2001
DOI: 10.1016/s0045-7930(00)00007-4
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Computation of turbulent wake flows in variable pressure gradient

Abstract: Transport aircraft performance is strongly in uenced by the e ectiveness of high-lift systems. Developing wakes generated by the airfoil elements are subjected to strong pressure gradients and can thicken very rapidly, limiting maximum lift. This paper focuses on the e ects of various pressure gradients on developing symmetric wakes and on the ability of a linear eddy viscosity model and a nonlinear explicit algebraic stress model to accurately predict their downstream evolution. In order to reduce the uncerta… Show more

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Cited by 10 publications
(3 citation statements)
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“…Numerous studies using this closure have reported differences in the quantities of interest such as the mean velocity and Reynolds stress profiles compared with experimental and HiFi data [22,23,24,25,26,27]. To better understand the reason for differences, the validity of the Boussinesq hypothesis was, for example, tested by Schmitt [28] through computing the alignment of the modelled anisotropy tensor with the reference one given by ρ RS = a re f i j a mod ji a re f mn a re f nm a mod pq a mod qp ,…”
Section: Machine Learning As a Toolmentioning
confidence: 99%
“…Numerous studies using this closure have reported differences in the quantities of interest such as the mean velocity and Reynolds stress profiles compared with experimental and HiFi data [22,23,24,25,26,27]. To better understand the reason for differences, the validity of the Boussinesq hypothesis was, for example, tested by Schmitt [28] through computing the alignment of the modelled anisotropy tensor with the reference one given by ρ RS = a re f i j a mod ji a re f mn a re f nm a mod pq a mod qp ,…”
Section: Machine Learning As a Toolmentioning
confidence: 99%
“…To this end, numerical methods using RANS provide a time efficient and cost effective approach. Application of steady RANS with k-e turbulence model to the prediction of zero-pressuregradient wakes of flat plates and two-dimensional lifting bodies is widespread; see for example Patel and Scheuerer (1982), Patel and Chen (1987), Tummers et al (2007), Carlson et al (2001), Nguyen and Gorski (1991) and Mulvany et al (2004), all of which found this turbulence model predicts the near-wake features reasonably well compared to the experimental data. Moreover, a recent study by Iaccarino et al (2003) claims that unsteady RANS (URANS) is highly capable of predicting flows with gross unsteadiness, given that the unsteadiness is deterministic and that the frequency spectrum shows a spike at the vortex shedding frequency.…”
mentioning
confidence: 97%
“…One way is through the method of manufactured solutions (MMS), but only limited solutions are available for turbulence (see, e.g., Eca et al [3]). Demonstrating consistency using the same model in different codes can provide some level of assurance [4], but optimally the testing should be conducted by independent groups [5] to minimize the possibility of repeating the same mistakes.…”
Section: Introductionmentioning
confidence: 99%