Fusion energy research is a global effort aimed at addressing the world's energy challenges. Magnetic confinement fusion reactors seek to harness this energy by confining extremely hot plasma within powerful magnetic fields. Developing these reactors is a complex engineering endeavor, requiring advanced technologies such as superconducting magnets, reduced-activation materials, and beam and wave heating devices. From a physics standpoint, predicting and controlling the behavior of confined plasma—where numerous charged particles and electromagnetic fields interact intricately—presents a fascinating challenge.

Figure 1. Improving Fusion Plasma Predictions with Data Science

Efforts to understand energy and particle transport in plasmas include theoretical studies, numerical simulations with supercomputers, and experimental measurements of plasma turbulence. While physics-based simulations can predict turbulent transport and align reasonably well with experimental observations, discrepancies often arise, highlighting limitations in quantitative reliability. Similarly, empirical models derived from experimental data face challenges when extrapolated to future fusion devices, as they rely solely on existing datasets. Consequently, theoretical/simulation-based and experimental data-driven approaches each have strengths and limitations, leaving gaps that neither can fully address alone. While machine learning techniques, such as neural networks, could develop turbulent transport models if ample high-quality data were available, data scarcity—whether in quantity or coverage of key parameter ranges—remains a barrier for predicting plasmas in future nuclear fusion reactors. Figure 1 shows Improving Fusion Plasma Predictions with Data Science.

To address these challenges, this study applies a multi-fidelity modeling approach to improve predictions using a limited amount of high-accuracy (high-fidelity) data. By incorporating abundant but less accurate low-fidelity data, this method bridges data gaps. Specifically, the study introduces the nonlinear auto-regressive Gaussian process regression (NARGP) method to plasma turbulent transport modeling. Unlike conventional regression, which relies on single-input/output pairs, multi-fidelity modeling handles multiple outputs of varying fidelities for the same input. NARGP predicts high-fidelity outputs as a function of inputs and low-fidelity data. Applying this approach demonstrated enhanced prediction accuracy in scenarios such as:

1. Integrating low- and high-resolution simulation data.
2. Predicting turbulent diffusion coefficients using experimental plasma datasets.
3. Combining simplified theoretical models with turbulence simulation data.

By leveraging the predictability of physics-based models and simulations as low-fidelity data, this method compensates for the lack of high-quality experimental data, significantly improving prediction accuracy. These findings, published in Scientific Reports (a journal from the Nature publishing group), underscore the effectiveness of multi-fidelity modeling in advancing plasma transport predictions.

Traditionally, turbulent transport modeling has relied on two distinct approaches: physical model-based predictions from theory and simulations, or empirical models tailored to experimental data. This research bridges the gap between the two, creating a hybrid methodology that integrates the predictive power of simulations with the quantitative accuracy of experimental observations. The result is a powerful predictive framework for future nuclear fusion burning plasmas that combines theoretical knowledge and empirical precision.

The versatility of the multi-fidelity approach extends beyond fusion plasma research. It can integrate diverse datasets—such as simulations with different resolutions, simplified theories, and experimental measurements—to construct fast and accurate prediction models in a range of fields. This methodology holds potential for optimizing fusion reactor design, advancing performance prediction, and driving innovation in other scientific and technological domains.

Source: EureAlert

Cite this article:

Priyadharshini S (2024), “Enhancing Predictive Accuracy of Fusion Plasma Performance Through Data Science,” Anatechmaz, pp.95