Exit Condition for Probabilistic Assessment Using Monte Carlo Method

2010 ◽  
Vol 10 (1) ◽  
pp. 1-9
Author(s):  
Jakub Valihrach ◽  
Petr Konečný
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Optimization Design ◽  
Random Variable ◽  
Probability Of Failure ◽  
Probabilistic Assessment ◽  
Theoretical Derivation ◽  
The Monte Carlo Method ◽  
The Relationship

Exit Condition for Probabilistic Assessment Using Monte Carlo Method This paper introduces a condition used to exit a probabilistic assessment using the Monte Carlo simulation, and to evaluate it with regard to the relationship between the computed estimate of the probability of failure and the target design probability. The estimation of probability of failure is treated as a random variable, considering its variance that is dependent on the number of performed Monte Carlo simulation steps. After theoretical derivation of the decision condition, it is tested numerically with regard to its accuracy and computational efficiency. The condition is suitable for optimization design using the Monte Carlo method.

scholarly journals Stochastic energy-demand analyses with random input parameters for the single-family house

2021 ◽  
Author(s):  
Marcin Koniorczyk ◽  
Witold Grymin ◽  
Marcin Zygmunt ◽  
Dalia Bednarska ◽  
Alicja Wieczorek ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Energy Consumption ◽  
Monte Carlo Method ◽  
Energy Demand ◽  
Stochastic Perturbation ◽  
Random Variable ◽  
Single Family ◽  
The Monte Carlo Method ◽  
Stochastic Perturbation Method ◽  
Family House

AbstractIn the analyses of the uncertainty propagation of buildings’ energy-demand, the Monte Carlo method is commonly used. In this study we present two alternative approaches: the stochastic perturbation method and the transformed random variable method. The energy-demand analysis is performed for the representative single-family house in Poland. The investigation is focused on two independent variables, considered as uncertain, the expanded polystyrene thermal conductivity and external temperature; however the generalization on any countable number of parameters is possible. Afterwards, the propagation of the uncertainty in the calculations of the energy consumption has been investigated using two aforementioned approaches. The stochastic perturbation method is used to determine the expected value and central moments of the energy consumption, while the transformed random variable method allows to obtain the explicit form of energy consumption probability density function and further characteristic parameters like quantiles of energy consumption. The calculated data evinces a high accordance with the results obtained by means of the Monte Carlo method. The most important conclusions are related to the computational cost reduction, simplicity of the application and the appropriateness of the proposed approaches for the buildings’ energy-demand calculations.

scholarly journals Technical-Economic Analysis of Grapple Saw

2020 ◽  
Vol 41 (2) ◽  
pp. 219-229 ◽  
Author(s):  
Ricardo Hideaki Miyajima ◽  
Paulo Torres Fenner ◽  
Gislaine Cristina Batistela ◽  
Danilo Simões
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Distributions ◽  
Simulation Method ◽  
Forest Harvesting ◽  
Production Costs ◽  
Wood Harvesting ◽  
Food And Agriculture ◽  
The Monte Carlo Method

The processing of Eucalyptus logs is a stage that follows the full tree system in mechanized forest harvesting, commonly performed by grapple saw. Therefore, this activity presents some associated uncertainties, especially regarding technical and silvicultural factors that can affect productivity and production costs. To get around this problem, Monte Carlo simulation can be applied, or rather a technique that allows to measure the probabilities of values from factors that are under conditions of uncertainties, to which probability distributions are attributed. The objective of this study was to apply the Monte Carlo method for determining the probabilistic technical-economical coefficients of log processing using two different grapple saw models. Field data were obtained from an area of forest planted with Eucalyptus, located in the State of São Paulo, Brazil. For the technical analysis, the time study protocol was applied by the method of continuous reading of the operational cycle elements, which resulted in production. As for the estimated cost of programmed hour, the applied methods were recommended by the Food and Agriculture Organization of the United Nations. The incorporation of the uncertainties was carried out by applying the Monte Carlo simulation method, by which 100,000 random values were generated. The results showed that the crane empty movement is the operational element that most impacts the total time for processing the logs; the variables that most influence the productivity are specific to each grapple saw model; the difference of USD 0.04 m3 in production costs was observed between processors with gripping area of 0.58 m2 and 0.85 m2. The Monte Carlo method proved to be an applicable tool for mechanized wood harvesting for presenting a range of probability of occurrences for the operational elements and for the production cost.

scholarly journals Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport

2020 ◽  
Vol 10 (12) ◽  
pp. 4229 ◽  
Author(s):  
Alexander Heilmeier ◽  
Michael Graf ◽  
Johannes Betz ◽  
Markus Lienkamp
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
State Of The Art ◽  
Probability Distributions ◽  
Strategic Decisions ◽  
The Monte Carlo Method ◽  
Random Events ◽  
Example Variability

