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Development of Advanced Methods for Analysis of Experimental Data in Diffusion
AuthorJaques, Alonso Victor
AdvisorLaCombe, Jeffrey C.
Mining and Metallurgical Engineering
StatisticsView Usage Statistics
There are numerous experimental configurations and data analysis techniques for the characterization of diffusion phenomena. However, the mathematical methods for estimating diffusivities traditionally do not take into account the effects of experimental errors in the data, and often require smooth, noiseless data sets to perform the necessary analysis steps. The current methods used for data smoothing require strong assumptions which can introduce numerical “artifacts” into the data, affecting confidence in the estimated parameters. The Boltzmann-Matano method is used extensively in the determination of concentration - dependent diffusivities, D(C), in alloys. In the course of analyzing experimental data, numerical integrations and differentiations of the concentration profile are performed. These methods require smoothing of the data prior to analysis. We present here an approach to the Boltzmann-Matano method that is based on a regularization method to estimate a differentiation operation on the data, i.e., estimate the concentration gradient term, which is important in the analysis process for determining the diffusivity. This approach, therefore, has the potential to be less subjective, and in numerical simulations shows an increased accuracy in the estimated diffusion coefficients. We present a regression approach to estimate linear multicomponent diffusion coefficients that eliminates the need pre-treat or pre-condition the concentration profile. This approach fits the data to a functional form of the mathematical expression for the concentration profile, and allows us to determine the diffusivity matrix directly from the fitted parameters. Reformulation of the equation for the analytical solution is done in order to reduce the size of the problem and accelerate the convergence. The objective function for the regression can incorporate point estimations for error in the concentration, improving the statistical confidence in the estimated diffusivity matrix. Case studies are presented to demonstrate the reliability and the stability of the method. To the best of our knowledge there is no published analysis of the effects of experimental errors on the reliability of the estimates for the diffusivities. For the case of linear multicomponent diffusion, we analyze the effects of the instrument analytical spot size, positioning uncertainty, and concentration uncertainty on the resulting values of the diffusivities. These effects are studied using Monte Carlo method on simulated experimental data. Several useful scaling relationships were identified which allow more rigorous and quantitative estimates of the errors in the measured data, and are valuable for experimental design. To further analyze anomalous diffusion processes, where traditional diffusional transport equations do not hold, we explore the use of fractional calculus in analytically representing these processes is proposed. We use the fractional calculus approach for anomalous diffusion processes occurring through a finite plane sheet with one face held at a fixed concentration, the other held at zero, and the initial concentration within the sheet equal to zero. This problem is related to cases in nature where diffusion is enhanced relative to the classical process, and the order of differentiation is not necessarily a second–order differential equation. That is, differentiation is of fractional order α, where 1 ≤ α < 2. For α = 2, the presented solutions reduce to the classical second-order diffusion solution for the conditions studied. The solution obtained allows the analysis of permeation experiments. Frequently, hydrogen diffusion is analyzed using electrochemical permeation methods using the traditional, Fickianbased theory. Experimental evidence shows the latter analytical approach is not always appropiate, because reported data shows qualitative (and quantitative) deviation from its theoretical scaling predictions. Preliminary analysis of data shows better agreement with fractional diffusion analysis when compared to traditional square-root scaling. Although there is a large amount of work in the estimation of the diffusivity from experimental data, reported studies typically present only the analytical description for the diffusivity, without scattering. However, because these studies do not consider effects produced by instrument analysis, their direct applicability is limited. We propose alternatives to address these, and to evaluate their influence on the final resulting diffusivity values.
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