scholarly journals Kinetic Monte Carlo Simulation of Clustering in an Al-Mg-Si-Cu Alloy

Materials ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4523
Author(s):  
Qilu Ye ◽  
Jianxin Wu ◽  
Jiqing Zhao ◽  
Gang Yang ◽  
Bin Yang
Keyword(s):  
Activation Energy ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Kinetic Monte Carlo ◽  
Complex Interaction ◽  
Cu Alloy ◽  
Cu Alloys ◽  
Simulation Results ◽  
Small Clusters ◽  
First Time

The mechanism of the clustering in Al-Mg-Si-Cu alloys has been a long-standing controversial issue. Here, for the first time, the mechanism of the clustering in the alloy was investigated by a Kinetic Monte Carlo (KMC) approach. In addition, reversion aging (RA) was carried out to evaluate the simulation results. The results showed that many small-size clusters formed rapidly in the early stages of aging. With the prolongation of aging time, the clusters merged and grew. The small clusters formed at the beginning of aging in Al-Mg-Si-Cu alloy were caused by initial vacancies (quenching vacancies). The merging and decomposition of the clusters were mainly caused by the capturing of vacancies, and the clusters had a probability to decompose before reaching a stable size. After repeated merging and decomposition, the clusters reach stability. During RA, the complex interaction between the cluster merging and decomposition leaded to the partial irregular change of the hardness reduction and activation energy.

Coping with uncertainties in integrated urban water management

1997 ◽  
Vol 36 (8-9) ◽  
pp. 265-269
Author(s):  
Govert D. Geldof
Keyword(s):  
Water Management ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Interdisciplinary Approach ◽  
Urban Water ◽  
Urban Water Management ◽  
Integrated Water Management ◽  
Good Design ◽  
Management Techniques ◽  
Simulation Results

In the practice of integrated water management we meet complexity, subjectivity and uncertainties. Uncertainties come into play when new urban water management techniques are applied. The art of a good design is not to reduce uncertainties as much as possible, but to find the middle course between cowardice and recklessness. This golden mean represents bravery. An interdisciplinary approach is needed to reach consensus. Calculating uncertainties by using Monte Carlo simulation results may be helpful.

scholarly journals Cheating at Craps

2021 ◽  
Vol 48 (4) ◽  
pp. 53-61
Author(s):  
Andrea Marin ◽  
Carey Williamson
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Modeling And Simulation ◽  
Analytical Model ◽  
Quantitative Evaluation ◽  
Mathematical Analysis ◽  
Simulation Modeling ◽  
Analytical Modeling ◽  
The World ◽  
Simulation Results

Craps is a simple dice game that is popular in casinos around the world. While the rules for Craps, and its mathematical analysis, are reasonably straightforward, this paper instead focuses on the best ways to cheat at Craps, by using loaded (biased) dice. We use both analytical modeling and simulation modeling to study this intriguing dice game. Our modeling results show that biasing a die away from the value 1 or towards the value 5 lead to the best (and least detectable) cheating strategies, and that modest bias on two loaded dice can increase the winning probability above 50%. Our Monte Carlo simulation results provide validation for our analytical model, and also facilitate the quantitative evaluation of other scenarios, such as heterogeneous or correlated dice.

scholarly journals A Comparison of Different Data-driven Procedures to Determine the Bunching Window

2021 ◽  
Vol 49 (2) ◽  
pp. 262-293
Author(s):  
Vincent Dekker ◽  
Karsten Schweikert
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Income Distribution ◽  
Prior Information ◽  
Structural Breaks ◽  
Data Driven ◽  
Taxable Income ◽  
Counterfactual Models ◽  
Simulation Results ◽  
Second Procedure

In this article, we compare three data-driven procedures to determine the bunching window in a Monte Carlo simulation of taxable income. Following the standard approach in the empirical bunching literature, we fit a flexible polynomial model to a simulated income distribution, excluding data in a range around a prespecified kink. First, we propose to implement methods for the estimation of structural breaks to determine a bunching regime around the kink. A second procedure is based on Cook’s distances aiming to identify outlier observations. Finally, we apply the iterative counterfactual procedure proposed by Bosch, Dekker, and Strohmaier which evaluates polynomial counterfactual models for all possible bunching windows. While our simulation results show that all three procedures are fairly accurate, the iterative counterfactual procedure is the preferred method to detect the bunching window when no prior information about the true size of the bunching window is available.

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