Uncertainty Assessment using Monte Carlo Simulation in Net Thrust Measurement at AETF

The objective is to develop an approach for the determination of the target reliability index for serviceability limit state (SLS) of single piles. This contributes to conducting the SLS reliability-based design (RBD) of piles. Based on a two-parameter, hyperbolic curve-fitting equation describing the load-settlement relation of piles, the SLS model factor is defined. Then, taking into account the uncertainties of load-settlement model, load and bearing capacity of piles, the formula for computing the SLS reliability index (βsls) is obtained using the mean value first order second moment (MVFOSM) method. Meanwhile, the limit state function for conducting the SLS reliability analysis by the Monte Carlo simulation (MCS) method is established. These two methods are finally applied to determine the SLS target reliability index. Herein, the limiting tolerable settlement (slt) is treated as a random variable. For illustration, four load test databases from South Africa are compiled again to conduct reliability analysis and present the recommended target reliability indices. The results indicate that the MVFOSM method overestimates βsls compared to that computed by the MCS method. Besides, both factor of safety (FS) and slt are key factors influencing βsls, so the combination of FS and βsls is welcome to be used for the SLS reliability analysis of piles when slt is determined. For smaller slt, pile types and soils conditions have significant influence on the SLS target reliability indices; for larger slt, slt is the major factor having influence on the SLS target reliability indices. This proves that slt is the most key parameter for the determination of the SLS target reliability index.

Monte Carlo (MC) simulation methods are widely used to solve complex engineering and scientific problems. Unlike other deterministic methods, MC methods use statistical sampling to produce approximate solutions. As the processed sample size N growths, the uncertainty of the solution is reduced. It is well known that the mean-square approximation error decreases as 1/N. However, for large problems like high-dimensional integrations and computationally intensive simulations, MC methods may take months or even years to obtain a solution with acceptable tolerance. The Super-Efficient (SE) Monte Carlo simulation method, originated by Umeno, produces a solution whose approximation error decreases as fast as 1/N2. However, it only applies to a small class of problems possessing certain properties. We describe an approximate SE Monte Carlo simulation method that is applicable to a wider class of problems than the original SE method, and yields a convergence rate as fast as 1/Na for 1<a<2.

This paper deals with the reliability assessment of electrical power supply systems under redundancy. For doing so, three probabilistic safety analysis (PSA) methods are proposed and developed namely fault tree analysis (FTA), reliability block diagram (RBD), and Monte Carlo Simulation (MC) method. The FTA method is performed using RiskSpectrumPSA® software and the RBD and MC methods are coded using Matlab software. The electrical power supply system reliability is analyzed and enhanced considering various cases of redundancy. The FTA method is based on qualitative and quantitative reliability assessment. The quantitative assessment calculates the failure probability of the top event, and the qualitative approach determines the minimal cut sets (MCS). However, the RBD and MC methods are proposed to compute the system failure probability. The considered methods are applied on three case studies and the obtained results are compared to each other. The obtained results show that the three methods provide almost the same results. Furthermore, the reliability of the electrical power supply is clearly enhanced with the number of redundant components.

Evaluation of concepts of fin efficiency using Monte Carlo Simulation method

This article aims to investigate the means to obtain optimal hot stamping process parameters and the influence of the stochastic variability of these parameters on forming quality. A multi-objective stochastic approach, integrating response surface methodology (RSM), multi-objective genetic algorithm optimization non-dominated sorting genetic algorithm II (NSGA-II) and the Monte Carlo simulation (MCS) method is proposed in this article to achieve this goal. RSM was used to establish the relationship between the process parameters and forming quality indices. NSGA-II was utilized to obtain a Pareto frontier, which consists of a series of optimal process parameters. The MCS method was employed to study and reduce the influence of a stochastic property of these process parameters on forming quality. The results confirmed the efficiency of the proposed multi-objective stochastic approach during optimization of the hot stamping process. Robust optimal process parameters guaranteeing good forming quality were also obtained using this approach.

Harmonics originated by non‐linear elements connected to power systems cause unwanted harmful technical problems. In this study a novel probabilistic harmonic load flow based on a point estimate method has been proposed. The proposed method surpasses the previous point estimate methods (PEMs) when generalising the approach to multiple random variables with various probabilistic distributions. Addressing the results of the Monte Carlo simulation (MCS) method as reference, the fast PEM has been applied to a modified version of the IEEE 14 bus test system and the advantages of this new method are highlighted. The results reveal less computational burden and time compared with the MCS method while the accuracy remains at a high level.

Uncertainty estimation and Monte Carlo simulation method

The aim of the research is to implement Markov Chain Monte Carlo (MCMC) simulation method to price the futures contract of cocoa commodities. The result shows that MCMC is more flexible than Standard Monte Carlo (SMC) simulation method because MCMC method uses hit-and-run sampler algorithm to generate proposal movements that are subsequently accepted or rejected with a probability that depends on the distribution of the target that we want to be achieved. This research shows that MCMC method is suitable to be used to simulate the model of cocoa commodity price movement. The result of this research is a simulation of future contract prices for the next three months and future contract prices that must be paid at the time the contract expires. Pricing future contract by using MCMC method will produce the cheaper contract price if it compares to Standard Monte Carlo simulation.

