Data for "Sensitivities of time-dependent temperature profile predictions for NSTX with the Multi-Mode Model"

Lestz, Jeff; Avdeeva, Galina; Kaye, Stanley; Gorelenkova, Marina; Halpern, Federico; McClenaghan, Joseph; Pankin, Alexei; Thome, Kathreen

doi:10.34770/yvhr-gj82

Title: Data for "Sensitivities of time-dependent temperature profile predictions for NSTX with the Multi-Mode Model"

Dataset
Other Related Research

Abstract

Data for figures contained in "Sensitivities of time-dependent temperature profile predictions for NSTX with the Multi-Mode Model"

Authors:

;

General Atomics (United States)
Princeton Plasma Physics Laboratory

Publication Date:: Fri Sep 19 00:00:00 EDT 2025

DOE Contract Number:: AC02-09CH11466; SC0021113

Research Org.:: Princeton Plasma Physics Laboratory

Sponsoring Org.:: United States Department of Energy

Subject:: MMM; NSTX; Reduced turbulent transport models; Spherical tokamaks; TRANSP; Time-dependent integrated modeling

OSTI Identifier:: 2589047

DOI:: https://doi.org/10.34770/yvhr-gj82

Citation Formats


                    Lestz, Jeff, Avdeeva, Galina, Kaye, Stanley, Gorelenkova, Marina, Halpern, Federico, McClenaghan, Joseph, Pankin, Alexei, and Thome, Kathreen. Data for "Sensitivities of time-dependent temperature profile predictions for NSTX with the Multi-Mode Model".  United States: N. p., 2025. 
        Web.  doi:10.34770/yvhr-gj82.

Copy to clipboard


                    Lestz, Jeff, Avdeeva, Galina, Kaye, Stanley, Gorelenkova, Marina, Halpern, Federico, McClenaghan, Joseph, Pankin, Alexei, & Thome, Kathreen. Data for "Sensitivities of time-dependent temperature profile predictions for NSTX with the Multi-Mode Model".  United States.  doi:https://doi.org/10.34770/yvhr-gj82

Copy to clipboard


                    Lestz, Jeff, Avdeeva, Galina, Kaye, Stanley, Gorelenkova, Marina, Halpern, Federico, McClenaghan, Joseph, Pankin, Alexei, and Thome, Kathreen. 2025.  
"Data for "Sensitivities of time-dependent temperature profile predictions for NSTX with the Multi-Mode Model"".  United States.  doi:https://doi.org/10.34770/yvhr-gj82.  https://www.osti.gov/servlets/purl/2589047. Pub date:Fri Sep 19 00:00:00 EDT 2025

Copy to clipboard


                    
@article{osti_2589047,

  title        = {Data for "Sensitivities of time-dependent temperature profile predictions for NSTX with the Multi-Mode Model"},

  author       = {Lestz, Jeff and Avdeeva, Galina and Kaye, Stanley and Gorelenkova, Marina and Halpern, Federico and McClenaghan, Joseph and Pankin, Alexei and Thome, Kathreen},

  abstractNote = {Data for figures contained in "Sensitivities of time-dependent temperature profile predictions for NSTX with the Multi-Mode Model"},

  doi          = {10.34770/yvhr-gj82},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Fri Sep 19 00:00:00 EDT 2025},

  month        = {Fri Sep 19 00:00:00 EDT 2025}

}

Copy to clipboard

Dataset:

View Dataset

DOI: https://doi.org/10.34770/yvhr-gj82

Save / Share:

Export Metadata

Save to My Library

Similar records in DOE Data Explorer and OSTI.GOV collections:

Sensitivities of time-dependent temperature profile predictions for NSTX with the multi-mode model

Journal Article Lestz, J B ; Avdeeva, G ; Kaye, S M ; ... - Plasma Physics and Controlled Fusion
Sensitivities of time-dependent temperature profile predictions for NSTX with the multi-mode model

Journal Article Lestz, J. B. ; Avdeeva, G. ; Kaye, S. M. ; ... - Plasma Physics and Controlled Fusion

The Multi-Mode Model (MMM) for turbulent transport was applied to a large set of well-analyzed discharges from the National Spherical Torus Experiment (NSTX) in order to evaluate its sensitivities to a wide range of plasma conditions. MMM calculations were performed for hundreds of milliseconds in each discharge by performing time-dependent predictive simulations with the 1.5D tokamak integrated modeling code TRANSP. A closely related study (Lestz et al 2025 Plasma Phys. Control. Fusion 67 105029) concluded that MMM predicted electron and ion temperature profiles that were in reasonable agreement with NSTX observations, generally outperforming a different reduced transport model, TGLF. Thismore »« less
Data for "Predictive modeling of NSTX discharges with the updated MULTI-MODE anomalous transport module"

Dataset Rafiq, T. ; Wilson, C. ; Clauser, C. ; ...
Prediction of electron density and pressure profile shapes on NSTX-U using neural networks

Dataset Boyer, Mark ; Chadwick, Jason

A new model for prediction of electron density and pressure profile shapes on NSTX and NSTX-U has been developed using neural networks. The model has been trained and tested on measured profiles from experimental discharges during the first operational campaign of NSTX-U. By projecting profiles onto empirically derived basis functions, the model is able to efficiently and accurately reproduce profile shapes. In order to project the performance of the model to upcoming NSTX-U operations, a large database of profiles from the operation of NSTX is used to test performance as a function of available data. The rapid execution time ofmore »« less
Predicting Resistive Wall Mode Stability in NSTX through Balanced Random Forests and Counterfactual Explanations

Dataset Piccione, Andrea ; Sabbagh, Steven ; Andreopoulos, Yiannis

Similar Records