Temporally sparse data assimilation for the small-scale reconstruction of turbulence

Wang, Yunpeng1,2,3; Yuan, Zelong1,2,3; Xie, Chenyue4; Wang, Jianchun1,2,3

2022-06-01

10.1063/5.0091391

PHYSICS OF FLUIDS   Impact Factor & Quartile

1070-6631

1089-7666

Previous works have shown that the small-scale information of incompressible homogeneous isotropic turbulence is fully recoverable as long as sufficient large-scale structures are continuously enforced through temporally continuous data assimilation (TCDA). In the current work, we show that the assimilation time step can be relaxed to values about 1-2 orders larger than that for TCDA, using a temporally sparse data assimilation (TSDA) strategy, while the accuracy is still maintained or even slightly better in the presence of non-negligible large-scale errors. One-step data assimilation (ODA) is examined to unravel the mechanism of TSDA. It is shown that the relaxation effect for errors above the assimilation wavenumber k(a) is responsible for the error decay in ODA. Meanwhile, the errors contained in the large scales can propagate into small scales and make the high-wavenumber ( k > k(a)) error noise decay slower with TCDA than TSDA. This mechanism is further confirmed by incorporating different levels of errors in the large scales of the reference flow field. The advantage of TSDA is found to grow with the magnitude of the incorporated errors. Thus, it is potentially more beneficial to adopt TSDA if the reference data contain non-negligible errors. Finally, an outstanding issue raised in previous works regarding the possibility of recovering the dynamics of sub-Kolmogorov scales using direct numerical simulation data at a Kolmogorov scale resolution is also discussed. Published under an exclusive license by AIP Publishing.

SCI ; EI

English

First ; Corresponding

National Natural Science Foundation of China (NSFC)[91952104,92052301,12172161,91752201] ; National Numerical Windtunnel Project[NNW2019ZT1-A04] ; Shenzhen Science and Technology Program[KQTD20180411143441009] ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)[GML2019ZD0103] ; Department of Science and Technology of Guangdong Province[2020B1212030001]

Mechanics ; Physics

Mechanics ; Physics, Fluids & Plasmas

WOS:000807731600003

AIP Publishing

20222412218021

Computational complexity ; Turbulence

Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1

PHYSICS

Web of Science

1.Southern Univ Sci & Technol, Natl Ctr Appl Math Shenzhen NCAMS, Shenzhen 518055, Peoples R China
2.Southern Univ Sci & Technol, Dept Mech & Aerosp Engn, Shenzhen 518055, Peoples R China
3.Southern Univ Sci & Technol, Guangdong Hong Kong Macao Joint Lab Data Driven Fl, Hong Kong 518055, Guangdong, Peoples R China
4.Hong Kong Univ Sci & Technol, Dept Ocean Sci, Hong Kong 999077, Peoples R China

Wang, Yunpeng,Yuan, Zelong,Xie, Chenyue,et al. Temporally sparse data assimilation for the small-scale reconstruction of turbulence[J]. PHYSICS OF FLUIDS,2022,34(6).

Wang, Yunpeng,Yuan, Zelong,Xie, Chenyue,&Wang, Jianchun.(2022).Temporally sparse data assimilation for the small-scale reconstruction of turbulence.PHYSICS OF FLUIDS,34(6).

Wang, Yunpeng,et al."Temporally sparse data assimilation for the small-scale reconstruction of turbulence".PHYSICS OF FLUIDS 34.6(2022).