23P046-P
Simulation of Heat Transport in LHD Plasmas Using TASK3D code
TASK3Dを用いたLHDにおける熱輸送シミュレーション	 	 Arimitsu Wakasa1, Atsushi Fukuyama1, Sadayoshi Murakami1, Masayuki Yokoyama2,Masahiko Sato2, Hisamichi Funaba2, Ryosuke Seki2, Kiyomasa Watanabe2, Noriyoshi Nakajima2
若狭有光1, 福山 淳1, 村上定義1, 横山雅之2,佐藤雅彦2, 舟場久芳2, 關 良輔2, 渡邊清政2, 中島徳嘉2
1

Kyoto University, Graduate School of Engineering, Department of Nuclear Engineering
Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan1
京都大学大学院工学研究科原子核工学専攻	  〒606-8501 京都府京都市左京区吉田本町2National Institute for Fusion Science (NIFS)
322-6, Oroshi-cho, Toki-city, GIFU, 509-5292, Japan2
核融合科学研究所 〒509-5292	  岐阜県土岐市下石町322-6

An integrated transport simulation code for helical plasmas, TASK3D, has been developed andapplied to the LHD plasmas. In this study, the heating module and TASK3D are newlyconnected. We applied TASK3D to predictive heat transport simulation for the LHD experimentand compared the simulation results with experimental results.
1. Introduction
Since the various phenomena in fusion
plasmas have a very wide range of time scale (1ns to 1000 s) and spatial scale (1 µm to 10 m),
and the dominant phenomena differ in eachregime, we cannot describe a plasma dischargeusing just a single physical model. Thus, it isessential to develop an integrated transport
simulation code that connects a variety ofphysical models describing the different timescale phenomena self-consistently. The goals of
the integrated simulation code are to: 1) verify thephysical models against experimental data andpromote understanding of physical phenomena, 2)predict the performance of burning plasmas, and,3) contribute to the study of equipment designand operation scenarios. For these purposes,
TASK3D [1] is being developed in collaboration
with Kyoto University and NIFS. TASK3D is an
integrated transport simulation code for thehelical plasma based on TASK [2] that is
applicable for 2D tokamak configurations.
2. TASK3DTASK3D has a modular structure and each
module describes different physical phenomena.
Through the data exchange interface, each module
is connected and the plasma is describedself-consistently. In non-axisymmetric systems,
the helical trapped particles increase theneoclassical transport as the temperature of theplasma is raised (1/ν regime). Moreover, in
non-axisymmetric systems, neoclassical transport

has a strong dependence on the radial electricfield. Therefore, it is important to accurately
evaluate the neoclassical transport. To evaluate
the neoclassical transport, we have constructed
the neoclassical transport database module,
DGN/LHD [3]. For calculating the neoclassicaldiffusion coefficient, we have used the DCOM [4]and GSRAKE [5] codes. We apply the Neural
Network method to fitting these results of thediffusion coefficients of LHD. Using theDGN/LHD, we can evaluate the neoclassicaltransport
coefficientaccuratelyeveninhigh-temperature plasmas and finite beta plasmas.
We also have developed the radial electric fieldcalculating module, ER, which calculates theradial electric field determined by the neoclassicalambipolar condition using DGN/LHD. And the TR
module solves the 1D diffusive transport equation
for the density, the temperature, and the toroidal
angular momentum of each plasma species and thepoloidal magnetic field. So far, heat transfer
analyses have been performed in various LHD
Plasma Using TASK3D. So far, the heat transport
analysis has been performed in various LHD
plasmas using TASK3D (mainly using TR,DGN/LHD, and ER modules)[6].
In this study, we newly connect the heatingmodule and TASK3D. The FIT3D code [7] hasbeen generally used for the NBI deposition
analyses in LHD plasmas. As a result, it became
possible to evaluate the power supply from the NBI
whenever there was a change in profile of magnetic
field, density, temperature, and so on. This madepossible the predictive simulation for the LHD

4. Summary
The development of integrated transport
code is an important issue. TASK3D is being
developed in collaboration with Kyoto Universityand NIFS, and the steady-state analysis has been
performed using TASK3D so far. By improving theconnection of TASK3D and the NBI heating
module newly, we can calculate the time evolution

Temerature [keV]

ei

42Ti

321Te1-3000.0 0.2 0.4 0.6 0.8 1.0
r/a
Fig.1. TASK3D simulation results of electron and iontemperature.

