Modeling Uncertainty

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where Uf is the total uncertainity propagated through the model, df/dx is the model sensitivity in the equation prescribed above, N is the total number of parameters, and Uxi is the input parameter uncertainty. This parameter uncertainty term will have to depend on previous studies with respect to it's variance on the response your system. Conservatively, it can be assumed that a 5% parameter uncertainty can be used.  
 
where Uf is the total uncertainity propagated through the model, df/dx is the model sensitivity in the equation prescribed above, N is the total number of parameters, and Uxi is the input parameter uncertainty. This parameter uncertainty term will have to depend on previous studies with respect to it's variance on the response your system. Conservatively, it can be assumed that a 5% parameter uncertainty can be used.  
  
===Step 1===
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==Example==
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In this example, we will vary a single MEAM parameter and look at the influence with respect to the dislocation velocity of a single material, called "Material 1".
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 +
Material 1 MEAM Parameters:
 +
[[File:MEAMParameters.jpg]]
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 +
==Step 1==
 
Calibrate your MEAM potential with respect to Density Functional Theory. This requires the use of elastic constants from experiments or literature to calibrate your material to DFT.  
 
Calibrate your MEAM potential with respect to Density Functional Theory. This requires the use of elastic constants from experiments or literature to calibrate your material to DFT.  
  

Revision as of 10:55, 1 April 2017

Contents

Uncertainty

This topic has been taken by Student 1 for 2017 Contribution

Objective

This page will provide information on how to model uncertainty using the MEAM parameter calibration (MPC) tool and Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). On this page, the central finite difference approximation is used as an example to help users to understand how to model the uncertainty of the response of your system with respect to certain variables.

In this example, the "response" of our system will be the dislocation velocity determined from LAMMPS. Additionally, "variables" can be inputs that contribute to the response of your system. In this example, these variables will be will be the MEAM parameters that are used to input into LAMMPS.

Theory

The uncertainty of the response of your system can be approximated using a one-factor-at-atime perturbation methodology. This method uses the central difference approximation to estimate the sensitivity of your response with respect to input variables. This sensitivity can be expressed as:

SensitivityEqn.jpg

where f() is the model function, Xi is the model input parameter, X0,i is the base value of a parameter, +/-i is the perturbation size around the base parameter, and DeltaXi is the difference between the perturbed input parameters. The perturbation size typically assumes a +/-1% perturbed factor. The uncertainty based on the sensitivity of an input can be determined from the following equation:

UncertaintyEqn.jpg

where Uf is the total uncertainity propagated through the model, df/dx is the model sensitivity in the equation prescribed above, N is the total number of parameters, and Uxi is the input parameter uncertainty. This parameter uncertainty term will have to depend on previous studies with respect to it's variance on the response your system. Conservatively, it can be assumed that a 5% parameter uncertainty can be used.

Example

In this example, we will vary a single MEAM parameter and look at the influence with respect to the dislocation velocity of a single material, called "Material 1".

Material 1 MEAM Parameters: MEAMParameters.jpg


Step 1

Calibrate your MEAM potential with respect to Density Functional Theory. This requires the use of elastic constants from experiments or literature to calibrate your material to DFT.

Step 2

References

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