Mean and Turbulent Flow Statistics in a Trellised Agricultural Canopy

Miller, Nathan E.; Stoll, Rob; Mahaffee, Walter F.; Pardyjak, Eric R.

doi:10.1007/s10546-017-0265-y

Cited by 19 publications

(5 citation statements)

References 70 publications

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“…As is explained in Miller et al. (2017) and demonstrated in Everard et al. (2020), when the wind is aligned with the row‐oriented vineyard, the flow behavior is analogous to that of a sparse canopy and less to the more canonical canopy explored by Su et al.…”

Section: Resultsmentioning

confidence: 81%

“…To further compare with other canonical canopy flow studies, we ask whether the flow inspected here is more representative of dense canopy or boundary-layer flows. As is explained in Miller et al (2017) and demonstrated in Everard et al (2020), when the wind is aligned with the row-oriented vineyard, the flow behavior is analogous to that of a sparse canopy and less to the more canonical canopy explored by Su et al (2000). Eddy advection velocities that are slower than the mean streamwise flow can be indicative of a more conventional turbulent boundary-layer flow (e.g., Willmarth & Wooldridge, 1962).…”

Section: 1029/2021gl093746mentioning

confidence: 93%

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Sweeping Effects Modify Taylor’s Frozen Turbulence Hypothesis for Scalars in the Roughness Sublayer

Everard

Katul

Lawrence

et al. 2021

Geophysical Research Letters

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Section: Resultsmentioning

confidence: 81%

Section: 1029/2021gl093746mentioning

confidence: 93%

Sweeping Effects Modify Taylor’s Frozen Turbulence Hypothesis for Scalars in the Roughness Sublayer

Everard

Katul

Lawrence

et al. 2021

Geophysical Research Letters

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“…This relationship has been exploited to calculate ε in a variety of applications (e.g. [29,30]) and has been shown to be a stable and accurate way for estimating dissipation with stereoscopic PIV data [9]. The present ε fields are shown in Fig.…”

Section: Variable C µ Rans Modelmentioning

confidence: 99%

Creating Data-Driven Turbulence Models Using PIV

Beresh

Miller

Parish

et al. 2022

LXLASER

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Data-driven turbulence models have been created for a k-ε RANS model based on PIV data in a jet in crossflow. In the simpler of two implementations, the nominal value of the model coefficient Cµ was replaced with an optimized value calibrated to the PIV results. Despite being based on only four flow cases of a canonical configuration, the optimized model demonstrated superior performance over 48 flow cases of increasing complexity. A second, more sophisticated data-driven model has been created by mapping a spatially variable Cµ to flow state variables using machine learning of experimentally measured flow field properties of the turbulence. This second model has been implemented in a production RANS code but requires further improvements before it can return results deviating from either the nominal or calibrated Cµ models.

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“…Второй объект моделирования в системе растения в посеве + патогенные грибы -грибы, их распространение в посеве и развитие инфекции (генерация нового потом ст ва спор). Моделирование распространения спор гриб ных инфекций в посевах основано на экспериментальном и теоретическом изучении транспорта твердых дисперсных систем в атмосфере и посевах на разных пространственных масштабах -от десятков и сотен километров до мет ров и сантиметров (Chamecki, 2012;Chamecki et al, 2012;Bailey et al, 2014;Miller et al, 2017). Скорость развития инфекции от момента инокуляции спор -важный параметр модели, сам он также является функцией от влажности и температуры среды (Duthie, 1997;Magarey et al, 2005).…”

Section: моделирование развития инфекции в посевеunclassified

A system approach to the modeling of fungal infections of the wheat leaf

et al. 2019

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Currently, studies on the mechanisms of the pathogenesis of plant diseases and their distribution in crops are intensively conducted in Russia and the world. First of all, this interest is associated with a significant effect of pathogens on the harvest. In Western Siberia, brown rust and powdery mildew are almost annually recorded in the crops of spring and winter wheat, reaching in some years up to the epiphytotic level. In this regard, methods for monitoring the condition of crops in order to predict their dynamics and plan agrotechnological events to control the state of plants in crops, including the development of fungal infection are developing. Models of fungal infections development on the wheat leaf (for example, brown rust) are used to monitor, predict and control the state of crops in order to optimize the growing process. Mathematical models allow computational experiments to make predictions about the risk dynamics of infections in different scenarios of global weather changes. Such designation of models assumes their hierarchical structure characteristic of multilevel modeling systems. This review presents models for the development of foliar fungal infections in crops, and formulates the methodological aspects of system modeling that can be used for adapting existing models and their units, and developing new models based on them. The article presents the structure of the hierarchical system for modeling the development of leafy infection, provides an overview of the units constituting the system, and discusses the issues of parametric adaptation of submodels. We demonstrated that, to date, plant growth and development models have been developed with varying degrees of detail. Currently, to develop a system for modeling the development of an infection in a crop, it is necessary to determine a large body of available experimental data and, by taking into account this data, we can put together a model as a system consisting of model modules, for which the models of basic processes have already been developed and described.

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Mean and Turbulent Flow Statistics in a Trellised Agricultural Canopy

Cited by 19 publications

References 70 publications

Sweeping Effects Modify Taylor’s Frozen Turbulence Hypothesis for Scalars in the Roughness Sublayer

Sweeping Effects Modify Taylor’s Frozen Turbulence Hypothesis for Scalars in the Roughness Sublayer

Creating Data-Driven Turbulence Models Using PIV

A system approach to the modeling of fungal infections of the wheat leaf

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