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Effect of thickness of planar nozzles on erosion depth of levee soils subjected to plunging water

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International Journal of Sediment Research 2018年第3期33卷 243-249页

作者： Chung Rak Song jinwon kim James Tyler Kidd Alexander Cheng David Admiraal Department of Civil Engineering University of Nebraska-Lincoln

In this study, the effect of the thickness of a planar jet on the erosion depth when the jet impinges on a surface composed of cohesive soil was analytically and numerically evaluated. The results showed that the erosion depth was practically independent of the nozzle thickness for erosion depths shallower than the potential core length(i.e. the region of the jet in which the central flow velocity is the same as the nozzle velocity). The relation between nozzle thickness and erosion depth was non-linear with continuously variable slope for erosion depths deeper than the potential core length. Finally, the relation was approximately linear when the erosion depth converged to the equilibrium erosion depth. The findings of this study indicate that direct and fast prediction of the erosion depth in the field is possible using the data from a small scale soil erosion test with similar flow velocities.

关键词： Nozzle thickness Levee erosion Erosion depth Plunging water Planar nozzle

Two Types of Diurnal Variations in Heavy Rainfall during July over Korea

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Advances in Atmospheric Sciences 2021年第12期38卷 2201-2211页

作者： Chang-Kyun PARK Minhee CHANG Chang-Hoi HO Kyung-Ja HA jinwon kim Byung-Ju SOHN School of Earth and Environmental Sciences Seoul National UniversitySeoul 08826Korea School of Earth and Environmental Systems Pusan National UniversityBusan 46241Korea Center for Climate Physics Institute for Basic ScienceBusan 46241Korea National Institute of Meteorological Sciences Korean Meteorological AdministrationJeju 63568Korea

This study examined the characteristics of the diurnal variations of heavy rainfall(≥110 mm in 12 hours)in Korea and the related atmospheric circulation for July from 1980−*** the analysis period,two dominant pattens of diurnal variation of the heavy rainfall emerged:all-day heavy rainfall(AD)and morning only heavy rainfall(MO)*** the AD-type,the heavy rainfall is caused by abundant moisture content in conjunction with active convection in the morning(0000−1200,LST;LST=UTC+9)and the afternoon hours(1200−2400 LST).These systems are related to the enhanced moisture inflow and upward motion induced by the strengthening of the western North Pacific subtropical high and upper-tropospheric *** the MO-type,heavy rainfall occurs mostly in the morning hours;the associated atmospheric patterns are similar to the *** find that the atmospheric pattern related to severe heavy rainfalls in 2020 corresponds to a typical AD-type and resembles the 1991 heavy-rainfall system in its overall synoptic/mesoscale *** present results imply that extremely heavy rainfall episodes in Korea during the 2020 summer may occur again in the future associated with the recurring atmospheric phenomenon related to the heavy rainfall.

关键词： heavy rainfall diurnal cycle of rainfall 2020 summer summer rainy season Korea

Antiinflammatory and analgesic effect of herbal cocktail Hongbaekjeong via inhibition of proinflammatory cytokines and prostaglandin E2 release

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Chinese Science Bulletin 2014年第25期59卷 3127-3133页

作者： jinwon Lim Hyo-Jung Lee Bonglee kim Jeong-Un Choi Yoeok Shin Eun Jung Sohn Sung-Hoon kim Cancer Preventive Material Development Research Center College of Oriental Medicine Kyung Hee University

Though Hongbaekjeong(HBJ),a hebal mixture of three medicinal plants,has been traditionally used for arthritis and muscular pain,its scientific evidence still remains ***,in the present study,analgesic and anti-inflammatory mechanism of HBJ was evaluated in vitro and in *** significantly reduced NO production and prostaglandin E2(PGE2)release and also attenuated the expression of cyclooxygenase 2(COX-2)in lipopolysaccharides(LPS)and interferon(IFN)-c treated RAW 264.7 ***,HBJ abrogated the production of proinflammatory cytokines such as interleukin(IL)1b,IL-6,IL-8 and monocyte chemoattractant protein-1(MCP-1)in LPS and IFN-c treated RAW 264.7 *** addition,HBJ significantly decreased the number of writhing syndrome induced by acetic acid,and also increased latency in hot-plate method and tail flick test in ***,HBJ significantly reduced the edema volume in the hind paw of the rats with arthritis induced by Freund’s complete adjuvant(FCA)compared to untreated ***,our findings demonstrate the antiinflammatory and analgesic potential of HBJ via inhibition of proinflammatory cytokines and PGE2 release for treatment of arthritis and muscular pain.

