DNS of passive scalars in turbulent pipe flow

Pirozzoli, Sergio; Romero, Joshua; Fatica, Massimiliano; Verzicco, Roberto; Orlandi, P.

doi:10.1017/jfm.2022.265

Cited by 18 publications

(47 citation statements)

References 60 publications

(143 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of the uncertainty estimation analysis are very similar to those reported in Pirozzoli et al. (2022), and are not reported here. Basically, the estimated sampling and discretization errors are for the Nusselt number, for the channel centreline temperature, and for the peak temperature variance.…”

Section: Methodssupporting

confidence: 91%

“…Additional tests aimed at establishing the effect of streamwise domain length and grid size have been carried out for the DNS-C flow case. The results of the uncertainty estimation analysis are very similar to those reported in Pirozzoli et al (2022), and are not reported here. Basically, the estimated sampling and discretization errors are 0.2 % for the Nusselt number, 0.4 % for the channel centreline temperature, and 0.7 % for the peak temperature variance.…”

Section: Methodssupporting

confidence: 82%

“…The present study is the continuation of previous efforts (Pirozzoli et al. 2016, 2022; Modesti & Pirozzoli 2022) targeted to study forced thermal turbulent convection by means of DNS.…”

Section: Introductionmentioning

confidence: 69%

“…The effect of molecular Prandtl number variation is also scrutinized, in the range 0.025 ≤ Pr ≤ 4. The present study is the continuation of previous efforts (Pirozzoli et al 2016(Pirozzoli et al , 2022Modesti & Pirozzoli 2022) targeted to study forced thermal turbulent convection by means of DNS.…”

Section: Introductionmentioning

confidence: 80%

“…where β accounts for change of the offset of the logarithmic layer with the Prandtl number (Kader & Yaglom 1972). The temperature profiles at Pr = 1 are shown in figure 3, which are both compared with (3.1a,b) by using k θ = 0.459 (same as in pipe flow; Pirozzoli et al 2022), with an additive constant resulting from best fitting β(1) = 6.14, a bit less than in pipe flow. Small deviations of the mean velocity and temperature profiles from a genuine logarithmic behaviour were observed in a number of previous studies (e.g.…”

Section: Temperature Field and Statistics At Unit Prandtl Numbermentioning

confidence: 99%

See 4 more Smart Citations

Direct numerical simulation of one-sided forced thermal convection in plane channels

Pirozzoli

Modesti

2023

J. Fluid Mech.

Self Cite

View full text Add to dashboard Cite

We carry out direct numerical simulations (DNS) of turbulent flow and heat transfer in pressure-driven plane channels, by considering cases with heating on both walls, as well as asymmetric heating limited to one of the channel walls. Friction Reynolds numbers up to ${Re}_{\tau } \approx 2000$ are considered, and Prandtl numbers from ${Pr}=0.025$ to ${Pr} = 4$ , the temperature field being regarded as a passive scalar. Whereas cases with symmetric heating show close similarity between the temperature and the streamwise velocity fields, with turbulent structures confined to either half of the channel, in the presence of one-sided heating the temperature field exhibits larger regions with coherent fluctuations extending beyond the channel centreline. Validity of the logarithmic law for the mean temperature is confirmed, as well as universality of the associated von Kármán constant, which we estimate to be $k_{\theta } = 0.459$ . Deviations from the logarithmic behaviour are much clearer in cases with one-sided heating, which feature a wide outer region with parabolic mean temperature profile. The DNS data are exploited to construct a predictive formula for the heat transfer coefficient as a function of both Reynolds and Prandtl number. We find that the reduction of the thermal efficiency in the one-sided case is approximately $20\,\%$ at unit Prandtl number; however, it can become much more significant at low Prandtl number.

show abstract

Section: Methodssupporting

confidence: 91%

Section: Methodssupporting

confidence: 82%

“…The present study is the continuation of previous efforts (Pirozzoli et al. 2016, 2022; Modesti & Pirozzoli 2022) targeted to study forced thermal turbulent convection by means of DNS.…”

Section: Introductionmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 80%

Section: Temperature Field and Statistics At Unit Prandtl Numbermentioning

confidence: 99%

See 3 more Smart Citations

Direct numerical simulation of one-sided forced thermal convection in plane channels

Pirozzoli

Modesti

2023

J. Fluid Mech.

Self Cite

View full text Add to dashboard Cite

show abstract

Accurately predicting turbulent heat transfer over rough walls: a review of measurement equipment and methods

Abu Rowin,

Xia,

Wang

et al. 2024

Exp Fluids

View full text Add to dashboard Cite

Studying turbulent heat transfer over rough surfaces is vital for enhancing heat transfer efficiency in various practical applications. This research presents an in-depth examination of the commissioning of a heated floor boundary layer wind tunnel facility, specifically focussing on addressing the uncertainties in measuring heat transfer over rough walls. Our findings show that minor variations in the slope of the inner-scaled mean temperature profile ($$\kappa _h$$ κ h ) on a heated smooth wall have a marginal effect on the estimates of friction temperature and heat transfer coefficients across a range of friction Reynolds numbers ($${900 \lesssim \textrm{Re}_\tau \lesssim 3700}$$ 900 ≲ Re τ ≲ 3700 ) when using the Clauser fit method. Direct heat transfer measurements using power metres validate this conclusion. Temperature measurements over a three-dimensional sinusoidal roughness indicate constant $$\kappa _h$$ κ h within uncertainty limits across the examined range $${2300 \lesssim \textrm{Re}_\tau \lesssim 10{,}400}$$ 2300 ≲ Re τ ≲ 10 , 400 , contingent on prior knowledge of the roughness’s virtual origin. Nevertheless, measuring heat transfer coefficients and roughness functions entails large uncertainty due to challenges in estimating heat losses and applying the modified Clauser method. Recommendations for enhancing accuracy in heated rough wall measurements include direct measurement of wall shear stress and heat flux, selecting low emissivity heated plates and ensuring precise control of heated wall conditions. This work also emphasises the significance of conducting a comprehensive uncertainty analysis as a valuable tool for identifying and addressing any shortcomings in the measurement facility and equipment.

show abstract