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189 results on '"Li, Zhengtong"'

21. Efficient Interfacial Solar Steam Generator with Controlled Macromorphology Derived from Flour via 'Dough Figurine' Technology

Author

Li, Zhengtong, Wang, Chengbing, Li, Zeyu, Deng, Lin, Su, Jinbu, Shi, Jing, and An, Meng

Subjects
Physics - Applied Physics
Abstract

Solar-driven interface steam generator (SISG) is a most promising technology for seawater desalination and wastewater purification. A shape- and size-controlled, low-cost, eco-friendly solar-absorber material is urgently desired for practical application of SISG. Herein, we proposed a facile, sustainable and scalable approach to produce tailored SISG with controlled macromorphology derived from flour via "dough figurine" technology which is originated from the China Han Dynasty. Three kinds of self-floated flour-based absorbers i.e. near-cylindrical (integrated), near-spherical (loose packing) and powdery (dense packing) absorber used as SISGs were discussed, we found that the macromorphology significantly influences water transport and interfacial thermal management of SISG, the integrated absorber has an overwhelming advantage, which possesses a high evaporation efficiency 71.9% at normal solar illumination. The proposed "dough figurine" technology breaks the limitations of the inherent geometry of reported biomass based SISG, which provides an important guidance for SISG use in remote and impoverished areas., Comment: 31 Pages, 5 Figures

Published
2019

41. Introduction

Author

Ming, Tingzhen, Peng, Chong, Gong, Tingrui, Li, Zhengtong, Ming, Tingzhen, Peng, Chong, Gong, Tingrui, and Li, Zhengtong

Published
2017
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46. Enhancing the accuracy of physics-informed neural networks for indoor airflow simulation with experimental data and Reynolds-averaged Navier–Stokes turbulence model.

Author

Zhang, Chi, Wen, Chih-Yung, Jia, Yuan, Juan, Yu-Hsuan, Lee, Yee-Ting, Chen, Zhengwei, Yang, An-Shik, and Li, Zhengtong

Subjects
AIR flow, TURBULENCE, DEEP learning, DIFFERENTIAL equations, RESEARCH personnel, ACQUISITION of data
Abstract

Physics-informed neural network (PINN) has aroused broad interest among fluid simulation researchers in recent years, representing a novel paradigm in this area where governing differential equations are encoded to provide a hybrid physics-based and data-driven deep learning framework. However, the lack of enough validations on more complex flow problems has restricted further development and application of PINN. Our research applies the PINN to simulate a two-dimensional indoor turbulent airflow case to address the issue. Although it is still quite challenging for the PINN to reach an ideal accuracy for the problem through a single purely physics-driven training, our research finds that the PINN prediction accuracy can be significantly improved by exploiting its ability to assimilate high-fidelity data during training, by which the prediction accuracy of PINN is enhanced by 53.2% for pressure, 34.6% for horizontal velocity, and 40.4% for vertical velocity, respectively. Meanwhile, the influence of data points number is also studied, which suggests a balance between prediction accuracy and data acquisition cost can be reached. Last but not least, applying Reynolds-averaged Navier–Stokes (RANS) equations and turbulence model has also been proved to improve prediction accuracy remarkably. After embedding the standard k–ε model to the PINN, the prediction accuracy was enhanced by 82.9% for pressure, 59.4% for horizontal velocity, and 70.5% for vertical velocity, respectively. These results suggest a promising step toward applications of PINN to more complex flow configurations. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Particle size optimization of metal–organic frameworks for superior capacitive deionization in oxygenated saline water

Author

Xing, Zhiyuan, primary, Xuan, Xiaoxu, additional, Hu, Haiyan, additional, Li, Mohua, additional, Gao, Huimin, additional, Alowasheeir, Azhar Salman, additional, Jiang, Dong, additional, Zhu, Liyang, additional, Li, Zhengtong, additional, Kang, Yunqing, additional, Zhang, Jing, additional, Yi, Xibin, additional, Yamauchi, Yusuke, additional, and Xu, Xingtao, additional

Published
2023
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