Simplified crossover droplet model for adsorption of pure fluids in slit pores

2004 ◽  
Vol 120 (17) ◽  
pp. 8241-8252 ◽  
Author(s):  
S. B. Kiselev ◽  
J. F. Ely
Keyword(s):  
Droplet Model ◽  
Pure Fluids ◽  
Slit Pores

Density of pure fluids

2017 ◽  
Author(s):  
A. Fenghour
Keyword(s):  
Pure Fluids

scholarly journals A Systematic Approach to Predict the Behavior of Cough Droplets Using Feedforward Neural Networks Method

Fluids ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 76
Author(s):  
Irfan Bahiuddin ◽  
Setyawan Bekti Wibowo ◽  
M. Syairaji ◽  
Jimmy Trio Putra ◽  
Cahyo Adi Pandito ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Transmission ◽  
Systematic Approach ◽  
Feedforward Neural Networks ◽  
Feedforward Neural Network ◽  
Horizontal Distance ◽  
Droplet Generator ◽  
Droplet Model ◽  
Droplet Behavior ◽  
Diameter Reduction

Coronavirus disease 2019 (Covid-19) has been identified as being transmitted among humans with droplets from breath, cough, and sneezes. Understanding the droplets’ behavior can be critical information to avoid disease transmission, especially while designing a device deals with human air respiratory. Although various studies have provided enormous computational fluid simulations, most cases are too specific and quite challenging to combine with other similar studies directly. Therefore, this paper proposes a systematic approach to predict the droplet behavior for coughing cases using machine learning. The approach consists of three models, which are droplet generator, mask model, and free droplet model modeled using feedforward neural network (FFNN). The evaluation has shown that the three FFNNs models’ accuracies are relatively high, with R-values of more than 0.990. The model has successfully predicted the evaporation effect on the diameter reduction and the completely evaporated state, which can be considered unlearned cases for machine learning models. The predicted horizontal distance pattern also agrees with the data in the literature. In summary, the proposed approach has demonstrated the capability to predict the diameter pattern according to the experimental or previous work data at various mask face types.

scholarly journals Experiments on the Capillary Condensation/Evaporation Hysteresis of Pure Fluids and Binary Mixtures in Cylindrical Nanopores

2021 ◽  
Vol 125 (10) ◽  
pp. 5802-5815
Author(s):  
Xingdong Qiu ◽  
Huan Yang ◽  
Morteza Dejam ◽  
Sugata P. Tan ◽  
Hertanto Adidharma
Keyword(s):  
Binary Mixtures ◽  
Capillary Condensation ◽  
Pure Fluids

RELATIVISTIC MEAN FIELD THEORETICAL FOUNDATION OF THE DROPLET MODEL FOR NUCLEI

2002 ◽  
Vol 11 (01) ◽  
pp. 55-65 ◽  
Author(s):  
CHUN-YUAN GAO ◽  
QI-REN ZHANG
Keyword(s):  
Mass Formula ◽  
Energy Surface ◽  
Theoretical Foundation ◽  
Mean Field Theory ◽  
Mean Field ◽  
Binding Energies ◽  
Droplet Model ◽  
Relativistic Mean Field ◽  
Theoretical Foundations ◽  
Type Mass

The binding energies per-nucleon for 1654 nuclei, whose mass numbers range from 16 to 263 and charge numbers range from 8 to 106, are calculated by the relativistic mean field theory, with finite nucleon size effect being taken into account. The calculated energy surface goes through the middle of experimental points, and the root mean square deviation for the binding energies per-nucleon is 0.08163 MeV. The numerical results may be well simulated by a droplet model type mass formula. The droplet model is therefore put on the relativistic mean field theoretical foundations.

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