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Your search keyword '"Sun, Yangting"' showing total 135 results
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135 results on '"Sun, Yangting"'

6. Mixed potential model for passivity characters of hyper-duplex stainless steel 2707 in ammonium carbonate solution containing chloride

Author

Sun, Li, Zhao, Tianyu, Qiu, Jie, Sun, Yangting, Li, Weihua, Zheng, Haibing, Lin, Zhifeng, Chen, Si, Jiang, Yiming, Li, Jin, and Macdonald, Digby D

Subjects
HDSS 2707, Point defect model, Mixed potential model, Passivity, Applied potential, Civil Engineering, Materials Engineering, Mechanical Engineering, Energy
Abstract

A mixed potential model (MPM) comprising the Point Defect Model (PDM) and the Generalized Butler Volmer Equation (GBVE) was successfully developed to study the passivity of HDSS 2707. The results show that the dominant point defect within the passive film is the Min+, thereby rendering n-type semiconductor behavior of the barrier layer. The anodic current is independent of the film formation potential whereas it increases with increasing temperature and follows the Arrhenius law. The partial cathodic current of hydrogen reaction is affected by the applied voltage. The anodizing constant of the passive film is calculated to be 2.91 nm/V.

Published
2022

7. FNAS: Uncertainty-Aware Fast Neural Architecture Search

Author

Liu, Jihao, Zhang, Ming, Sun, Yangting, Liu, Boxiao, Song, Guanglu, Liu, Yu, and Li, Hongsheng

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Reinforcement learning (RL)-based neural architecture search (NAS) generally guarantees better convergence yet suffers from the requirement of huge computational resources compared with gradient-based approaches, due to the rollout bottleneck -- exhaustive training for each sampled generation on proxy tasks. In this paper, we propose a general pipeline to accelerate the convergence of the rollout process as well as the RL process in NAS. It is motivated by the interesting observation that both the architecture and the parameter knowledge can be transferred between different experiments and even different tasks. We first introduce an uncertainty-aware critic (value function) in Proximal Policy Optimization (PPO) to utilize the architecture knowledge in previous experiments, which stabilizes the training process and reduces the searching time by 4 times. Further, an architecture knowledge pool together with a block similarity function is proposed to utilize parameter knowledge and reduces the searching time by 2 times. It is the first to introduce block-level weight sharing in RLbased NAS. The block similarity function guarantees a 100% hitting ratio with strict fairness. Besides, we show that a simply designed off-policy correction factor used in "replay buffer" in RL optimization can further reduce half of the searching time. Experiments on the Mobile Neural Architecture Search (MNAS) search space show the proposed Fast Neural Architecture Search (FNAS) accelerates standard RL-based NAS process by ~10x (e.g. ~256 2x2 TPUv2 x days / 20,000 GPU x hour -> 2,000 GPU x hour for MNAS), and guarantees better performance on various vision tasks.

Published
2021

8. Point defect model for passivity breakdown on hyper-duplex stainless steel 2707 in solutions containing bromide at different temperatures

Author

Sun, Li, Zhao, Tianyu, Qiu, Jie, Sun, Yangting, Li, Kuijiao, Zheng, Haibing, Jiang, Yiming, Li, Yanhui, Li, Jin, Li, Weihua, and Macdonald, Digby D

Subjects
HDSS 2707, Bromide solution, Passivity breakdown, Point Defect Model, Civil Engineering, Materials Engineering, Mechanical Engineering, Energy
Abstract

Passivity breakdown on HDSS 2707 has been studied and the electrochemical data are interpreted in terms of the Point Defect Model. Pitting parameters of HDSS 2707 are determined, including the polarizability at bl/s interface, the defect annihilation rate, and the defect diffusion coefficient. The breakdown potential is demonstrated to be linearly related to log [Br-], pH, and the square root of potential scan rate (v1/2), and follows a near-normal distribution. The critical vacancy concentration calculated from the PDM is consistent with that estimated crystallographically from the chromic barrier layer, and the dominant point defect is further confirmed by Mott-Schottky measurements.

Published
2022

35. Cover Picture: Materials and Corrosion. 5/2023

Author

Tan, Xin, primary, Lan, Rulei, additional, Yao, Qiong, additional, Tu, Qiyong, additional, Lei, Longlin, additional, Zhang, Jun, additional, Sun, Yangting, additional, Li, Jin, additional, and Jiang, Yiming, additional

Published
2023
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41. Influence of the machined surface roughness on the pitting behavior of 304 stainless steel.

Author

Tan, Xin, Lan, Rulei, Yao, Qiong, Tu, Qiyong, Lei, Longlin, Zhang, Jun, Sun, Yangting, Li, Jin, and Jiang, Yiming

Subjects
PITTING corrosion, SURFACE roughness, STAINLESS steel, CRITICAL temperature, ARITHMETIC mean, IRON chlorides
Abstract

The relationship between specimens with different machined surface roughness and pitting resistance was explored using the critical pitting temperature (CPT), critical pitting potential (Ep), and FeCl3 pitting immersion tests. Compared with the arithmetic mean deviation of the surface contour (Ra), the depth–width ratio could characterize the mathematical relationship between the machined surface roughness and pitting parameters more simply. With an increase in the depth–width ratio, the CPT and Ep of 304 stainless steel decreased, the average corrosion rate increased, the number of pits decreased, and the pit sizes increased. A larger depth–width ratio led to a larger effective diffusion length of metal cations in the pits, thus, the critical pitting criteria could be reached easily, which resulted in the reduction of the pitting resistance of 304 stainless steel. [ABSTRACT FROM AUTHOR]

Published
2023
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