Applying an optimal race strategy is a decisive factor in achieving the best possible result in a motorsport race. This mainly implies timing the pit stops perfectly and choosing the optimal tire compounds. Strategy engineers use race simulations to assess the effects of different strategic decisions (e.g., early vs. late pit stop) on the race result before and during a race. However, in reality, races rarely run as planned and are often decided by random events, for example, accidents that cause safety car phases. Besides, the course of a race is affected by many smaller probabilistic influences, for example, variability in the lap times. Consequently, these events and influences should be modeled within the race simulation if real races are to be simulated, and a robust race strategy is to be determined. Therefore, this paper presents how state of the art and new approaches can be combined to modeling the most important probabilistic influences on motorsport races—accidents and failures, full course yellow and safety car phases, the drivers’ starting performance, and variability in lap times and pit stop durations. The modeling is done using customized probability distributions as well as a novel “ghost” car approach, which allows the realistic consideration of the effect of safety cars within the race simulation. The interaction of all influences is evaluated based on the Monte Carlo method. The results demonstrate the validity of the models and show how Monte Carlo simulation enables assessing the robustness of race strategies. Knowing the robustness improves the basis for a reasonable determination of race strategies by strategy engineers.

scholarly journals C-beta energy converter efficiency modeling

2019 ◽  
Vol 222 ◽  
pp. 02012
Author(s):  
Oleg Kuznetsov ◽  
Viktor Chepurnov ◽  
Albina Gurskaya ◽  
Mikhail Dolgopolov ◽  
Sali Radzhapov
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Theoretical Models ◽  
Maximum Efficiency ◽  
Equivalent Circuits ◽  
Optimal Parameters ◽  
Energy Converter ◽  
Electron Hole ◽  
The Monte Carlo Method

To construct beta converters with maximum efficiency it is necessary to carry out the theoretical calculation in order to determine their optimal parameters - the geometry of the structure, the thickness of the deposition of the radioisotope layer, the depth and the width of the p-n junction, and others. To date, many different theoretical models and calculations methods had been proposed. There are fairly simple theoretical models based on the Bethe-Bloch formula and the calculation of the rate of generation of electron-hole pairs, and on calculations by equivalent circuits. Also, the Monte-Carlo method is used for theoretical modeling of beta converters. This paper explores beta converter optimization using the Monte-Carlo method. The purpose of the study is to conduct Monte-Carlo simulation of the beta converter to determine its optimal parameters.

scholarly journals Optimization design of the stick of an excavator under uncertain loading (in the conditions of the Syrian Arab Republic)

2021 ◽  
Vol 341 ◽  
pp. 00014
Author(s):  
Jalal Mohammad ◽  
Irina Odinokova ◽  
Vitaly Gaevskiy ◽  
Evgeni Nosko
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Optimization Design ◽  
Hydraulic Excavator ◽  
Original Design ◽  
Lower Stress ◽  
Interaction Models ◽  
The Monte Carlo Method ◽  
Syrian Arab Republic

The article is devoted to the optimization design of the stick of a hydraulic excavator at the most frequent and greatest value loads. The uncertain loads acting on the working equipment of the excavator were obtained by the Monte Carlo method based on 3D analytical soil-tool interaction models. The results show that the adopted methodology can be used to optimize the design of elements of the excavator working equipment, since the design of stick was obtained with less weight and lower stress values than the original design.

A probability model for fibrous composites with local load sharing

1980 ◽  
Vol 372 (1751) ◽  
pp. 539-553 ◽  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Extreme Value Theory ◽  
Probabilistic Analysis ◽  
Asymptotic Theory ◽  
Probability Model ◽  
Probability Of Failure ◽  
Value Theory ◽  
Fibrous Composites ◽  
The Monte Carlo Method

The chain-of-bundles model for fibrous composites is reviewed, and an approximation to the probability of failure is derived. This leads to formulae for predicting the strength of such a composite. These formulae are developed in the context of an asymptotic theory, and the Monte Carlo method is used to study a specific case in more detail. We also discuss the size effect. The probabilistic analysis relies heavily on extreme value theory, and a brief survey of the relevant parts of that theory is included.

