Reliability Against Drifting Ice—An Adjunct to Monte Carlo Simulation

1991 ◽  
Vol 113 (3) ◽  
pp. 253-259
Author(s):  
A. B. Dunwoody
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Normal Family ◽  
Probability Distributions ◽  
Load Model ◽  
Ice Load ◽  
Ice Loads ◽  
Drifting Ice ◽  
Log Normal ◽  
Monte Carlo Simulation Program

A method is presented for the calculation of the reliability of a structure against drifting ice subject to restrictions on the form of the ice load model and on the form of the probability distributions of the ice feature characteristics. The ice load model must have the form that the ice load is proportional to the product of the characteristics of the impacting ice feature raised to individual powers. Results from a Monte Carlo simulation program are presented to demonstrate that the ice loads for a number of useful ice interaction scenarios can be modeled by an equation of this form. The probability distributions of the ice feature characteristics must be from the log-normal family. A realistic example using publicly available ice data and ice load model is presented.

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 STARlight: A Monte Carlo simulation program for ultra-peripheral collisions of relativistic ions

2017 ◽  
Vol 212 ◽  
pp. 258-268 ◽  
Author(s):  
Spencer R. Klein ◽  
Joakim Nystrand ◽  
Janet Seger ◽  
Yuri Gorbunov ◽  
Joey Butterworth
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Program ◽  
Peripheral Collisions ◽  
Monte Carlo Simulation Program

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.

A Parametric Unit Commitment Method for Revenue Adequacy Assessment

2007 ◽  
Vol 8 (3) ◽  
Author(s):  
Gianpietro Granelli ◽  
Mario Montagna ◽  
Paolo Marannino ◽  
Fabio Zanellini
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Unit Commitment ◽  
Market Price ◽  
Multivariate Normal ◽  
Market Prices ◽  
Strategic Role ◽  
Adequacy Assessment ◽  
Monte Carlo Simulation Program

Unit commitment (UC), originally employed to minimize the generating cost of a vertically integrated utility, has become a basic tool for revenue adequacy evaluation by GENeration COmpanies (GENCOs). The strategic role of UC is apparent in those market environments where GENCOs are asked to internalize all costs and the relevant UC decisions within their day–ahead market bids. In this paper, a parametric UC method is presented to assess the convenience of making energy bids for marginal or nearly marginal generation units. The convenience is gauged by the basis of revenue adequacy that is on each unit's ability to cover all expenses and generate profits. Beside UC choices, the parametric UC procedure provides a market price behavior that would make it profitable to commit a certain unit for generation. A Monte Carlo simulation program is employed to find the probability that actual market prices will fulfill the revenue adequacy requirements. Market prices are handled as random variables with multivariate normal distribution, taking into account the correlation between prices at successive time periods. The parametric UC procedure and the Monte Carlo simulation are carried out by using reasonable data of generation units available to some Italian GENCOs.

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