4.6 Article

Contextual Predictions for Parker Solar Probe. II. Turbulence Properties and Taylor Hypothesis

期刊

出版社

IOP Publishing Ltd
DOI: 10.3847/1538-4365/ab16d7

关键词

magnetohydrodynamics (MHD); solar wind; Sun: corona; turbulence

资金

  1. NASA from Princeton University [SUB0000165]
  2. NASA Heliophysics Grand Challenge program [NNX14AI63G]
  3. NASA Living With a Star program [NNX15AB88G]
  4. NASA Heliospheric Guest Investigator program [NNX17AB79G]
  5. NASA Heliospheric Supporting Research grants [80NSSC18K1210, 80NSSC18K1648]
  6. NASA High-End Computing Program through the NASA Advanced Supercomputing Division at Ames Research Center [SMD-17-1580, SMD-17-1617]
  7. NASA [NNX15AB88G, 809033, 681607, NNX14AI63G] Funding Source: Federal RePORTER

向作者/读者索取更多资源

The Parker Solar Probe (PSP) primary mission extends seven years and consists of 24 orbits of the Sun with descending perihelia culminating in a closest approach of similar to 9.8 R-circle dot. In the course of these orbits PSP will pass through widely varying conditions, including anticipated large variations of turbulence properties, such as energy density, correlation scales, and cross helicities. Here we employ global magnetohydrodynamic simulations with self-consistent turbulence transport and heating to preview conditions that will likely be encountered by PSP by assuming suitable boundary conditions at the coronal base. The code evolves large-scale parameters-such as the velocity, magnetic field, and temperature-as well as the turbulent energy density, cross helicity, and correlation scale. These computed quantities provide the basis for evaluating additional useful parameters that are derivable from the primary model outputs. Here we illustrate one such possibility in which computed turbulence and large-scale parameters are used to evaluate the accuracy of the Taylor frozen-in hypothesis along the PSP trajectory. Apart from the immediate purpose of anticipating turbulence conditions that PSP will encounter, as experience is gained in comparisons of observations with simulated data, this approach will be increasingly useful for planning and interpretation of subsequent observations.

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