Interval Identification of Structural Parameters Using Interval Deviation Degree and Monte Carlo Simulation

2019 ◽  
Vol 16 (07) ◽  
pp. 1850103 ◽  
Author(s):  
Zhaopu Guo ◽  
Zhongmin Deng
Keyword(s):  
Monte Carlo ◽  
Optimization Problems ◽  
Structural Parameters ◽  
Uncertainty Propagation ◽  
Interval Estimation ◽  
Interval Length ◽  
Modal Data ◽  
Structural Responses ◽  
Deviation Degree ◽  
Mc Simulation

This paper is devoted to the interval identification of structural parameters in the aspect of uncertainty propagation and uncertainty quantification. The accurate interval estimation of structural responses can be efficiently obtained by application of Monte Carlo (MC) simulation combined with surrogate models. By means of the concept of interval length, a novel quantitative metric named as interval deviation degree (IDD) is constructed to characterize the disagreement of interval distributions between analytical modal data and measured modal data. The nominal values and interval radii of the system parameters are well estimated by solving two optimization problems. Finally, numerical and experimental case studies are given to illustrate the feasibility of the proposed method in the interval identification of structural parameters.

Multilevel Monte Carlo by using the Halton sequence

2020 ◽  
Vol 26 (3) ◽  
pp. 193-203
Author(s):  
Shady Ahmed Nagy ◽  
Mohamed A. El-Beltagy ◽  
Mohamed Wafa
Keyword(s):  
Monte Carlo ◽  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Computational Cost ◽  
Random Numbers ◽  
Multilevel Monte Carlo ◽  
Halton Sequence ◽  
Random Generator ◽  
Mc Simulation ◽  
Multilevel Monte Carlo Method

AbstractMonte Carlo (MC) simulation depends on pseudo-random numbers. The generation of these numbers is examined in connection with the Brownian motion. We present the low discrepancy sequence known as Halton sequence that generates different stochastic samples in an equally distributed form. This will increase the convergence and accuracy using the generated different samples in the Multilevel Monte Carlo method (MLMC). We compare algorithms by using a pseudo-random generator and a random generator depending on a Halton sequence. The computational cost for different stochastic differential equations increases in a standard MC technique. It will be highly reduced using a Halton sequence, especially in multiplicative stochastic differential equations.

A Study on First Principle Calculation of Alloy Phase Diagram by Monte Carlo Simulation

1992 ◽  
Vol 291 ◽  
Author(s):  
Hideaki Sawada ◽  
Atsushi Nogami ◽  
Wataru Yamada ◽  
Tooru Matsiuniya
Keyword(s):  
Phase Diagram ◽  
Monte Carlo ◽  
Alloy Phase Diagram ◽  
Lattice Constant ◽  
Local Concentration ◽  
First Principle ◽  
Principle Calculation ◽  
First Principle Calculation ◽  
Local Lattice ◽  
Mc Simulation

ABSTRACTA method of first principle calculation of alloy phase diagram was developed by the combination of first principle energy band calculation, cluster expansion method (CEM) and Monte Carlo (MC) simulation, where the effective multi-body potential energy for the flip test in MC simulation was obtained by the decomposition of the total energy by CEM. This method was applied to Cu-Au binary system. The calculated phase diagram agreed with that of CVM by introducing the dependence of the lattice constant on the concentration of the whole system. Furthermore an attempt of introducing the effect of local lattice relaxation was performed by the consideration of the local concentration. The order-disorder transition temperature became closer to the experimental value by adjustment of the local lattice constant depending on the concentration in the local region consisted of up to the second nearest neighbors of the atom tested for flipping.

scholarly journals Benchmarking of Siemens Linac in Electron Modes: 8-14 MeV Electron Beams

