Numerical investigations on the deposition characteristics of lunar dust in the human bronchial airways--颗粒学报PARTICUOLOGY

Jing, H., Xue, Y., Wu, B., Wang, Y., Xi, Z., & Cui, X. (2025). Numerical investigations on the deposition characteristics of lunar dust in the human bronchial airways. Particuology, 97, 25-38. https://doi.org/10.1016/j.partic.2024.11.018

Numerical investigations on the deposition characteristics of lunar dust in the human bronchial airways

Hao Jing^a, Yuan Xue^b, Bin Wu^b *, Yixiao Wang^{a c}, Zhaojun Xi^a, Xinguang Cui^a *

a School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
b China Astronaut Research and Training Center, Beijing, China
c China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan, China

10.1016/j.partic.2024.11.018

Volume 97, February 2025, Pages 25-38

Received 14 August 2024, Revised 13 October 2024, Accepted 25 November 2024, Available online 7 December 2024, Version of Record 7 January 2025.

E-mail: Wubinacc@sina.com; xinguang_cui@mail.hust.edu.cn

Highlights

• LD deposition is determined by activity intensity, body posture, and its size.

• Difference of LD DE between standing and lying flat postures reaches up to 29%.

• Difference of LD DE between rest and intense activities can reach 70%.

• MLA is more capable to predict the LD DE compared to fitting functions.

Abstract

Although it is widely acknowledged that lunar dust (LD) is toxic to the human health, its deposition characteristics in the bronchial airways remain unknown, which is significantly important to understand its toxicity. Therefore, this study employs computational fluid dynamics and machine learning algorithm methods to address this issue considering the difficulty of conducting the experiments of LD deposition. The major results are: (1) the deposition efficiencies (DE) of micrometer-sized LD in the terminal bronchioles vary significantly depending on the human body posture, with a notable difference of DE up to 29% between standing and lying flat postures; (2) LD deposition in various bronchial regions shows differences under activity intensities, with higher DE in segmental bronchi and terminal bronchioles under intense and lower intensive activities, respectively; (3) In predicting DE of LD, machine learning algorithms outperform fitting functions, achieving higher precision and smaller errors, reducing the root mean square error by approximately 60%–80%. These results indicate that LD deposition characteristics in the bronchial airways under lunar environment are also influenced by the combined factors of particle size, activity intensity, and body posture.

Graphical abstract

Keywords

Deposition of lunar dust; Bronchial airways; Computational fluid dynamics; Machine learning algorithms

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