| Project/Area Number |
13640452
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Space and upper atmospheric physics
|
|---|
| Research Institution | Kyushu University |
|---|
Principal Investigator |
HADA Tohru Kyushu University, Interdisciplinary Graduate School of Engineering Sciences, Associate Professor, 大学院・総合理工学研究院, 助教授 (30218490)
|
|---|
| Project Period (FY) |
2001 – 2002
|
| Project Status |
Completed (Fiscal Year 2002)
|
| Budget Amount *help |
¥3,300,000 (Direct Cost: ¥3,300,000)
Fiscal Year 2002: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2001: ¥2,200,000 (Direct Cost: ¥2,200,000)
|
|---|
| Keywords | particle diffusion / particle acceleration / super / sub-diffusion / MHD turbulence / phase coherence / Levy process / 非線形波動 / 超拡散・準拡散 / 準線形理論 / レビ過程 |
|---|
| Research Abstract |
Diffusive transport of charged particles in a collisionless plasma is one of the central issues in space plasma research. Most of the conventional arguments are based on a classical diffusion model, a second order linear diffusion equation with a prescribed diffusion coefficient. However, recent progress in spacecraft observations as well as numerical simulation studies imply that several essential modifications are necessary to the model. In particular, the use of the Levy statistics appears to be quite useful, in which the transport properties have explicit dependence to relevant spatial and temporal scales. This is a direct consequence of that (1)the observed MHD turbulence in space has large amplitude and (2)the MHD waves have finite phase coherence. In this research project we evaluated various transport coefficients of energetic particles in MHD turbulence by performing numerical simulations (mainly test particle simulations), and related the diffusion properties to statistics of the turbulence. The presence of finite phase correlation among the MHD waves make the transport properties drastically deviate from quasi-linear predictions.
|
