Parameter Study of Asymptotic Sampling for Truss Structures Reliability

2014 ◽  
Vol 969 ◽  
pp. 288-293 ◽  
Author(s):  
Adéla Hlobilová ◽  
Matěj Lepš
Keyword(s):  
Sampling Method ◽  
Optimal Parameter ◽  
Latin Hypercube Sampling ◽  
High Sensitivity ◽  
Truss Structures ◽  
Parameter Study ◽  
Asymptotic Results ◽  
New Techniques ◽  
Simulation Techniques ◽  
Asymptotic Sampling

Sampling methods for predicting a reliability index such as Monte Carlo or Latin Hypercube Sampling are very time consuming thus advanced simulation techniques are frequently used. The asymptotic sampling is one of new techniques based on asymptotic results from reliability theory supported with a simple regression. Since the high sensitivity of the asymptotic sampling method to the control parameters has been reported, this contribution is focused on a study of the optimal parameter setting. The well-known truss structure is introduced and results with different parameter settings are presented.

Parameter Study on Subset Simulation for Reliability Assessment

2017 ◽  
Vol 1144 ◽  
pp. 128-135
Author(s):  
Adéla Hlobilová ◽  
Matěj Lepš
Keyword(s):  
Monte Carlo ◽  
Reliability Assessment ◽  
Latin Hypercube Sampling ◽  
Structural Safety ◽  
Parameter Study ◽  
Subset Simulation ◽  
Mathematical Functions ◽  
New Techniques ◽  
Failure Event ◽  
Truss Bridge

Small probability of failure characterizes a good structural design. Prediction of such a structural safety is time consuming considering that sampling methods such as Monte Carlo method or Latin Hypercube sampling are used. Therefore, more specialized methods are developed. A Subset simulation is one of the new techniques based on modifying the failure event as an intersection of nested intermediate events that are easier to solve. This paper deals with a parameter study of the Subset simulation with modified Metropolis algorithm for Markov chain Monte Carlo using distinct proposal distributions. Different setting is then compared on reliability assessment benchmarks, namely on two mathematical functions with different failure probabilities and on a 23-bar planar truss bridge.

scholarly journals Stochastic Risk and Uncertainty Analysis for Shale Gas Extraction in the Karoo Basin of South Africa

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Abdon Atangana ◽  
Gerrit van Tonder
Keyword(s):  
South Africa ◽  
Mass Transport ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Transport Equations ◽  
Upward Migration ◽  
Karoo Basin ◽  
Fracking Fluid ◽  
Stochastic Risk ◽  
Flow And Mass Transport

We made use of groundwater flow and mass transport equations to investigate the crucial potential risk of water pollution from hydraulic fracturing especially in the case of the Karoo system in South Africa. This paper shows that the upward migration of fluids will depend on the apertures of the cement cracks and fractures in the rock formation. The greater the apertures, the quicker the movement of the fluid. We presented a novel sampling method, which is the combination of the Monte Carlo and the Latin hypercube sampling. The method was used for uncertainties analysis of the apertures in the groundwater and mass transport equations. The study reveals that, in the case of the Karoo, fracking will only be successful if and only if the upward methane and fracking fluid migration can be controlled, for example, by plugging the entire fracked reservoir with cement.

Optimization of the OOP Children Restraint System

2012 ◽  
Vol 155-156 ◽  
pp. 386-390
Author(s):  
Zhong Hao Bai ◽  
Jing Fei ◽  
Wei Jie Ma
Keyword(s):  
Genetic Algorithm ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Optimization Method ◽  
Approximate Model ◽  
Parameters Optimization ◽  
Fe Model ◽  
Restraint System ◽  
Effective Protection ◽  
Multi Body

Based on the study of SAE J1980-2008 and FMVSS 208, MADYMO7.1 is used to establish a Multi-body and FE model for two OOP children, and the statistic test is implemented to verify the accuracy of the model. The airbag parameters impacting OOP children greatly and their ranges are selected to determine the objective function. With the Latin Hypercube Sampling method, the Kring approximate model is constructed, and multi-island genetic algorithm is used in subsequently parameters optimization. The results show that the proposed optimization method can provide effective protection for 6-year-old OOP children.

SAMS: Stochastic Analysis With Minimal Sampling—A Fast Algorithm for Analysis and Design Under Uncertainty

2004 ◽  
Vol 127 (4) ◽  
pp. 558-571 ◽  
Author(s):  
A. Mawardi ◽  
R. Pitchumani
Keyword(s):  
System Performance ◽  
Stochastic Analysis ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Analysis And Design ◽  
Process Outcomes ◽  
Input Parameters ◽  
Computationally Intensive ◽  
Uncertain Input ◽  
Reliability And Robustness

Design of processes and devices under uncertainty calls for stochastic analysis of the effects of uncertain input parameters on the system performance and process outcomes. The stochastic analysis is often carried out based on sampling from the uncertain input parameters space, and using a physical model of the system to generate distributions of the outcomes. In many engineering applications, a large number of samples—on the order of thousands or more—is needed for an accurate convergence of the output distributions, which renders a stochastic analysis computationally intensive. Toward addressing the computational challenge, this article presents a methodology of S̱tochastic A̱nalysis with M̱inimal S̱ampling (SAMS). The SAMS approach is based on approximating an output distribution by an analytical function, whose parameters are estimated using a few samples, constituting an orthogonal Taguchi array, from the input distributions. The analytical output distributions are, in turn, used to extract the reliability and robustness measures of the system. The methodology is applied to stochastic analysis of a composite materials manufacturing process under uncertainty, and the results are shown to compare closely to those from a Latin hypercube sampling method. The SAMS technique is also demonstrated to yield computational savings of up to 90% relative to the sampling-based method.

