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1,478 results on '"Li, Qingyu"'

109. The finite element modeling of the impacting process of hard particles on pump components

Author

Li Qingyu, Jing Laiyi, Sun Qunying, Ji Li, and Chen Songying

Subjects
finite element method, hard particles, shielded pump, flow passage components, erosion wear, crack propagation, Physics, QC1-999
Abstract

Shield pumps which transport a variety of complex media are widely used in chemical industry, mineral industry, and other fields. However, the hard particles in the media erode the flow passage in the shield pump, seriously affecting the service life of the pump. Therefore, it is important to study the erosion process in the flow passage to extend the service life of the pump. This erosion process in the shield pump can be investigated by experiments, but it is difficult for the current experimental method to copy the real industrial service environment of impellers. For example, the environment temperature and speed range are found difficult to be reached by the current experimental method. Comparatively, the simulation by finite element method can overcome the above deficiency. In this article, the ANASYS software is used to simulate the erosion process in shield pump flow passage due to hard particles. Specifically, the structural static analysis module is used to build the model of thermal barrier coating components and hard particle components. Then the variation law of stress, strain, and material deformation of the flow passage components under concentrated force is obtained. Second, the extended finite element method is used to study the crack propagation caused by the erosion process. A linear elastic–linear softening constitutive model is established to simulate the stress variation during the crack propagation and material removal under the continuous erosion from hard particles. The proposed law regarding stress variation and crack propagation in the erosion process in this study contributes to theoretical support for damage detection and service life extension of shield pumps.

Published
2022
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144. Ensemble-learning error mitigation for variational quantum shallow-circuit classifiers

Author

Li, Qingyu, Huang, Yuhan, Hou, Xiaokai, Li, Ying, Wang, Xiaoting, Bayat, Abolfazl, Li, Qingyu, Huang, Yuhan, Hou, Xiaokai, Li, Ying, Wang, Xiaoting, and Bayat, Abolfazl

Abstract

Classification is one of the main applications of supervised learning. Recent advancements in developing quantum computers have opened a new possibility for machine learning on such machines. Due to the noisy performance of near-term quantum computers, error mitigation techniques are essential for extracting meaningful data from noisy raw experimental measurements. Here, we propose two ensemble-learning error mitigation methods, namely, bootstrap aggregating and adaptive boosting, which can significantly enhance the performance of variational quantum classifiers for both classical and quantum datasets. The idea is to combine several weak classifiers, each implemented on a shallow noisy quantum circuit, to make a strong one with high accuracy. While both of our protocols substantially outperform error-mitigated primitive classifiers, the adaptive boosting shows better performance than the bootstrap aggregating. The protocols have been exemplified for classical handwriting digits as well as quantum phase discrimination of a symmetry-protected topological Hamiltonian, in which we observe a significant improvement in accuracy. Our ensemble-learning methods provide a systematic way of utilizing shallow circuits to solve complex classification problems. © 2024 authors. Published by the American Physical Society.

Published
2024

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