Sensitivity analysis in stochastic flow networks using the Monte Carlo method

Networks ◽  
1993 ◽  
Vol 23 (7) ◽  
pp. 605-621 ◽  
Author(s):  
Christos Alexopoulos ◽  
George S. Fishman
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Stochastic Flow ◽  
Flow Networks ◽  
The Monte Carlo Method

Characterizing stochastic flow networks using the monte carlo method

Networks ◽  
1991 ◽  
Vol 21 (7) ◽  
pp. 775-798 ◽  
Author(s):  
Christos Alexopoulos ◽  
George S. Fishman
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Stochastic Flow ◽  
Flow Networks ◽  
The Monte Carlo Method

Effect of Influential Factors on the Reliability of the Mechanical Behavior of a Composite Plate Holes Fatigue Loaded

2013 ◽  
Vol 682 ◽  
pp. 161-167
Author(s):  
C. Bouraoui ◽  
Med. Bouassaadia ◽  
R. Ben Sghaier ◽  
Moussa Karama
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Cyclic Loading ◽  
Composite Plate ◽  
Influential Factors ◽  
Geometrical Parameters ◽  
Composite Component ◽  
Fiber Volume ◽  
The Monte Carlo Method

This work presents a sensitivity analysis of mechanical and geometrical parameters on a composite component under a cyclic loading. A design of experiments approach has been used. This study in improved by a reliability approach where the scatterings of all the characteristic properties are taken into account. The Monte Carlo method where used to compute the reliability. An application has been carried out on a composite rectangular plate with a hole in its center. Where a useful surface responses diagrams of the reliability are determined. A significant nonlinear effect of the fiber volume faction is observed on the behaviors reliability with no significant interactions effect with the others factors.

PERMUTATIONAL METHODS FOR PERFORMANCE ANALYSIS OF STOCHASTIC FLOW NETWORKS

2013 ◽  
Vol 28 (1) ◽  
pp. 21-38 ◽  
Author(s):  
Ilya Gertsbakh ◽  
Reuven Rubinstein ◽  
Yoseph Shpungin ◽  
Radislav Vaisman
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Performance Analysis ◽  
Failure Probability ◽  
Stochastic Flow ◽  
Maximal Flow ◽  
Convergence Properties ◽  
Flow Networks ◽  
Flow Network ◽  
Random Failures

In this paper we show how the permutation Monte Carlo method, originally developed for reliability networks, can be successfully adapted for stochastic flow networks, and in particular for estimation of the probability that the maximal flow in such a network is above some fixed level, called the threshold. A stochastic flow network is defined as one, where the edges are subject to random failures. A failed edge is assumed to be erased (broken) and, thus, not able to deliver any flow. We consider two models; one where the edges fail with the same failure probability and another where they fail with different failure probabilities. For each model we construct a different algorithm for estimation of the desired probability; in the former case it is based on the well known notion of the D-spectrum and in the later one—on the permutational Monte Carlo. We discuss the convergence properties of our estimators and present supportive numerical results.

scholarly journals Methodological assessment of uncertainty in forecasting hydrocarbon reserves of the North Varieganskoye field (Western Siberia)

2021 ◽  
Vol 1 ◽  
pp. 46-54
Author(s):  
Irina Aleksandrovna LEBEDEVA ◽  
◽  
Stephan Grigor’evich PANYAK ◽  
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Reservoir Properties ◽  
Crystal Ball ◽  
The North ◽  
The Monte Carlo Method ◽  
Methodological Assessment ◽  
Hydrocarbon Reserves

