Spreading Characteristics of Molecularly Thin Lubricant on Surfaces With Groove-Shaped Textures: Monte Carlo Simulation and Measurement Using PFPE Film

2002 ◽  
Vol 124 (3) ◽  
pp. 575-583 ◽  
Author(s):  
Hedong Zhang ◽  
Yasunaga Mitsuya ◽  
Maiko Yamada
Keyword(s):  
Thin Film ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Ising Model ◽  
Solid Surface ◽  
Smooth Surface ◽  
The Monte Carlo Method ◽  
Grooved Surface ◽  
Molecularly Thin Lubricant

Spreading characteristics of molecularly thin lubricant on a grooved surface have been studied numerically by Monte Carlo simulations and compared with measurements obtained by perfluoropolyether (PFPE) thin film spreading on a solid surface with minute grooves. In the simulations, by incorporating the interactions between molecules and the side surfaces of a groove, the Monte Carlo method based on the Ising model was extended to the case of a surface with grooves and applied to simulate the spreading of non-polar lubricant inside a groove. Compared with the spreading on a smooth surface, lubricant spreads rapidly inside a groove, indicating an acceleration of the spreading along the groove. In the experiments, lubricant spreading on a surface with groove-shaped textures was measured by making use of the phenomenon in which diffracted light decreases or vanishes in the lubricant-covered region. Based on the results showing lubricant spreading predominantly along the groove, the accelerating effect obtained in the simulations is well confirmed by the measurements.

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.

Spreading Characteristics of Molecularly Thin Lubricant on Surfaces With Groove-Shaped Textures: Effects of Molecular Weight and End-Group Functionality

2003 ◽  
Vol 125 (2) ◽  
pp. 350-357 ◽  
Author(s):  
Hedong Zhang ◽  
Yasunaga Mitsuya ◽  
Maiko Yamada
Keyword(s):  
Molecular Weight ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Ising Model ◽  
Monte Carlo Simulations ◽  
Simulation Method ◽  
Solid Surfaces ◽  
The Monte Carlo Method ◽  
End Groups ◽  
End Group

Effects of molecular weight and end-group functionality on spreading of molecularly thin perfluoropolyether (PFPE) film over solid surfaces with groove-shaped textures have been studied by experiments and Monte Carlo simulations. In the experiments, lubricant spreading on a surface with groove-shaped textures was measured by making use of the phenomenon in which diffracted light weakens in the lubricant-covered region. It is found that grooves serve to accelerate spreading and this effect increases for deeper grooves, and also the accelerating rate becomes larger for a lubricant having a larger molecular weight or functional end-groups. In the simulations, the Monte Carlo method based on the Ising model was extended to enable us to evaluate the effect of molecular weight on the spreading of non-functional lubricant inside a groove. The validity of the newly developed simulation method was well confirmed from the agreement between the simulation and experimental results.

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 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.  

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