Towards Fast and Accurate Predictions of Radio Frequency Power Deposition and Current Profile via Data-driven Modeling
- OSTI
Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modeling and real-time control applications. The machine learning models use a database of 16,000+ GENRAY/CQL3D simulations for training, validation, and testing. Latin hypercube sampling methods ensure that the database covers the range of 9 input parameters ($$n_{e0}$$, $$T_{e0}$$, $$I_p$$, $$B_t$$, $$R_0$$, $$n_{||}$$, $$Z_{eff}$$, $$V_{loop}$$, $$P_{LHCD}$$) with sufficient density in all regions of parameter space. The surrogate models reduce the inference time from minutes to ~ms with high accuracy across the input parameter space.
- Research Organization:
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Plasma Science and Fusion Center
- Sponsoring Organization:
- USDOE Office of Science (SC), Fusion Energy Sciences (FES)
- DOE Contract Number:
- SC0021202
- OSTI ID:
- 1887948
- Country of Publication:
- United States
- Language:
- English
Towards fast and accurate predictions of radio frequency power deposition and current profile via data-driven modelling: applications to lower hybrid current drive
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journal | August 2022 |