Method for Estimating Failure Probability of Mechanical Structures Using Number Theoretical Net (NT-net) Simulation

2009 ◽  
Vol 628-629 ◽  
pp. 239-244
Author(s):  
Z.J. Wen ◽  
De Shun Liu ◽  
Shu Yi Yang
Keyword(s):  
Failure Probability ◽  
Simulation Method ◽  
Computational Effort ◽  
Side Impact ◽  
Computational Accuracy ◽  
Simulation Techniques ◽  
Net Method ◽  
Good Lattice ◽  
Good Lattice Point ◽  
Point Set

According to poor computational accuracy at small to median sample sizes of Monte Carlo ( MC ) simulation techniques in estimating the probability failure of mechanical structures, the number theoretical net ( NT-net ) simulation method is proposed to reduce computing effort. Several key concepts, such as good point set, good-lattice point ( glp ), discrepancy and NT-net method, are defined. The sampling stategy is improved by introducing NT-net that can provide better convergent rate over MC. The new method is used to estimate failure probability of the side impact bar on the car door. Results indicate the computational effort needed by NT-net for the same accuracy is about 1/12 of that needed by the MC-based method, and the obtained results are more stable.

Application of a Particle Filter-Based Subset Simulation Method to the Reliability Assessment of a Marine Structure

2012 ◽  
Author(s):  
Zakoua Guédé ◽  
Alexandru Tantar ◽  
Emilia Tantar ◽  
Pierre Del Moral
Keyword(s):  
Particle Filter ◽  
Failure Probability ◽  
Branching Process ◽  
Simulation Method ◽  
Classical Particle ◽  
Filter Method ◽  
Subset Simulation ◽  
Simulation Techniques ◽  
Marine Structure ◽  
Best Parameter

The present study aims at investigating advanced subset simulation techniques, which are based on the theory of particle filter, for the assessment of the failure probability of a marine structure under extreme loading conditions. Three approaches are considered, namely the classical particle filter method, the subset simulation with a branching process and one using the minimum values of the samples as levels. They are, first, intensively applied on a simple example for which a known analytical solution is available, in order to investigate their parameter settings. Then, they are applied, with good performance, using their respective best parameter settings, to the assessment of failure probability of a FPSO subjected to extreme roll motion.

Evaluation Method and Application of Assembly Yield in Two-Dimension Multi-Station Assembly Processes Based on Number-Theoretical Net

2012 ◽  
Vol 522 ◽  
pp. 921-926
Author(s):  
Ze Jun Wen ◽  
Zheng Qiang Zhu ◽  
Yan Ming Zhao ◽  
Fan Zhang
Keyword(s):  
Evaluation Method ◽  
State Space Model ◽  
Total Sample ◽  
Two Dimension ◽  
Automotive Body ◽  
Variation Propagation ◽  
Net Method ◽  
Good Lattice ◽  
Good Lattice Point ◽  
Component Assembly

Method for calculating assembly yield in two-dimension multi-station assembly processes is developed based on Number-Theoretical Net (NT-net). The discrepancy of NT-net is analyzed, and the principle of generating good lattice point (glp) based on NT-net method is introducted. Afterwards, taking fixture locating variations which are sampled using NT-net method for input vectors, the samples are substituted into state space model of dimension variation propagation in multi-station assembly processes to get output vectors. The statistics for qualified sample is accomplished, after comparing output vectors with the variations of measuring points on component. Assembly yield in two-dimension multi-station assembly processes is gained when qualified sample divided by total sample. Finally, a real case in automotive body floor assembly is given as an example to calculate the assembly yield in two-dimension three-station assembly processes. The result is validated by using Monte carlo simulation. It provides a new way to predict assembly yield in two-dimension multi-station assembly processes.

