Abstract
Preferential pathway flows can form as the result of physical-chemical interactions between soils and chemically aggressive permeants. Preferential pathway flow models have been developed to predict increases in the permeability of hydraulic barriers due to aggressive permeant interactions. The parameters of these models are difficult to determine experimentally. Equation nonlinearities, the number of model coefficients, parameter constraints and randomness of experimental data make the inverse problem of parameter estimation quite challenging. A modified Levenberg-Marquardt algorithm in a Monte Carlo multi-start implementation (Ravi and Jennings, 1990) has been developed into a software package to resolve these difficulties. This package helps users calibrate preferential flow models using transient permeability data. The package, Parameter Estimation Algorithm for Preferential Pathway Models (PEAPPM), has been designed with graphing and contour plotting capabilities to help user improve the calibration process. The package also offers statistical analysis of parameter estimation results.
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