Bayesian Uncertainty Reduction of Generalised <i>k</i>-<i>ω</i> Turbulence Model for Prediction of Film-Cooling Effectiveness

Misaka, Takashi; Nakazawa, Takashi; Obayashi, Shigeru; Kubo, Seiji; Asaumi, Norio; Ideta, Takeomi

doi:10.1080/10618562.2022.2074001

Search citation statements

Order By: Relevance

Paper Sections

Select...

Turbulence Model1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Other1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mixed standard k-ε turbulence model was used for numerical calculation [16][17] . The hybrid standard k-ε turbulence model is an extension of the single-phase turbulence model.…”

Section: Turbulence Modelmentioning

confidence: 99%

Numerical simulation of pressure loss of sliding sleeve in typical fracturing

Yuan¹,

Su²,

Guo³

et al. 2023

Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)

View full text Add to dashboard Cite

“…The mixed standard k-ε turbulence model was used for numerical calculation [16][17] . The hybrid standard k-ε turbulence model is an extension of the single-phase turbulence model.…”

Section: Turbulence Modelmentioning

confidence: 99%

Numerical simulation of pressure loss of sliding sleeve in typical fracturing

Yuan¹,

Su²,

Guo³

et al. 2023

Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)

View full text Add to dashboard Cite

Parameter adaptation of k − ω SST turbulence model for improving resolution of moderately separated flows around 2D wing and 3D ship hulls via EnKF data assimilation

Sakamoto,

Hino,

Kobayashi

et al. 2024

J Mar Sci Technol

View full text Add to dashboard Cite

Parameters of turbulence model have rarely been changed to solve flow around ship hulls. Since these values are determined using relatively simple flows such as two-dimensional flat plate, there may be other parameter sets which are more suitable for resolving complex turbulent flow around ship hulls. Present study investigates suitable parameters of the k − ω SST model, one of the most popular turbulence models among ship flow simulations, throughout the data assimilation with ensemble Kalman filter aiming to improve computational accuracy of stern wake. Five parameters of k − ω SST model are first selected and are subjected to data assimilation using two-dimensional NACA4412 airfoil with moderate separation under 13.87 degrees of attack angle. Then, assimilated value “$$\beta_{1} = 0.{16}$$ β 1 = 0.16 (originally 0.075)” which controls the production and destruction of ω is applied to solve four different blunt hull forms under towed condition. Bulk flow and available turbulence quantities on the propeller plane are subjected to validation. Consequently, the model parameter “$$\beta_{1} = 0.{16}$$ β 1 = 0.16 (originally 0.075)” has been found to improve axial velocity distribution in model scale on the propeller plane in four different blunt hull forms maintaining the change of total resistance coefficient less than 0.8% difference compared to the original parameter. This research is of its first kind to bring the idea of parameter adaptation of turbulence model via data assimilation in the field of ship hydrodynamics.

show abstract

Bayesian Uncertainty Reduction of Generalised k-ω Turbulence Model for Prediction of Film-Cooling Effectiveness

Cited by 2 publications

References 14 publications

Numerical simulation of pressure loss of sliding sleeve in typical fracturing

Numerical simulation of pressure loss of sliding sleeve in typical fracturing

Parameter adaptation of k − ω SST turbulence model for improving resolution of moderately separated flows around 2D wing and 3D ship hulls via EnKF data assimilation

Contact Info

Product

Resources

About