scholarly journals Application of Monte Carlo Simulation to Find Travel Time of Groundwater in the Iraqi Western Desert

2017 ◽  
Vol 8 (1) ◽  
pp. 17
Author(s):  
Dawood Eisa Sachit ◽  
Hayat Kareem Shukur Azawi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Distribution Function ◽  
Hydraulic Conductivity ◽  
Groundwater Flow ◽  
Travel Time ◽  
Input Data ◽  
Western Desert ◽  
Triangular Distribution ◽  
Entire Area

In this study, a computerized mathematical method represented by Monte Carlo simulation was used to predict the travel time of the groundwater flow in the Iraqi western desert. During the run of the simulations, all the hydraulic parameters of Darcy’s Law were fixed but the hydraulic conductivity. The input data of the hydraulic conductivity is compared to the triangular distribution function to find the best number of iteration to run the simulations. The results showed that an iteration number of 5000 was enough to achieve best match between the input data of the hydraulic conductivity and the fitted distribution function. In addition, the estimated travel time of the groundwater flow is broadly varied through the entire area and ranges from 1983 years to 113 741 years based on 10 000 m of travel distance. Furthermore, hydraulic conductivity of the aquifer has high impact on the estimated travel time of the groundwater flow. However, head difference of groundwater elevation among the selected wells considerably influences the expected travel time of the groundwater flow.

Supply Chain Inventory Coordination under Uncertain Demand via Combining Monte Carlo Simulation and Fitness Inheritance PSO

2015 ◽  
Vol 6 (1) ◽  
pp. 1-22
Author(s):  
Heting Cao ◽  
Xingquan Zuo
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Distribution Function ◽  
Supply Chain ◽  
Simulation Model ◽  
Uncertain Demand ◽  
Computational Time ◽  
Arbitrary Distribution ◽  
Demand Distribution ◽  
Inventory Coordination

Supply chain coordination consists of multiple aspects, among which inventory coordination is the most widely used in practice. Inventory coordination is challenging due to the uncertainty of customers' demand. Existing researches typically assume that the demand is either a deterministic constant or a stochastic variable following a known distribution function. However, the former cannot reflect the practical costumers' demand, and the later make the model inaccurate when the demand distribution is ambiguous or highly variable. In this paper, the authors propose a Monte Carlo simulation model of such problem, which can mimic the inventory changing procedure of a supply chain with uncertain demand following an arbitrary distribution function. Then, a PSO is combined with the simulation model to achieve a coordination decision scheme to minimize the total inventory cost. Experiments show that their approach is able to produce a high quality solution within a short computational time and outperforms comparative approaches.

Influence of Applied Tensile and Bending Strains on Local and Overall Critical Current of Multifilamentary Bi2223-Composite Superconductor

2007 ◽  
Vol 539-543 ◽  
pp. 739-744
Author(s):  
Shojiro Ochiai ◽  
D. Doko ◽  
Hiroshi Okuda ◽  
Sang Soo Oh ◽  
Dong Woo Ha ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Distribution Function ◽  
Weibull Distribution ◽  
Critical Current ◽  
Experimental Result ◽  
N Value ◽  
Local Current ◽  
Composite Superconductor ◽  
Extent Of Damage

Influence of applied tensile and bending strains on the local and overall transport critical current Ic and n-value at 77 K of multifilamentary Bi2223-composite superconductor was studied, where the n-value refers to the sharpness of the transition from super- to normal conducting state. Under both tensile and bending strains, the damage such as transverse and longitudinal cracking of the Bi2223 filaments and interfacial debonding between the filament and silver progressed. The extent of damage and accordingly the critical current was different among the local portions. The relation of the local current and n-value to overall ones was analyzed with a voltage summation model, with which the experimental result was described well. Further analysis revealed that the distribution of local critical current could be described by the Weibull distribution function and n-value could be expressed as a function of critical current. Based on these results, a Monte Carlo simulation was carried out to predict the overall critical current from the distribution of local critical current, with which the experimental results could be described.

scholarly journals Topological analysis in Monte Carlo simulation for uncertainty propagation

Solid Earth ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 1663-1684 ◽  
Author(s):  
Evren Pakyuz-Charrier ◽  
Mark Jessell ◽  
Jérémie Giraud ◽  
Mark Lindsay ◽  
Vitaliy Ogarko
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Input Data ◽  
Topological Analysis ◽  
Uncertainty Propagation ◽  
Population Heterogeneity ◽  
Plausible Model ◽  
Uncertainty Index ◽  
Uncertainty Estimates ◽  
Geological Models

Abstract. This paper proposes and demonstrates improvements for the Monte Carlo simulation for uncertainty propagation (MCUP) method. MCUP is a type of Bayesian Monte Carlo method aimed at input data uncertainty propagation in implicit 3-D geological modeling. In the Monte Carlo process, a series of statistically plausible models is built from the input dataset of which uncertainty is to be propagated to a final probabilistic geological model or uncertainty index model. Significant differences in terms of topology are observed in the plausible model suite that is generated as an intermediary step in MCUP. These differences are interpreted as analogous to population heterogeneity. The source of this heterogeneity is traced to be the non-linear relationship between plausible datasets' variability and plausible model's variability. Non-linearity is shown to mainly arise from the effect of the geometrical rule set on model building which transforms lithological continuous interfaces into discontinuous piecewise ones. Plausible model heterogeneity induces topological heterogeneity and challenges the underlying assumption of homogeneity which global uncertainty estimates rely on. To address this issue, a method for topological analysis applied to the plausible model suite in MCUP is introduced. Boolean topological signatures recording lithological unit adjacency are used as n-dimensional points to be considered individually or clustered using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The proposed method is tested on two challenging synthetic examples with varying levels of confidence in the structural input data. Results indicate that topological signatures constitute a powerful discriminant to address plausible model heterogeneity. Basic topological signatures appear to be a reliable indicator of the structural behavior of the plausible models and provide useful geological insights. Moreover, ignoring heterogeneity was found to be detrimental to the accuracy and relevance of the probabilistic geological models and uncertainty index models. Highlights. Monte Carlo uncertainty propagation (MCUP) methods often produce topologically distinct plausible models. Plausible models can be differentiated using topological signatures. Topologically similar probabilistic geological models may be obtained through topological signature clustering.

A Comprehensive Procedure to Estimate the Probability of Extreme Vibration Levels Due to Mistuning

2010 ◽  
Vol 132 (11) ◽  
Author(s):  
Y.-J. Chan ◽  
D. J. Ewins
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Distribution Function ◽  
Importance Sampling ◽  
Gradient Method ◽  
Sampling Method ◽  
Optimization Analysis ◽  
Amplification Factors ◽  
Bladed Disk ◽  
Importance Sampling Method

A new procedure is developed to find the probabilities of extremely high amplification factors in mistuned bladed disk vibration levels, typical of events which occur rarely. While a rough estimate can be made by curve-fitting the distribution function generated in a Monte Carlo simulation, the procedure presented here can determine a much more accurate upper bound and the probabilities of amplification factors near to that bound. The procedure comprises an optimization analysis based on the conjugate gradient method and a stochastic simulation using the importance sampling method. Two examples are provided to illustrate the efficiency of the procedure, which can be 2 or 3 orders of magnitude more efficient than Monte Carlo simulations.

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