Validation and Analysis Boston, MA USA June 1, 2017 · B.A. Grierson / IAEA Tech Fus. Data / Jun 2017 Data Processing and Preparation for TRANSP Varies Widely Across the Tokamak Community

B.A. Grierson / IAEA Tech Fus. Data / Jun 2017

by

B.A. Grierson1, N. Logan1, S.R. Haskey1, L. Cui1, S.P. Smith2, O. Meneghini2, J. Buchanan3

1Princeton Plasma Physics Laboratory, Princeton, NJ 08540, USA2General Atomics, San Diego, CA 92121, USA3CCFE, Culham Science Centre, Abingdon, OX14 3DB, UK

Presented at the 2nd IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis

Boston, MA USA

June 1, 2017

Interpretive Analysis and Predictive Discharge Modeling with TRANSP


OMFIT and TRANSP are Fulfilling Experimental and Core Predictive Whole Device Modeling Needs

● US DOE community workshops1 identified

need for streamlined experiment/theory

comparison— OMFIT2 framework provides such

workflows

● Motivates common set of tools across

machines for processing tokamak data,

managing code runs, visualizing results

● Community based development leverages

expert knowledge for emerging needs

✓✓

✓

1science.energy.gov link2O. Meneghini, et. al., Nucl. Fusion 55 (2015)

https://science.energy.gov/~/media/fes/pdf/workshop-reports/2016/ISFusionWorkshopReport_11-12-2015.pdf


Accurate Tokamak Power Balance Analysis Is the Cornerstone of Transport Interpretation and Model Validation1

● Understanding the balance of energy, particles and

momentum fluxes relies on:— High quality time-dependent profile data2

— Accurate source calculations

● TRANSP3 is a commonly used time-dependent

transport code for interpretive transport analysis and

predictive simulations— Wide use in the US and international

● OMFIT4 is streamlining data preparation, diagnostic

consistency, and interpretive → predictive workflows

1C. Holland, Phys. Plasmas 23 060901 (2016) and references therein

2N. Logan, This Afternoon, O-323http://transpweb.pppl.gov 4S.P. Smith, Tomorrow, O-43

Time derivative of i.e. X = n(x,t)

S = sources/sinks from neutral beams, RF, recycling, 3D fields

Spatial derivative of i.e. X = n(x,t)

http://transpweb.pppl.gov


Data Processing and Preparation for TRANSP Varies Widely Across the Tokamak Community

● Contrasting DIII-D, JET, NSTX, C-Mod, etc…

displayed wide range of inputs, diagnostics

and mappings

— All flavors handled inside of OMFIT

● New multi-machine strategy— Tokamak independence for common tasks

such as OMFIT equilibria and profiles

— Tokamak dependent for device specific inputs

such as auto EQ, profiles, heating, ...

● At highest level alleviate reliance on existing

tokamak data

— New ability to design shots


DIII-D Reference Shot Provides Power Scan For Time-Dependent TRANSP Demonstration

● Power ramp-up over five

seconds

— Assesses L-H power threshold

— All profile diagnostics enabled

● Sawtoothing plasma with little

other core MHD

● Sufficient information for

assessing time-dependent

diagnostic verification


Goal of Experimental Analysis Determines TRANSP Operating Mode and Fidelity of Heating Sources

● TRANSP input namelist is configurable to

meet wide range of needs

● Common use cases:— Survey, diagnostic checkout BEAST mode

— Power/particle/momentum balance

analysis mode

— Current diffusion for kinetic equilibrium

reconstruction

— Energetic particle physics and fast-ion

distribution function studies

Mode BEAST Analysis kEFIT EP

ZONES 20 50 100+ 50

Timestep 0.020 0.010 0.020 0.001

Particles 5k 32k 5k 128k++

Ex. Time NBI only

20min/s 3.2h/s 20min/s ++

Fastest possible without being worthless, useful for global and vol. integrated quantities

Sufficient space/time resolution for transport fluxes

Spatial resolution for edge bootstrap current, low heating for fast particle stored energy and neutrons

Short timestep for capturing rapid beam turn-on, highest MC statistics for dist. function

Many namelist switches to set

Operating modes scripted in OMFIT with drop-down menu

16 TRANSP variables7 NUBEAM variables


Increase of Heating Package Fidelity Required for Accurate Statistics for Torque Calculations

● Global quantities weakly affected by

poor monte-carlo statistics

— Total neutron rate

— Vol. integrated quantities Wth

, Wfast

— Surf. integrated quantities INBI

● Derived profile quantities require

increased fidelity

— NBI prompt JxB torque and

momentum diffusivity particularly

sensitive to MC statistics1

Analysis

BEAST

1Mantica, et. al. ,Phys. Plasmas 17 (2010)

EP

Analysis

BEAST neutrons/sec

Torque (Nm/cm3)


Increasing Fidelity Required for Accurate Fast-particle Distribution Function

● EP modes driven by

gradients in phase space

● Requires extremely high

monte-carlo statistics

BEAST Mode 0.1 hrs x1

unresolved

Step-wise evolution







Analysis Mode 1.0 hrs x8

qualitative

peaking

Smooth response







EP Mode High Fidelity 7.6 hrs x64

quantitative


Data Consistency Checks Allow the Quantification of Systematic Uncertainties in the Plasma Measurements

