Usage

To use RESSPyLab in a project:

import RESSPyLab

The current version includes primarily functions to perform material parameter calibration of the Voce and Chaboche (VC) metal plasticity model.

There are five main useful functions in the library

def NTR_SVD_Solver(f,gradF,Hf,x_0):
        # Newton-Trust Region solver with SVD preconditioning
        # f - function taking an array "x" of n floats as an argument and returning the real value of f
        # gradF - function taking an array "x" of n floats as an argument and returning the gradient of f, an array sized n
        # Hf - function taking an array "x" of n floats as an argument and returning Hessian of f, array sized n by n
        # x_0 - initial starting point; array sized n
        # x_min - local minimum of f; array sized n
        #...
        return x_min

def NTR_J_Solver(f,gradF,Hf,x_0):
        # Newton-Trust Region solver with Jacobi preconditioning
        # f - function taking an array "x" of n floats as an argument and returning the real value of f
        # gradF - function taking an array "x" of n floats as an argument and returning the gradient of f, an array sized n
        # Hf - function taking an array "x" of n floats as an argument and returning Hessian of f, array sized n by n
        # x_0 - initial starting point; array sized n
        # x_min - local minimum of f; array sized n
        #...
        return x_min

def VCcurve(x,test):
        # x - set of parameters for the Voce and Chaboche material model
        # The order in x should be the following:
        # x=[E, sy0, Qinf, b, C_1, gamma_1, C_2, gamma_2, ..., ..., C_k, gamma_k]
        #
        # test is a pandas dataframe with columns named 'e_true', for the true strain and 'Sigma_true', for the true stress
        # simCurve is a pandas dataframe containing the simulated curve with the VC model. Integration is conducted with the discretization in "test"
        #...
        return simCurve

def VCopt_SVD(x_0,listTests):
        # This function performs parameter optimization of the VC model for an ensemble of experimental data with SVD preconditioning.
        #
        # listTests is a python list containg pandas dataframes of multiple "test"
        return x_min

def VCopt_J(x_0,listTests):
        # This function performs parameter optimization of the VC model for an ensemble of experimental data with Jacobi preconditioning.
        #
        # listTests is a python list containg pandas dataframes of multiple "test"
        return x_min

A working example using a Jupyter notebook, along its data files can be found on gitHub.