Statistical Distance-Based Validation Metrics for Probabilistic Plasma Turbulence Validation Studies

We investigate how uncertainties in experimentally-derived transport model inputs impact model predictions, using the quasilinear TGLF transport model. We use the rapidly converging and computationally inexpensive probabilistic collocation method (PCM) to propagate probability distribution functions (PDFs) of experimentally-derived input parameters through TGLF, yielding PDFs of predicted transport fluxes and local flux-matching gradient scale lengths. The experimental fidelity of the TGLF predictions is then assessed by comparing the predicted PDFs to PDFs of the corresponding experimental measurements and power balance calculations, using a new validation metric based upon the Wasserstein distance [1]. The utility of these metrics are illustrated via transport modeling of a DIII-D ITER baseline plasma in which the mix of neutral beam injection (NBI) and electron cyclotron heating (ECH) is varied. We find the ion temperature gradient is the dominant uncertainty driver in the NBI-only phase, whereas multiple parameters are important in plasmas with NBI+ECH heating.

[1] Rubner, Yossi, et. al. Sixth International Conference on Computer Vision. IEEE, 1998.