Assessing the time-step dependency of calculating supraglacial debris thermal diffusivity from vertical temperature profiles

2021 ◽  
Author(s):  
Calvin Beck ◽  
Lindsey Nicholson
Keyword(s):  
Thermal Conductivity ◽  
Thermal Diffusivity ◽  
Web Application ◽  
Sampling Interval ◽  
Heat Diffusion ◽  
Time Interval ◽  
High Mountain ◽  
Time Step ◽  
Ice Melt ◽  
Apparent Thermal Conductivity

<p>Debris thermal conductivity is a critical parameter in calculating a glacier’s sub-debris ice melt. The method widely used in publications to calculate apparent thermal conductivity of supraglacial debris layers is based on an estimate of volumetric heat capacity of the debris and simple heat diffusion principles and is presented in . The analysis of heat diffusion requires a vertical array of temperature measurements through the supraglacial debris cover. This study explores the effect of the temperature sampling interval on the thermal conductivity values derived using this method. Initial results indicate that the thermal diffusivity decreases linearly with an increasing sampling time from 30min to 6h by 0.2-0.4 mm²/s for glaciers in high mountain Asia during the monsoon season. These results suggest that care must be taken in choosing the analysis time interval for computing debris thermal conductivity and for comparing values between datasets sampled at different intervals. Current research aims to further investigate the cause of the artifact and determine how this problem can be solved. An open-source web application is therefore developed to help other scientists investigate the effect of the sampling interval on their calculated sub-debris ice melt. This study falls under the remit of the on debris-covered glaciers and is supported by data provided from within this group.</p>

Thermal Properties of Thin Metallic Layer Deposited on Insulators: Effect of the Interface on the Independent Determination of the Thermal Diffusivity and Conductivity of the Layer

2008 ◽  
Author(s):  
Danie`le Fournier ◽  
Jean Paul Roger ◽  
Christian Fretigny
Keyword(s):  
Thermal Conductivity ◽  
Thermal Properties ◽  
Thermal Diffusivity ◽  
Thermal Resistance ◽  
Thermal Contact ◽  
Heat Diffusion ◽  
Metallic Layer ◽  
Good Thermal Contact ◽  
Lateral Heat

Lateral heat diffusion thermoreflectance is a very powerful tool for determining directly the thermal diffusivity of layered structures. To do that, experimental data are fitted with the help of a heat diffusion model in which the ratio between the thermal conductivity k and the thermal diffusivity D of each layer is fixed, and the thermal properties of the substrate are known. We have shown in a previous work that it is possible to determine independently the thermal diffusivity and the thermal conductivity of a metallic layer deposited on an insulator, by taking into consideration all the data obtained at different modulation frequencies. Moreover, it is well known that to prevent a lack of adhesion of a gold film deposited on substrates like silica, an intermediate very thin (Cr or Ti) layer is deposited to assure a good thermal contact. We extend our previous work: the asymptotic behaviour determination of the surface temperature wave at large distances from the modulated point heat source for one layer deposited on the substrate to the two layers model. In this case (very thin adhesion coating whose thermal properties and thickness are known), it can be establish that the thermal diffusivity and the thermal conductivity of the top layer can still be determined independently. It is interesting to underline that the calculus can also be extended to the case of a thermal contact resistance which has often to be taken into account between two solids. We call thermal resistance a very thin layer exhibiting a very low thermal conductivity. In this case, the three parameters we have to determine are the thermal conductivity and the thermal diffusivity of the layer and the thermal resistance. We will show that, in this case, the thermal conductivity of the layer is always obtained independently of a bound of the couple thermal resistance – thermal diffusivity, the thermal diffusivity being under bounded and the thermal resistance lower bounded. Experimental results on thin gold layers deposited on silica with and without adhesion layers are presented to illustrate the method. Discussions on the accuracy will also be presented.

Theoretical Study on Transient Hot-Strip Method by Numerical Analysis

2009 ◽  
Author(s):  
Gaosheng Wei ◽  
Xiaoze Du ◽  
Xinxin Zhang ◽  
Fan Yu
Keyword(s):  
Thermal Conductivity ◽  
Heat Capacity ◽  
Thermal Diffusivity ◽  
Numerical Analysis ◽  
Measurement Error ◽  
Time Interval ◽  
Strip Surface ◽  
Transient Hot Strip ◽  
Hot Strip ◽  
Thermal Diffusivities

This paper presented the effects of finite dimensions of the sample and non-zero heat capacity of the strip on thermal conductivity determination with the transient hot-strip (THS) method. Through numerical analysis of temperature field within the system composed of the samples and the strip, the temperature transients at the strip surface were obtained to calculate the thermal conductivities of materials, which were compared to the exact values. The effect of heat losses out of the external surfaces of the sample and the heat capacity of the strip on thermal conductivity determination were then analyzed comprehensively. It is shown that the sample finite dimensions have great effect on thermal conductivity determination, especially on the materials with relatively higher thermal diffusivities, and the measured thermal conductivity always lower than the exact value due to the lower convective heat transfer coefficient out of the external surfaces of the sample. The measurement error is estimated less than 2.2 percent for the material with thermal diffusivity less than 4.0×10−6 m2/s with the sample dimensions of 120 mm × 60 mm (width × thickness) and the fitting time interval of 20–450s. The non-zero heat capacity of the strip has great effect on thermal conductivity determinations of the materials with relatively lower thermal diffusivities. The measurement error is estimated less than 5 percent for the material with thermal diffusivity larger than 0.8×10−7 m2/s with Cr20Ni80 alloy as the strip.

scholarly journals Numerical and experimental determination of the minimum and maximum measuring times for the hot wire parallel technique

Cerâmica ◽  
2003 ◽  
Vol 49 (309) ◽  
pp. 29-35 ◽  
Author(s):  
W. N. dos Santos ◽  
R. Gregório
Keyword(s):  
Thermal Conductivity ◽  
Thermal Diffusivity ◽  
Ceramic Materials ◽  
Time Interval ◽  
Hot Wire ◽  
High Thermal Diffusivity ◽  
Different Dimensions ◽  
Thermal Diffusivities ◽  
Parallel Technique

The hot wire technique is considered to be an effective and accurate means of determining the thermal conductivity of ceramic materials. However, specifically for materials of high thermal diffusivity, the appropriate time interval to be considered in calculations is a decisive factor for getting accurate and consistent results. In this work, a numerical simulation model is proposed with the aim of determining the minimum and maximum measuring time for the hot wire parallel technique. The temperature profile generated by this model is in excellent agreement with that one experimentally obtained by this technique, where thermal conductivity, thermal diffusivity and specific heat are simultaneously determined from the same experimental temperature transient. Eighteen different specimens of refractory materials and polymers, with thermal diffusivities ranging from 1x10-7 to 70x10-7 m²/s, in shape of rectangular parallelepipeds, and with different dimensions were employed in the experimental programme. An empirical equation relating minimum and maximum measuring times and the thermal diffusivity of the sample is also obtained.

scholarly journals Thermal Conductivity and Thermal Diffusivity of Biodiesel And Bioethanol Samples

2013 ◽  
Vol 16 (4) ◽  
pp. 90-94
Author(s):  
Monika Božiková ◽  
Peter Hlaváč
Keyword(s):  
Thermal Conductivity ◽  
Thermal Diffusivity ◽  
Thermophysical Parameter ◽  
Hydroxyl Group ◽  
Coefficient Of Determination ◽  
Time Interval ◽  
Animal Fats ◽  
Thermophysical Parameters ◽  
Polynomial Coefficients ◽  
Two Samples

Abstract This article deals with thermal properties of selected biodiesel and bioethanol samples (biodiesel No 1, No 2 and bioethanol No 1, No 2). Biodiesel is renewable fuel that can be manufactured from vegetable oils, animal fats, or recycled restaurant grease for use in diesel vehicles. Biodiesel‘s physical properties are similar to those of petroleum diesel, but it is a cleaner-burning alternative fuel. Ethanol (CH3CH2OH) is a clear liquid. Also known as ethyl alcohol, grain alcohol, and EtOH, the molecules in this fuel contain a hydroxyl group (OH-) bonded to a carbon atom. Ethanol is made of the same chemical compound regardless of whether it is produced from starch and sugar-based feedstocks, such as corn grain, sugar cane, etc. The hot wire method was used for thermal parameters measurements. The experiment is based on measuring the temperature rise vs. time evaluation of an electrically heated wire embedded in the tested material. Thermal conductivity is derived from the resulting change in temperature over a known time interval. For two samples of biodiesel and two samples of bioethanol, there were determined basic thermophysical parameters - thermal conductivity and thermal diffusivity. Two series of measurements were made for each sample of biodiesel and bioethanol. In the first series, there were measured the thermal conductivity and thermal diffusivity at constant room temperature 20 °C. Every thermophysical parameter was measured 10 times for each sample. The results were statistically processed. In the second series of measurements, there were measured the relations of thermal conductivity and thermal diffusivity to temperature in temperature range 20-29 °C. It was evident from results that all measured dependencies are nonlinear. Polynomial functions described by polynomial coefficients were obtained for both thermophysical parameters. The type of function was selected according to statistical evaluation based on the coefficient of determination for every thermophysical parameter graphical dependency. All obtained results are presented in Figures 1-4 and in Tables 1-4. The results of thermophysical parameters measurements of biodiesel and bioethanol could be compared with the values presented in literature.

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