Temperature Dependent Thermal Conductivity Measurement on Biological Materials by Solving Inverse Heat Conduction Problem: A Theoretical Study

Author(s):  
Dawei Luo ◽  
Tzu Fang Chen ◽  
Sui Lin ◽  
Liqun He ◽  
Dayong Gao
2009 ◽  
Vol 132 (3) ◽  
Author(s):  
Jianhua Zhou ◽  
Yuwen Zhang ◽  
J. K. Chen ◽  
Z. C. Feng

The inverse heat conduction problem (IHCP) in a one-dimensional composite slab with rate-dependent pyrolysis chemical reaction and outgassing flow effects is investigated using the iterative regularization approach. The thermal properties of the composites are considered to be temperature-dependent. A nonlinear conjugate gradient method formulation is developed and applied to solve the IHCP in an organic composite slab whose front-surface is subjected to high intensity periodic laser heating.


2011 ◽  
Vol 243-249 ◽  
pp. 89-92
Author(s):  
Shi Liang Xu

The Artificial Ground Freezing (AGF) Method play an important role in the geotechnical engineering and the back analysis of thermal conductivity of frozen soil is the main inverse heat conduction problem of temperature field. In this paper the physical modelling test of AGF is carried out with double-row-pipe freezing in the lab. According to the measured temperature, the back analysis of thermal conductivity of frozen soil is solved based on the two-dimensional finite element simulation and the least square principle. It is helpful to investigate the freezing process and determine the frozen wall thickness.


1997 ◽  
Vol 119 (1) ◽  
pp. 38-45 ◽  
Author(s):  
A. M. Osman ◽  
K. J. Dowding ◽  
J. V. Beck

This paper presents a method for calculating the heat flux at the surface of a body from experimentally measured transient temperature data, which has been called the inverse heat conduction problem (IHCP). The analysis allows for two-dimensional heat flow in an arbitrarily shaped body and orthotropic temperature dependent thermal properties. A combined function specification and regularization method is used to solve the IHCP with a sequential-in-time concept used to improve the computational efficiency. To enhance the accuracy, the future information used in the sequential-in-time method and the regularization parameter are variable during the analysis. An example using numerically simulated data is presented to demonstrate the application of the method. Finally, a case using actual experimental data is presented. For this case, the boundary condition was experimentally measured and hence, it was known. A good comparison is demonstrated between the known and estimated boundary conditions for the analysis of the numerical, as well as the experimental data.


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