Research

Overview

Turbulence is a state of fluid motion prevalent in nature and various engineering applications, such as air flows and river rapids. An essential feature of turbulence is the randomness of fluid motion, characterized by chaotic changes in flow velocity. Another feature of turbulent flow is the existence of many vortices or eddies of different sizes that oscillate, squeeze, collide, connect, and merge. In this nonlinear interaction, the kinetic energy is transferred from the large eddies to the small eddies, and then to the smaller eddies until being dissipated at the smallest eddies by the viscosity. This transfer of energy across scales is called the energy cascade.

I study the turbulent dynamics of conducting fluids such as plasmas. The involvement of dynamics at many scales, from macroscopic fluid scales to sub-electron scales, makes possible the wealth of physical behaviors that occur in plasma turbulence. Depending on the plasma properties of interest, e.g., whether dominant at small or large scales, a plasma can be described in various limits, such as kinetic and magnetohydrodynamic (MHD) models. MHD and plasma turbulence is more complicated than its counterpart in hydrodynamic turbulence in many ways. Most of my researches are about statistical properties. I use numerical simulations and spacecraft observations, focusing on turbulence theory and its application to space plasmas such as solar wind. I am also interested in conducting flows at low magnetic Reynolds number always encountered in industrial applications such as molten metals.

Research interests

• Solar Wind and Plasma Turbulence I: Energy dissipation. Unlike MHD model, solar wind and plasma turbulence in most astrophysical contexts is typically of weak collisionality, and frequently modeled as collisionless. A mystery that pervades decades of studies without a consensus solution has been to identify the energy dissipation mechanism in collisionless plasmas by which heat is deposited to accelerated solar wind and corona. I address this problem within the framework of the Vlasov approximation, and focus on the questions: (1) What methods can be used to account for energy dissipation at kinetic scales? (2) How is the dissipated energy partitioned between different plasma species? (3) How does the variability of plasma conditions (such as the degree of compressibility) regulate energy dissipation?

[1] Y. Yang, W. H. Matthaeus, T. N. Parashar, P. Wu, M. Wan, et al. Physical Review E 95, 061201(R) (2017)

[2] Y. Yang, W. H. Matthaeus, T. N. Parashar, C. C. Haggerty, V. Roytershteyn, et al. Physics of Plasmas 24(7), 072306 (2017)

[3] A. Chasapis, Y. Yang, W. H. Matthaeus, T. N. Parashar, C. C. Haggerty, et al. Astrophysical Journal 862:32 (2018)

[4] Y. Yang, M. Wan, W. H. Matthaeus, L. Sorriso-Valvo, T. N. Parashar, et al. Monthly Notices of the Royal Astronomical Society 482, 4933-4940 (2019)

[5] O. Pezzi, Y. Yang, F. Valentini, S. Servidio, A Chasapis, et al. Physics of Plasmas 26, 072301 (2019)

[6] W. H. Matthaeus, Y. Yang, M. P. Wan, et al. Astrophysical Journal 891: 101 (2020)

[7] R. Bandyopadhyay, W. H. Matthaeus, T. N. Parashar, Y. Yang, et al. Physical Review Letters 124, 255101 (2020)

[8] Y. Wang, R. Bandyopadhyay, R. Chhiber, W. H. Matthaeus, A. Chasapis, Y. Yang, et al. Journal of Geophysical Research: Space Physics 126, e2020JA029000 (2021)

[9] M. Zhou, H. Man, Y. Yang, Z. Zhong, and X. Deng. Geophysical Research Letters, e2021GL096372 (2021)

[10] Y. Yang, W. H. Matthaeus, S. Roy, V. Roytershteyn, et al. Astrophysical Journal 929:142 (2022)

• Solar Wind and Plasma Turbulence II: Multiscale Nature of Turbulence. Turbulence is featured with multi spatial and temporal scales, which are linked dynamically through cascade and conversion processes. Current spacecraft missions and modern simulation methods have made it possible to investigate heliospheric plasma turbulence at scales ranging from the injection or energy-containing scales to the kinetic dissipative scales. I focus on the cross-scale energy transfer process in plasma turbulence.