Efficient Simulation Method to Predict Green Water Loads on Superstructures

2015 ◽  
Author(s):  
Lasse Theilen ◽  
Ole Detlefsen ◽  
Moustafa Abdel-Maksoud ◽  
Michael Bohm
Keyword(s):  
Simulation Method ◽  
Computational Effort ◽  
Numerical Prediction ◽  
Green Water ◽  
Impact Loads ◽  
Operational Conditions ◽  
Efficient Simulation ◽  
Non Linear ◽  
Linear Behaviour

The numerical prediction of green water loads on super-structures is challenging due to the high number of required calculations to identify the critical operational conditions in the seaway which lead to overcoming seawater on deck. Further, the simulation of the non-linear behaviour of water on the deck and the prediction of impact loads require high computational effort. This paper presents an efficient three-step approach to simulate green water loads. The application of the developed procedure will be demonstrated on a mega yacht geometry.

scholarly journals Parametric Sensitivity Analysis for Importance Measure on Failure Probability and Its Efficient Kriging Solution

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Yishang Zhang ◽  
Yongshou Liu ◽  
Xufeng Yang
Keyword(s):  
Sensitivity Analysis ◽  
Failure Probability ◽  
Computational Effort ◽  
Importance Measure ◽  
Sensitivity Index ◽  
Reliability Engineering ◽  
Parametric Sensitivity ◽  
Distribution Parameters ◽  
The Moment ◽  
Sensitivity Indicator

The moment-independent importance measure (IM) on the failure probability is important in system reliability engineering, and it is always influenced by the distribution parameters of inputs. For the purpose of identifying the influential distribution parameters, the parametric sensitivity of IM on the failure probability based on local and global sensitivity analysis technology is proposed. Then the definitions of the parametric sensitivities of IM on the failure probability are given, and their computational formulae are derived. The parametric sensitivity finds out how the IM can be changed by varying the distribution parameters, which provides an important reference to improve or modify the reliability properties. When the sensitivity indicator is larger, the basic distribution parameter becomes more important to the IM. Meanwhile, for the issue that the computational effort of the IM and its parametric sensitivity is usually too expensive, an active learning Kriging (ALK) solution is established in this study. Two numerical examples and two engineering examples are examined to demonstrate the significance of the proposed parametric sensitivity index, as well as the efficiency and precision of the calculation method.

The X-bar control chart with restriction of the capability indices

2017 ◽  
Vol 34 (1) ◽  
pp. 38-52 ◽  
Author(s):  
Pedro Carlos Oprime ◽  
Glauco Henrique de Sousa Mendes
Keyword(s):  
Phase I ◽  
Control Chart ◽  
Design Methodology ◽  
Simulation Method ◽  
Operating Conditions ◽  
Suggested Approach ◽  
Content Type ◽  
Capability Index ◽  
Simulation Techniques ◽  
Capability Indices

Purpose The purpose of this paper is to find the configuration of the number (m) and size (n) of the sample in Phase I that would make it possible to detect the out-of-control (OOC) state of the process with the smallest number of samples and ensure a capability index (Cpk) that would meet the customer’s requirements. Design/methodology/approach The suggested approach addresses this problem using simulation techniques and design of experiments (DOE). The simulation techniques made it possible to reproduce the normal operating conditions of the process. The DOE was used to construct a predictive model for control chart performance and thus to determine combinations of m and n in Phase I that would meet the capability objectives of the process. A numerical example and a simulation study were conducted to illustrate the proposed method. Findings Using simulation techniques and DOE, the authors can find the number (m) and size (n) of the sample in Phase I that would make it possible to detect the OOC state of the process with the smallest number of samples and ensure a Cpk that would meet the customer’s requirements. Originality/value In the real situations of many companies, choosing the numbers and sizes of samples (m and n) in Phases I and II is a crucial decision in relation to implementing a control chart. The paper shows that the simulation method and use of linear regression are effective alternatives because they are better known and more easily applied in industrial settings. Therefore, the need for alternatives to the X control chart comes into play.

scholarly journals Theoretical Prediction of Mechanical Strength and Desalination Performance of One-Atom-Thick Hydrocarbon Polymer in Pressure-Driven Separation

