Bahiuddin, Irfan and Wibowo, Setyawan Bekti and Syairaji, M. and Putra, Jimmy Trio and Pandito, Cahyo Adi and Maulana, Ahdiar Fikri and Prastica, Rian Mantasa Salve and Nazmi, Nurhazimah (2021) A systematic approach to predict the behavior of cough droplets using feedforward neural networks method. Fluids, 6 (2). ISSN 23115521
Full text not available from this repository. (Request a copy)Abstract
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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Item Type: | Article |
---|---|
Additional Information: | Cited by: 2; All Open Access, Gold Open Access |
Uncontrolled Keywords: | droplet; cough; feedforward neural network; machine learning; respiratory system; empirical model |
Subjects: | T Technology > TJ Mechanical engineering and machinery |
Divisions: | Vocational School |
Depositing User: | Sri JUNANDI |
Date Deposited: | 28 Oct 2024 08:23 |
Last Modified: | 28 Oct 2024 08:23 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/8531 |