scholarly journals Applying Monte Carlo Simulation in New Tech

2020 ◽  
Author(s):  
Levent Yilmaz
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Analysis Tool ◽  
Hydrogen Energy ◽  
Energy Potential ◽  
Highly Nonlinear ◽  
Statistical Analysis Tool ◽  
Engineering Models ◽  
Complex Engineering ◽  
Physical Phenomena

Monte Carlo in Monaco is given to the theory for mathematics, whose simulation process involves generating chance variables and exhibiting random behaviours in nature. This simulation is a powerful statistical analysis tool and widely used in both non-engineering fields and engineering fields for new perspectives. This simulation has been applied to diverse problems ranging from the simulation of complex physical phenomena such as atom collisions, to the simulation of river boundary layers as meanders and Dow Jones forecasting. It can deal with many random variables, various distribution types and highly nonlinear engineering models, while Monte Carlo is also suitable for solving complex engineering problems in two areas which are varying randomly. Monte Carlo simulation is given as an application for hydrogen energy potential determination.

Comparison of Assembly Tolerance Analysis by the Direct Linearization and Modified Monte Carlo Simulation Methods

1995 ◽  
Author(s):  
Jinsong Gao ◽  
Kenneth W. Chase ◽  
Spencer P. Magleby
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Closed Loop ◽  
Great Influence ◽  
Tolerance Analysis ◽  
Linearization Method ◽  
Direct Linearization ◽  
Highly Nonlinear ◽  
Assembly Features ◽  
Assembly Constraints

Abstract Two methods for performing statistical tolerance analysis of mechanical assemblies are compared: the Direct Linearization Method (DLM), and Monte Carlo simulation. A selection of 2-D and 3-D vector models of assemblies were analyzed, including problems with closed loop assembly constraints. Closed vector loops describe the small kinematic adjustments that occur at assembly time. Open loops describe critical clearances or other assembly features. The DLM uses linearized assembly constraints and matrix algebra to estimate the variations of the assembly or kinematic variables, and to predict assembly rejects. A modified Monte Carlo simulation, employing an iterative technique for closed loop assemblies, was applied to the same problem set. The results of the comparison show that the DLM is accurate if the tolerances are relatively small compared to the nominal dimensions of the components, and the assembly functions are not highly nonlinear. Sample size is shown to have great influence on the accuracy of Monte Carlo simulation.

scholarly journals Construction Diversion Risk Assessment for Hydropower Development on Sediment-Rich Rivers

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 938
Author(s):  
Zida Song ◽  
Quan Liu ◽  
Zhigen Hu ◽  
Chunsheng Zhang ◽  
Jinming Ren ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Assessment Method ◽  
Energy Potential ◽  
Hydropower Development ◽  
Development Environment ◽  
Construction Diversion ◽  
The World ◽  
Overtopping Probability

Hydropower is an important renewable energy, and Construction Diversion Risk (CDR) should be highlighted and assessed during hydropower development. Since sediment-rich rivers are widely existing around the world and have great hydro-energy potential, assessing CDR for hydropower development on sediment-rich rivers in terms of engineering feasibility is of significance. This paper proposes a CDR assessment method for the sediment-rich hydropower development environment. The method is concise and practical, reflects diversion uncertainties and correlation, and mainly adopts the Gumbel–Hougaard Copula and the Monte Carlo Simulation. Through simulating flood evolution and sediment impact during diversion, the method can assess CDR basing on the cofferdam overtopping probability. Case results show that the proposed method can achieve CDR assessment on a sediment-rich river and highlights sediment impact on the diversion risk. Through results discussion, the risk feature of construction diversion on sediment-rich rivers is revealed, that sediment impact causes the dynamic and yearly-risen CDR. Hence, our conclusions are: (1) the proposed method is feasible, effective and has industrial potential, and (2) a diversion scheme on sediment-rich rivers is suggested that adopts the design with high or yearly-heightening cofferdams, based on the advanced CDR assessment to cope with the risk features of sediment-rich diversion environments.

Mixed Efficient Global Optimization for Time-Dependent Reliability Analysis

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Zhen Hu ◽  
Xiaoping Du
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Global Optimization ◽  
Reliability Analysis ◽  
Surrogate Model ◽  
Extreme Value ◽  
Time Dependent ◽  
Efficient Global Optimization ◽  
Highly Nonlinear ◽  
Adaptive Kriging

Time-dependent reliability analysis requires the use of the extreme value of a response. The extreme value function is usually highly nonlinear, and traditional reliability methods, such as the first order reliability method (FORM), may produce large errors. The solution to this problem is using a surrogate model of the extreme response. The objective of this work is to improve the efficiency of building such a surrogate model. A mixed efficient global optimization (m-EGO) method is proposed. Different from the current EGO method, which draws samples of random variables and time independently, the m-EGO method draws samples for the two types of samples simultaneously. The m-EGO method employs the adaptive Kriging–Monte Carlo simulation (AK–MCS) so that high accuracy is also achieved. Then, Monte Carlo simulation (MCS) is applied to calculate the time-dependent reliability based on the surrogate model. Good accuracy and efficiency of the m-EGO method are demonstrated by three examples.

