Modeling Temperature Profiles in the Pedestal of NSTX with Reduced Models

TL;DR

This paper develops reduced models combining neoclassical, gyrokinetic, and surrogate transport models to predict H-mode pedestal temperature profiles in NSTX, revealing dominant transport mechanisms and improving predictive capabilities.

Contribution

It introduces a coupled modeling framework integrating neoclassical, ETG, and KBM transport models with a quasi-linear surrogate, advancing predictive pedestal modeling in spherical tokamaks.

Findings

Neoclassical transport dominates the ion channel across the pedestal.

ETG turbulence significantly affects the electron channel at the plasma edge.

KBM/MHD modes contribute substantially to overall thermal transport.

Abstract

This paper describes new modeling capabilities for predicting H-mode pedestal profiles in spherical tokamaks. Temperature profiles for NSTX discharges 132543 and 132588 are modeled by coupling the \textsc{astra} transport solver with neoclassical transport and gyrokinetic-based reduced models for electron temperature gradient (ETG) and kinetic ballooning mode (KBM) instabilities. A quasi-linear surrogate model for ion-scale transport is developed using linear \textsc{gene} simulations, requiring only a single free parameter calibrated to one discharge. Time-evolving the temperatures with fixed density yields good agreement with experiments for both discharges. Systematic analysis of the transport mechanisms reveals that neoclassical transport is huge across the entire pedestal region for the ion channel. ETG turbulence is large in the plasma edge and low density gradient region, contributing substantially to the electron channel. However, KBM/MHD-like modes also drive significant transport in both the ion and electron thermal channels, making them essential for accurate pedestal modeling. Further refinements, including explicit $E \times B$ shear suppression and scaled ETG transport, produce quantitative but not qualitative improvements. This work lays the foundation for predictive modeling of future devices.