Journal of Fusion Energy最新文献

The ITER Electron Cyclotron Heating and Current Drive (ECH) plays a pivotal role in heating and controlling fusion plasmas, with the Steering Mirrors being a crucial component of this actuator. A representative model of the ECH is compulsory in the development and validation of the Plasma Control System (PCS). This manuscript aims to propose a Control-Oriented model of the Steering Mirrors based on the design tested at the Swiss Plasma Centre. In this design a steering mirror rotates on some frictionless flexure pivots due to the action of a set of externally pressurized bellows acting against pre-compressed springs. This system is referred to as the Steering Mirror Assembly (SMA). The adherence of the model is tested by comparing the simulations with the experimental results, while considering ITER’s most recent requirements. Performances, generally increased in terms of accuracy, are in line with the prototype’s results.

The Superconducting Conductor Testing Facility, which is developed to evaluate the reliability of engineering technology and safe operation in a fusion reactor operation environment, is under construction by the Institute of Plasma Physics, Chinese Academy of Sciences (ASIPP). Since the Nb₃Sn layer coil of this test facility adopts the manufacturing process of the wind & react, the high-strength glass fiber used as the inter-turn insulation material will carbonize after high-temperature heat treatment at 665℃, thereby reducing the mechanical and electrical properties of the winding. The surface decarburization and modification process of high-strength glass fiber was developed to improve the properties of glass fiber after heat treatment. It is verified that the developed glass fiber tape treatment process can meet manufacturing process requirements of layered Nb₃Sn superconducting magnets through the coil winding radial pressure test and VPI sample performance test. This processing technology has been successfully applied in the manufacturing of experimental magnets, providing technical support for the insulation manufacturing of a large Nb₃Sn layer coil.

Radiative heat transfer is a fundamental process in high energy density physics and inertial fusion. Accurately predicting the behavior of Marshak waves across a wide range of material properties and drive conditions is crucial for design and analysis of these systems. Conventional numerical solvers and analytical approximations often face challenges in terms of accuracy and computational efficiency. In this work, we propose a novel approach to model Marshak waves using Fourier Neural Operators (FNO). We develop two FNO-based models: (1) a base model that learns the mapping between the drive condition and material properties to a solution approximation based on the widely used analytic model by Hammer & Rosen (2003), and (2) a model that corrects the inaccuracies of the analytic approximation by learning the mapping to a more accurate numerical solution. Our results demonstrate the strong generalization capabilities of the FNOs and show significant improvements in prediction accuracy compared to the base analytic model.

Open field line currents are intrinsic to DC helicity injection plasma startup and pose a challenge for inferring the plasma equilibrium with standard reconstruction analysis. Local helicity injection (LHI) is a type of DC helicity injection which uses small, modular current sources to drive force-free current along helical field lines to produce tokamak plasmas. MHD modeling and magnetic measurements during LHI indicate the injected current streams remain coherent as helical structures on the outboard edge of a core toroidal plasma that is tokamak-like in a toroidally averaged sense. To extract core plasma equilibrium properties, external magnetic diagnostics corrected for contributions from the injected current streams are fitted by a standard Grad-Shafranov equilibrium code. An iterative approach for estimating and subtracting the stream contributions from the diagnostic signals is described and applied to a model equilibrium database to reduce systematic errors introduced by the streams. Convergence is usually attained with 2 to 4 iterations, with derived equilibrium parameters matching the prescribed axisymmetric core values to within estimated experimental uncertainties. Accurate recovery of core parameters occurs when the ratio of the net toroidal windup current from the streams to the core plasma current is less than 0.2, which is typically satisfied in most experiments.

This study focuses on leveraging powerful machine learning approaches to determine neutron- alpha reaction cross-sections within the 14–15 MeV energy range. The investigation utilizes an experimental dataset comprising measurements of 133 nuclei concerning (n, α) reaction cross- sections. These data are divided into training and validation subsets, following established protocols, with 80% allocated for model training and 20% for testing. Key nucleus characteristics, including neutron number (N), mass number (A), and symmetry representation [(N-Z)²/A], were used as input variables for the machine learning models. SVR and XGBoost methods showed superior performance among the other machine learning methods used in the present study. In addition, a machine learning based online calculation tool was developed to estimate the reaction cross section.