Analysis of Ultrasonic Machining Using Monte Carlo Simulation

2011 ◽  
Author(s):  
Chittaranjan Sahay ◽  
Suhash Ghosh ◽  
Hari Kiran Kammila
Keyword(s):  
Monte Carlo Simulation ◽  
Grain Size ◽  
Monte Carlo ◽  
Removal Rate ◽  
Abrasive Particle ◽  
Negative Relationship ◽  
Machining Process ◽  
Analysis Tool ◽  
Ultrasonic Machining ◽  
Strong Positive Relationship

Proper selection of manufacturing conditions is one of the most important aspects in Ultrasonic Machining process, as these conditions determine the Material Removal Rate (MRR). In this work, two very popular mathematical models proposed by Miller and Shaw have been investigated using Monte Carlo simulation based Crystal Ball analysis tool. Effects of abrasive particle size, particle concentration, amplitude of tool vibration, tool radius and depth of hole on MRR have been analyzed for both models. Miller’s model indicates a strong positive relationship between abrasive grain size, concentration and MRR. Contrary to the literature search on experimental data, Shaw’s mathematical model indicates a negative relationship between MRR and grain size, and a very weak relationship between MRR and concentration. No definite relationship could be established between either tool radius and MRR or amplitude and MRR. A negative relationship between depth of hole and MRR was obtained for Shaw’s model.

scholarly journals VMCMC: a graphical and statistical analysis tool for Markov chain Monte Carlo traces

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Raja H. Ali ◽  
Mikael Bark ◽  
Jorge Miró ◽  
Sayyed A. Muhammad ◽  
Joel Sjöstrand ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Statistical Analysis ◽  
Markov Chain Monte Carlo ◽  
Analysis Tool ◽  
Statistical Analysis Tool

Application of Quasi-Monte Carlo Method Based on Good Point Set in Tolerance Analysis

2016 ◽  
Vol 16 (2) ◽  
Author(s):  
Yanlong Cao ◽  
Huiwen Yan ◽  
Ting Liu ◽  
Jiangxin Yang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Tolerance Analysis ◽  
Simulation Method ◽  
Analysis Tool ◽  
Monte Carlo Simulation Method ◽  
Worst Case ◽  
Quasi Monte Carlo ◽  
Good Point

Tolerance analysis is increasingly becoming an important tool for mechanical design, process planning, manufacturing, and inspection. It provides a quantitative analysis tool for evaluating the effects of manufacturing variations on performance and overall cost of the final assembly. It boosts concurrent engineering by bringing engineering design requirements and manufacturing capabilities together in a common model. It can be either worst-case or statistical. It may involve linear or nonlinear behavior. Monte Carlo simulation is the simplest and the most popular method for nonlinear statistical tolerance analysis. Monte Carlo simulation offers a powerful analytical method for predicting the effects of manufacturing variations on design performance and production cost. However, the main drawbacks of this method are that it is necessary to generate very large samples to assure calculation accuracy, and that the results of analysis contain errors of probability. In this paper, a quasi-Monte Carlo method based on good point (GP) set is proposed. The difference between the method proposed and Monte Carlo simulation lies in that the quasi-random numbers generated by Monte Carlo simulation method are replaced by ones generated by the method proposed in this paper. Compared with Monte Carlo simulation method, the proposed method provides analysis results with less calculation amount and higher precision.

scholarly journals Machine Learning & Mechanics of “Investment Matrix”: “Performance Optimisation & Risk Measurement of Bank Nifty”

2020 ◽  
Vol 8 (6) ◽  
pp. 3298-3302
Keyword(s):  
Machine Learning ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Analysis ◽  
Practical Implication ◽  
Moving Average ◽  
Technical Analysis ◽  
Research Paper ◽  
Risk Measurement ◽  
Analysis Tool

Purpose: The zeal and reason to write this research paper are to evaluate the performance & risk measurement of Bank Nifty based on Machine learning, Technical Analysis & Monte Carlo Simulation. Design /Methodologies/Approach: To achieve our desired results for this study, we use moving average (auto-optimization method) as a technical analysis return optimization tool & Monte Carlo Simulation as a risk analysis tool, & at the end harmonize both of the results, & compare with buy hold strategy. We use Bank Nifty end of day historical closing data of past five years i.e.1 Jan 2015 – 31 Dec 2019, For this study using Amibroker software. Originality & Value: This research paper is beneficial for anyone who wants understand Bank Nifty on the ground of technical analysis & risk measurement technique (MCs), & also to synergies the strength of two studies. Research Limitations: In appropriate input can lead to creating wrong simulation result, there are no. of unknown factors that simulation cannot truly understand or account during the process. Practical implication: Understanding stock market results is essential to make further decisions related to risk & reward ratio. The results imply that Moving average give outstanding returns on Bank Nifty in medium to long run, & Monte Carlo Simulation having functional judgemental abilities on probabilities basis towards risk & returns. Furthermore, by apply both the technique for risk analysis, simultaneously give outstanding risk & return optimization of Bank Nifty.

