IMPERFECTION SENSITIVITY AND RELIABILITY USING SIMPLE BAR-SPRING MODELS FOR STABILITY

2013 ◽  
Vol 13 (03) ◽  
pp. 1250075 ◽  
Author(s):  
VAHID ZEINODDINI MEIMAND ◽  
LORI GRAHAM-BRADY ◽  
BENJAMIN WILLIAM SCHAFER
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Ultimate Strength ◽  
Structural Reliability ◽  
Plastic Hinge ◽  
Continuous Systems ◽  
Plastic Collapse ◽  
Imperfection Sensitivity ◽  
Element Analysis ◽  
First Order

The objective of this paper is to demonstrate how simple bar-spring models can illustrate elementary and advanced structural behavior, including stability, imperfection sensitivity, and plastic collapse. In addition, the same bar-spring models also provide a ready means for assessing structural reliability. Bar-spring models for a column (both post-buckling stable and unstable), a frame, and a plate are all developed. For each model the influence of geometric imperfections are explicitly introduced and the ultimate strength considering plastic collapse of the supporting springs derived. The developed expressions are compared to material and geometric nonlinear finite element analysis models of analogous continuous systems, using yield surface based plastic hinge beam elements (in MASTAN) for the column and frame and shell elements (in ABAQUS) for the plate. The results show excellent qualitative agreement, and surprisingly good quantitative agreement. The developed bar-spring models are used in Monte Carlo simulations and in the development of first order Taylor Series approximations to provide the statistics of the ultimate strength as used in structural reliability calculations. Good agreement between conventional first order second moment assumptions and the Monte Carlo simulations of the bar-spring models is demonstrated. It is intended that the developed models provide a useful illustration of basic concepts central to structural stability and structural reliability.

An Equilibrium Prediction Method for Control and Fault Detection of Energy Systems

2021 ◽  
Author(s):  
Austin Rogers ◽  
Fangzhou Guo ◽  
Bryan Rasmussen
Keyword(s):  
Monte Carlo ◽  
Fault Detection ◽  
Monte Carlo Simulations ◽  
Case Studies ◽  
Air Conditioning ◽  
Prediction Method ◽  
Worker Safety ◽  
Static System ◽  
Control Logic ◽  
First Order

Abstract Many fault detection, optimization, and control logic methods rely on sensor feedback that assumes the system is operating at steady state conditions, despite persistent transient disturbances. While filtering and signal processing techniques can eliminate some transient effects, this paper proposes an equilibrium prediction method for first order dynamic systems using an exponential regression. This method is particularly valuable for many commercial and industrial energy system, whose dynamics are dominated by first order thermo-fluid effects. To illustrate the basic advantages of the proposed approach, Monte Carlo simulations are used. This is followed by three distinct experimental case studies to demonstrate the practical efficacy of the proposed method. First, the ability to predict the carbon dioxide level in classrooms allows for energy efficient control of the ventilation system and ensures occupant comfort. Second, predicting the optimal time to end the cool-down of an industrial sintering furnace allows for maximum part throughput and worker safety. Finally, fault detection and diagnosis methods for air conditioning systems typically use static system models; however, the transient response of many air conditioning signals may be approximated as first order, and therefore, the prediction model enables the use of static fault detection methods with transient data. In this paper, the equilibrium prediction method's performance will be quantified using both Monte Carlo simulations and case studies.

Improving the first-order structural reliability estimation by Monte Carlo simulation

2017 ◽  
Vol 170 (7) ◽  
pp. 532-540 ◽  
Author(s):  
Mohsen Ali Shayanfar ◽  
Mohammad Ali Barkhordari ◽  
Moien Barkhori ◽  
Mohammad Barkhori
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Structural Reliability ◽  
Reliability Estimation ◽  
First Order

scholarly journals Deterministic Sensitivity Analysis for First-Order Monte Carlo Simulations: A Technical Note

2009 ◽  
Vol 12 (1) ◽  
pp. 96-97 ◽  
Author(s):  
Benjamin P. Geisler ◽  
Uwe Siebert ◽  
G. Scott Gazelle ◽  
David J. Cohen ◽  
Alexander Göhler
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Technical Note ◽  
First Order ◽  
Deterministic Sensitivity Analysis

Surface and size effects on the Ferroelectric Phase Transition and Surface Polarization of Thin Films: Monte Carlo Simulations

1997 ◽  
Vol 493 ◽  
Author(s):  
J. Romero ◽  
L. F. Fonseca
Keyword(s):  
Thin Films ◽  
Phase Transition ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Ferroelectric Phase Transition ◽  
Ferroelectric Phase ◽  
First Order ◽  
Surface Polarization ◽  
Ferroelectric Phase Transition Temperature ◽  
Model Hamiltonian

ABSTRACTThe macroscopic polarization of ferroelectric thin films was studied by Monte Carlo simulations using a Transverse Ising Model Hamiltonian with four-spins interactions. The dependence of the ferroelectric phase transition temperature, Tc, on the thickness of the film was obtained resulting in a shifting of Tc towards lower temperatures and a change from first-order to second-order phase transition as the thickness of the film is reduced. Comparison between the surface and internal order was carried out by the calculation of layer-averaged polarizations as a function of the sample temperature and the surface interaction parameters. These comparisons show that increasing disorder at the surface can be reverted by increasing the four-spins surface interactions.

Sign in / Sign up

Export Citation Format

Share Document