Newsletter  2022.2  Index

Theme :"The Conference of Fluid Engineering Division (February issue)" Preface （T. Hashimoto，S. Matsuda，H.J. Park） Glimpses of Jomon lifestyle from interdisciplinary research Nobuhiko KAMIJO (Hirosaki University) Stress of needle-free injection by focused microjet and study on stability of the jet velocity using machine learning Daichi IGARASHI, Yuta Miyazaki, Masashi USAWA, Shuma KAWAI, Jingzu YEE, Masakazu MUTO, Shoto SEKIGUCHI, Yoshiyuki TAGAWA (Tokyo University of Agriculture and Technology) Numerical Simulation on Nano Fibril Orientational Control by Electric Field in Cellulose Dispersed Flow Takumi USUI (Tohoku University), Hidemasa TAKANA (Tohoku University) Evaluation of Particle Behavior in Radial Turbine for Marine Turbocharger Based on Lagrangian Particle Tracking Model Nao TANIGUCHI, Fumito HIRATANI (MITSUBISHI HEAVY INDUSTRIES, LTD.), Takeshi TSUJI and Hidetaka NISHIMURA (Mitsubishi Heavy Industries Marine Machinery & Equipment, Ltd.) Diary of Flow Sommelier and Sound Sommelier Mari Kasai (Hokkaido University)

 

Stress of needle-free injection by focused microjet and study on stability of the jet velocity using machine learning

Daichi IGARASHI, Yuta Miyazaki, Masashi USAWA, Shuma KAWAI, Jingzu YEE, Masakazu MUTO, Shoto SEKIGUCHI, Yoshiyuki TAGAWA Tokyo University of Agriculture and Technology

Abstract

In this study, we visualized the stress field in human tissue simulant during penetration of needle-free injections of a highly focused microjet and a non-focused microjet⁽¹⁾. We also investigated the interaction between these injection methods and human tissue simulant by evaluating the dynamics of the induced cavity. We measured temporal evolution of cavity shape, stress intensity distribution, and stress vector field by using a high-speed polarization camera. As for the results, the cavity induced by focused microjet was dominated by shear stress and penetrated deep into tissue simulant with stress intensity lower than that by non-focused microjet. In contrast, the cavity induced by non-focused microjet was dominated by compressive stress in high stress intensity and the penetration depth was not as deep as that of a focused microjet. Such results indicate that the jet shape of focused microjet makes it advantageous for the development of minimally invasive medical devices. As the next step for the development of needle-free injection device with the focused microjet, the mechanism of the jet is being investigated with the aid of machine learning. We investigated the important features of laser-induced microjet images that affect the jet velocity by visualizing the image classification process of microjet images by a Feedforward Neural Network (FNN). The visualization of the trained weights suggested that the vapor bubbles and cavitation generated by the laser affect the jet velocity. Furthermore, cavitation has a larger effect on the jet velocity than vapor bubbles. Therefore, this study suggests that the air content in the liquid affects the stability of the jet velocity.

Key words

 needle-free injector, microjet, polarization, stress visualization, machine learning, weight visualization

Figures

Figure 1   (a) Schematic diagram of experimental setup for the photoelastic measurement of stress field induced by (b) focused microjet, (c) non-focused microjet.

Figure 2   (a) Image sequence of stress fields and (b) stress vector fields induced by the penetration of(i) the focused microjet and (ii) the non- focused microjet.

Figure 3   (a) Jet velocity Ujet vs. laser energy E in the experiment and (b) architecture of the FNN used in this study.

Figure 4  (a) Image sequence of the laser-induced microjet injection and (b) colormap of weight W₁for classification of initial bubbles.

References

(1)	Miyazaki, Y. et al.,“Dynamic mechanical interaction between injection liquid and human tissue simulant induced by needle-free injection of a highly focused microjet”, Scientific Reports, vol. 11, No. 1, (2021), pp. 1-10.
(2)	Mitragotri, S., “Current status and future prospects of needle-free liquid jet injectors”, Natural Reviews Drug Discovery, vol. 5, No. 7, (2006), pp. 543–548.
(3)	Tagawa, Y. et al.,“Highly focused supersonic microjets”, Physical Review X, vol. 2, No. 3, (2012), 031002.
(4)	Brunton, S. L. et al., "Machine learning for fluid mechanics", Annual Review of Fluid Mechanics, vol. 52, (2020).
(5)	Adadi, A. et al.,“Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)”, IEEE access, vol. 6, (2018), pp. 52138-52160.
(6)	Pan, Z. et al., “Cavitation onset caused by acceleration”, Proceedings of the National Academy of Science, 114(32), (2017), 8470-8474.