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RISS 인기검색어
- 검색키워드
- 저자 : Xu Yikai (검색결과 3 건)
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Elbow flow analysis and optimal exhaust port profile design
Yang Xu,Shuhe Huang,Liwang Su,Xiangyu Zhang,Yikai Li 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.3
In this study, the exhaust port profile of a diesel engine was numerically analyzed and optimized. First, the elbow profile results show that the separation loss of lower profile plays the main role in overall separation loss, it decreases sharply and then slowly with an increase of r lower /d. Within the range of 0 ≤ r lower /d ≤ 5, the total secondary flow loss increases sharply and then slowly with an increase of r lower /d. Under the same condition of relative pressure difference and lower profile, the elbow discharge coefficient increases with an increase of d mid /r upper and remains almost constant when d mid /r upper >1. The maximum discharge coefficient under a certain r lower condition increases with an increase of r lower . Afterwards, the applicability of elbow profile results to exhaust port were verified and an exhaust port profile design method was established based on a circular arc profile. Finally the feasibility of exhaust port profile design method was verified experimentally.
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FLOW ANALYSIS AND THEORETICAL DESIGN OF DIESEL ENGINE EXHAUST PORT
Fushui Liu,Yang Xu,Jingmin Rui,Zhongjie Shi,Yikai Li 한국자동차공학회 2020 International journal of automotive technology Vol.21 No.1
Exhaust port is an important part of a diesel engine which affects the combustion and emission. It is important to have a good design for exhaust port. In this paper, the calculation formula of the optimal area ratio (Kopt) of outlet and inlet of exhaust port was derived. The formula shows that Kopt is only determined by relative pressure difference (RPD). Then influence of the area ratio (K) of outlet and inlet on flow capacity of exhaust port and the trend of Kopt with relative pressure difference were studied numerically to verify the validity and applicability of the formula. The numerical results shows that the mass flow rate increases first and then keeps constant with the increase of K at the same RPD. Kopt exists and decreases first and then keeps constant with the increase of RPD. The numerical trend is consistent with theoretical trend and the errors between maximum mass flow rates of numerical results and actual mass flow rates at theoretical optimal area ratios are all less than 5 % meeting engineering accuracy. It is indicated that the derived formula is correct and applicable. When an exhaust port is designed, the outlet area can be estimated by this formula.
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Yan Chenggong,Lin Jie,Li Haixia,Xu Jun,Zhang Tianjing,Chen Hao,Woodruff Henry C.,Wu Guangyao,Zhang Siqi,Xu Yikai,Lambin Philippe 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.6
Objective: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. Materials and Methods: Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signedrank test were performed to compare the objective measurements and the subjective image quality scores, respectively. Results: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 ± 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. Conclusion: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.
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