• Corpus ID: 124510602

Uncertainty analysis for computer simulations through validation and calibration

  title={Uncertainty analysis for computer simulations through validation and calibration},
  author={Sankaran Mahadevan and John M. McFarland},
Approaches to Describe and Quantify Uncertainty in Bio-physical Agricultural Computer Simulation Models
This thesis is for research or private study purposes only, and the author's right to be identified as the author of this thesis is recognized.
Uncertainty quantification and integration in engineering systems
Model Calibration for Fatigue Crack Growth Analysis under Uncertainty
A Bayesian methodology for model calibration applied to fatigue crack growth analysis of structures with complicated geometry and subjected to multi-axial variable amplitude loading conditions, using the concept of equivalent initial flaw size to replace small crack growth calculations and makes direct use of a long crack growth model.
A framework for the complex, but very practical problem of quantification of uncertainty in system-level model predictions is proposed based on Bayes networks and uses the available data at multiple levels of complexity (i.e., components, subsystem, etc.).
An efficient global reliability analysis method for turbine disc’s multi-failure modes considering importance distribution technology
An improved efficient global reliability analysis (EGRA) method for a turbine disc considering the importance distribution of multi-failure modes, such as low cycle fatigue (LCF) and creep-fatigue (CF) is presented.
Experimental Validation of the Adaptive Gaussian Process Regression Model Used for Prediction of Stress Intensity Factor as an Alternative to Finite Element Method
Currently, in the oil and gas industry, finite element method (FEM)-based commercial software (such as ANSYS and abaqus) is commonly employed for determining the stress intensity factor (SIF). In
Remaining Fatigue Life Prediction of Topside Piping Using Response Surface Models
  • A. Keprate, R. Ratnayake
  • Computer Science, Mathematics
    2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)
  • 2018
The paper examines the applicability of various RSMs, namely, multi-linear regression (MLR), polynomial regression (PR) (with interaction), Gaussian process regression (GPR), gradient boosting regression (GBR), and support vector regression (SVR), for estimating RFL and selected GPR as the best RSM for estimating the RFL of topside piping.
Sensitivity study of a full-scale industrial spray-injected fluidized bed reactor
Abstract The industrial fluidized bed reactor (FBR) described here is designed to convert an aqueous solid laden stream into a consistent granular product. The FBR is heated to 650 °C and chemically
A calibration procedure for increasing the accuracy of microscopic traffic simulation models
The results suggest that this approach offers a robust and effective method of calibrating simulation models where disaggregate-level vehicle data are available—which is becoming more prevalent with further advancements in mobile sensor and connected vehicle technologies.


Formulation of the thermal problem
This paper describes the thermal problem and presents the experimental data for validation. The thermal problem involves validating a model for heat conduction in a solid. The mathematical model is
Sandia National Laboratories Validation Workshop: Structural dynamics application☆
This article specifies a virtual structural dynamic subsystem and some systems, as well as mathematical models that approximately simulate them. The purpose is to define a setting for model
Bayesian analysis of computer code outputs: A tutorial
  • A. OHagan
  • Mathematics, Computer Science
    Reliab. Eng. Syst. Saf.
  • 2006
This tutorial is to introduce the more general reader to the Bayesian approach to quantifying, analysing and reducing uncertainty in the application of complex process models.
Probabilistic modeling of mechanical joints
  • Technical Report SAND2003-1456C, Sandia National Laboratories,
  • 2003
Methods of Multivariate Statistics
Abbreviations and Notations. Preface. Multivariate Methods: An Overview. Multivariate Normal Distributions. Outliers Detection and Normality Check. Inference on Location--Hotelling's T2. Repeated
Bayesian Calibration of computer models
We consider prediction and uncertainty analysis for systems which are approximated using complex mathematical models. Such models, implemented as computer codes, are often generic in the sense that
Bayesian Statistics: An Introduction
Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lees book appeared in 1989, but
Density Estimation for Statistics and Data Analysis
Maximum likelihood estimation of models for residual covariance in spatial regression
We describe the maximum likelihood method for fitting the linear model when residuals are correlated and when the covariance among the residuals is determined by a parametric model containing unknown
Verification and Validation in Computational Fluid Dynamics
This paper presents an extensive review of the literature in V and V in computational fluid dynamics (CFD), discusses methods and procedures for assessing V andV, and develops a number of extensions to existing ideas.