A dataset from the Plasma Science and Fusion Center Dataverse contains results from statistical Plasma OPerating CONtour (POPCON) analyses for the SPARC tokamak. The work by Saltzman et al. introduces Monte Carlo methods to quantify the impact of scaling law, profile, and impurity uncertainties on performance predictions. It was last updated on 2026-06-19.
Use Cases
- Quantifying the sensitivity of fusion reactor performance goals to input uncertainties based on the described Monte Carlo analysis.
- Identifying optimal operating points that balance H-mode access, confinement, and auxiliary power based on the multi-fidelity Bayesian optimization workflow.
- Comparing deterministic vs. uncertainty-aware predictions for tokamak operational scenarios as described in the analysis.
Strengths
- Analysis is based on the SPARC tokamak design referenced in Creely et al. 2020.
- The methodology incorporates physically motivated gradient-based functional forms for plasma profiles.
- A multi-fidelity Bayesian optimization workflow is described, offering a significant speed-up over brute force search.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- The dataset's specific file formats and size are not described.
Provenance
- Source
- Plasma Science and Fusion Center Dataverse
- Collection Method
- Results from statistical modeling and Monte Carlo analysis of tokamak physics.
- Freshness
- Last updated 2026-06-19 22:29:34; freshness should be verified.