Study on the Microfiber Attenuation in Melt-Blowing Airflow Field

Wanli Han,Yudong Wang,Xinhou Wang,Honglei Yi

TEXTILE RESEARCH JOURNAL（2023）

Jiaxing Univ | Guangxi Univ Sci & Technol | Donghua Univ

Cited 3|Views9

Abstract

Melt blowing (MB) is a process for producing microfiber or nanofiber using high-velocity air to attenuate the polymer jet. Compared to research on the melt-blowing airflow field analysis, the melt blown die optimization, and new materials development, little work has been done on fiber attenuation and whipping in the melt-blowing process. In this study, the airflow field distribution and turbulence fluctuation of the melt-blowing die are investigated using numerical simulation. The fiber motion process is simulated with the bead-viscoelastic element model and obtained using the high-speed camera. The underlying mechanisms leading to melt-blowing fiber attenuation have been discussed. The results show that the airflow field can be separated into three regions. The turbulent fluctuations in Regions I are related to the fiber whipping in the melt-blowing process. The fiber motion in Regions II and III are unsteady, resulting in higher fiber attenuation. Region II is the main zone of fiber attenuation where the fiber whipping increases and the drawing ratio is the largest. The fluctuation of air velocity causes fiber whipping which plays an important role in fiber attenuation. Experimental results of fiber diameter reduction and fiber motion are consistent with the simulation results.

Translated text

Key words

Melt blowing,fiber attenuation,airflow field,simulation

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper