A versatile inversion approach for space/temperature/time-related thermal conductivity via deep learning

2022 ◽  
Vol 186 ◽  
pp. 122444
Author(s):  
Yinpeng Wang ◽  
Qiang Ren
Keyword(s):  
Thermal Conductivity ◽  
Deep Learning ◽  
Temperature Time

Study on Application Properties of PCM Mortars

2014 ◽  
Vol 638-640 ◽  
pp. 1499-1502
Author(s):  
Xiao Na Hu ◽  
Yin Li
Keyword(s):  
Thermal Conductivity ◽  
Heat Storage ◽  
Thermal Inertia ◽  
Lauryl Alcohol ◽  
Measuring Instrument ◽  
Temperature Level ◽  
Storage Coefficient ◽  
Temperature Time

Lauryl alcohol mixed paraffin to prepare PCM mortars. Study on the heat storage using DSC equipment; study on the temperature level and temperature time by boilingwater; the DRM-II type "heat storage coefficient measuring instrument is used to check heat storage coefficient,and analysis the thermal inertia through heat storage coefficient and thermal conductivity.

scholarly journals Investigation of the thermophysical characteristics of triticale grain of the Gorka variety by the method of non-stationary thermal re-gime

2021 ◽  
Vol 83 (2) ◽  
pp. 17-22
Author(s):  
A. V. Drannikov ◽  
T. N. Tertychnaya ◽  
A. A. Shevtsov ◽  
N. V. Zasypkin ◽  
A. A. Ryndin
Keyword(s):  
Thermal Conductivity ◽  
Thermal Characteristics ◽  
Essential Amino Acids ◽  
Thermophysical Characteristics ◽  
New Variety ◽  
Grain Crop ◽  
Temperature Time ◽  
Micro Elements ◽  
Measuring Unit

In solving the problem of vegetable protein deficiency, triticale grain is of great interest – a unique hybrid that com-bines the best hereditary qualities of wheat and rye. The protein content of triticale is 1.0 – 1.5% higher than that of wheat and 3–4% higher than that of rye. Obtained a new variety of grain triticale – "Slide". Products prepared from this grain crop have a high nutritional value, since the protein that is part of triticale is characterized by an increased content of essential amino acids, and is not inferior to wheat grain in terms of the content of macro – and micro-elements. It contains a lot of copper, phosphorus, potassium, magnesium, calcium, sodium, zinc, manganese and iron, as well as vitamins В9, В5, В1, PP and E. Information about thermal characteristics as functions of tempera-ture plays a key role in engineering calculations and mathematical modeling of the processes of drying and heat treatment of triticale grain. The method of nonstationary thermal regime, based on the solution of the problem of thermal conductivity over two temperature-time points, developed by B.C. Wolkenstein, was used to determine the thermophysical characteristics. The determination of the thermophysical characteristics of the grain was carried out on the Kossfield RT 1394 N measuring unit (National Instruments). Revealed the character of the dependence of diffusivity, thermal conductivity and specific heat capacity on temperature. Equations describing the thermophysi-cal characteristics of grain at a humidity of 13.57 and 21.83% in the temperature range of 20–100 оС are obtained

The Thermal Properties of Human Blood during the Freezing Process

1973 ◽  
Vol 95 (2) ◽  
pp. 246-249 ◽  
Author(s):  
F. C. Wessling ◽  
P. L. Blackshear
Keyword(s):  
Thermal Conductivity ◽  
Thermal Properties ◽  
Water Solution ◽  
Time History ◽  
Steel Tube ◽  
Ice Crystals ◽  
Freezing Process ◽  
Stainless Steel Tube ◽  
History Of ◽  
Temperature Time

This paper presents calculations of the density, thermal conductivity, and enthalpy of blood during the freezing process. The calculations are based upon the premise that blood freezes similarly to a mixture of fats, proteins, and sodium chloride in a water solution and freezes so that ice crystals align themselves with the direction of heat flow. The properties were checked by calculating the theoretical temperature–time history of blood freezing in a Teflon-coated stainless-steel tube and comparing the results with experiments. The agreement was within 10 percent over the entire ranges of temperature and time. Hence the derived thermal properties are concluded to be good approximations to the real properties.

scholarly journals Effective thermal conductivity of unsaturated soils based on deep learning algorithm

2020 ◽  
Vol 205 ◽  
pp. 04006
Author(s):  
Zarghaam Haider Rizvi ◽  
Syed Jawad Akhtar ◽  
Wurood Talib Sabeeh ◽  
Frank Wuttke
Keyword(s):  
Thermal Conductivity ◽  
Deep Learning ◽  
Effective Thermal Conductivity ◽  
Unsaturated Soils ◽  
Learning Algorithm ◽  
Critical Role ◽  
Degree Of Saturation ◽  
Ann Model ◽  
Soil Thermal Conductivity ◽  
Validation Data

Soil thermal conductivity plays a critical role in the design of geo-structures and energy transportation systems. Effective thermal conductivity (ETC) of soil depends primarily on the degree of saturation, porosity and mineralogical composition. These controlling parameters have nonlinear dependencies, thus making prediction a nontrivial task. In this study, an artificial neural network (ANN) model is developed based on the deep learning (DL) algorithm to predict the effective thermal conductivity of unsaturated soil. A large dataset is constructed including porosity, degree of saturation and quartz content from literature to train and validate the developed model. The model is constructed with a different number of hidden layers and neurons in each hidden layer. The standard errors for training and testing are calculated for each variation of hidden layers and neurons. The network with the least error is adopted for prediction. Two sand types independent of training and validation data reported in the literature are considered for prediction of the ETC. Five simulation runs are performed for each sand, and the computed results are plotted against the reported experimental results. The results conclude that the developed ANN model provides an efficient, easy and straightforward way to predict soil thermal conductivity with reasonable accuracy.

Rapid measurement of thermal conductivity by transient heating of a fine thermo-junction

1957 ◽  
Vol 239 (1219) ◽  
pp. 476-486 ◽  
Keyword(s):  
Thermal Conductivity ◽  
Room Temperature ◽  
Simultaneous Recording ◽  
Effective Length ◽  
Measuring Time ◽  
Transient Heating ◽  
Rapid Measurement ◽  
Heated Wire ◽  
Temperature Time ◽  
Frequency Current

Transient methods of measuring thermal conductivities of poor conductors are very rapid and can be applied to small systems. By recording the temperature of an electrically heated wire, the conductivity of the environment can be determined in a fraction of a second. Thus with a wire of radius 10 -2 cm the measuring time is less than 10 -1 s and the effective overall radius of the system is about 10 -1 cm; furthermore, the effective length can be reduced to about 1·0 cm by making the wire a thermo-junction. Apparatus operating on this principle consists of a source of radio-frequency current for heating the junction wires and an oscillograph for simultaneous recording of the thermo-e. m. f. Temperature-time curves are used to deduce the thermal conductivity of substances placed in contact with the wires. The method is very flexible and can be applied to small, rapidly changing systems at high temperatures. The conductivities of certain liquids have been measured at room temperature. The values obtained generally lie within 10% of the accepted values.

Synthesis and Thermoelectric Properties of N-Type Bi2Te2.7Se0.3 Compounds by Spark Plasma Sintering

2005 ◽  
Vol 486-487 ◽  
pp. 654-657
Author(s):  
Dong Choul Cho ◽  
Cheol Ho Lim ◽  
Ki Tae Kim ◽  
Seung Y. Shin ◽  
D.M. Lee ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Electrical Resistivity ◽  
Seebeck Coefficient ◽  
Thermoelectric Properties ◽  
Hydrogen Reduction ◽  
Sintering Temperature ◽  
Reduction Process ◽  
Plasma Sintering ◽  
Spark Plasma ◽  
Temperature Time

Thermoelectric properties of the spark plasma sintered n-type Bi2Te2.7Se0.3 compounds were characterized with the sintering temperature, time and hydrogen reduction process. The Seebeck coefficient, electrical resistivity and thermal conductivity were dependent on hydrogen reduction process as well as sintering temperature. The Seebeck coefficient and electrical resistivity decreased and thermal conductivity increased with reduction treatment and sintering temperature. The results suggest that the carrier density varies with the dissolved oxygen and Te vacancies generated during the pulverization process. The highest figure of merit of 3.11×10-3/K was obtained for the compounds spark plasma sintered at 460°C for 16min by using the reduced powders.

Identification of pipe inner surface in heat conduction problems by deep learning and effective thermal conductivity transform

2020 ◽  
Vol 37 (9) ◽  
pp. 3505-3523
Author(s):  
Haolong Chen ◽  
Zhibo Du ◽  
Xiang Li ◽  
Huanlin Zhou ◽  
Zhanli Liu
Keyword(s):  
Thermal Conductivity ◽  
Deep Learning ◽  
Effective Thermal Conductivity ◽  
Identification Problem ◽  
Learning Model ◽  
Surface Shape ◽  
Transform Method ◽  
Content Type ◽  
Inner Surface ◽  
Deep Learning Model

Purpose The purpose of this paper is to develop a transform method and a deep learning model to identify the inner surface shape based on the measurement temperature at the outer boundary of the pipe. Design/methodology/approach The training process is assisted by the finite element method (FEM) simulation which solves the direct problem for the data preparation. To avoid re-meshing the domain when the inner surface shape varies, a new transform method is proposed to transform the shape identification problem into the effective thermal conductivity identification problem. The deep learning model is established to set up the relationship between the measurement temperature and the effective thermal conductivity. Then the unknown geometry shape is acquired by the mapping between the inner shape and the effective thermal conductivity through the inverse transform method. Findings The new method is successfully applied to identify the internal boundary of a pipe with eccentric circle, ellipse and nephroid inner geometries. The results show that as the measurement points increased and the measurement error decreased, the results became more accurate. The position of the measurement point and mesh density of the FEM model have less effect on the results. Originality/value The deep learning model and the transform method are developed to identify the pipe inner surface shape. There is no need to re-mesh the domain during the computation progress. The results show that the proposed method is a fast and an accurate tool for identifying the pipe inner surface.

scholarly journals The Use of Complete Temperature?Time Curves for Determination of Thermal Conductivity with Particular Reference to Rocks

1959 ◽  
Vol 12 (3) ◽  
pp. 203 ◽  
Author(s):  
JC Jaeger
Keyword(s):  
Thermal Conductivity ◽  
Time Curve ◽  
Transient Methods ◽  
Temperature Time

Most of the transient methods at present in use for the determination of thermal conductivity involve the study of the asymptote of a temperature-time curve. This implies that they require relatively long times of experiment and make no use of the information contained in measurements of temperature at smaller times.

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