scholarly journals Effects of material uncertainties on vibration performance of cross laminated timber floors

2021 ◽  
Vol 64 (3) ◽  
pp. 153-157
Author(s):  
Marija Milojević ◽  
Emilija Damnjanović ◽  
Marija Nefovska-Danilović ◽  
Miroslav Marjanović
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Wood Species ◽  
Material Properties ◽  
Random Variables ◽  
Weight Ratio ◽  
Cross Laminated Timber ◽  
Vibration Serviceability ◽  
Material Uncertainties ◽  
Vibration Performance

Variety of wood species and complexity of their structure make the reliable material properties of cross-laminated timber (CLT) difficult to obtain, which can lead to inaccurate prediction of CLT behavior. Due to high stiffness-to-weight ratio, CLT floors can suffer from vibration serviceability issues. This paper aims to quantify the uncertainties induced by material properties and investigate their effect on vibration performance of CLT floors. Analysis based on Monte Carlo simulations, considering material properties as random variables, is developed. Based on the conducted analysis, appropriate conclusions have been derived.

scholarly journals Benefits of on-wafer calibration standards fabricated in membrane technology

2011 ◽  
Vol 9 ◽  
pp. 19-26
Author(s):  
M. Rohland ◽  
U. Arz ◽  
S. Büttgenbach
Keyword(s):  
Thin Film ◽  
Monte Carlo ◽  
Electrical Properties ◽  
Monte Carlo Simulations ◽  
Material Properties ◽  
Membrane Technology ◽  
Coplanar Waveguides ◽  
Calibration Standards ◽  
Electromagnetic Waveguide ◽  
Propagation Of Uncertainties

Abstract. In this work we compare on-wafer calibration standards fabricated in membrane technology with standards built in conventional thin-film technology. We perform this comparison by investigating the propagation of uncertainties in the geometry and material properties to the broadband electrical properties of the standards. For coplanar waveguides used as line standards the analysis based on Monte Carlo simulations demonstrates an up to tenfold reduction in uncertainty depending on the electromagnetic waveguide property we look at.

Orientational phase behavior of polymer-grafted nanocubes

Nanoscale ◽  
2019 ◽  
Vol 11 (34) ◽  
pp. 15939-15957 ◽  
Author(s):  
Brian Hyun-jong Lee ◽  
Gaurav Arya
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Phase Behavior ◽  
Material Properties ◽  
Scaling Relations

Monte Carlo simulations and analytical scaling relations are used to identify the stable interparticle configurations (phases) exhibited by polymer-grafted nanocubes and to study their phase behavior as a function of material properties.

Reliability Optimization Design of Gear Box

2011 ◽  
Vol 284-286 ◽  
pp. 2509-2512
Author(s):  
Wen Hui Mo
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Material Properties ◽  
Optimization Design ◽  
Random Variables ◽  
Reliability Optimization ◽  
Objective Functions ◽  
Feasible Method ◽  
Reliability Calculation ◽  
Gear Box

Geometry parameters, material properties and applied loads of the gear box are regarded as normal random variables. A model of reliability optimization design of the gear box is introduced. Two objective functions are selected. The Monte Carlo simulation of reliability calculation is presented. With rapid increasing of the speed of CPU, it is a feasible method. The optimization effect is very good.

scholarly journals Analysis of approximations of GUM supplement 2 based non-Gaussian PDFs of measurement models with Rosenblatt Gaussian transformation mappings

2020 ◽  
Vol 11 ◽  
pp. 2
Author(s):  
Vishal Ramnath
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Random Variables ◽  
Measurement Model ◽  
Linear Measurement ◽  
Statistical Simulation ◽  
Implicit Assumption ◽  
Measurement Models ◽  
Gaussian System ◽  
Non Gaussian