Uncertainty Quantification of Turbulence Model Coefficients via Latin Hypercube Sampling Method

2010 ◽  
Author(s):  
Matthew C. Dunn ◽  
Babak Shotorban ◽  
Abdelkader Frendi
Keyword(s):  
Uncertainty Quantification ◽  
Turbulent Flows ◽  
Turbulence Model ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Recirculation Region ◽  
Free Shear Layer ◽  
Latin Hypercube ◽  
Reattachment Point ◽  
Turbulent Dissipation

This paper is concerned with the propagation of uncertainties in the values of turbulence model coefficients and parameters in turbulent flows. These coefficients and parameters are determined from experiments performed on elementary flows and they are subject to uncertainty. The widely used k–ε turbulence model is considered. It consists of model transport equations for the turbulence kinetic energy and rate of turbulent dissipation. Both equations involve various model coefficients about which adequate knowledge is assumed known in the form of probability density functions. The study is carried out for the flow over a 2D backward-facing step configuration. The Latin Hypercube Sampling method is employed for the uncertainty quantification purposes as it requires a smaller number of samples compared to the conventional Monte-Carlo method. The mean values are reported for the flow output parameters of interest along with their associated uncertainties. The results show that model coefficient variability has significant effects on the streamwise velocity component in the recirculation region near the reattachment point and turbulence intensity along the free shear layer. The reattachment point location, pressure, and wall shear are also significantly affected.

Development of Statistical Core Thermal Design Methodology Using a Modified Latin Hypercube Sampling Method

1991 ◽  
Vol 94 (3) ◽  
pp. 407-415 ◽  
Author(s):  
Seung-Hyuk Lee ◽  
Hyun-Koon Kim ◽  
Sang-Ryeol Park ◽  
Soon-Heung Chang
Keyword(s):  
Design Methodology ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Thermal Design ◽  
Latin Hypercube

scholarly journals Design and Evaluation of LYSO/SiPM LIGHTENING PET Detector with DTI Sampling Method

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5820
Author(s):  
Zhenzhou Deng ◽  
Yushan Deng ◽  
Guandong Chen
Keyword(s):  
High Performance ◽  
Sampling Method ◽  
Average Energy ◽  
High Sensitivity ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Time Interval ◽  
Silicon Photomultiplier ◽  
Experimental Conditions ◽  
Wide Range

Positron emission tomography (PET) has a wide range of applications in the treatment and prevention of major diseases owing to its high sensitivity and excellent resolution. However, there is still much room for optimization in the readout circuit and fast pulse sampling to further improve the performance of the PET scanner. In this work, a LIGHTENING® PET detector using a 13 × 13 lutetium-yttrium oxyorthosilicate (LYSO) crystal array read out by a 6 × 6 silicon photomultiplier (SiPM) array was developed. A novel sampling method, referred to as the dual time interval (DTI) method, is therefore proposed to realize digital acquisition of fast scintillation pulse. A semi-cut light guide was designed, which greatly improves the resolution of the edge region of the crystal array. The obtained flood histogram shown that all the 13 × 13 crystal pixels can be clearly discriminated. The optimum operating conditions for the detector were obtained by comparing the flood histogram quality under different experimental conditions. An average energy resolution (FWHM) of 14.3% and coincidence timing resolution (FWHM) of 972 ps were measured. The experimental results demonstrated that the LIGHTENING® PET detector achieves extremely high resolution which is suitable for the development of a high performance time-of-flight PET scanner.

A Probabilistic Safety Assessment Method Based on Latin Hypercube Sampling Method

2015 ◽  
Vol 757 ◽  
pp. 159-163
Author(s):  
Ying Juan Yue ◽  
Fei Chen ◽  
Hong Li ◽  
Hai Xia Du ◽  
Xiao Jun Du
Keyword(s):  
Change Process ◽  
Safety Assessment ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Assessment Method ◽  
Probabilistic Assessment ◽  
Latin Hypercube ◽  
Probabilistic Safety Assessment ◽  
Failure Assessment ◽  
Probabilistic Safety

Based on the shortcomings of traditional probabilistic assessment methods, an improved probabilistic safety assessment method was proposed, which used Latin hypercube sampling, considered the change process about fatigue crack propagation, as well as the effect of random variables on the failure assessment curve. The paper also analyzed the specific example with this method. The results showed that this method was simpler and more effective, which had some value of applications in engineering.

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