K-ε Turbulence Model Parameter Estimates Using an Approximate Self-similar Jet-in-Crossflow Solution

Dechant, Lawrence; Ray, Jaideep; Lefantzi, Sophia; Ling, Julia; Arunajatesan, Srinivasan

doi:10.2514/6.2017-4167

Cited by 8 publications

(8 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also plot the profiles due to the analytically predicted values C a from Ref. 16, which also agree with experiments. In Fig.…”

Section: Iid Bayesian Calibrationsupporting

confidence: 72%

“…This work, along with our companion paper 16 and our previous papers (Ref. 17,26) lead us to believe that the main cause of RANS prediction errors for jet-in-crossflow may be due to inappropriate RANS constants.…”

Section: Discussionmentioning

confidence: 68%

“…The results presented here are best viewed within the context provided by our previous publications, 17,26 and our companion paper Ref. 16. These studies seek to uncover the cause behind RANS's inability to simulate compressible JIC interactions with much precision.…”

Section: Iiic Discussionmentioning

confidence: 85%

“…We addressed this question in two ways. In our companion paper, 16 we developed an analytical model for JIC interactions, which yielded C a , the analytical counterpart of C opt,1 . Since C a does not involve fitting to data, there is no question of conflating model-form and parametric uncertainties.…”

Section: Iiic Discussionmentioning

confidence: 99%

“…Results due to C nom , the nominal value of the parameters, and C a , parameters estimated analytically in Ref. 16 are also plotted. We find that the ensemble mean predictions using P (ue,ve) and P ωe are practically indistinguishable, which is remarkable given that the two PDFs were calibrated to different observations (mid-plane velocity and crossplane vorticity respectively).…”

Section: Iid Bayesian Calibrationmentioning

confidence: 99%

See 4 more Smart Citations

Robust Bayesian Calibration of a RANS Model for Jet-in-Crossflow Simulations

Ray

Lefantzi

Arunajatesan

et al. 2017

8th AIAA Theoretical Fluid Mechanics Conference

Self Cite

View full text Add to dashboard Cite

Compressible jet-in-crossflow interactions are poorly simulated using Reynolds-Averaged Navier Stokes (RANS) equations. This is due to model-form errors (physical approximations) in RANS as well as the use of parameter values simply picked from literature (henceforth, the nominal values of the parameters). Previous work on the Bayesian calibration of RANS models has yielded joint probability densities of C = (Cµ, C 2, C 1), the most influential parameters of the RANS equations. The calibrated values were far more predictive than the nominal parameter values and the advantage held across a range of freestream Mach numbers and jet strengths. In this work we perform Bayesian calibration across a range of Mach numbers and jet strengths and compare the joint densities, with a view of determining whether compressible jet-in-crossflow could be simulated with either a single joint probability density or a point estimate for C. We find that probability densities for C 2 agree and also indicate that the range typically used in aerodynamic simulations should be extended. The densities for C 1 agree, approximately, with the nominal value. The densities for Cµ do not show any clear trend, indicating that they are not strongly constrained by the calibration observables, and in turn, do not affect them much. We also compare the calibrated results to a recently developed analytical model of a jet-in-crossflow interaction. We find that the values of C estimated by the analytical model delivers prediction accuracies comparable to the calibrated joint densities of the parameters across a range of Mach numbers and jet strengths. Nomenclature(u e , v e ) Experimental counterpart of (u m , v m ) (u m , v m ) Modeled streamwise velocity deficit and normalized vertical velocities at probes (C µ , C 2 , C 1 ) Parameters in the k − RANS model requiring calibration C a Analytical estimates of (C µ , C 2 , C 1 ) C nom Nominal value of (C µ , C 2 , C 1 ) R Physically realistic part of the parameter space CVP Counter-rotating vortex pair JIC Jet-in-crossflow

show abstract

“…We also plot the profiles due to the analytically predicted values C a from Ref. 16, which also agree with experiments. In Fig.…”

Section: Iid Bayesian Calibrationsupporting

confidence: 72%

Section: Discussionmentioning

confidence: 68%

Section: Iiic Discussionmentioning

confidence: 85%

Section: Iiic Discussionmentioning

confidence: 99%

Section: Iid Bayesian Calibrationmentioning

confidence: 99%

See 3 more Smart Citations

Robust Bayesian Calibration of a RANS Model for Jet-in-Crossflow Simulations

Ray

Lefantzi

Arunajatesan

et al. 2017

8th AIAA Theoretical Fluid Mechanics Conference

Self Cite

View full text Add to dashboard Cite

show abstract

Adaptive wavelet compression of large additive manufacturing experimental and simulation datasets

et al. 2018

View full text Add to dashboard Cite

Calibration of κ-ε turbulence model for thermal–hydraulic analyses in rib-roughened narrow rectangular channels using genetic algorithm

2021

View full text Add to dashboard Cite

Nowadays, applications of turbulent fluid flow in removing high heat flux in rib-roughened narrow channels are drawing much interest. In this work, an improved version of the κ-ε turbulence model is proposed for better prediction of thermal–hydraulic characteristics of flow inside rib-roughened (pitch-to-rib height (p/k) ratio = 10 and 20) narrow channels (channel height, H = 1.2 mm and 3.2 mm). For this, the four turbulence model parameters, Cμ, Cε1, Cε2, and σk, are calibrated. These parameters are adjustable empirical constants provided for controlling the accuracy of the turbulence model results when needed. The simulated data are used to develop correlations between the relative errors in predicting the friction factor (f), Nusselt number (Nu), and the model parameters using a multivariate nonlinear regression method. These correlations are used to optimize the errors using genetic algorithm. Results reveal that the calibrated parameters are not the same for all the narrow channel configurations. After calibration, the overall predictive improvements are up to 35.83% and 27.30% for p/k = 10 and p/k = 20 respectively when H = 1.2 mm. Also, up to 15.48% and 18.05% improvements are obtained for p/k = 10 and p/k = 20 respectively when H = 3.2 mm. The role of the two parameters Cε1 and Cε2 are found to be of primary importance. Furthermore, three types of nanofluids i.e. Al2O3-water, CuO-water, and TiO2-water are studied using the calibrated model to check the potentiality of heat transfer enhancement. Among them, CuO-water nanofluid is predicted to have around 1.32 times higher value of Nu than pure water for the same narrow channel configuration. Article Highlights κ-ε turbulence model is calibrated for rib-roughened narrow rectangular channels using genetic algorithm. Cε1 and Cε2 are the most influential parameters on the performance of the model inside rib-roughened narrow channel. Suggested calibration process is more effective for channel height of 1.2 mm than 3.2 mm.

show abstract

K-ε Turbulence Model Parameter Estimates Using an Approximate Self-similar Jet-in-Crossflow Solution

Cited by 8 publications

References 23 publications

Robust Bayesian Calibration of a RANS Model for Jet-in-Crossflow Simulations

Robust Bayesian Calibration of a RANS Model for Jet-in-Crossflow Simulations

Adaptive wavelet compression of large additive manufacturing experimental and simulation datasets

Calibration of κ-ε turbulence model for thermal–hydraulic analyses in rib-roughened narrow rectangular channels using genetic algorithm

Contact Info

Product

Resources

About