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Velocity and temperature scalings leading to compressible laws of the wall

Published online by Cambridge University Press:  22 December 2023

P.G. Huang
Affiliation:
Mechanical and Materials Engineering, Wright State University, Dayton, OH 45435, USA
G.N. Coleman
Affiliation:
Computational AeroSciences, NASA Langley Research Center, Hampton, VA 23681, USA
P.R. Spalart
Affiliation:
Boeing Commercial Airplanes, Seattle, WA 98124 (retired), USA
X.I.A. Yang*
Affiliation:
Mechanical Engineering, Pennsylvania State University, University Park, PA 16802, USA
*
†Email address for correspondence: xzy48@psu.edu

Abstract

We exploit the similarity between the mean momentum equation and the mean energy equation and derive transformations for mean temperature profiles in compressible wall-bounded flows. In contrast to prior studies that rely on the strong Reynolds analogy and the presumed similarity between the instantaneous and mean velocity and temperature signals, the discussion in this paper involves the Farve-averaged equations only. We establish that the compressible momentum and energy equations can be made identical to their incompressible counterparts under appropriate normalizations and coordinate transformations. Two types of transformations are explored for illustration purposes: Van Driest (VD)-type transformations and semi-local-type or Trettel–Larsson (TL)-type transformations. In our derivations, it becomes clear that VD-type velocity and temperature transformations hold exclusively within the logarithmic layer. On the other hand, TL-type transformations extend their applicability to incorporate wall-damping effects, at least in principle. Each type of transformation serves its distinct purpose and has its applicable range. However, it is noteworthy that while VD-type transformations can be assessed using measurements obtained from laboratory experiments, TL-type transformations necessitate viscosity and density information typically accessible only through numerical simulations. Finally, we justify the omission of the turbulent kinetic energy transfer term, a term that is unclosed, in the energy equation. This omission leads to closed-form temperature transformations that are valid for both adiabatic and isothermal walls.

Information

Type
JFM Papers
Copyright
© The Author(s), 2023. Published by Cambridge University Press
Figure 0

Figure 1.(a) Turbulent Prandtl number. (b) Total Prandtl number. Dataset AA is from Abe & Antonia (2019) and LHP is from Lluesma-Rodriguez et al. (2018) and the $Pr_{total}$ model correlation is from (2.12), (2.15) and (2.16).

Figure 1

Figure 2.(a) Turbulent conductivity. (b) Temperature profiles. Dataset AA refers to Abe & Antonia (2019) and LHP refers to Lluesma-Rodriguez et al. (2018). The model correlation in (a) is from (2.17) and the temperature correlation is from the solution of (2.8) using (2.17).

Figure 2

Figure 3.Schematic of the DNS by Lusher & Coleman (2022). The DNS studies both isothermal and adiabatic wall conditions in a channel. The coordinate $y_e$ is where the maximum velocity is located, and $y_0$ is the location where the molecular viscosity experiences a step change; see Lusher & Coleman (2022) for further details about the step change in the molecular viscosity. In the present study, whichever of $y_e$ and $y_0$ is closer to the adiabatic or isothermal wall defines the effective thickness of the boundary layer adjacent to that particular wall.

Figure 3

Table 1.Details of cold/isothermal-wall-side cases, where ${Re}_{\tau _{iw}}=\rho _{iw} u_{\tau,iw} h/\mu _{iw}$, $M_{\tau _{iw}}=u_{\tau,iw}/c_{\tau,iw}$, $B_q=\langle q_{iw} \rangle /(\rho _{iw} u_{\tau,iw} c_p T_{iw}$) and $\langle T_e \rangle$ is the temperature at the free stream, as defined by figure 3.

Figure 4

Table 2.Details of hot/adiabatic-wall-side cases, where ${Re}_{\tau _{aw}}=\langle \rho _{aw} \rangle u_{\tau,aw} h/\langle \mu _{aw} \rangle$, $M_{\tau _{aw}}=u_{\tau,aw}/\langle c_{\tau,aw} \rangle$ and $\langle T_e \rangle$ is the temperature at the free stream, as defined by figure 3.

Figure 5

Figure 4.Energy fluxes, (3.2), for cases iF2 and aF2. The quantities are scaled by $\rho _b V_\tau ^3$, where $\rho _b=\int _{-h}^{+h}\langle \rho \rangle \,{\rm d} y/2h$ and $V_\tau ^2=(\tau _{iw}+\tau _{aw})/2 \rho _b$; see Lusher & Coleman (2022).

Figure 6

Figure 5.Plots of (a) $\mu _t/\mu$ and (b) total Prandtl number scaling with $y^*$ for isothermal-wall DNS data.

Figure 7

Figure 6.Plots of (a) $\mu _t/\mu$ and (b) total Prandtl number scaling with $y^*$ for adiabatic-wall DNS data.

Figure 8

Figure 7.Van Driest's velocity transformation in (4.5) of DNS data. The legends in (a,b) are the same as in figures 5(a) and 6(a).

Figure 9

Figure 8.Van Driest's temperature transformation in (4.6) of DNS data. The legends in (a,b) are the same as in figures 5(a) and 6(a).

Figure 10

Figure 9.Comparison of VD1, i.e. the transformation in (4.5) and (4.6), and VD2, i.e. the transformation in (4.12) and (4.13), for case iE.

Figure 11

Figure 10.Results of the TL velocity transformation. The legends in (a,b) are the same as in figures 5(a) and 6(a).

Figure 12

Figure 11.Results of the TL-type temperature transformation. The legends in (a,b) are the same as in figures 5(a) and 6(a).

Figure 13

Figure 12.Results of the TL and VD transformations for a boundary-layer flow in Zhang et al. (2014).