2018 ◽  
Vol 8 (2) ◽  
Author(s):  
H Dowlatabadi ◽  
A A Mowlavi ◽  
M Ghorbani ◽  
S Mohammadi ◽  
F Akbari
Keyword(s):  
Monte Carlo ◽  
Radiation Treatment ◽  
Electron Beams ◽  
Dose Profile ◽  
Depth Dose ◽  
Mcnpx Code ◽  
Gamma Index ◽  
Simulation Results ◽  
Mc Simulation ◽  
Good Agreement

Introduction: Radiation therapy using electron beams is a promising method due to its physical dose distribution. Monte Carlo (MC) code is the best and most accurate technique for forespeaking the distribution of dose in radiation treatment of patients.Materials and Methods: We report an MC simulation of a linac head and depth dose on central axis, along with profile calculations. The purpose of the present research is to carefully analyze the application of MC methods for the calculation of dosimetric parameters for electron beams with energies of 8–14 MeV at a Siemens Primus linac. The principal components of the linac head were simulated using MCNPX code for different applicators. Results: The consequences of measurements and simulations revealed a good agreement. Gamma index values were below 1 for most points, for all energy values and all applicators in percent depth dose and dose profile computations. A number of states exhibited rather large gamma indices; these points were located at the tail of the percent depth dose graph; these points were less used in in radiotherapy. In the dose profile graph, gamma indices of most parts were below 1. The discrepancies between the simulation results and measurements in terms of Zmax, R90, R80 and R50 were insignificant. The results of Monte Carlo simulations showed a good agreement with the measurements. Conclusion: The software can be used for simulating electron modes of a Siemens Primus linac when direct experimental measurements are not feasible.

scholarly journals The Weibull Generalized Exponential Distribution with Censored Sample: Estimation and Application on Real Data

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hisham M. Almongy ◽  
Ehab M. Almetwally ◽  
Randa Alharbi ◽  
Dalia Alnagar ◽  
E. H. Hafez ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Exponential Distribution ◽  
Estimation Method ◽  
Interval Estimation ◽  
Likelihood Estimation ◽  
Real Data ◽  
Mcmc Methods ◽  
Generalized Exponential Distribution ◽  
Censored Sample ◽  
Sample Maximum

This paper is concerned with the estimation of the Weibull generalized exponential distribution (WGED) parameters based on the adaptive Type-II progressive (ATIIP) censored sample. Maximum likelihood estimation (MLE), maximum product spacing (MPS), and Bayesian estimation based on Markov chain Monte Carlo (MCMC) methods have been determined to find the best estimation method. The Monte Carlo simulation is used to compare the three methods of estimation based on the ATIIP-censored sample, and also, we made a bootstrap confidence interval estimation. We will analyze data related to the distribution about single carbon fiber and electrical data as real data cases to show how the schemes work in practice.

Investigation of field output factors using IAEA-AAPM TRS-483 code of practice recommendations and Monte Carlo simulation for 6 MV photon beams

2021 ◽  
pp. 1-6
Author(s):  
Sumalee Yabsantia ◽  
Sivalee Suriyapee ◽  
Nakorn Phaisangittisakul ◽  
Sornjarod Oonsiri ◽  
Taweap Sanghangthum ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Field Method ◽  
Correction Factors ◽  
Photon Beams ◽  
Small Field Dosimetry ◽  
Code Of Practice ◽  
Edge Detectors ◽  
Output Factors ◽  
Measured Field ◽  
Mc Simulation

Abstract Introduction: This study aims to experimentally determine field output factors using the methodologies suggested by the IAEA-AAPM TRS-483 for small field dosimetry and compare with the calculation from Monte Carlo (MC) simulation. Methods: The IBA-CC01, Sun Nuclear EDGE and IBA-SFD detectors were employed to determine the uncorrected and the corrected field output factors for 6 MV photon beams. Measurements were performed at 100 cm source to axis distance, 10 cm depth in water, and the field sizes ranged from 1 × 1 to 10 × 10 cm2. The use of field output correction factors proposed by the TRS-483 was utilised to determine field output factors. The measured field output factors were compared to that calculated using the egs_chamber user code. Results: The decrease in the percentage standard deviation of the measured three detectors was observed after applying the field output correction factors. Measured field output factors using CC01 and EDGE detectors agreed with MC values within 3% for field sizes down to 1 × 1 cm2, except the SFD detector. Conclusions: The corrected field output factors agree with the calculation from MC, except the SFD detector. CC01 and EDGE are suitable for determining field output factors, while the SFD may need more implementation of the intermediate field method.

