scholarly journals Improvement of k-epsilon turbulence model for CFD simulation of atmospheric boundary layer around a high-rise building using stochastic optimization and Monte Carlo Sampling technique

2017 ◽  
Vol 171 ◽  
pp. 366-379 ◽  
Author(s):  
Mohammadreza Shirzadi ◽  
Parham A. Mirzaei ◽  
Mohammad Naghashzadegan
Keyword(s):  
Boundary Layer ◽  
Monte Carlo ◽  
Stochastic Optimization ◽  
Atmospheric Boundary Layer ◽  
Turbulence Model ◽  
Cfd Simulation ◽  
Sampling Technique ◽  
Monte Carlo Sampling ◽  
High Rise ◽  
High Rise Building

CFD simulation of external CHTC on a high-rise building with and without façade appurtenances

2019 ◽  
Vol 165 ◽  
pp. 106350 ◽  
Author(s):  
Meseret T. Kahsay ◽  
Girma T. Bitsuamlak ◽  
Fitsum Tariku
Keyword(s):  
Cfd Simulation ◽  
High Rise ◽  
High Rise Building

Verification of reference ranges by using a Monte Carlo sampling technique

1994 ◽  
Vol 40 (12) ◽  
pp. 2216-2222 ◽  
Author(s):  
E W Holmes ◽  
S E Kahn ◽  
P A Molnar ◽  
E W Bermes
Keyword(s):  
Monte Carlo ◽  
Sampling Technique ◽  
Monte Carlo Sampling ◽  
Monte Carlo Technique ◽  
Reference Ranges ◽  
Operating Characteristics ◽  
Significance Tests ◽  
Software Program

Abstract We have investigated the application of Monte Carlo significance tests to the verification of reference ranges in the context of the transfer of an established range from one laboratory to another. Here we present an introduction to the Monte Carlo technique, outline a procedure for performing these tests using a commercially available software program, and demonstrate some of the operating characteristics of the tests when they are used to compare samples of different sizes and variances.

Capacity Expansion Problem by Monte Carlo Sampling Method

2009 ◽  
Vol 13 (6) ◽  
pp. 697-703
Author(s):  
Takayuki Shiina ◽  
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Sampling Method ◽  
Sampling Technique ◽  
Capacity Expansion ◽  
Monte Carlo Sampling ◽  
Previous Approach ◽  
Importance Sampling Technique ◽  
Stochastic Vector ◽  
Additive Form

We consider the stochastic programming problem with recourse in which the expectation of the recourse function requires a large number of function evaluations, and its application to the capacity expansion problem. We propose an algorithm which combines an L-shaped method and a Monte Carlo method. The importance sampling technique is applied to obtain variance reduction. In the previous approach, the recourse function is approximated as an additive form in which the function is separable in the components of the stochastic vector. In our approach, the approximate additive form of the recourse function is perturbed to define the new density function. Numerical results for the capacity expansion problem are presented.

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