In scientific metrology practise the application of Monte Carlo simulations with the aid of the GUM Supplement 2 (GS2) technique for performing multivariate uncertainty analyses is now more prevalent, however a key remaining challenge for metrologists in many laboratories is the implicit assumption of Gaussian characteristics for summarizing and analysing measurement model results. Whilst non-Gaussian probability density functions (PDFs) may result from Monte Carlo simulations when the GS2 is applied for more complex non-linear measurement models, in practice results are typically only reported in terms of multivariate expected and covariance values. Due to this limitation the measurement model PDF summary is implicitly restricted to a multivariate Gaussian PDF in the absence of additional higher order statistics (HOS) information. In this paper an earlier classical theoretical result by Rosenblatt that allows for an arbitrary multivariate joint distribution function to be transformed into an equivalent system of Gaussian distributions with mapped variables is revisited. Numerical simulations are performed in order to analyse and compare the accuracy of the equivalent Gaussian system of mapped random variables for approximating a measurement model’s PDF with that of an exact non-Gaussian PDF that is obtained with a GS2 Monte Carlo statistical simulation. Results obtained from the investigation indicate that a Rosenblatt transformation offers a convenient mechanism to utilize just the joint PDF obtained from the GS2 data in order to both sample points from a non-Gaussian distribution, and also in addition which allows for a simple two-dimensional approach to estimate coupled uncertainties of random variables residing in higher dimensions using conditional densities without the need for determining parametric based copulas.

UNCERTAINTY QUANTIFICATION IN STOCHASTIC SYSTEMS USING POLYNOMIAL CHAOS EXPANSION

2010 ◽  
Vol 02 (02) ◽  
pp. 305-353 ◽  
Author(s):  
K. SEPAHVAND ◽  
S. MARBURG ◽  
H.-J. HARDTKE
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Uncertainty Quantification ◽  
Stochastic Systems ◽  
Polynomial Chaos ◽  
Random Variables ◽  
Polynomial Chaos Expansion ◽  
Uncertain Parameters ◽  
Chaos Expansion ◽  
Generalized Polynomial

In recent years, extensive research has been reported about a method which is called the generalized polynomial chaos expansion. In contrast to the sampling methods, e.g., Monte Carlo simulations, polynomial chaos expansion is a nonsampling method which represents the uncertain quantities as an expansion including the decomposition of deterministic coefficients and random orthogonal bases. The generalized polynomial chaos expansion uses more orthogonal polynomials as the expansion bases in various random spaces which are not necessarily Gaussian. A general review of uncertainty quantification methods, the theory, the construction method, and various convergence criteria of the polynomial chaos expansion are presented. We apply it to identify the uncertain parameters with predefined probability density functions. The new concepts of optimal and nonoptimal expansions are defined and it demonstrated how we can develop these expansions for random variables belonging to the various random spaces. The calculation of the polynomial coefficients for uncertain parameters by using various procedures, e.g., Galerkin projection, collocation method, and moment method is presented. A comprehensive error and accuracy analysis of the polynomial chaos method is discussed for various random variables and random processes and results are compared with the exact solution or/and Monte Carlo simulations. The method is employed for the basic stochastic differential equation and, as practical application, to solve the stochastic modal analysis of the microsensor quartz fork. We emphasize the accuracy in results and time efficiency of this nonsampling procedure for uncertainty quantification of stochastic systems in comparison with sampling techniques, e.g., Monte Carlo simulation.

Effect of the Randomness of the Relative Humidity on the Phenomenon of Carbonation of the Reinforced Concrete

2011 ◽  
Vol 340 ◽  
pp. 181-183 ◽  
Author(s):  
A. Badaoui ◽  
M. Badaoui ◽  
F. Kharchi
Keyword(s):  
Monte Carlo ◽  
Reinforced Concrete ◽  
Relative Humidity ◽  
Probability Distribution ◽  
Monte Carlo Simulations ◽  
Probabilistic Analysis ◽  
Random Variables ◽  
Carbonation Depth ◽  
Lognormal Probability

This paper deals with shows the randomness effect of the relative humidity on the carbonation phenomenon of the reinforced concrete. This analysis concentrates on the evaluation of carbonation depth (Xc) of the reinforced concrete from a probabilistic analysis. Monte Carlo simulations are realized under the assumption that the relative humidity at the surface of the concrete is random variables with a lognormal probability distribution.

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