To split or not to split: case studies on Monte Carlo analysis of illumination ray tracing concerning the usefulness of ray-splitting

2006 ◽  
Author(s):  
Patrick Le Houillier ◽  
Edward Freniere
Keyword(s):  
Monte Carlo ◽  
Case Studies ◽  
Ray Tracing ◽  
Monte Carlo Analysis

scholarly journals Pairing Directional Solar Inputs From Ray Tracing to Solar Receiver/Reactor Heat Transfer Models on Unstructured Meshes: Development and Case Studies

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
H. Evan Bush ◽  
Andrew J. Schrader ◽  
Peter G. Loutzenhiser
Keyword(s):  
Heat Transfer ◽  
Monte Carlo ◽  
Case Studies ◽  
Ray Tracing ◽  
Three Dimensional ◽  
Transfer Model ◽  
Reactor Model ◽  
Ansys Fluent ◽  
Ray Tracing Model ◽  
Transfer Models

Abstract A novel method for pairing surface irradiation and volumetric absorption from Monte Carlo ray tracing to computational heat transfer models is presented. The method is well-suited to directionally and spatially complex concentrated radiative inputs (e.g., solar receivers and reactors). The method employs a generalized algorithm for directly mapping absorbed rays from a Monte Carlo ray tracing model to boundary or volumetric source terms in the computational mesh. The algorithm is compatible with unstructured, two and three-dimensional meshes with varying element shapes. Four case studies were performed on a directly irradiated, windowed solar thermochemical reactor model to validate the method. The method was shown to conserve energy and preserve spatial variation when mapping rays from a Monte Carlo ray tracing model to a computational heat transfer model in ansys fluent.

Generation of Correlated Logistic-Normal Random Variates for Medical Decision Trees

1998 ◽  
Vol 37 (03) ◽  
pp. 235-238 ◽  
Author(s):  
M. El-Taha ◽  
D. E. Clark
Keyword(s):  
Monte Carlo ◽  
Decision Trees ◽  
Computer Algebra ◽  
Taylor Series ◽  
Computer Algebra System ◽  
Random Variable ◽  
Monte Carlo Analysis ◽  
Normal Random Variable ◽  
Medical Decision ◽  
Theoretical Results

AbstractA Logistic-Normal random variable (Y) is obtained from a Normal random variable (X) by the relation Y = (ex)/(1 + ex). In Monte-Carlo analysis of decision trees, Logistic-Normal random variates may be used to model the branching probabilities. In some cases, the probabilities to be modeled may not be independent, and a method for generating correlated Logistic-Normal random variates would be useful. A technique for generating correlated Normal random variates has been previously described. Using Taylor Series approximations and the algebraic definitions of variance and covariance, we describe methods for estimating the means, variances, and covariances of Normal random variates which, after translation using the above formula, will result in Logistic-Normal random variates having approximately the desired means, variances, and covariances. Multiple simulations of the method using the Mathematica computer algebra system show satisfactory agreement with the theoretical results.

Monte Carlo Analysis of Nonequilibrium Ionized Flows.

1996 ◽  
Author(s):  
Iain D. Boyd ◽  
Xiaoming Liu ◽  
Jitendra Balakrishnan
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Analysis

scholarly journals Li-ion batteries for peak shaving, price arbitrage, and photovoltaic self-consumption in commercial buildings: A Monte Carlo Analysis

2021 ◽  
Vol 234 ◽  
pp. 113889
Author(s):  
Pietro Elia Campana ◽  
Luca Cioccolanti ◽  
Baptiste François ◽  
Jakub Jurasz ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Analysis ◽  
Commercial Buildings ◽  
Li Ion Batteries ◽  
Peak Shaving ◽  
Li Ion

A detailed experimental and Monte Carlo analysis of gold nanoparticle dose enhancement using 6 MV and 18 MV external beam energies in a macroscopic scale

2021 ◽  
Vol 171 ◽  
pp. 109638
Author(s):  
Tara Gray ◽  
Nema Bassiri ◽  
Shaquan David ◽  
Devanshi Yogeshkumar Patel ◽  
Sotirios Stathakis ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Gold Nanoparticle ◽  
Monte Carlo Analysis ◽  
External Beam ◽  
Macroscopic Scale ◽  
Dose Enhancement
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