src.domains.Verification module

This module is responsible for the model verification process.

It includes one class for the model verification. See details in its own documentation.

Contact person: Stefan Riedmaier Creation date: 20.04.2020 Python version: 3.8

class src.domains.Verification.Verification(config)

Bases: object

gci_uncertainty(discretization_error_da)

This functions calculates the numerical discretization uncertainty based on the Grid Convergence Index (GCI).

The theory can be found in [1, Ch. 8.6].

Literature: [1] W. L. Oberkampf and C. J. Roy, Verification and Validation in Scientific Computing, Cambridge, Cambridge University Press, 2010, ISBN: 9780511760396.

Param:

xr.DataArray: array of discretization errors

Returns:

array of numerical discretization uncertainties

Return type:

xr.DataArray

process()

This method runs through each step of the model verification process.

It contains the following steps: 1.1) Generation of verification scenarios for the simulation model. 1.2) Execution of the generated scenarios. 1.3) Assessment of the model responses in the executed scenarios. 2) Richardson extrapolation to determine the discretization error. 3) Application of the Grid Convergence Index to convert the error to a numerical uncertainty.

The interfaces are based on xarrays, recognizable by the “_da”-endings of the variables.

Returns:

array of numerical uncertainties

Return type:

xr.DataArray

static richardson_extrapolation(scenarios_model_da, qois_kpi_model_da)

This function calculates the discretization error of a simulation model via Richardson Extrapolation.

It requires simulations with three step sizes of equal ratio.

The theory can be found, e.g., in [1, Sec. 2.1.1] and [2, Ch. 8.4.2.1].

Literature: [1] S. Sankararaman and S. Mahadevan, „Integration of model verification, validation, and calibration for uncertainty quantification in engineering systems,“ Reliability Engineering & System Safety, vol. 138, pp. 194–209, 2015. [2] W. L. Oberkampf and C. J. Roy, Verification and Validation in Scientific Computing, Cambridge, Cambridge University Press, 2010, ISBN: 9780511760396.

Parameters:

• scenarios_model_da (xr.DataArray) – array with input scenarios

• qois_kpi_model_da (xr.DataArray) – array with output kpis

Returns:

array of numerical discretization errors

Return type:

xr.DataArray