Sensitivity analysis of an improved method for measuring the radon diffusion coefficient of porous materials

2005 ◽  
pp. 740-748 ◽  
Author(s):  
W.H. van der Spoel ◽  
M. van der Pal
Keyword(s):  
Sensitivity Analysis ◽  
Diffusion Coefficient ◽  
Porous Materials ◽  
Improved Method ◽  
Radon Diffusion

Global sensitivity analysis of multiscale properties of porous materials

2018 ◽  
Vol 123 (7) ◽  
pp. 075103 ◽  
Author(s):  
Kimoon Um ◽  
Xuan Zhang ◽  
Markos Katsoulakis ◽  
Petr Plechac ◽  
Daniel M. Tartakovsky
Keyword(s):  
Sensitivity Analysis ◽  
Porous Materials ◽  
Global Sensitivity Analysis ◽  
Global Sensitivity

scholarly journals Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees

Materials ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1544 ◽  
Author(s):  
Hai-Bang Ly ◽  
Eric Monteiro ◽  
Tien-Thinh Le ◽  
Vuong Minh Le ◽  
Morgan Dal ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Diffusion Coefficient ◽  
Decision Trees ◽  
Mean Squared Error ◽  
Selective Laser Sintering ◽  
Laser Sintering ◽  
Coefficient Of Determination ◽  
Chamber Pressure ◽  
Dissolution Time ◽  
Initial Concentration

The presence of defects like gas bubble in fabricated parts is inherent in the selective laser sintering process and the prediction of bubble shrinkage dynamics is crucial. In this paper, two artificial intelligence (AI) models based on Decision Trees algorithm were constructed in order to predict bubble dissolution time, namely the Ensemble Bagged Trees (EDT Bagged) and Ensemble Boosted Trees (EDT Boosted). A metadata including 68644 data were generated with the help of our previously developed numerical tool. The AI models used the initial bubble size, external domain size, diffusion coefficient, surface tension, viscosity, initial concentration, and chamber pressure as input parameters, whereas bubble dissolution time was considered as output variable. Evaluation of the models’ performance was achieved by criteria such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and coefficient of determination (R2). The results showed that EDT Bagged outperformed EDT Boosted. Sensitivity analysis was then conducted thanks to the Monte Carlo approach and it was found that three most important inputs for the problem were the diffusion coefficient, initial concentration, and bubble initial size. This study might help in quick prediction of bubble dissolution time to improve the production quality from industry.

Sensitivity analysis of heat losses in collective heat distribution systems using an improved method of EPBD calculations

Energy ◽  
2017 ◽  
Vol 140 ◽  
pp. 850-860 ◽  
Author(s):  
Julio Efrain Vaillant Rebollar ◽  
Eline Himpe ◽  
Jelle Laverge ◽  
Arnold Janssens
Keyword(s):  
Sensitivity Analysis ◽  
Distribution Systems ◽  
Heat Losses ◽  
Heat Distribution ◽  
Improved Method

New method and installation for rapid determination of radon diffusion coefficient in various materials

2014 ◽  
Vol 130 ◽  
pp. 7-14 ◽  
Author(s):  
Andrey Tsapalov ◽  
Loren Gulabyants ◽  
Mihail Livshits ◽  
Konstantin Kovler
Keyword(s):  
Diffusion Coefficient ◽  
Rapid Determination ◽  
New Method ◽  
Radon Diffusion
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