Dengzun Yao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Dengzun Yao has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Guiyi Wu
Centre of Excellence for Advanced Materials, Dongguan 523808, China
1 shared publication - Haifei Lu
Wuhan University of Technology
1 shared publication - Hongyun Gao
School of Physics and Mechanics, Wuhan University of Technology, Wuhan 430070, China
1 shared publication - Hui Chen
School of Material Science and Engineering, Southwest Jiaotong University, Chengdu, China
1 shared publication - Lei Sun
School of Physics and Mechanics, Wuhan University of Technology, Wuhan 430070, China
1 shared publication - Min Li
School of Physics and Mechanics, Wuhan University of Technology, Wuhan 430070, China
1 shared publication
Latest Publications
- Open Access
Dual-Resonance Plasmonic Nanocavity with Differential Thermo-Optic Response for Enhanced Fiber-Optic Thermal Flowmeters
Optic-fiber-based thermal flowmeters have the merits of compact size and high sensitivity, which typically require two light beams separately acting as a pump for heating the sensing unit and a probe for sensing temperature with the variation of external flow. Here, we propose a metallic nanostructure with multiple plasmonic resonance modes for the application of an optic-fiber-based thermal flowmeter. The optical properties of a nanostructure comprised of a double-width gold grating, a poly (methylmethacrylate) (PMMA) layer, and a gold film are numerically simulated in the spectral range of 600–1800 nm. The optical resonances of different modes are systematically investigated with the variation of the structural parameters. Interestingly, two optical resonance modes with distinct spectral shift under the same temperature variation, i.e., 21.34 pm/°C vs. 269.2 pm/°C, are obtained after the strategic optimization of the nanostructure. Finally, the sensitivity of the flowmeter with the proposed nanostructure is investigated by adopting the low-temperature sensitivity mode for optical pumping and the high-temperature sensitivity mode for temperature sensing, proving its significant potential as an optic-fiber-based thermal flowmeter.
Open AccessDual-Resonance Plasmonic Nanocavity with Differential Thermo-Optic Response for Enhanced Fiber-Optic Thermal Flowmeters
Optic-fiber-based thermal flowmeters have the merits of compact size and high sensitivity, which typically require two light beams separately acting as a pump for heating the sensing unit and a probe for sensing temperature with the variation of external flow. Here, we propose a metallic nanostructure with multiple plasmonic resonance modes for the application of an optic-fiber-based thermal flowmeter. The optical properties of a nanostructure comprised of a double-width gold grating, a poly (methylmethacrylate) (PMMA) layer, and a gold film are numerically simulated in the spectral range of 600–1800 nm. The optical resonances of different modes are systematically investigated with the variation of the structural parameters. Interestingly, two optical resonance modes with distinct spectral shift under the same temperature variation, i.e., 21.34 pm/°C vs. 269.2 pm/°C, are obtained after the strategic optimization of the nanostructure. Finally, the sensitivity of the flowmeter with the proposed nanostructure is investigated by adopting the low-temperature sensitivity mode for optical pumping and the high-temperature sensitivity mode for temperature sensing, proving its significant potential as an optic-fiber-based thermal flowmeter.
- Open Access
Characterization of Fatigue Damage in Hadfield Steel Using Acoustic Emission and Machine Learning-Based Methods
Structural health monitoring (SHM) of fatigue cracks is essential for ensuring the safe operation of engineering equipment. The acoustic emission (AE) technique is one of the SHM techniques that is capable of monitoring fatigue-crack growth (FCG) in real time. In this study, fatigue-damage evolution of Hadfield steel was characterized using acoustic emission (AE) and machine learning-based methods. The AE signals generated from the entire fatigue-load process were acquired and correlated with fatigue-damage evolution. The AE-source mechanisms were discussed based on waveform characteristics and bispectrum analysis. Moreover, multiple machine learning algorithms were used to classify fatigue sub-stages, and the results show the effectiveness of classification of fatigue sub-stages using machine learning algorithms. The novelty of this research lies in the use of machine learning algorithms for the classification of fatigue sub-stages, unlike the existing methodology, which requires prior knowledge of AE-loading history and calculation of ∆K.
Open AccessCharacterization of Fatigue Damage in Hadfield Steel Using Acoustic Emission and Machine Learning-Based Methods
Structural health monitoring (SHM) of fatigue cracks is essential for ensuring the safe operation of engineering equipment. The acoustic emission (AE) technique is one of the SHM techniques that is capable of monitoring fatigue-crack growth (FCG) in real time. In this study, fatigue-damage evolution of Hadfield steel was characterized using acoustic emission (AE) and machine learning-based methods. The AE signals generated from the entire fatigue-load process were acquired and correlated with fatigue-damage evolution. The AE-source mechanisms were discussed based on waveform characteristics and bispectrum analysis. Moreover, multiple machine learning algorithms were used to classify fatigue sub-stages, and the results show the effectiveness of classification of fatigue sub-stages using machine learning algorithms. The novelty of this research lies in the use of machine learning algorithms for the classification of fatigue sub-stages, unlike the existing methodology, which requires prior knowledge of AE-loading history and calculation of ∆K.