Correlation of Steel Yield Stress and Ultimate Strength - Monte Carlo Simulation via Non-Gaussian Random Variables

2017 ◽  
Vol 893 ◽  
pp. 223-228 ◽  
Author(s):  
Petr Konečný
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Yield Stress ◽  
Ultimate Strength ◽  
Joint Distribution ◽  
Assessment Method ◽  
Joint Distributions ◽  
Steel Strength ◽  
Simulation Based ◽  
Non Gaussian

This paper describes a Monte Carlo simulation of the correlated steel characteristics of yield stress and ultimate strength of steel S235 grade from Northern Moravia region in the Czech Republic. Their joint distribution is described by a correlation index and frequency histograms. The paper step-by-step describes simulation process of the transformation of a correlated Gaussian joint distribution to a general joint distribution, because the yield stress as well as ultimate steel strength random parameters do not follow a Gaussian distribution. Their marginal distribution can be easily described by a suitable parametric distribution or frequency histogram suitable for use with the Simulation-based Reliability Assessment method (SBRA). Describing joint distributions of non-Gaussian processes is overcome by application of fractile correlation.

scholarly journals Voronoi diagram and Monte-Carlo simulation based finite element optimization for cost-effective 3D printing

2021 ◽  
Vol 50 ◽  
pp. 101301
Author(s):  
A.Z. Zheng ◽  
S.J. Bian ◽  
E. Chaudhry ◽  
J. Chang ◽  
H. Haron ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Finite Element ◽  
Monte Carlo ◽  
3D Printing ◽  
Voronoi Diagram ◽  
Cost Effective ◽  
Simulation Based

Monte Carlo Simulation of Texture and Microstructure Transformation during Annealing of Steel

2007 ◽  
Vol 129 ◽  
pp. 83-87
Author(s):  
Hua Long Li ◽  
Jong Tae Park ◽  
Jerzy A. Szpunar
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Grain Boundary ◽  
Computer Model ◽  
Graphical Representation ◽  
Structural Characteristics ◽  
Polycrystalline Materials ◽  
Virtual Experiments ◽  
Grain Boundary Character ◽  
Simulation Based

Controlling texture and microstructure evolution during annealing processes is very important for optimizing properties of steels. Theories used to explain annealing processes are complicated and always case dependent. An recently developed Monte Carlo simulation based model offers an effective tool for studying annealing process and can be used to verify the arbitrarily defined theories that govern such processes. The computer model takes Orientation Image Microscope (OIM) measurements as an input. The abundant information contained in OIM measurement allows the computer model to incorporate many structural characteristics of polycrystalline materials such as, texture, grain boundary character, grain shape and size, phase composition, chemical composition, stored elastic energy, and the residual stress. The outputs include various texture functions, grain boundary and grain size statistics that can be verified by experimental results. Graphical representation allows us to perform virtual experiments to monitor each step of the structural transformation. An example of applying this simulation to Si steel is given.

Structural Modal Multifurcation With Internal Resonance: Part 2—Stochastic Approach

1993 ◽  
Vol 115 (2) ◽  
pp. 193-201 ◽  
Author(s):  
R. A. Ibrahim ◽  
B. H. Lee ◽  
A. A. Afaneh
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Axial Load ◽  
Random Excitation ◽  
Stochastic Bifurcation ◽  
Density Level ◽  
Analytical Treatment ◽  
Asymmetric Mode ◽  
Non Gaussian ◽  
Gaussian Closure

Stochastic bifurcation in moments of a clamped-clamped beam response to a wide band random excitation is investigated analytically, numerically, and experimentally. The nonlinear response is represented by the first three normal modes. The response statistics are examined in the neighborhood of a critical static axial load where the normal mode frequencies are commensurable. The analytical treatment includes Gaussian and non-Gaussian closures. The Gaussian closure fails to predict bifurcation of asymmetric modes. Both non-Gaussian closure and numerical simulation yield bifurcation boundaries in terms of the axial load, excitation spectral density level, and damping ratios. The results of both methods are in good agreement only for symmetric response characteristics. In the neighborhood of the critical bifurcation parameter the Monte Carlo simulation yields strong nonstationary mean square response for the asymmetric mode which is not directly excited. Experimental and Monte Carlo simulation exhibit nonlinear features including a shift of the resonance peak in the response spectra as the excitation level increases. The observed shift is associated with a widening effect in the response bandwidth.

scholarly journals Fault Risk Assessment of Underwater Vehicle Steering System Based on Virtual Prototyping and Monte Carlo Simulation

2016 ◽  
Vol 23 (3) ◽  
pp. 97-105
Author(s):  
Deyu He ◽  
Niaoqing Hu ◽  
Lei Hu ◽  
Ling Chen ◽  
YiPing Guo ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Fault Injection ◽  
Fault Simulation ◽  
Virtual Prototyping ◽  
Underwater Vehicle ◽  
Steering System ◽  
Assessment Method ◽  
Risk Indicator

Abstract Assessing the risks of steering system faults in underwater vehicles is a human-machine-environment (HME) systematic safety field that studies faults in the steering system itself, the driver’s human reliability (HR) and various environmental conditions. This paper proposed a fault risk assessment method for an underwater vehicle steering system based on virtual prototyping and Monte Carlo simulation. A virtual steering system prototype was established and validated to rectify a lack of historic fault data. Fault injection and simulation were conducted to acquire fault simulation data. A Monte Carlo simulation was adopted that integrated randomness due to the human operator and environment. Randomness and uncertainty of the human, machine and environment were integrated in the method to obtain a probabilistic risk indicator. To verify the proposed method, a case of stuck rudder fault (SRF) risk assessment was studied. This method may provide a novel solution for fault risk assessment of a vehicle or other general HME system.

scholarly journals A Novel Monte-Carlo Simulation-Based Model for Malware Detection (eRBCM)

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2881
Author(s):  
Muath Alrammal ◽  
Munir Naveed ◽  
Georgios Tsaramirsis
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Information Systems ◽  
Malware Detection ◽  
New Model ◽  
Malicious Activity ◽  
Model Based ◽  
Simulation Based ◽  
Computer Based ◽  
Reinforcement Model

The use of innovative and sophisticated malware definitions poses a serious threat to computer-based information systems. Such malware is adaptive to the existing security solutions and often works without detection. Once malware completes its malicious activity, it self-destructs and leaves no obvious signature for detection and forensic purposes. The detection of such sophisticated malware is very challenging and a non-trivial task because of the malware’s new patterns of exploiting vulnerabilities. Any security solutions require an equal level of sophistication to counter such attacks. In this paper, a novel reinforcement model based on Monte-Carlo simulation called eRBCM is explored to develop a security solution that can detect new and sophisticated network malware definitions. The new model is trained on several kinds of malware and can generalize the malware detection functionality. The model is evaluated using a benchmark set of malware. The results prove that eRBCM can identify a variety of malware with immense accuracy.

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