The Case for Truly Integrated Cost and Schedule Risk Analysis

2017 ◽  
pp. 76-108
Author(s):  
Colin H. Cropley
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
Complex Projects ◽  
Schedule Risk ◽  
Project Risks ◽  
Mc Simulation ◽  
Integrated Cost ◽  
Schedule Risk Analysis ◽  
Relationship Of ◽  
Cost Outcomes

Time and cost outcomes of large and complex projects are forecast poorly across all sectors. Over recent years, Monte Carlo (MC) simulation has increasingly been adopted to forecast project time and cost outcomes more realistically. It is recognised that the simultaneous analysis of time and cost impacts makes sense as a modelling objective, due to the well-known relationship of time and money in projects. But most MC practitioners advocate the use of Schedule Risk Analysis (SRA) feeding into Cost Risk Analysis (CRA) because they believe it is too hard to perform Integrated Cost & Schedule Risk Analysis (IRA) realistically. This chapter elaborates an IRA methodology that produces realistic forecasts without relying on questionable assumptions and enables identification and ranking of all sources of cost uncertainty for risk optimisation as part of the process. It also describes an extension of IRA methodology to include assessment of the assets produced by the project as well as the project itself, thus enabling the analysis of business risks as well as project risks.

scholarly journals Variance and Dimension Reduction Monte Carlo Method for Pricing European Multi-Asset Options with Stochastic Volatilities

2019 ◽  
Vol 11 (3) ◽  
pp. 815 ◽  
Author(s):  
Yijuan Liang ◽  
Xiuchuan Xu
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Convergence Rates ◽  
Option Price ◽  
Correlation Coefficients ◽  
Control Variate ◽  
Stochastic Volatilities ◽  
Conditional Monte Carlo ◽  
Mc Simulation ◽  
Martingale Representation Theorem

Pricing multi-asset options has always been one of the key problems in financial engineering because of their high dimensionality and the low convergence rates of pricing algorithms. This paper studies a method to accelerate Monte Carlo (MC) simulations for pricing multi-asset options with stochastic volatilities. First, a conditional Monte Carlo (CMC) pricing formula is constructed to reduce the dimension and variance of the MC simulation. Then, an efficient martingale control variate (CV), based on the martingale representation theorem, is designed by selecting volatility parameters in the approximated option price for further variance reduction. Numerical tests illustrated the sensitivity of the CMC method to correlation coefficients and the effectiveness and robustness of our martingale CV method. The idea in this paper is also applicable for the valuation of other derivatives with stochastic volatility.

Damage Identification in Continuum Structures From Vibration Modal Data

1997 ◽  
Vol 503 ◽  
Author(s):  
H. P. Chen ◽  
N. Bicanic
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Structural Parameters ◽  
Computational Technique ◽  
Gauss Point ◽  
Continuum Structures ◽  
Modal Data ◽  
Stiffness Matrices ◽  
Direct Iteration ◽  
Vibration Modal

ABSTRACTA novel procedure for damage identification of continuum structures is proposed, where both the location and the extent of structural damage in continuum structures can be correctly determined using only a limited amount of measurements of incomplete modal data. On the basis of the exact relationship between the changes of structural parameters and modal parameters, a computational technique based on direct iteration and directly using incomplete modal data is developed to determine damage in structure. Structural damage is assumed to be associated ith a proportional (scalar) reduction of the original element stiffness matrices, equivalent to a scalar reduction of the material modulus, which characterises at Gauss point level. Finally, numerical examples for plane stress problem and plate bending problem are utilised to demonstrate the effectiveness of the proposed approach.

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