A computational model of cooperation dynamics: Sensitivity analysis

Abstract This work discusses the fundamentals of designing deep excavation support by means of observational method. The effective tools for optimum designing with the use of the observational method are both inclinometric and geodetic monitoring, which provide data for the systematically updated calibration of the numerical computational model. The analysis included methods for selecting data for the design (by choosing the basic random variables), as well as methods for an on-going verification of the results of numeric calculations (e.g., MES) by way of measuring the structure displacement using geodetic and inclinometric techniques. The presented example shows the sensitivity analysis of the calculation model for a cantilever wall in non-cohesive soil; that analysis makes it possible to select the data to be later subject to calibration. The paper presents the results of measurements of a sheet pile wall displacement, carried out by means of inclinometric method and, simultaneously, two geodetic methods, successively with the deepening of the excavation. This work includes also critical comments regarding the usefulness of the obtained data, as well as practical aspects of taking measurement in the conditions of on-going construction works.

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Sensitivity Analysis of Stress State of Welded Tanks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.769.3 ◽

2015 ◽

Vol 769 ◽

pp. 3-8

Author(s):

Zdenek Kala ◽

Jakub Gottvald ◽

Jakub Stonis ◽

Stanislav Vejvoda ◽

Abayomi Omishore

Keyword(s):

Sensitivity Analysis ◽

Stress State ◽

Hydrostatic Pressure ◽

Crude Oil ◽

Computational Model ◽

Sampling Method ◽

Latin Hypercube Sampling ◽

Load Case ◽

Cylindrical Tank ◽

Stochastic Sensitivity Analysis

This article focuses on the reliability analysis of a welded cylindrical tank, which is used for the storage of crude oil. The dominant load case is loading of the inner shell by hydrostatic pressure of the crude oil. Stochastic sensitivity analysis was used to study the effect of the variability of the thickness of the ith course on the stress state of adjacent courses. The computational model was developed in the programme ANSYS. Meshing was performed using SHELL181 elements. The Latin Hypercube Sampling method was implemented during analysis. Sensitivity analysis based on the evaluation of the correlation between the random plate thicknesses and the stress state was performed.

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Multi-scale Modeling and Sensitivity Analysis in Biological Systems

10.5642/cguetd/208 ◽

2021 ◽

Author(s):

◽

An Do Dela

Keyword(s):

Sensitivity Analysis ◽

Computational Model ◽

Type I Diabetes ◽

Model Fitting ◽

Type I ◽

Stochastic Fluctuations ◽

Highly Nonlinear ◽

Critical Issues ◽

The Face

We develop a multi-method sensitivity framework, which incorporates two variance-based methods, namely Sobol's method, eFAST and Derivative-based global measures to identify which parameters are most influential to the model outputs. A new implementation version of eFAST, namely DeFAST, was developed to address some critical issues in an existing published algorithm. Sensitivity analysis is a powerful tool in the modeling process that can be leveraged in various ways including model reduction and model fitting to data. There are two novel models that have been developed in this work where sensitivity analysis was applied. A stochastic computational model was constructed to understand mechanistic division event in Caulobacter crecentus bacterium in order to investigate how precise measurements can be made at the micron scale in the face of stochastic fluctuations. In this context, sensitivity analysis is used to derive a minimal PDE model in a minimal intermittent-search framework that could capture key results of the computational model closely. In addition, a new single compartment mathematical model for type I diabetes was analyzed to understand which parameters are the main driver of the blood glucose dynamics with the intention to understand the curative potential of dendritic-cell-based vaccine therapies. In this case, the sensitivity analysis was used to rank parameters and reduce the parameter space so that we can calibrate the model with in-vivo data in the future. The novelty of this work is that we validate our sensitivity analysis approach on highly nonlinear and stochastic models. These complex models present significant challenges for the application of sensitivity analysis algorithms as compared to the simpler case-study models that are typically used for testing sensitivity analysis methods.

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