Polymers ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1358 ◽  
Author(s):  
Shuangqing Sun ◽  
Fei Shan ◽  
Qiang Lyu ◽  
Chunling Li ◽  
Songqing Hu
Keyword(s):  
Mechanical Strength ◽  
Simulation Method ◽  
Hydraulic Pressure ◽  
Gas Purification ◽  
Simulation Techniques ◽  
Separation Membrane ◽  
Hydrocarbon Polymers ◽  
Hydrocarbon Polymer ◽  
Mechanical Strengths ◽  
Pressure Driven

One-atom-thick materials hold promise for the future of membrane-based gas purification and water filtration applications. However, there are a few investigations on the mechanical properties of these materials under pressure-driven condition. Here, by employing molecular simulation techniques and continuum mechanics simulation, we investigate the mechanical strength of two-dimensional hydrocarbon polymers containing sub-nanometer pores with various topologies. We demonstrate that the mechanical strengths of the membranes are correlated with their pore sizes and geometries. In addition, when the pore size of substrates is controlled within a reasonable range, all of the membrane candidates can withstand the practical hydraulic pressure of few megapascal. The studied materials also exhibit better seawater desalination performance as compared to the traditional polymeric reverse osmosis membrane. This work presents a new route to design new separation membrane, and also propose a simulation method to evaluate the mechanical strength and desalination performance.

scholarly journals Modification Research of the Internal Substructure Method for Seismic Wave Input in Deep Underground Structure-Soil Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Xin Bao ◽  
Jingbo Liu ◽  
Dongyang Wang ◽  
Shutao Li ◽  
Fei Wang ◽  
...  
Keyword(s):  
Free Surface ◽  
Seismic Wave ◽  
Soil Structure ◽  
Underground Structure ◽  
Computational Effort ◽  
Seismic Loads ◽  
Underground Structures ◽  
Computational Accuracy ◽  
Substructure Method ◽  
Deep Underground

A new internal substructure method for seismic wave input in soil-structure systems was recently proposed. This method simplifies the calculation of equivalent input seismic loads and avoids the participation of artificial boundaries in the process of seismic wave input. However, in previous research and applications, the internal substructures are usually intercepted down from the free surface, which forms large substructures and increases the computational effort for data management on the substructure nodes, especially for deep underground structures. In this study, the internal substructure method is modified by intercepting the internal substructures entirely beneath the free surface and adjacently around the underground structures. Then, the equivalent input seismic loads are obtained through the dynamic analysis of the internal substructures and applied to the corresponding positions of the total soil-structure models. Thus, the earthquake energy can be more efficiently input into the region near the underground structures without losing computational accuracy. We provide the detailed implementation procedures of this modified method and validate its applicability and accuracy through the scattered problems of underground cavities in homogeneous and layered half-space sites.

Toward Unified Hybrid Simulation Techniques for Spiking Neural Networks

2014 ◽  
Vol 26 (6) ◽  
pp. 1055-1079 ◽  
Author(s):  
Michiel D'Haene ◽  
Michiel Hermans ◽  
Benjamin Schrauwen
Keyword(s):  
Neural Network ◽  
Hybrid Approach ◽  
Simulation Method ◽  
Time Step ◽  
Simulation Techniques ◽  
Simulation Engine ◽  
Weak Points ◽  
The Common ◽  
Network Simulators ◽  
The Ideal

In the field of neural network simulation techniques, the common conception is that spiking neural network simulators can be divided in two categories: time-step-based and event-driven methods. In this letter, we look at state-of-the art simulation techniques in both categories and show that a clear distinction between both methods is increasingly difficult to define. In an attempt to improve the weak points of each simulation method, ideas of the alternative method are, sometimes unknowingly, incorporated in the simulation engine. Clearly the ideal simulation method is a mix of both methods. We formulate the key properties of such an efficient and generally applicable hybrid approach.

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