Comparison of Different Ann Approaches in Daily Pan Evaporation Prediction

Nowadays, one of the most important effects on water resources under climate change is increasing of free water surface evaporation which depends on the increasing of temperature. In basins, where there are no observed data, free water surface evaporation is taken into account depending on historical temperature and similar data and their long-term statistics. Predicting of real value of evaporation contains some uncertainties. The modeling of evaporation with a small number of predictors has crucial importance on the regions and basins where measurements are not sufficient and/or not exist. In this presented study, daily evaporation prediction models were prepared by using empirical Penman equation, Levenberg-Marquardt algorithm based on 'Feed Forward Back Propagation Artificial Neural Networks (LMANN)', radial basis neural networks (RBNN), generalized regression neural networks (GRNN). When the models were compared, it was noticed that the results of neural network models are statistically more meaningful than the Penman equation.

An evaporation duct prediction model coupled with the MM5

Evaporation duct is an abnormal refractive phenomenon in the marine atmosphere boundary layer. It has been generally accepted that the evaporation duct prominently affects the performance of the electronic equipment over the sea because of its wide distribution and frequent occurrence. It has become a research focus of the navies all over the world. At present, the diagnostic models of the evaporation duct are all based on the Monin-Obukhov similarity theory, with only differences in the flux and character scale calculations in the surface layer. These models are applicable to the stationary and uniform open sea areas without considering the alongshore effect. This paper introduces the nonlinear factor a and the gust wind item wg into the Babin model, and thus extends the evaporation duct diagnostic model to the offshore area under extremely low wind speed. In addition, an evaporation duct prediction model is designed and coupled with the fifth generation mesoscale model （MMS）. The tower observational data and radar data at the Pingtan island of Fujian Province on May 25-26, 2002 were used to validate the forecast results. The outputs of the prediction model agree with the observations from 0 to 48 h. The relative error of the predicted evaporation duct height is 19.3% and the prediction results are consistent with the radar detection.

Evaporate prediction and compensation of intake port wall-wetting fuel film for spark ignition engines fueled with ethanol-gasoline blends

The fuel dynamic transfer process,including fuel injection,fuel film deposition and evaporation in the intake port,was analyzed for spark ignition(SI) engines with port fuel injection(PFI).The influence of wall-wetting fuel film,especially its evaporation rate,upon the air-fuel ratio of in-cylinder mixtures was also discussed.According to the similarity principle,Fick's law,the ideal gas equation and the Gilliland correlation,an evaporate prediction model of wall-wetting fuel film was set up and an evaporate prediction based dynamic fuel film compensator was designed.Through engine cold start tests,the wall-wetting temperature,which is the key input of the fuel film evaporate prediction model,was also modeled and predicted.Combined with the experimental data of the evaporation characteristics of ethanol-gasoline blends and engine calibration tests,all the parameters of the wall-wetting fuel film evaporate prediction model used in the fuel film compensator were identified.Square-wave disturbance tests of fuel injection showed that with the help of the fuel film compensator the response of the in-cylinder air-fuel ratio was significantly improved and the real air-fuel ratio always closely matched the expected ratio.The fuel film compensator was then integrated into the final air-fuel ratio controller,and the engine tests showed that the air-fuel ratio control error was less than 2% in steady-state conditions,and less than 4% in transient conditions.The fuel film compensator also showed good adaptability to different ethanol-gasoline blends.