Maybe a little bug exists in F1 function in PANS SST model

[lpz456]

template tmp PANSkOmegaSST::PANSkOmegaSST::F1 ( const volScalarField& CDkOmega ) const { tmp CDkOmegaPlus = max ( CDkOmega, dimensionedScalar("1.0e-10", dimless/sqr(dimTime), 1.0e-10) );

tmp<volScalarField> arg1 = min
(
    min
    (
        max
        (
            (scalar(1)/this->betaStar_)*sqrt(kU_)/(omegaU_*this->y_),
            scalar(500)*(this->mu()/this->rho_)/(sqr(this->y_)*omegaU_)
        ),
        (4*this->alphaOmega2_*(fK_/fOmega_))*kU_
        /(CDkOmegaPlus*sqr(this->y_))
    ),
    scalar(10)
);

return tanh(pow4(arg1));

}

the term “(4this->alphaOmega2_(fK/fOmega))” seems to be “(4this->alphaOmega2_(fOmega/fK))”

[ckpark96]

You could be right. I overlooked the derivation of this F1 term during my studies. Do you have a deerivation of this term I could look at? Thanks!

[lpz456]

Recently, I want to do some hybird RANS-LES method work based on RSM.

After reading some literature, it seems that there is no strict derivation for the PANS SST F1 function.

Referring to these two references, the F1 formula you provided seems to have some issues.

——"Prediction of heat transfer from a circular cylinder in a cross-flow at a low sub-critical Reynolds number with the Partially-Averaged number with the Partially-Averaged Navier-Stokes method", International Journal of Thermal Sciences, 2021.

——“A PANS Method Based on Rotation-Corrected Energy Spectrum for Efficient Simulation of Rotating Flow”, Frontiers in Energy Research, 2022.

Hope this can help you!

[ckpark96]

Thank you! I'll definitely check it out and test out the corrected version.

How is your experience with the PANS model?

[lpz456]

Hi, I’ve recently started studying the hybrid RANS/LES methods. I began testing them in delayed isotropic turbulence (DIT) cases in OpenFOAM, such as DDES-SST, IDDES-SST, Smagorinsky LES, k Eqn LES, PANS fixed fK, and Dym fK. During the tests, I encountered some issues. For example, when using the dynamic fK you provided (k = kU / fK, omega = omegaU / fW), fK rapidly decreases and approaches the lower limit set in the model, such as lowLim = 0.1 or lowLim = 0.01. However, the turbulence decay rate is significantly higher compared to the result with a fixed value of fK = 0.1 and 0.01. I don't understand why there is a significant difference in the results between the fixed-value and dynamic versions of fK, even though their values are similar. Do you have a similar experience? :-)

[lpz456]

Perhaps the spatially varying fK introduces additional errors during the discretization process.