● Plasma total stored energy and neutron rates

commonly used for data consistency checks

— Stored energy from profile analysis should

match equilibrium energy from magnetics

— Neutron rate should match classical prediction

in absence of EP modes

● Further metrics for resistive current evolution

— Internal inductance li

— Surface loop voltage (plasma resistivity)

— MSE pitch-angle evolution

Impurity dilution reduces Wth through ni

ne peaking and QNImpurity dilution reduces nD

Peaked near axis


Common Experimental Uncertainty is the Plasma Impurity Content

● Electron density, temperature profiles

typically well-constrained— Thomson + interf, ECE

● Single-ion density profile may be

available through charge-exchange— Survey spectroscopy may indicate

many other low-Z impurities

— Visible bremsstrahlung may indicate

Zeff

above single-impurity

● Motivates systematic Zeff

variation to assess

data consistency

— TRANSP SCAN provided by OMFIT

Total Stored Energy

Total Neutron Rate

⨉0.6

⨉1.4

⨉0.6

⨉1.4


Neutron Rate and Stored Energy Exhibit Distinct Responses to Variation in Z

eff

● Increasing Zeff

directly depletes the

thermal deuterium density— Target for beam-plasma neutrons

— Absolute neutron calibration ~20%

● Increasing Zeff

reduces nD

more than it

increases nZ

— Response of Wtot

weaker

than neutrons

— Absolute equilibrium

stored energy ~ few %

● Confidence depends on particular machine

● Bounds Zeff for further analysis (i.e. GK)

Both metrics indicate overall Zeff > only carbon

Derived Zeff Derived Zeff


Resistive Current Evolution Provides MSE Pitch Angles for Assessing Neoclassical Resistive Current Diffusion

Measured

Simulation


Resistive Current Evolution Provides MSE Pitch Angles for Assessing Neoclassical Resistive Current Diffusion

Measured

Simulation


Routine Predictive Simulations Maximize the Scientific Utilization of Scarce Resources; Experimental Runtime

While interpretive analysis seeks to accurately

quantify power flows and transport coefficients

Core transport simulations seek to

validate transport models


Predictive Simulation Capabilities Now Being Routinely Utilized Through Standardized Workflow and Best Practices

● Core transport simulations replace the

fluid variables with profiles derived from

transport models

— Local transport models provide flux given

gradient

— Profile is defined by integral of local

gradients

● Enabling a predictive TGLF1 simulation in

TRANSP with PT_SOLVER requires setting

at least 50 namelist variables

Start with experimental profiles

→ Choose transport model→ Choose transport channels→ Set radial boundary condition→ Set transition time to predictive

Many namelist switches to set

Operating modes scripted in OMFIT with drop-down menus guiding switches and logic checking


Standardized OMFIT Visualizations and Post-Processing Provides Profile Evolution and Validation Metrics

● TRANSP run interpretively in

analysis mode

● TRANSP run repeated with

TGLF to predict Te, Ti

— 15 hrs wall time

— 8 NUBEAM,128 TGLF CPUs

● How did TGLF do,

quantitatively?




analysis mode






quantitatively?




analysis mode






quantitatively?


Time-Dependent Transport Model Validation Metrics Provided by OMFIT for List of TRANSP Runs

● Historically validation metrics1,2

produced for single timeslice

● Time-dependent simulations provide

metrics as EQ, heating are varied

CORE

PEDESTAL1ITER Physics Basis T&C Nucl. Fusion 39 (1999)2Kinsey, J. et. al., Phys. Plasmas 15 (2008)

GLF23 TGLF

Expt

GLF23

TGLF


Predictive Transport Simulations with TGLF Provide Time-Evolving Turbulence Characteristics

● Validation metrics quantify

accuracy and utility of model

● Insight into the nature of the

turbulence provided by linear

modes and flux spectra

— Multi-dimensional space for ⍵

and ᶕ in (⍴,t,k)

● Ultimately, knowledge of

instability informs control

strategy


Predictive Transport Simulations with TGLF Provide Time-Evolving Turbulence Characteristics

● Validation metrics quantify

accuracy and utility of model

● Insight into the nature of the

turbulence provided by linear

modes and flux spectra

— Multi-dimensional space for ⍵

and ᶕ in (⍴,t,k)

● Ultimately, knowledge of

instability informs control

strategy

Growth rate increases in time with heating power

Early

Late

Ion

Electron


OMFIT and TRANSP are Fulfilling Experimental and Core Predictive Whole Device Modeling Needs

● Community workshops1 identified need for

streamlined experiment/theory comparison

— OMFIT2 framework provides such workflows

● Achieved common set of tools across

machines for processing tokamak data,

managing code runs, visualizing results

● Community based development is

leveraging expert knowledge for emerging

needs for the future

✓✓

✓

1science.energy.gov link2O. Meneghini, et. al., Nucl. Fusion 55 (2015)

https://science.energy.gov/~/media/fes/pdf/workshop-reports/2016/ISFusionWorkshopReport_11-12-2015.pdf

Documents

Validation and Analysis Boston, MA USA June 1, 2017 · B.A. Grierson / IAEA Tech Fus. Data / Jun 2017 Data Processing and Preparation for TRANSP Varies Widely Across the Tokamak Community