[1] Y. Yang, Y. Shi, M. Wan, W. H. Matthaeus, and S. Chen. Physical Review E 93, 061102(R) (2016)

[2] Y. Yang, M. Wan, W. H. Matthaeus, L. Sorriso-Valvo, T. N. Parashar, et al. Monthly Notices of the Royal Astronomical Society 482, 4933-4940 (2019)

[3] M. P. Wan, Y. Yang. Acta Aerodynamica Sinica 38(1): 160-170 (2020)

[4] W. H. Matthaeus, Y. Yang, M. P. Wan, et al. Astrophysical Journal 891: 101 (2020)

[5] Y. Yang, M. Linkmann, L. Biferale, and M. P. Wan. Astrophysical Journal 909: 175 (2021)

[6] Y. Yang, W. H. Matthaeus, S. Roy, V. Roytershteyn, et al. Astrophysical Journal 929:142 (2022)

[7] Y. Wang, R. Chhiber, S. Adhikari, Y. Yang, R. Bandyopadhyay, et al. Astrophysical Journal 937:76 (2022)

• Solar Wind and Plasma Turbulence III. The magnetic Kelvin-Helmholtz (KH) instability is a magnetohydrodynamic (MHD) shear-driven instability frequently observed in solar system plasmas. This shear instability is fundamental and can be found in many flow shear systems throughout the Universe. It plays an important role in the mixing of plasmas and in triggering solar wind fluctuations. We provide robust evidence of KH wave development in the solar wind using Solar Orbiter, which sheds new light on the process of shear-driven turbulence as mediated by the KH waves with implications for the driving of solar wind fluctuations. The shear-driven turbulence has been invoked to account for "switchbacks" observed by Parker Solar Probe (PSP).

[1] D. Ruffolo, W. H. Matthaeus, R. Chhiber, A. V. Usmanov, Y. Yang, et al. Astrophysical Journal 902, 94 (2020)

[2] R. Kieokaew, B. Lavraud, Y. Yang, W. H. Matthaeus, D. Ruffolo, et al. Astronomy & Astrophysics 656, A12 (2021)

• Magnetohydrodynamic (MHD) turbulence: The turbulent dynamics of conducting fluids such as liquid metals and plasmas, are relevant to a variety of astro- and geophysical processes. These systems can be described to a good approximation by MHD model at some parameter ranges. I am interested in energy spectrum, intermittency, coherent structures, energy transfer across scales and possible effects of compressibility, from theory and simulations.

[1] Y. Yang, Y. Shi, M. Wan, W. H. Matthaeus, and S. Chen. Physical Review E 93, 061102(R) (2016)

[2] Y. Yang, W. H. Matthaeus, Y. Shi, M. Wan, and S. Chen. Physics of Fluids 29, 035105 (2017)

[3] M. P. Wan, Y. Yang. Acta Aerodynamica Sinica 38(1): 160-170 (2020)

[4] Y. Yang, M. Linkmann, L. Biferale, and M. P. Wan. Astrophysical Journal 909: 175 (2021)

[5] Y. Yang, M. Wan, W. H. Matthaeus, and S. Chen. Journal of Fluid Mechanics 916, A4 (2021)

• Interplay of Turbulence and Magnetic Reconnection. Many naturally occurring and manmade plasmas are observed to be in a turbulent state. Magnetic reconnection, frequently observed in these systems, is itself a nonlinear process, though largely studied independently of turbulence. In many cases, turbulence is either a consequence or a driver of the reconnection process. The interplay between turbulence and magnetic reconnection is of great interest.

[1] C. C. Haggerty, T. N. Parashar, W. H. Matthaeus, M. A. Shay, Y. Yang, et al. Physics of Plasmas 24, 102308 (2017)

[2] Y. Yang, M. Wan, W. H. Matthaeus, Y. Shi, T. N. Parashar, et al. Physics of Plasmas 26, 072306 (2019)

[3] R. Bandyopadhyay, Y. Yang, W. H. Matthaeus, et al. Astrophysical Journal Letters 893: L25 (2020)

[4] H. Sun, Y. Yang, Q. Lu, S. Lu, M. Wan, et al. Astrophysical Journal 926: 97 (2022)

• Computational fluid dynamics: Direct Numerical Simulation (DNS) is applied in fundamental studies of turbulent flows. The vast range of spatial and temporal scales in turbulent fields lead to a strict requirement on the small-scale resolution of numerical methods. I primarily apply the well-established pseudo-spectral method to incompressible MHD turbulence, but also augment my studies with compressible MHD turbulence. In particular, I, in collaboration with my colleagues, developed a hybrid high-order finite difference scheme capable of capturing shock-turbulence interactions.

[1] Y. Yang, M. Wan, Y. Shi, K. Yang, and S. Chen. Journal of Computational Physics 306, 73-91 (2016)