Propeller is one of the important marine propulsors for generating thrust to overcome the ship resistance. This paper presents the anlaysis of the skew propeller and propulsion for a submarine by using the Boundary Element Method (BEM). This method is useful for analysis and design of lifting bodies like hydrofoils and propellers. A five-bladed Highly Skewed Propeller (HSP) has been chosend for a submarine of 120 [Tones] with forward constant speed 8 [knots] and the calculated results of the hydrodynamic performance have presented. The efficiency values have been obtained 0.65 and 0.68 at two surfaces and submerged conditions, respectively.

Evaluating the groundwater level in arid and semi-arid regions of the country requires accurate prediction and efficiency of its fluctuations. The use of modern methods, including evolutionary algorithms, artificial neural networks and fuzzy methods, is very useful for prediction the groundwater level and generating artificial water surface data due to its high efficiency. In this research, by using Election and Imperialist Competitive Algorithms, artificial neural network, monthly data for 9 years as well as groundwater level of 10 wells, predicted the 7-year the groundwater level of Reshtkhar plain in khorasan-Razavi. In order to train the models, the statistic data was provided on 10 observation wells with a 9-year (2002-2014), which 70% of the data was introduced as training data to the model and 30% of the data was used as a test for calibration of the model. The results of the Election Algorithm predicted Reshtkhar groundwater level for the year 1400, between 14 to 16. 5 meters in diffirent areas of the plain. Based on the calculations and the results obtained from the statistical parameters, the Election algorithm was RMSE, R2 and NSE, 0. 029, 0. 90 and 0. 73 respectively, compared with the two methods of artificial neural network and Imperialist Competitive Algorithm has a significant ability to predicte the groundwater level.

In this paper, a new approach for orbital position prediction of satellites, is introduced. As traditional methods are based on keplerian equations of motion, orbital disturbances are uasualy neglected for simplicity. This paper, suggests artificial intelligent time series peridiction methods for orbital position prediction of satellites. The advantage of this method is based on usage of actual data, so all disturbances are taken into account. For this reason use of TLE as the most reachable actual data is considered. Compariosion of output of this method with actual data, proofs the accuracy of proposed method.