| Title |
An LES Turbulent Inflow Generator using A Recycling and Rescaling Method
|
|---|---|
| Published in |
Applied Scientific Research, Section B, October 2016
|
| DOI | 10.1007/s10494-016-9778-6 |
| Pubmed ID | |
| Authors |
F. Xiao, M. Dianat, J. J. McGuirk |
| Abstract |
The present paper describes a recycling and rescaling method for generating turbulent inflow conditions for Large Eddy Simulation. The method is first validated by simulating a turbulent boundary layer and a turbulent mixing layer. It is demonstrated that, with input specification of mean velocities and turbulence rms levels (normal stresses) only, it can produce realistic and self-consistent turbulence structures. Comparison of shear stress and integral length scale indicates the success of the method in generating turbulent 1-point and 2-point correlations not specified in the input data. With the turbulent inlet conditions generated by this method, the growth rate of the turbulent boundary/mixing layer is properly predicted. Furthermore, the method can be used for the more complex inlet boundary flow types commonly found in industrial applications, which is demonstrated by generating non-equilibrium turbulent inflow and spanwise inhomogeneous inflow. As a final illustration of the benefits brought by this approach, a droplet-laden mixing layer is simulated. The dispersion of droplets in the near-field immediately downstream of the splitter plate trailing edge where the turbulent mixing layer begins is accurately reproduced due to the realistic turbulent structures captured by the recycling/rescaling method. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Argentina | 1 | 50% |
| United States | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 51 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 25% |
| Student > Master | 7 | 14% |
| Researcher | 6 | 12% |
| Student > Doctoral Student | 5 | 10% |
| Professor | 3 | 6% |
| Other | 5 | 10% |
| Unknown | 12 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 23 | 45% |
| Computer Science | 2 | 4% |
| Environmental Science | 1 | 2% |
| Nursing and Health Professions | 1 | 2% |
| Physics and Astronomy | 1 | 2% |
| Other | 5 | 10% |
| Unknown | 18 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 October 2016.
All research outputs
#16,596,200
of 25,394,764 outputs
Outputs from Applied Scientific Research, Section B
#141
of 597 outputs
Outputs of similar age
#195,066
of 318,741 outputs
Outputs of similar age from Applied Scientific Research, Section B
#6
of 21 outputs
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So far Altmetric has tracked 597 research outputs from this source. They receive a mean Attention Score of 1.1. This one has gotten more attention than average, scoring higher than 72% of its peers.
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We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.