关键词：前列腺素E2 炎性细胞因子和镇痛抗炎单核细胞趋化蛋白-1 鸡尾酒 IFN-γ

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基于双层数据分解混合模型预测鄱阳湖COD

农业工程学报 2022年第5期38卷 296-302页

作者：陈伟金柱成俞真元王晓丽彭士涛朱哲魏燕杰天津理工大学环境科学与安全工程学院安全处置与资源化技术重点实验室天津300384 理科大学数学系平壤朝鲜999091 交通运输部天津水运工程科学研究所天津300456

化学需氧量(Chemical Oxygen Demand,COD)是衡量水质状况的最重要参数之一,反映水体受还原性物质污染的程度。该研究采用改进的完全集合经验模式分解(Improved Complete Ensemble Empirical Mode Decomposition With Adaptive Noise,ICEEMDAN)、变分模式分解(Variational Mode Decomposition,VMD)相结合的双层数据分解算法,并利用双向长短期记忆(Bidirectional Long Short-term Memory,BLSTM)神经网络,提出了一种混合模型IVB(Improved Complete Ensemble Empirical Mode Decomposition With Adaptive Noise-Variational Mode Decomposition-Bidirectional Long Short-term Memory),并以鄱阳湖高锰酸盐指数(Permanganate index,COD_(Mn))监测数据为研究对象,进行案例研究。结果表明,IVB模型具有良好的预测性能:1 d以后的COD_(Mn)预测中,IVB模型的平均绝对百分比误差为2.21%,与单一BLSTM神经网络模型相比降低了10.57个百分点,而与IB(Improved Complete Ensemble Empirical Mode Decomposition With Adaptive Noise-Bidirectional Long Short-term Memory)模型相比降低了4.62个百分点;7 d以后的COD_(Mn)预测中,IVB模型的平均绝对百分比误差为8.18%,与单一BLSTM神经网络模型相比降低了16.34个百分点,而与IB模型相比降低了4.68个百分点。这项研究表明,所开发的IVB模型可以用作水资源管理的有效分析与决策工具。

关键词：水质机器学习 COD 数据分解样本熵(SE)

基于经验小波变换的鄱阳湖COD_(Mn)预测

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环境工程技术学报 2023年第1期13卷 180-187页

作者：陈伟金柱成俞真元王晓丽彭士涛魏燕杰天津理工大学环境科学与安全工程学院朝鲜理科大学数学系交通运输部天津水运工程科学研究院

高锰酸盐指数(COD_(Mn))是衡量水质状况的最重要参数之一,能反映水体受还原性物质污染的程度。结合经验小波变换(EWT)和双向长短期记忆(BLSTM)神经网络,提出了一种先利用EWT将原始的COD_(Mn)时间序列分解成若干成分,然后利用BLSTM神经... 详细信息

高锰酸盐指数(COD_(Mn))是衡量水质状况的最重要参数之一,能反映水体受还原性物质污染的程度。结合经验小波变换(EWT)和双向长短期记忆(BLSTM)神经网络,提出了一种先利用EWT将原始的COD_(Mn)时间序列分解成若干成分,然后利用BLSTM神经网络对分解出来的每个成分进行预测,最后将所有成分的预测结果重建获得最终COD_(Mn)预测值的新的混合模型EWT-BLSTM;并以2017年8月—2020年4月鄱阳湖COD_(Mn)监测数据为研究对象,进行模型性能验证。结果表明:EWTBLSTM模型具有良好的预测性能,预测未来1 d以后的COD_(Mn)时,EWT-BLSTM模型的平均绝对百分比误差为2.25%,与单一BLSTM神经网络模型相比降低了10.53%;预测未来7 d以后的COD_(Mn)时,EWT-BLSTM模型的平均绝对百分比误差为8.36%,与单一BLSTM神经网络模型相比降低了16.16%。在COD_(Mn)峰值处,该模型依然保持较高稳定的预测性能,说明在数据相对复杂、极端的情况下,该模型依然适用。

关键词：水质预测 COD_(Mn) 经验小波变换(EWT) 双向长短期记忆(BLSTM) 机器学习数学模拟鄱阳湖