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Keywords = subgrid activity sensor

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13 pages, 21929 KB  
Article
Dynamic Mixed Modeling in Large Eddy Simulation Using the Concept of a Subgrid Activity Sensor
by Josef Hasslberger
Fluids 2023, 8(8), 219; https://doi.org/10.3390/fluids8080219 - 28 Jul 2023
Cited by 5 | Viewed by 1950
Abstract
Following the relative success of mixed models in the Large Eddy Simulation of complex turbulent flow configurations, an alternative formulation is suggested here which incorporates the concept of a local subgrid activity sensor. The general idea of mixed models is to combine the [...] Read more.
Following the relative success of mixed models in the Large Eddy Simulation of complex turbulent flow configurations, an alternative formulation is suggested here which incorporates the concept of a local subgrid activity sensor. The general idea of mixed models is to combine the advantages of structural models (superior alignment properties), usually of the scale similarity type, and functional models (superior stability), usually of the eddy viscosity type, while avoiding their disadvantages. However, the key question is the mathematical realization of this combination, and the formulation in this work accounts for the local level of underresolution of the flow. The justification and evaluation of the newly proposed mixed model is based on a priori and a posteriori analysis of homogeneous isotropic turbulence and laminar–turbulent transition in the Taylor–Green vortex, respectively. The suggested model shows a robust and accurate behavior for the cases investigated. In particular, it outperforms the separate structural and functional base models as well as the simulation without an explicit subgrid-scale model. Full article
(This article belongs to the Collection Feature Paper for Mathematical and Computational Fluid Mechanics)
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12 pages, 637 KB  
Article
Effect of Land Cover Heterogeneity on Soil Moisture Retrieval Using Active Microwave Remote Sensing Data
by Tarendra Lakhankar, Hosni Ghedira, Marouane Temimi, Amir E. Azar and Reza Khanbilvardi
Remote Sens. 2009, 1(2), 80-91; https://doi.org/10.3390/rs1020080 - 13 May 2009
Cited by 36 | Viewed by 13368
Abstract
This study addresses the issue of the variability and heterogeneity problems that are expected from a sensor with a larger footprint having homogenous and heterogeneous sub-pixels. Improved understanding of spatial variability of soil surface characteristics such as land cover and vegetation in larger [...] Read more.
This study addresses the issue of the variability and heterogeneity problems that are expected from a sensor with a larger footprint having homogenous and heterogeneous sub-pixels. Improved understanding of spatial variability of soil surface characteristics such as land cover and vegetation in larger footprint are critical in remote sensing based soil moisture retrieval. This study analyzes the sub-pixel variability (standard deviation of sub-grid pixels) of Normalized Difference Vegetation Index and SAR backscatter. Back-propagation neural network was used for soil moisture retrieval from active microwave remote sensing data from Southern Great Plains of Oklahoma. The effect of land cover heterogeneity (number of different vegetation species within pixels) on soil moisture retrieval using active microwave remote sensing data was investigated. The presence of heterogeneous vegetation cover reduced the accuracy of the derived soil moisture using microwave remote sensing data. The results from this study can be used to characterize the uncertainty in soil moisture retrieval in the context of Soil Moisture Active and Passive (SMAP) mission which will have larger footprint. Full article
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