Systems,” clock synchronization protocol to achieve the demands of clock synchronization at a submicrosecond level, synchronously triggering other subsystems through the network trigger and hardware trigger. The TSS successfully implemented synchronization accuracy in less than 1 μs, and the network trigger is more efficient than the hardware trigger, which is about 80 μs faster. The TSS meets the requirements of timing synchronization accuracy of the NNBIS.

The designed neural networks are trained to appraise the risk of ion sources breakdown events in the neutral beam injector (NBI) experimental device using several offline diagnostic signals as inputs. A saliency analysis proves the reasonableness of the chosen inputs, some of which are helpful to improve the network performance. The experimental tests that were carried out refer to data collected from successfully-terminated and breakdown-terminated shots performed during three years of experimental advanced superconducting tokamak NBI experiments, from 2016 to 2019. Results show that it is very possible to develop a predictor base on neural network that intervenes well in advance to avoid ion sources breakdown or mitigate its effects during the beam extraction in the high-power and long-pulse mode.