A Method for Characterizing the Properties of Industrial Foams A Method for Characterizing the Properties of Industrial Foams
A conjugate-gradient algorithm was used to minimize the difference between simulated Monte Carlo measurements and diffusion theory predicted measurements. A large set of simulated measurements, calculated at various source-detector separations and three discrete frequencies were used to predict the layer properties. Ten blind cases were considered and property predictions were made for each. The predicted properties were within approximately 10% of the actual values, and on average the errors were approximately 4%. Predictions of the reduced scattering coefficient were all within approximately 5% with the majority being within 3%. Predictions of L were all within approximately 10% with the majority being within 7%. Attempts to separate g from the reduced scattering coefficient were unsuccessful, and it was determined that implementation of different source models might make such attempts possible.
It was shown that with a large number of measurements, properties could be accurately predicted. A method for reducing the number of measurements needed for accurate property estimation was developed. Starting with a single measurement location, property predictions were made. An approach for updating the optimal detector location, based on the current estimate of the properties, was developed and applied to three cases. Property predictions for the three cases were made to within 10% of the actual values. A maximum of three measurement locations were necessary to obtain such predictions, a significant reduction as compared to the previously illustrated method.
School:Brigham Young University
School Location:USA - Utah
Source Type:Master's Thesis
Keywords:spectroscopy frequency domain time diffusion theory inverse problems conjugate gradient monte carlo foam layers
Date of Publication:08/03/2005