DNS of turbulent heat transfer in channel flow with low to medium-high Prandtl number fluid

Kawamura, Hiroshi; Ohsaka, Kouichi; Abe, Hiroyuki; Yamamoto, Kiyoshi

doi:10.1016/s0142-727x(98)10026-7

Cited by 335 publications

(292 citation statements)

References 13 publications

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“…This behavior is similar to constant property cases with the same Pr w , but different Re τ values (see, e.g., Ref. [2]). Similarly, cases LL and VλSPr LL , which exhibit quasisimilar Pr profiles, also show quasisimilar conductive heat flux profiles, despite the different Re τ profiles.…”

Section: B Re τ Invariant Mean Scalar Scalingsupporting

confidence: 81%

“…A Prandtl-number-dependent shift also occurs when comparing profiles of θ/θ τ for constant property cases at different Prandtl numbers, as can be seen by comparing case CP395 Pr1 with CP395 Pr4 (also see, e.g., Ref. [2]). Similar to u vD , which does not collapse for cases with Re τ gradients, θ vD also deviates significantly for cases with Re τ gradients.…”

Section: A Conventional Mean Scalar Scalingmentioning

confidence: 77%

“…One of the important aspects in all these studies involve investigating the analogy between momentum transfer and scalar transport, since it results in a simple modeling approach, where the turbulent scalar flux is determined by the turbulent eddy viscosity μ t and a turbulent Prandtl (or Schmidt) number Pr (or Sc t ), which is defined as the ratio of eddy viscosity μ t to eddy conductivity α t . Kim and Moin [1] and Kawamura et al [2] performed DNS of turbulent heat transfer in a channel at a friction Reynolds number of Re τ = 180 and Prandtl numbers Pr w ranging from 0.1 to 2 in the former study and 0.025 to 5 in the latter. For fluids with Pr w > 0.1, the turbulent Prandtl number was found to be independent of Pr w , with values of the order of unity in the regions away from the center.…”

Section: Introductionmentioning

confidence: 99%

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Scalar statistics in variable property turbulent channel flows

2017

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Direct numerical simulation of fully developed, internally heated channel flows with isothermal walls is performed using the low-Mach-number approximation of Navier-Stokes equation to investigate the influence of temperature-dependent properties on turbulent scalar statistics. Different constitutive relations for density ρ, viscosity μ, and thermal conductivity λ as a function of temperature are prescribed in order to characterize the turbulent scalar statistics. It is shown that the dominant effect caused by property variations on scalar statistics can be parameterized by two nondimensional parameters, namely the semilocal Reynolds number Re τ ≡ Re τ √ (ρ/ρ w )/(μ/μ w ) (the bar and subscript w denote Reynolds averaging and wall value respectively, while Re τ is the friction Reynolds number based on wall values), and the local Prandtl number Pr = Pr w (μ/μ w )/(λ/λ w ) (Pr w is the molecular Prandtl number based on wall values). Near-wall gradients in Re τ modulate the turbulent heat flux generation mechanism because of structural changes in turbulence. However, the influence of these modulations on the inner scaling of turbulent heat conductivity normalized by local mean viscosity is shown to be weak. Using this observation, a temperature transformation is derived that is invariant of Re τ variations and only exhibits a Pr -dependent shift.

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Section: B Re τ Invariant Mean Scalar Scalingsupporting

confidence: 81%

Section: A Conventional Mean Scalar Scalingmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

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Scalar statistics in variable property turbulent channel flows

2017

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“…It can be seen from the DNS predictions that as the Prandtl number increases, the near-wall peak moves closer to the wall and the RMS increases indicating an increase in the scalar fluctuations. The RMS profiles also show a linear scaling with y + in the near-wall region, see also Kawamura et al (1998). The RMS profile has two peaks.…”

Section: Mean and Root-mean-square Profiles Of The Scalarmentioning

confidence: 73%

An explicit algebraic model for the subgrid-scale passive scalar flux

2013

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“…While a much broader research effort has been devoted to the modeling of turbulent flow [4,5], the effort to simulate and develop models for turbulent heat transfer has been more limited [10][11][12][13][14][15][16][17]. The simulations are mainly divided into two categories: those that are based on the Eulerian approach to describing transport phenomena (and where the system of reference is not moving), and those that…”

Section: Introductionmentioning

confidence: 99%

Lagrangian Modeling of Turbulent Dispersion from Instantaneous Point Sources at the Center of a Turbulent Flow Channel

Nguyen

Feher

Papavassiliou

2017

Fluids

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Abstract:The paper is focused on the simulation and modeling of the dispersion from an instantaneous source of heat or mass located at the center of a turbulent flow channel. The flow is modeled with a direct numerical simulation, and the dispersion is modeled with Lagrangian methods based on Lagrangian scalar tracking (LST). The LST technique allows the simulation of scalar sources that span a range of Prandtl or Schmidt numbers that cover orders of magnitude. The trajectories of individual heat or mass markers are tracked, generating a probability distribution function that describes the behavior of instantaneous point sources of a scalar in the turbulent field. The effect of the Prandtl or Schmidt number on turbulent dispersion is examined, with emphasis on the dispersion pattern. Results for Prandtl or Schmidt numbers between 0.1 and 15,000 are presented. For an instantaneous source at the channel center, it is found that there are two zones of cloud development: one where molecular diffusion plays a role at very small times (early stage of the dispersion), and one where turbulent convection dominates. The asphericity of the scalar marker cloud is found to increase monotonically, in contrast to published results for isotropic, homogenous turbulence, where the asphericity goes through a maximum.

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DNS of turbulent heat transfer in channel flow with low to medium-high Prandtl number fluid

Cited by 335 publications

References 13 publications

Scalar statistics in variable property turbulent channel flows

Scalar statistics in variable property turbulent channel flows

An explicit algebraic model for the subgrid-scale passive scalar flux

Lagrangian Modeling of Turbulent Dispersion from Instantaneous Point Sources at the Center of a Turbulent Flow Channel

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