Monte Carlo uncertainty assessment of ultrasonic beam parameters from immersion transducers used to non-destructive testing

Blackbody (BB) cavities as standard radiation sources are used in calibration for various infrared (IR) instruments in IR detection, imaging, material emissivity measuring etc. A surface pitted with cell-like mini cavities as honeycombs whose radiant characteristics is evaluated by Monte Carlo method (MCM) is applied to develop a new surface BB. To improve the precision of MCM, make it more reliable, three requisite factors are considered. First, set up correct physical model in which the radiation of a surface comes from light emitting points across the surface consistently. Second, the uniformity of random number generators are investigated, which is the precondition for the MCM simulation. Third, to perform simulating with tremendous sampling number, after all light points and their rays are tracked, the statistical results become stable, fluctuate slightly, which increase the exactitude of MCM. To implement MCM simulation, every ray of the IR lights is tracked, the position of a light point, the direction of a ray, the state that if the ray is absorbed or reflected at a reflecting point etc. are all regarded as random variables which are sampled by random number according to their probability distribution. The effective emissivity of the surface is evaluated statistically via tracking numerous rays which involves setting up the ray equation, and coordinates transformation etc. The relationships between the accuracy of computation results and simulating model, sampling method, uniformity of pseudo-random number, and sampling number have been discussed. The results show the surface with honeycombs can provide a consistent radiation near to be a diffuse emitter even the material of the surface including spectral components, and the MCM simulating results provide the criteria for surface BB design.

Monte Carlo Simulation and Response Surface Method are two very powerful reliability analysis methods. Normally, in the reliability analysis of complex structures, the limit state function often can not be expressed in a closed-form. Usually, the codes for probabilistic analysis need to be combined with finite element models. ANSYS Probabilistic Design System (PDS) has provided a package to conduct probabilistic analysis automatically. This paper is going to compare the performance of these methods through an easy engineering problem in ANSYS. The results are going to be derived to show the feature of applying the corresponding reliability methods.

A combined Monte Carlo (MC) simulation and Genetic Algorithm (GA) method was proposed by other researchers for the optimisation of spare parts allocation. From case studies, it was found that the number of simulation trials of the existing method tended to be either excessive or inadequate. Thus, a simulation replication number control method making full use of the advance simulation effort is proposed and implemented into the existing method. A numerical example shows significant improvement on overall simulation efficiency and that at the same time the required accuracy is guaranteed. Furthermore, it is argued that application-specific knowledge should be embedded into the general GA procedure so that the evolution process can be more efficient. Heuristic methods for initial population preparation for GA with and without considering component cost difference are proposed and illustrated for spare parts allocation. A computing experiment was designed and performed to examine the influence of parameters for replication number control and initial population preparation. The generation of availability–cost curve further indicates the necessity to adopt heuristic methods to improve searching efficiency in GA.

This paper applies the stochastic finite element method (SFEM) to perform the natural frequency analysis of functionally graded material (FGM). It is assumed that the elastic modulus and width of the FGM beam vary along the thickness and width directions following exponential functions. The stochastic eigenvalue problem is solved independently by first-order perturbation and Monte Carlo simulation (MCS) method through changing elastic modulus as spatial randomness. The results show that the first-order perturbation method based SFEM produces a very close value to MCS method.

, ^ Introduction. Uncertainty-analysis methods 5 Monte Carlo simulation 10 First-order error analysis 10 Uncertainty analysis of the simulations of effects of discharging treated wastewater during ice-cover conditions 11 Specific conductance ^ Total ammonia 14 Dissolved oxygen 16 Uncertainty analysis of the simulations of effects of discharging treated wastewater during ice-free conditions 18 Specific conductance ; ^ Total ammonia 21 Dissolved oxygen 23 Summary and conclusions 24 References ^^ FIGURES 1. Map showing location of study subreach on the Red River of the North and data-collection sites from Fargo, North Dakota, to Georgetown, Minnesota 3 TABLES 1. Site number, description, and location of data-collection sites 4 2. Coefficients of variation for input variables for the Red River at Fargo Water-Quality Model for ice-cover and ice-free conditions 6 3. Selected statistics for specific-conductance values simulated with the Red River at Fargo Ice-Cover Water-Quality Model and with the Monte Carlo simulation method, calculated with the first-order error analysis method, and measured during ice-cover conditions, February 23-24,1995, on the Red River of the North at Fargo, North Dakota, and Moorhead, Minnesota 12 4. Selected statistics for total ammonia concentrations simulated with the Red River at Fargo Ice-Cover Water-Quality Model and with the Monte Carlo simulation method, calculated with the first-order error analysis method, and measured during ice-cover conditions, February 23-24,1995, on the Red River of the North at Fargo, North Dakota, and Moorhead, Minnesota 13 5. Selected statistics for dissolved-oxygen concentrations simulated with the Red River at Fargo Ice-Cover Water-Quality Model and with the Monte Carlo simulation method, calculated with the first-order error analysis method, and measured during ice-cover conditions, February 23-24,1995, on the Red River of the North at Fargo, North Dakota, and Moorhead, Minnesota 14 6. Results of first-order error analysis of specific-conductance values simulated with the Red River at Fargo Ice-Cover Water-Quality Model for data collected February 23-24, 1995, and the Red River at Fargo Water-Quality Model for data collected August 29-30, 1989 ^

It has been reported that SiC/SiC composite has high strength and toughness, but is degraded when exposed in air at high temperatures due to the propagation of the crack made by the premature fracture of the SiO2 layer. The present work aimed to describe such a behavior with a computer simulation. For this aim, the shear lag - Monte Carlo simulation method was applied. The variation of strength of the composite as a function of thickness of the SiO2 layer and change of fracture morphology with progressing oxidation could be reproduced satisfactorily by this method.