Multi-fidelity machine learning methods for sputtering yield calculations relevant to magnetic fusion energy systems

Sputtering of plasma-facing components in magnetic fusion energy systems is an area of significant concern in fusion research. Sputtering yield data in this regime is difficult to obtain both experimentally and computationally. A substantial database of sputtering yields for a range of fusion-relevant materials is systematically generated from empirical formulas, binary collision approximation simulations, and published experimental and calculated results. A machine learning pipeline is optimized for the sputtering yield prediction problem. Linear regression, artificial neural network, and gradient boosting models are assessed in combination with various feature engineering methods. A multi-fidelity gradient boosting tree demonstrates a gain in computational efficiency on the order of 10^6 compared with high-energy binary collision approximation simulations. The gradient boosting model accurately and robustly predicts sputtering yields for a selection of ion materials incident on tungsten and boron across a broad range of ITER-relevant incident ion energies and angles. Feature importance analysis is employed to enhance model interpretability and inform the development of a semi-empirical formula applicable for all angles of ion incidence. Generalizability of the model is assessed for unknown ion and target material parameters. The database and multi-fidelity machine learning model are made available online for web retrieval.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Nuclear, Plasma, Radiolgc Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Valaitis, Sonata

Contributors dc:contributor

• Curreli, Davide
• Vergari, Lorenzo

• Multi-fidelity
• Machine Learning
• Sputtering
• Sputtering Yield
• Magnetic Fusion Energy Systems
• Tokamaks
• Plasma-material Interactions
• Gradient Boosting Model
• Artificial Neural Network
• Yamamura
• Feature Engineering
• Binary Collision Approximation Simulations
• Rustbca
• Feature Importance Analysis
• Shap
• Plasma-facing Components
• Tungsten
• Boron

Statement dc:rights

• Copyright 2024 Sonata Valaitis

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/127510

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F127510