Journal article
Generation of turbulent inflow data from realistic approximations of the covariance tensor
- Abstract:
-
This study presents a novel synthetic inflow generator capable of producing a random field matching a realistic set of two-point statistics with minimal input. The method is based on two main elements. The first element is a procedure to infer realistic two-point covariance tensors from readily available data (e.g. freestream velocity, boundary layer thickness, and turbulence intensity) by a preliminary Reynolds averaged Navier-Stokes (RANS) simulation with an explicit algebraic Reynolds stre...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Authors
Bibliographic Details
- Publisher:
- AIP Publishing Publisher's website
- Journal:
- Physics of Fluids Journal website
- Volume:
- 34
- Article number:
- 115140
- Publication date:
- 2022-10-22
- Acceptance date:
- 2022-10-18
- DOI:
- EISSN:
-
1089-7666
- ISSN:
-
1070-6631
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1287137
- Local pid:
- pubs:1287137
- Deposit date:
- 2022-10-26
Terms of use
- Copyright holder:
- Hao et al.
- Copyright date:
- 2022
- Rights statement:
- Copyright 2022 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http:// creativecommons.org/licenses/by/4.0/).
- Licence:
- CC Attribution (CC BY)
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