Zobrazeno 1 - 10
of 724
pro vyhledávání: '"D. Sandberg"'
Strategies for Enhancing One-Equation Turbulence Model Predictions Using Gene-Expression Programming
Publikováno v:
Fluids, Vol 9, Iss 8, p 191 (2024)
This paper introduces innovative approaches to enhance and develop one-equation RANS models using gene-expression programming. Two distinct strategies are explored: overcoming the limitations of the Boussinesq hypothesis and formulating a novel one-e
Externí odkaz:
https://doaj.org/article/24391eea9d494c3780d7dcd0f9f0e466
Autor:
Sivaramakrishnan Malathi Ananth, Aditya Vaid, Nagabhushana Rao Vadlamani, Massimiliano Nardini, Melissa Kozul, Richard D. Sandberg
Publikováno v:
AIAA Journal. 61:1986-2001
Several high-resolution scale-resolving simulations are carried out to examine the effect of riblets on the mean and turbulent statistics of a zero-pressure-gradient boundary layer. The Reynolds number is chosen such that the riblets are exposed to b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::497f11458d0eddc07cb9cca3e9a313e2
https://doi.org/10.2514/1.j062418
https://doi.org/10.2514/1.j062418
Publikováno v:
AIAA Journal. 61:2100-2115
The accuracy of machine-learned turbulence models often diminishes when applied to flow cases outside the training data set. In an effort to improve the predictive accuracy of data-driven models for an expanded set of cases, an extension of a computa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8076ed0e831d5137ea5705437f456fe0
https://doi.org/10.2514/1.j062572
https://doi.org/10.2514/1.j062572
Autor:
Danny Fritsch, Vidya Vishwanathan, Christopher J. Roy, Todd Lowe, William J. Devenport, Paul Croaker, Graeme Lane, Oksana Tkachenko, David Pook, Shubham Shubham, Richard D. Sandberg
Publikováno v:
AIAA Journal. 61:2002-2021
A wide variety of models and methods for the prediction of the surface pressure spectrum beneath turbulent boundary layers is presented and assessed. A thorough review is made of the current state of the art in empirical and analytical pressure spect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bccada4e4755c55249e45307a8b227f2
https://doi.org/10.2514/1.j062074
https://doi.org/10.2514/1.j062074
Autor:
Roberto Pacciani, Michele Marconcini, Francesco Bertini, Simone Rosa Taddei, Ennio Spano, Yaomin Zhao, Harshal D. Akolekar, Richard D. Sandberg, Andrea Arnone
Publikováno v:
Energies, Vol 14, Iss 24, p 8327 (2021)
This paper presents an assessment of machine-learned turbulence closures, trained for improving wake-mixing prediction, in the context of LPT flows. To this end, a three-dimensional cascade of industrial relevance, representative of modern LPT bladin
Externí odkaz:
https://doaj.org/article/cd1ce1e007164092913ae58b413a7bb1
Publikováno v:
Energies, Vol 14, Iss 15, p 4680 (2021)
Existing Reynolds Averaged Navier–Stokes-based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this paper, a novel framework based on computational fluids dynamics (CFD) driven mach
Externí odkaz:
https://doaj.org/article/b15a16fadc6d4d0187927bbbe7716846
Publikováno v:
Andrology. 10:1361-1367
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e99f3396a000897fa37280f59f108bca
https://doi.org/10.1111/andr.13223
https://doi.org/10.1111/andr.13223
Akademický článek
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Akademický článek
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Publikováno v:
Annual Review of Fluid Mechanics. 54:255-285
The current generation of axial turbomachines are the culmination of decades of experience, and detailed understanding of the underlying flow physics has been a key factor for achieving high efficiency and reliability. Driven by advances in numerical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ab21e323b52e2fadeb711b1dc8bed6c4
https://doi.org/10.1146/annurev-fluid-031221-105530
https://doi.org/10.1146/annurev-fluid-031221-105530