A continuously variable displacement mechanism, which is composed of a hydraulic control valve with mechanical-positional feedback to camshaft, was designed for changing the displacement of traditional camshaft connec...A continuously variable displacement mechanism, which is composed of a hydraulic control valve with mechanical-positional feedback to camshaft, was designed for changing the displacement of traditional camshaft connecting-rod low speed high torque (LSHT) hydraulic motor continuously. The new type of continuously variable displacement mechanism is simple and easy to be made. The structure and principle of a continuously variable displacement mechanism was introduced. The mathematic model of the continuously variable displacement mechanism was set up and its static and dynamic characteristics were analyzed with the help of computer simulation. It can be seen that the cam ring on camshaft of the traditional LSHT hydraulic motor can stop at any position between minimum and maximum eccentricity, according to an input fluid pressure signal. And it can also stay anywhere stably through self-adjusting. Besides, it can work stabilized when load impact or oil leakage exists.展开更多
Hydrological risk is highly dependent on the occurrence of extreme rainfalls.This fact has led to a wide range of studies on the estimation and uncertainty analysis of the extremes.In most cases,confidence intervals(C...Hydrological risk is highly dependent on the occurrence of extreme rainfalls.This fact has led to a wide range of studies on the estimation and uncertainty analysis of the extremes.In most cases,confidence intervals(CIs)are constructed to represent the uncertainty of the estimates.Since the accuracy of CIs depends on the asymptotic normality of the data and is questionable with limited observations in practice,a Bayesian highest posterior density(HPD)interval,bootstrap percentile interval,and profile likelihood(PL)interval have been introduced to analyze the uncertainty that does not depend on the normality assumption.However,comparison studies to investigate their performances in terms of the accuracy and uncertainty of the estimates are scarce.In addition,the strengths,weakness,and conditions necessary for performing each method also must be investigated.Accordingly,in this study,test experiments with simulations from varying parent distributions and different sample sizes were conducted.Then,applications to the annual maximum rainfall(AMR)time series data in South Korea were performed.Five districts with 38-year(1973–2010)AMR observations were fitted by the three aforementioned methods in the application.From both the experimental and application results,the Bayesian method is found to provide the lowest uncertainty of the design level while the PL estimates generally have the highest accuracy but also the largest uncertainty.The bootstrap estimates are usually inferior to the other two methods,but can perform adequately when the distribution model is not heavy-tailed and the sample size is large.The distribution tail behavior and the sample size are clearly found to affect the estimation accuracy and uncertainty.This study presents a comparative result,which can help researchers make decisions in the context of assessing extreme rainfall uncertainties.展开更多
文摘A continuously variable displacement mechanism, which is composed of a hydraulic control valve with mechanical-positional feedback to camshaft, was designed for changing the displacement of traditional camshaft connecting-rod low speed high torque (LSHT) hydraulic motor continuously. The new type of continuously variable displacement mechanism is simple and easy to be made. The structure and principle of a continuously variable displacement mechanism was introduced. The mathematic model of the continuously variable displacement mechanism was set up and its static and dynamic characteristics were analyzed with the help of computer simulation. It can be seen that the cam ring on camshaft of the traditional LSHT hydraulic motor can stop at any position between minimum and maximum eccentricity, according to an input fluid pressure signal. And it can also stay anywhere stably through self-adjusting. Besides, it can work stabilized when load impact or oil leakage exists.
基金supported by Hanyang University(Grant No.HY-2014)
文摘Hydrological risk is highly dependent on the occurrence of extreme rainfalls.This fact has led to a wide range of studies on the estimation and uncertainty analysis of the extremes.In most cases,confidence intervals(CIs)are constructed to represent the uncertainty of the estimates.Since the accuracy of CIs depends on the asymptotic normality of the data and is questionable with limited observations in practice,a Bayesian highest posterior density(HPD)interval,bootstrap percentile interval,and profile likelihood(PL)interval have been introduced to analyze the uncertainty that does not depend on the normality assumption.However,comparison studies to investigate their performances in terms of the accuracy and uncertainty of the estimates are scarce.In addition,the strengths,weakness,and conditions necessary for performing each method also must be investigated.Accordingly,in this study,test experiments with simulations from varying parent distributions and different sample sizes were conducted.Then,applications to the annual maximum rainfall(AMR)time series data in South Korea were performed.Five districts with 38-year(1973–2010)AMR observations were fitted by the three aforementioned methods in the application.From both the experimental and application results,the Bayesian method is found to provide the lowest uncertainty of the design level while the PL estimates generally have the highest accuracy but also the largest uncertainty.The bootstrap estimates are usually inferior to the other two methods,but can perform adequately when the distribution model is not heavy-tailed and the sample size is large.The distribution tail behavior and the sample size are clearly found to affect the estimation accuracy and uncertainty.This study presents a comparative result,which can help researchers make decisions in the context of assessing extreme rainfall uncertainties.
