Computational Methods for Creep Modeling
Creep is an important phenomenon present in the power generation and aerospace industry. Creep is a mechanism that degrades the quality of a part, such as the turbines used in different industries, when elevated temperatures and loads are applied. Creep damage can cause critical failures that can cost millions of dollars and as such, prevention is important. A multitude of creep models exist to help with design and failure prevention. These models are applied using computational tools which allows the engineer to properly predict the life of a part or other material properties. In this work various creep numerical optimization methods are explored. In the first study, minimum-creep-strain rate (MCSR) models are collected to create a “metamodel”. A metamodel is a model of models which in this study, is composed of 9 MCSR models. The MCSR is an important value for creep design as it tends to be the base of more complex models. Finding the best data fitting MCSR model is important as it will help build complex models properly by finding the corresponding MCSR based on needed stresses and temperatures. The metamodel can be regressed to the original MCSR models to rapidly and consistently fit any of the base component models; this is called the constrained metamodeling approach. The metamodel is also used with a pseudo-constrained approach, a metamodeling approach that aims to automatically find the best model autonomously while exploring new possible model forms. In the second study, the collected MCSR models are used to create “material specific” creep continuum damage mechanics-based constitutive models. Herein, material specific is defined as a constitutive model based on the mechanism-informed MCSR equations found in deformation mechanism maps and calibrated to available material data. The material specific models are created by finding the best MCSR model for a dataset. Once the best MCSR model is found, the Monkman Grant inverse relationship between the MCSR and rupture time is employed to derive a rupture equation. The equations are substituted into continuum damage mechanics-based creep strain rate and damage evolution equations to furnish predictions of creep deformation and damage. Material specific modeling allows for the derivation of creep constitutive models that are better tuned to the specific material of interest and available material data. The material specific framework is also advantageous since it has a systematic framework that moves from finding the best MCSR model, to rupture time, to damage evolution and, creep strain rate. In the final study, objective functions for creep constitutive models are evaluated and the best objective function for different categories of creep data are determined. A plethora of creep constitutive models have been developed to predict the stress-rupture, minimum-creep-strain-rate, creep deformation, and stress relaxation of materials. Sometimes these models can be calibrated analytically but oftentimes numerical optimization is required to generate the best predictions. In numerical optimization, the model, calibration data, optimization algorithm, objective function, and error tolerance influence the accuracy of predictions. The objective function is the function that compares the calibration data to model predictions and is either minimizing or maximizing to a desired error tolerance. In the creep modeling community, the objective function and error tolerance are rarely reported. Without this information, it is extremely difficult to reproduce research published by the community. In this study, twelve objective functions for creep are compiled. Four categories of creep data are collected. A detailed analysis is performed to determine the best objective function and error tolerance for each category of creep data.
Vega, Ricardo, "Computational Methods for Creep Modeling" (2020). ETD Collection for University of Texas, El Paso. AAI27999100.