Relevance. The assessment of uncertainties for the calculation of hydrocarbon reserves is a timely topic. The quality of hydrocarbon reserves is declining as old oil fields are depleted. Rates of discovery are in decline since most of the territories prospectively rich in hydrocarbon resources have already been explored. Newly discovered fields are classified as small and medium in terms of the amount of hydrocarbon reserves and difficult in terms of the quality of reserves. Purpose of the research is to determine important reservoir properties that have the greatest impact on the variability of the initial hydrocarbon reserves using sensitivity analysis and conducting a probabilistic hydrocarbon reserves assessment by the Monte Carlo method. Methods of research. The probabilistic hydrocarbon reserves assessment was carried out by the Monte Carlo method with the corresponding frequency curves of the probability distribution of volumetric parameters, which were performed using the Oracle Crystal Ball application. The sensitivity analysis of volumetric parameters that have the greatest impact on the amount of the initial hydrocarbons reserves was performed using the Oracle Crystal Ball application as well. Results and their application. An assessment of uncertainties and risks is necessary both for newly discovered fields in order to negate the risk of drilling unproductive wells and mature fields for a targeted program of geological and technological measures (GTM). Conclusions. The formation of bedded sand bodies of the Vasyugan (SE11, SE12, SE13) and Tyumen (SE2) suites took place in various sedimentation conditions, which could not but affect the quality of the reservoir of the studied formations. The analysis of the sensitivity of parameters has shown that different parameters affect the amount of hydrocarbon reserves in places SE11, SE12, SE13 and place SE2 .

UGR CALCULATION BASED ON THE LUMINANCE SPATIAL-ANGULAR DISTRIBUTION

2020 ◽  
Vol 2020 (4) ◽  
pp. 25-32
Author(s):  
Viktor Zheltov ◽  
Viktor Chembaev
Keyword(s):  
Monte Carlo ◽  
Angular Distribution ◽  
Monte Carlo Method ◽  
Visual Task ◽  
New Method ◽  
Lighting System ◽  
Preliminary Assessment ◽  
System A ◽  
The Monte Carlo Method

The article has considered the calculation of the unified glare rating (UGR) based on the luminance spatial-angular distribution (LSAD). The method of local estimations of the Monte Carlo method is proposed as a method for modeling LSAD. On the basis of LSAD, it becomes possible to evaluate the quality of lighting by many criteria, including the generally accepted UGR. UGR allows preliminary assessment of the level of comfort for performing a visual task in a lighting system. A new method of "pixel-by-pixel" calculation of UGR based on LSAD is proposed.

PSHA software review based on the Monte Carlo method

2020 ◽  
pp. 14-24
Author(s):  
V.A. Mironov ◽  
S.A. Peretokin ◽  
K.V. Simonov
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Seismic Hazard ◽  
Hazard Analysis ◽  
Software Review ◽  
Seismic Hazard Analysis ◽  
Probabilistic Seismic Hazard ◽  
Test Calculation ◽  
Seismic Surveys ◽  
The Monte Carlo Method

The article is a continuation of the software research to perform probabilistic seismic hazard analysis (PSHA) as one of the main stages in engineering seismic surveys. The article provides an overview of modern software for PSHA based on the Monte Carlo method, describes in detail the work of foreign programs OpenQuake Engine and EqHaz. A test calculation of seismic hazard was carried out to compare the functionality of domestic and foreign software.

Application of the Monte Carlo Method for the Prediction of Behavior of Peptides

2019 ◽  
Vol 20 (12) ◽  
pp. 1151-1157 ◽  
Author(s):  
Alla P. Toropova ◽  
Andrey A. Toropov
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Natural Sciences ◽  
Monte Carlo Technique ◽  
Quantitative Structure ◽  
Structure Property ◽  
The Monte Carlo Method ◽  
Modern Natural

Prediction of physicochemical and biochemical behavior of peptides is an important and attractive task of the modern natural sciences, since these substances have a key role in life processes. The Monte Carlo technique is a possible way to solve the above task. The Monte Carlo method is a tool with different applications relative to the study of peptides: (i) analysis of the 3D configurations (conformers); (ii) establishment of quantitative structure – property / activity relationships (QSPRs/QSARs); and (iii) development of databases on the biopolymers. Current ideas related to application of the Monte Carlo technique for studying peptides and biopolymers have been discussed in this review.

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