54χTBii32e€)( )5n = n are fixed19(3density [10 m ]3. Simulation Condition and Results
We applied TASK3D to predictive heattransport simulation of the LHD experiment. The	 characteristic parameters of assumed plasma are
Raxis is 3.6m and B0= -2.75T. In this simulation, we
assume that the thermal transport coefficients aregiven as the sum of a turbulent term χTB and aneoclassical term χNC and assume that the turbulent
diffusivity of the electrons and the ions are equal.
We have implemented the turbulent transportmodel as per the gyro-Bohm model;
TBχ gyro-Bohm= C gyro-Bohm T eB ρ L,
where Cgyro-Bohm is a constant factor to match thesimulation results with experimental results; in thissimulation, we use the value of 16, which had been
determined on the previous transport analysis in
LHD plasma (#88343, Te and Ti measured) so as toproperly fit the electron and ion temperatures
obtained by TASK3D simulation to experimentalresults. In this simulation, the density profile isfixed as shown in Fig.1. We assumed the NBI
heating plasma and each NBI power of port throughis: #1=2.15MW, #2=3.19MW, #4=3.6MW,#5=5.6MW, and not incident from #3. We
re-calculate the NBI heating power deposition
along with the time evolution of electron and iontemperatures. By repeating the procedure, thesteady temperature profile is obtained as shown in
Fig.1. Experiments were performed to validate thesimulation. In the experimental results, the electronand ion temperature at r/a=0 was 2.7keV and
2.1keV respectively. The electron temperature
obtained by the predictive simulation is about 10%
lower than the experimental result, and the iontemperature obtained by simulation is in good
agreement with the experimental result. Thus, thesimulation results are found to be reasonable. The
obtained electron and ion thermal diffusivities withthe gyro-Bohm models are shown in Fig.2. Here,
we also show the neoclassical thermal diffusivity. It
is found that the turbulent transport dominates inthe electron thermal transport.
We will show and compare the simulation
results considering other turbulence models.

of LHD plasma with calculating NBI power
deposition. Predictive simulations are performed
using TASK3D in LHD plasmas and are comparedwith experimental results. In this presentation, wewill show more detailed comparisons.

χ , χ [m2sec-1]

plasma, especially NBI plasma.

1 χi

χ

TBeNC

χ

NC

e

00.0 0.2 0.4 0.6 0.8 1.0
r/a
Fig.2. The obtained thermal diffusivities of ions andelectrons with the gyro-Bohm model and the neoclassicaltransport by TASK3D simulation.

Acknowledgments
This work is supported by Grant-in-Aid for
Scientific Research (S) (20226017) from JSPS.
References[1] M. Sato et al., Plasma Fusion Res., 3, S1063 (2008).
[2] A. Fukuyama et al., Proc. 20th Fusion Energy Conf.,
Villamoura,Portugal,2004,
IAEA-CSP-250CD/TH/P2-3, International Atomic
Energy Agency (2004).
[3] A. Wakasa et al., Contributions to Plasma
Phys. 50 (2010) 582.
[4] A. Wakasa et al., J. Plasma Fusion Res. 4 (2001)
408.
[5] C.D. Beidler et al., Plasma Phys. Control. Fusion 36(1994) 317.
[6] A. Wakasa et al., 23rd IAEA-FEC (2010).
[7] S. Murakami et al., Trans. Fusion Technol. 27(1995) 256.