Abstract

Thermal diffusivity in an important thermophysical property that quantifies the ratio of the rate at which heat is conducted through a material to the amount of energy stored in a material. The pulsed laser diffusion (PLD) method is a widely used technique for measuring thermal diffusivities of materials. This technique is based on the fact that the diffusivity of a sample may be inferred from measurement of the time-dependent temperature profile at a point on the surface of a sample that has been exposed to a pulse of radiant energy from a laser or flash lamp. An accepted standard approach for the PLD method is based on a simple model of a PLD measurement system. However, the standard approach is based on idealizations that are difficult to achieve in practice. Therefore, models that treat a PLD measurement system with greater fidelity are desired. The objective of this research is to develop and test a higher fidelity model that more accurately represents the spatial and temporal variations in the input power. This higher fidelity model is referred to as Distributed Source Finite Absorption (DSFA) model. The cost of the increased fidelity associated with the DSFA model is an increase in the complexity of inferring values of the thermal diffusivity. A new method of extracting values from time dependent temperature measurements based on a genetic algorithm and on reduced order modeling was developed. The primary contribution of this thesis is a detailed discussion of the development and numerical verification of this proposed new method for measuring the thermal diffusivity of various materials. Verification of the proposed new method was conducted using numerical experiments. A detailed model of a PLD system was created using advanced engineering software, and detailed simulations, including conjugate heat transfer and solution of the full Navier-Stokes equations, were used to generate multiple numerical data sets. These numerical data sets were then used to infer the thermal diffusivity and other properties of the sample using the proposed new method. These numerical data sets were also used as inputs to the standard approach. The results of this verification study show that the proposed new method is able to infer the thermal diffusivity of samples to within 4.93%, the absorption coefficient to within 10.57 % and the heat capacity of the samples to within 5.37 %. Application of the standard approach to these same data sets gave much poorer estimates of the thermal diffusivity, particularly when the absorption coefficient of the material was relatively low.

Degree

MS

College and Department

Ira A. Fulton College of Engineering and Technology; Mechanical Engineering

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2012-11-27

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd5712

Keywords

heat transfer, thermal diffusivity, reduced order modeling, genetic algorithm

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