Determination of the resistance to sensible heat flux density from turfgrass for estimation of its evapotranspiration rate

The determination of the turbulent fluxes in very stable conditions is done, generally, through parameterizations. In this work the turbulent fluxes are estimated, by using a simplified model, through prognostic equations for the turbulent intensity, the sensible heat flux and the temperature variance. The results indicate that the model is able to reproduce both atmospheric coupling and the intermittent character of the turbulence in very stable conditions.

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Determination of nonuniform heat flux density on boundary in inverse heat conduction problem

Wärme- und Stoffübertragung ◽

10.1007/bf01541107 ◽

1993 ◽

Vol 28 (3) ◽

pp. 117-122 ◽

Cited By ~ 2

Author(s):

J. M. Gu

Keyword(s):

Heat Flux ◽

Heat Conduction ◽

Flux Density ◽

Heat Flux Density ◽

Heat Conduction Problem ◽

Inverse Heat Conduction Problem ◽

Inverse Heat Conduction

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In-situ determination of the heat flux density at the glass/mould interface during a glass pressing production cycle

International Journal of Thermal Sciences ◽

10.1016/j.ijthermalsci.2008.05.010 ◽

2009 ◽

Vol 48 (2) ◽

pp. 428-439 ◽

Cited By ~ 3

Author(s):

Gilles Dusserre ◽

Gilles Dour ◽

Gérard Bernhart

Keyword(s):

Heat Flux ◽

Flux Density ◽

Heat Flux Density ◽

Production Cycle

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Surface heat transfer coefficient measurement by immersed temperature sensor and inverse modelling

tm - Technisches Messen ◽

10.1515/teme-2016-0019 ◽

2016 ◽

Vol 83 (11) ◽

Author(s):

Mirko Javurek ◽

Andreas Mittermair

Keyword(s):

Heat Flux ◽

Inverse Problem ◽

Flux Density ◽

Temperature Sensor ◽

Surface Heat Flux ◽

Heat Flux Density ◽

Surface Heat ◽

Problem Solution ◽

Inverse Problem Solution

AbstractA transient surface heating or cooling process of a solid is considered. A procedure for the determination of surface temperature and surface heat flux density during such a process is presented using a submersed temperature sensor in the solid. From this measured temperature the surface temperature and surface heat flux density are calculated by inverse process modelling. This method is prone to errors since measurement errors are amplified in the inverse process modelling and can thus easily become unacceptably large. The LSQR regularisation algorithm is optimised for fast performance as well as less memory requirement and applied to the inverse problem solution. The proposed method allows to simulate an experimental setup and to determine the accuracy of the results gained from the simulated experiment. This is essential for the determination of the accuracy of a planned or existing test facility. The influence of process parameters like sensor depth, sensor noise level, sampling rate, heat flux density amplitude and cooling/heating process duration is investigated. In most cases it is very important to carefully adjust the process parameters in order to obtain reliable and accurate results. Additionally the proper selection of the regularisation parameter required for the inverse problem solution is analysed.

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