# Model of non-stationary, inhomogeneous turbulence

## Abstract

Here, we compare results from a spectral model for non-stationary, inhomogeneous turbulence (Besnard et al. in Theor Comp Fluid Dyn 8:1–35, 1996) with direct numerical simulation (DNS) data of a shear-free mixing layer (SFML) (Tordella et al. in Phys Rev E 77:016309, 2008). The SFML is used as a test case in which the efficacy of the model closure for the physical-space transport of the fluid velocity field can be tested in a flow with inhomogeneity, without the additional complexity of mean-flow coupling. The model is able to capture certain features of the SFML quite well for intermediate to long times, including the evolution of the mixing-layer width and turbulent kinetic energy. At short-times, and for more sensitive statistics such as the generation of the velocity field anisotropy, the model is less accurate. We propose two possible causes for the discrepancies. The first is the local approximation to the pressure-transport and the second is the a priori spherical averaging used to reduce the dimensionality of the solution space of the model, from wavevector to wavenumber space. DNS data are then used to gauge the relative importance of both possible deficiencies in the model.

- Authors:

- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Univ. of New Mexico, Albuquerque, NM (United States)

- Publication Date:

- Research Org.:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

- Sponsoring Org.:
- USDOE

- OSTI Identifier:
- 1331267

- Report Number(s):
- LA-UR-15-29117

Journal ID: ISSN 0935-4964

- Grant/Contract Number:
- AC52-06NA25396

- Resource Type:
- Journal Article: Accepted Manuscript

- Journal Name:
- Theoretical and Computational Fluid Dynamics

- Additional Journal Information:
- Journal Volume: 2016; Journal ID: ISSN 0935-4964

- Publisher:
- Springer

- Country of Publication:
- United States

- Language:
- English

- Subject:
- 42 ENGINEERING; Mathematics

### Citation Formats

```
Bragg, Andrew D., Kurien, Susan, and Clark, Timothy T..
```*Model of non-stationary, inhomogeneous turbulence*. United States: N. p., 2016.
Web. doi:10.1007/s00162-016-0401-1.

```
Bragg, Andrew D., Kurien, Susan, & Clark, Timothy T..
```*Model of non-stationary, inhomogeneous turbulence*. United States. doi:10.1007/s00162-016-0401-1.

```
Bragg, Andrew D., Kurien, Susan, and Clark, Timothy T.. Fri .
"Model of non-stationary, inhomogeneous turbulence". United States.
doi:10.1007/s00162-016-0401-1. https://www.osti.gov/servlets/purl/1331267.
```

```
@article{osti_1331267,
```

title = {Model of non-stationary, inhomogeneous turbulence},

author = {Bragg, Andrew D. and Kurien, Susan and Clark, Timothy T.},

abstractNote = {Here, we compare results from a spectral model for non-stationary, inhomogeneous turbulence (Besnard et al. in Theor Comp Fluid Dyn 8:1–35, 1996) with direct numerical simulation (DNS) data of a shear-free mixing layer (SFML) (Tordella et al. in Phys Rev E 77:016309, 2008). The SFML is used as a test case in which the efficacy of the model closure for the physical-space transport of the fluid velocity field can be tested in a flow with inhomogeneity, without the additional complexity of mean-flow coupling. The model is able to capture certain features of the SFML quite well for intermediate to long times, including the evolution of the mixing-layer width and turbulent kinetic energy. At short-times, and for more sensitive statistics such as the generation of the velocity field anisotropy, the model is less accurate. We propose two possible causes for the discrepancies. The first is the local approximation to the pressure-transport and the second is the a priori spherical averaging used to reduce the dimensionality of the solution space of the model, from wavevector to wavenumber space. DNS data are then used to gauge the relative importance of both possible deficiencies in the model.},

doi = {10.1007/s00162-016-0401-1},

journal = {Theoretical and Computational Fluid Dynamics},

number = ,

volume = 2016,

place = {United States},

year = {Fri Jul 08 00:00:00 EDT 2016},

month = {Fri Jul 08 00:00:00 EDT 2016}

}