Engineering topics

Wallace, Greg

Publications and source records attributed to Wallace, Greg.

5 records

DOE OSTI2024

Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science

This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science

Artificial Intelligence (cs.AI)↗

DOE OSTI2023

Conceptual design of the LHCD system on CFETR

With the capability of saving flux consumption in the current ramp up phase, controlling the safety factor (q) profile, providing the required off-axis current drive (CD), a lower hybrid current drive (LHCD) system was eventually determined to be used on the China Fusion Engineering Test Reactor (CFETR). Here, in this paper, a 20 MW/4.6 GHz system composed of 40 units of 500 kW continuous wave (CW) klystron amplifier is preliminarily designed. The feasibility of the frequency choice of 4.6 GHz in the physical aspect is discussed. In order to minimize the transmission loss, a TE01 over-mode of circular waveguide will be used in the transmission system. Although previous calculations indicates that high field side (HFS) launcher location results in waves damping at inner region than low field side (LFS) launcher, which is favorable for plasma stability, HFS launcher will be extremely difficult in engineering due to the limited space. Calculations show that the tritium breeding ratio (TBR) will be decreased by ~ 1.5% with HFS launcher, while it will be decreased by ~ 0.22% only with LFS launcher. As a result, the LH power will be coupled to plasma from the top port at LFS. The designed passive active multi-junction (PAM) launcher is arranged in an array of 5 rows and 8 columns with the height of 1181 mm and the width of 794 mm. A shielding block with a thickness of 20 cm will be equipped around the feeding waveguides to protect the feeding waveguides and to prevent neutron leakage. A preliminary scheme of the remote maintenance for the LFS launcher is given. The modeling results shows that the density for optimum coupling is ~ 1.8 × 10 17 /m 3 , lower than the 4.6 GHz cut-off density n e_co = 2.6 × 10 17 /m 3 . Around this density, the power directivity (D p ) can be high as 71%.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Realization of thousand-second improved confinement plasma with Super I-mode in Tokamak EAST

Mastering nuclear fusion, which is an abundant, safe, and environmentally competitive energy, is a great challenge for humanity. Tokamak represents one of the most promising paths toward controlled fusion. Obtaining a high-performance, steady-state, and long-pulse plasma regime remains a critical issue. Recently, a big breakthrough in steady-state operation was made on the Experimental Advanced Superconducting Tokamak (EAST). A steady-state plasma with a world-record pulse length of 1056 s was obtained, where the density and the divertor peak heat flux were well controlled, with no core impurity accumulation, and a new high-confinement and self-organizing regime (Super I-mode = I-mode + e-ITB) was discovered and demonstrated. These achievements contribute to the integration of fusion plasma technology and physics, which is essential to operate next-step devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Fusion RF Modeling Machine Learning (FusionML_RF) v1.0

FusionML_RF consists of multiple codes and trained machine learning (ML) models that perform low-cost output modeling from the Genray-CQL3D. Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. For example, completing a single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time. On the other hand, these ML models achieve ~ms of inference time with high accuracy across the input parameter space. This software collection consists of multiple components. (1) codes that use ML methods and precomputed Genray-CQL3D simulation output to build regression models that enable approximate computations of Genray-CLQ3D outputs from arbitrary but physically meaningful input parameters (surrogate modeling); (2) three trained models created by the team, using a database of 16,000+ GENRAY/CQL3D simulations, to study the performance of ML models for surrogate modeling; (3) codes that load the trained models and simulation data, and then compute mean squared error between the models' predictions and the ground truth of simulation output data. This collection is being made available in conjunction with a scientific publication about the work to promote reusability and provide an artifact of the scientific work.

Bai, Zhe↗