TRANSP
{{Short description|Computer code for analyzing tokamak plasma experiments}}
TRANSP is a computational tool developed at the Princeton Plasma Physics Laboratory (PPPL) for the interpretive and predictive modeling of plasma behavior in magnetic confinement fusion experiments. It has been primarily used to analyze data from tokamak experiments and is also applied to other magnetic confinement devices. TRANSP supports studies related to plasma transport, fast ion dynamics, heating, fueling, and momentum transport.
TRANSP uses Fortran, C/C++, Java, Python, Perl, Bash, and C shell scripts. It supports OpenMP, Open MPI, and Open ACC. TRANSP is stored on GitHub. TRANSP implements Monte Carlo methods with MPI to calculate with message passing interface (MPI) processing for computing kinetic properties of fast ions, such as neutral beam injected ions and fusion alpha particles.'The tokamak Monte Carlo fast ion module NUBEAM in the National Transport Code Collaboration library' Alexei Pankin, et al., 2024 Computer Physics Communications 159 (2004) 157–184 The properties computed include the distributions fast ions energy in space, energy,
and the ratio of parallel to the plasma current velocity to perpendicular to the plasma current. It incorporates an electromagnetic wave solver for computing effects of Ion cyclotron resonance heating of the plasma ions and electrons.
The development of TRANSP started in the late 1970s.
Hawryluk, R. J., 1980, in Physics of Plasmas Close to Thermo-nuclear Condition, edited by B. Coppi, G. G. Leotta, D. Pfirsch, R. Pozzoli, and E. Sindoni (Pergoma, New York/CEC, Brussels), Vol. 1, p. 19.
It was first used to model plasmas from experiments in the Tokamak Fusion Test Reactor (TFTR) at PPPL.'Results from deuterium-tritium Tokamak confinement experiments' R.J. Hawryluk, 1998
Reviews of Modern Physics 70 537
https://doi.org/10.1103/RevModPhys.70.537
As of 2025, the program continues to be developed and maintained at PPPL, with ongoing contributions documented in recent updates and publications. TRANSP has been used in studies and publications related to experiments conducted in tokamaks including
Joint European Torus, ASDEX Upgrade, KSTAR, EAST Experimental Advanced Superconducting Tokamak Tore Supra, and NSTX-U National Spherical Torus Experiment.
TRANSP was employed in predictive modeling studies, such as those related to expected fusion reaction rates in TFTR's deuterium-tritium campaigns. An early example is a prediction of fusion reaction rates expected from later experiments in TFTR using deuterium and tritium.
TRANSP was the first integrated computer program used for studying phenomena within the plasma boundary of tokamak discharges.{{Cn|date=March 2025}} It is used to compute properties which cannot be measured directly, such as the radial transport of plasma species, energy, toroidal momentum, and angular momentum. It computes the effects of actuators used to heat and fuel the plasma. The program generates parameters that can be compared with real measurements to verify the accuracy and credibility of the digital model.
Applications in Fusion Research
TRANSP was used to accurately model a precursor TFTR experiment with deuterium plasma, and then was further used to substitute a mix of deuterium and tritium into the model. The predicted fusion gain, (QDT), defined as the ratio of fusion energy produced to the external heating power applied to the plasma, was 0.32. Later, deuterium-tritium experiments in 1993–1996 achieved a maximum Q{{sub|DT}} of 0.28{{cite Q| Q134468646}}
indicating that there were foreseen processes besides the straight forward mix of tritium with deuterium.
Publications using TRANSP for JET results include
a summary of analysis of modeling of deuterium-tritium experiments in JET
'Overview of interpretive modelling of fusion performance in JET DTE2 discharges with TRANSP',
Z. Stancar, et al.,
Nucl. Fusion 63 126058
https://doi.org/10.1088/1741-4326/ad0310
and calculations of the fusion gain ratio in the plasma core
'Core fusion power gain and alpha heating in JET, TFTR, and ITER'
R.V. Budny, J.G. Cordey and TFTR Team and JET Contributors
Nuclear Fus. (2016) <56> 056002
https://iopscience.iop.org/article/10.1088/0029-5515/56/5/056002
and simulation of multiple fast ion species
Podesta M., et al., 2022
'Extension of the energetic particle transport kick model in TRANSP to multiple fast ion species'
Nuclear Fusion. 62 (12): 126047
htpps://doi.org/10.1088/1741-4326/ac99ee. ISSN 0029-5515
and studies of optimizing non-thermal fusion power.
'JET D-T scenario with optimized non-thermal fusion'
M. Maslov, et al., 2023
Nucl. Fusion 63 112002
https://doi.org/10.1088/1741-4326/ace2d8
Publications of results from experiments in NSTX-U National Spherical Torus Experiment also rely on TRANSP-generated results.
Studies of ways to create reverse magnetic shear are in
Galante,M.E. et al.,
'Reversed magnetic shear scenario development in NSTX-U using TRANSP'
Nuclear Fusion 65 026035 https://doi.org/10.1088/1741-4326/ad9e03
TRANSP is being used in studies of fast ion transport and
Alfvén wave interactions.
Podestà, M, et al., 2009
'Experimental studies on fast-ion transport by Alfven wave avalanches on the National Spherical Torus Experiment'
Physics of Plasmas 16, 056104
https://doi.org/10.1063/1.3080724
TRANSP is being used to predict results from future experiments in ITER. One early exampleR.V.Budny, et al 'Predictions of H-mode performance in ITER' 2008
Nuclear Fusion 48 075005 supports the prediction of achieving Q{{sub|DT}} in the range 5–14, and A study predicted QDT values in the range 5–14, based on TRANSP modeling under specific assumptions. Other examples include
Murakami M. et al 2011
'Integrated modelling of steady-state scenarios and heating and current drive
mixes for ITER' Nuclear Fusion 51 103006
https://iopscience.iop.org/article/10.1088/0029-5515/56/5/056002
Kessel C. et al 2007 'Simulation of the hybrid and steady state advanced operating
modes in ITER' Nuclear Fusion 47(9) 1274-1284
which projected fusion gains of 3.5-7 in steady-state mode and 5.6-8.3 in hybrid mode,
depending on the assumptions used for transport and source modeling.