Estimation of Random Variable Distribution Parameters by the Monte Carlo Method

2013 ◽  
Vol 20 (2) ◽  
pp. 249-262 ◽  
Author(s):  
Sergiusz Sienkowski
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Random Variable ◽  
Statistical Parameters ◽  
The Monte Carlo Method ◽  
Variable Distribution ◽  
Distribution Parameters ◽  
The Subject ◽  
The Mean ◽  
Typical Measurement

Abstract The paper is concerned with issues of the estimation of random variable distribution parameters by the Monte Carlo method. Such quantities can correspond to statistical parameters computed based on the data obtained in typical measurement situations. The subject of the research is the mean, the mean square and the variance of random variables with uniform, Gaussian, Student, Simpson, trapezoidal, exponential, gamma and arcsine distributions.

scholarly journals Examining the Value of Monte Carlo Simulation for Project Time Management

2018 ◽  
Vol 24 (1) ◽  
pp. 11
Author(s):  
Goran Avlijas
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Time Management ◽  
Research Question ◽  
Relevant Literature ◽  
Management Tool ◽  
Project Duration ◽  
Schedule Management ◽  
The Monte Carlo Method

Research Question: This paper investigates whether the Monte Carlo simulation can be widely used as a practicable method for the analysis of the risks that impact project duration. Motivation: The main goal was to explore the use of the Monte Carlo simulation for project time management, and shed some light on the key benefits and drawbacks of this method. The paper reviewed the existing literature considering traditional use of the Monte Carlo for quantitative project risk analysis (such as Kwak & Ingall, 2007; Hulett, 2017) and elaborated the issue by suggesting potential improvements in terms of method modification for schedule management, such as event chain methodology proposed by Agarwal & Virine (2017). Another goal was to examine the capability of user-friendly software to provide project managers with some of these benefits. Idea: The core idea of this paper was to evaluate the value of the Monte Carlo method for project time and schedule management, by matching traditional foundations with modern techniques. Data: The paper used the secondary data extracted from relevant literature and project examples. A literature review reveals how the application of the Monte Carlo simulation evolved as a project management tool, along with specific benefits and concerns for its application. Tools: A detailed application of the Monte Carlo in predicting project duration is provided, and the applicability and viability of the method are proven through a case demonstration. Following the presentation of a practical example and discussion of the main features, some limitations and potential improvements to the Monte Carlo method are suggested. Findings: Even with the existence of certain limitations, the Monte Carlo simulation remains the primary method for quantitative analysis of project risks. Despite the Monte Carlo having been found to be applicable, adaptable and predictive of total project duration, it is found to be insufficiently used by practitioners. Contribution: The paper urges the need for research on successful diffusion of the Monte Carlo simulation and helps practitioners to understand the adaptability of the Monte Carlo simulation as a tool for risk quantification and its use for effective duration planning of their projects.  

Optimal Design and Simulation on the Major Linkage Parameters of a Harvesting Manipulator

2010 ◽  
Vol 44-47 ◽  
pp. 651-655
Author(s):  
Yu Jie Cui ◽  
Jian Ning Hua ◽  
Pu Shi
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Optimal Design ◽  
Optimization Design ◽  
Robotic Manipulators ◽  
The Monte Carlo Method ◽  
Simulation Results ◽  
Optimization Methodology

A general optimization methodology for the optimal design of robotic manipulators is presented and illustrated by its application to a harvesting manipulator. The optimization design of the manipulator framework and structure is made according to the tomato' s growth circumstance and distribution space.The workspace of the manipulator is carried out and simulated through the Monte Carlo method. Simulation results show that the manipulator with 4 degree- of- freedoms meets the expectations for picking tomato in greenhouse. The rationality of the structure and frame is validated.

scholarly journals Risk management of a torrential flood construction project using the Monte Carlo simulation

2016 ◽  
pp. 29-44 ◽  
Author(s):  
Aleksandar Baumgertel ◽  
Nada Dragovic ◽  
Tijana Vulevic
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Risk Analysis ◽  
Quantitative Methods ◽  
Flow Profile ◽  
Total Cost ◽  
The Monte Carlo Method ◽  
Relevant Variables

Projects for the regulation of torrent basins carry various unforeseen adverse effects that may result in breached deadlines, increased costs, a reduction of quality etc. The paper presents the basic characteristics and most frequent risks associated with erosion control. Furthermore, it provides an overview of risk management through its basic stages - starting from risk identification and risk analysis to risk responses, including the methods used for risk analysis. As a part of quantitative methods for risk analysis, the Monte Carlo method is presented as the one most frequently used in simulations. The Monte Carlo method is a stochastic simulation method consisting of the following stages: the identification of criterion and relevant variables, the allocation of probability for relevant variables, the determination of correlation coefficient among relevant variables, simulation execution and result analysis. This method was applied in the analysis of the total cost of the project for the basin regulation of the Dumaca River in order to determine the funding that would be used as a backup in case of unforeseen events with a negative impact. The project for the regulation of the Dumaca River includes basin regulation in the form of complex flow profile and the lining of zones where necessary in terms of stability. The total cost is presented as a sum of costs of all works (preliminary works, earthworks, masonry works, concrete works and finishing works). The Monte Carlo simulation for cost analysis is carried out using the Oracle Crystal Ball software with its basic steps described in the paper. A sum of funding needed as a financial backup in case of unforeseen events with negative effects is obtained as the simulated total cost of the project.

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