Revisiting a drag partition model for canopy-like roughness elements

التفاصيل البيبلوغرافية
العنوان: Revisiting a drag partition model for canopy-like roughness elements
المؤلفون: Buono, Elia, Katul, Gabriel G., Vettori, Davide, Poggi, Davide, Manes, Costantino
سنة النشر: 2024
المجموعة: Physics (Other)
مصطلحات موضوعية: Physics - Fluid Dynamics
الوصف: Turbulent flows over a large surface area (S) covered by n obstacles experience an overall drag due to the presence of the ground and the protruding obstacles into the flow. The drag partition between the roughness obstacles and the ground is analyzed using an analytical model proposed by Raupach (1992) and is hereafter referred to as R92. The R92 is based on the premise that the wake behind an isolated roughness element can be described by a shelter area A and a shelter volume V. The individual sizes of A and V without any interference from other obstacles can be determined from scaling analysis for the spread of wakes. To upscale from an individual roughness element to n/S elements where wakes may interact, R92 adopted a background stress re-normalizing instead of reducing A or V with each element addition. This work shows that R92's approach only converges to a linear reduction in A and V for small n/S where wakes have low probability of interacting with one another. This probabilistic nature suggests that up-scaling from individual to multiple roughness elements can be re-formulated using stochastic averaging methods proposed here. The two approaches are shown to recover R92 under plausible conditions. Comparisons between R92 and available data on blocks and vegetation-like roughness elements confirm the practical utility of R92 and its potential use in large-scale models provided the relevant parameters accommodate certain features of the roughness element type (cube versus vegetation-like) and, to a lesser extent, their configuration throughout S.
نوع الوثيقة: Working Paper
الوصول الحر: http://arxiv.org/abs/2404.07893Test
رقم الانضمام: edsarx.2404.07893
قاعدة البيانات: arXiv