scholarly journals A novel Monte Carlo simulation procedure for modelling COVID-19 spread over time

2020 ◽  
Author(s):  
Gang Xie
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Model ◽  
Model Performance ◽  
Analysis Tool ◽  
Simulation Studies ◽  
Stochastic Point Process ◽  
Spread Dynamics ◽  
Monte Carlo Simulation Model ◽  
Modelling Approach

Abstract The coronavirus disease 2019 (COVID-19) has now spread throughout most countries in the world causing heavy life losses and damaging social-economic impacts. Following a stochastic point process modelling approach, a Monte Carlo simulation model was developed to represent the COVID-19 spread dynamics. First the simulation study was to examine various expected properties of the simulation model performance based on a number of arbitrarily defined scenarios. Then the simulation studies were performed in analysis of the real COVID-19 data reported for Australia and United Kingdom (UK). Given the initial number of active cases before 1 March were around 10 for both countries, the model estimated that the number of active COVID-19 cases was to peak around 30 March in Australia (≈ 1630 cases) and around 11 April in UK (≈ 24600 cases); ultimately the total confirmed cases could sum to 6610 for Australia in about 70 days and 136000 for UK in about 90 days. The analysis results also confirmed the reproduction number ranges as reported in the literature. This simulation model was considered as an effective and adaptable decision making/what-if analysis tool in battling COVID-19 in the immediate need, and in battling any other infectious diseases in the future.

Decision-Making Model for Emergency Evacuation Based on the Lens Model Using Machine Learning and Monte-Carlo Simulation for Incomplete Information Environment

2020 ◽  
Vol 64 (1) ◽  
pp. 229-233
Author(s):  
Miriam Alabi ◽  
Younho Seong ◽  
Sun Yi
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Incomplete Information ◽  
Emergency Evacuation ◽  
Supervised Machine Learning ◽  
Analysis Tool ◽  
Lens Model ◽  
Ecological Data

Preparing for emergencies reduces significant losses to infrastructure and the economy. In this study, a decision-making analysis tool, Lens model (LM), is used to characterize the decision behavior during an emergency evacuation based on multiple cues. Five Supervised Machine Learning (SML) algorithms were used to derive the LM parameters. The decision to evacuate under uncertain and incomplete information is always challenging. However, the LM consisting of the ecological-judgment models was created to understand evacuation behavior in uncertain environments fully. The judgment model was consolidated from historical data, whereas the ecological data, the incomplete information, was simulated using the Monte-Carlo Simulation (MCS). The SML models were evaluated using prediction accuracy (PA), and their performance validated by comparing the measures to the LM parameters. Experimental results show that k-nearest neighbor (KNN) achieved the least error in the ecology model as the LM parameter, Re corresponds to the performance of the algorithm model.

Reliability Analysis With Monte Carlo Simulation and Dependent Kriging Predictions

2016 ◽  
Vol 138 (12) ◽  
Author(s):  
Zhifu Zhu ◽  
Xiaoping Du
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Analysis ◽  
Surrogate Model ◽  
Limit State ◽  
Convergence Criterion ◽  
State Function ◽  
Kriging Method ◽  
Learning Function ◽  
Highly Nonlinear

Reliability analysis is time consuming, and high efficiency could be maintained through the integration of the Kriging method and Monte Carlo simulation (MCS). This Kriging-based MCS reduces the computational cost by building a surrogate model to replace the original limit-state function through MCS. The objective of this research is to further improve the efficiency of reliability analysis with a new strategy for building the surrogate model. The major approach used in this research is to refine (update) the surrogate model by accounting for the full information available from the Kriging method. The existing Kriging-based MCS uses only partial information. Higher efficiency is achieved by the following strategies: (1) a new formulation defined by the expectation of the probability of failure at all the MCS sample points, (2) the use of a new learning function to choose training points (TPs). The learning function accounts for dependencies between Kriging predictions at all the MCS samples, thereby resulting in more effective TPs, and (3) the employment of a new convergence criterion. The new method is suitable for highly nonlinear limit-state functions for which the traditional first- and second-order reliability methods (FORM and SORM) are not accurate. Its performance is compared with that of existing Kriging-based MCS method through five examples.

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