IRANIAN JOURNAL OF MARINE TECHNOLOGY
Year:2017 | Volume:4 | Issue:1 |Papers Count:7

Intelligent and autonomous UAV’s navigation, which is based on the compliance of received images from UAVs with those from satellites, is one of the newest types of navigation that has received much attentions from researchers and industrialists of this area. This approach is effective, in terms of electronic warfare and efficiency, when high-quality images are available so that the effective features of images can be extracted. However, blurring is one of the main destructive factors leading to decrease the extraction rate and then weak satellites’ images adaptation. So, Image de-blurring has become a new challenging issue for researchers. In this paper, a new method is proposed for improving image quality, using super-resolution techniques based on Convolutional Neural Network (CNN) with non-linear multi-layer mapping, which plays an important role in de-blurring and removing noises from UAV images. The simulation results show that the proposed method has much better performance compared to the other benchmark techniques in term of peak signal to noise ratio (PSNR) so that the proposed method increases the aforementioned criteria about 5%.

In this paper, the problem of improved parameter estimation and modelling of two subsystems of Autonomous Underwater Vehicle (AUV) in the horizontal and vertical planes is addressed, in the presence of the measurement noise. To this end, two identification strategies including the adaptive parameter identifier and the error least square (LS) identifier are designed and investigated, as two low-cost methods to identify the system model based on the information, extracted from practical tests. In both of the proposed identifiers, the selection problem of the appropriate values of the identifiers design parameters, as an important factor in order to improve process performance estimation, by using Particle Swarm Optimization (PSO) algorithm. It is shown that, this approach reduces the limitations in the practical methods. By applying the appropriate input signal, the system parameters have been finally given by utilizing both the adaptive identifier and the error least square identifier. Taking into account the measurement noise, it is shown that the latter approach provide better performance concerning the convergene rate and the accuracy. The simulation results are also presented to demonstrate and compare the performance of the mentioned identification schemes.

One of the most important of ship motions in sea waves is roll, which causes transverse and vertical accelerations and loss of operational performance of the frigate warship by making limitation in comfort, workability, safety, reduce the performance of weapons and navigation equipment as well as increase the mission time and energy consumption. To prevent these events it is essential selection of suitable stabilizer and control system .The purpose of this research is, analayze of roll motion and suitable controller design for decreasing the amplitude of this motion. for this is extracted suitable fin-roll model as well as computational fluid dynamics method for the fin-roll stabilizer, and flow analysis was done. Because there are fin constraints, in particular the dynamic stall and mechanical fin angle saturation, the constrained control was designed for fin-roll system and the results were compared with PID controller results. The results showed the LQR control had positive effects on significant reduction in the roll amplitude and achieving to the roll operational performance of the frigate warship according to NATO standard.

Designing a proper hull-form has always been considered an important issue in hydrodynamics of planing boats. Any small variation of geometry may result in significant changes in boat performance. In the current paper, it is attempted to develop a mathematical model for prediction of the performance of planing boats with variable deadrise in longitudinal direction. To accomplish this task, the 2.5D theory and water impact problem are utilized. Effects of deadrise variation from one section to another has been taken into account in the equations. Hydrodynamic, hydrostatic, and drag forces are determined. A new equation is extracted for computing the whisker spray area. By using the current method, effects of deadrise variation on the hydrodynamic performance of a planing boat are examined. It is concluded that a slight slope variation of deadrise angle may positively affect the performance and lead to a smaller trim angle and resistance in comparison with the prismatic hull-forms.

Trimaran is a type of multihull vessels that consists of one long slender centre hull with two outriggers or side hulls connected to main hull by a cross structure. However these side hulls have a significant effect on the stability, dynamic behavior, seakeeping performance and wave-induced structural loads. Due to its superior seagoing performance this structure has been of interest to naval architects, especially in commercial and military jobs.Compared to mono-hull ships, a trimaran will be exposed to different structure loads. Because of existence of cross structure, this vessel experience transverse wave loads except for longitudinal wave loads. These loads include transverse bending moments, transverse torsional moments and transverse shear forces that arise from interaction between side hulls and main hull. Furthermore, the fluid load acting on a side hull will transfer to the main hull by cross structure. In this article hydrodynamic loads of cross deck of trimaran was calculated using a 3D panel method based on the linear potential flow theory in the frequency domain using Maestro software. The results showed that in beam seas the responses are not sensitive to changes in the vessel speeds and only depended on wave frequencies. Moreover peak magnitudes accrueat the vessel speed of 25 knots in the Bow Quartering Seas (135 degree).

In this study, we have evaluated generation and development of sandy bed ripples under controlled wave conditions using physical modeling.Experiments of this study have been done in experimental flume of Soil Conservation and Watershed Management Research Institute (SCWMRI). Wave flume has 20m length, 5.5m width and 2m height that have been separated to three sections by two walls. Our model has been setup in 3 parts with different slopes of 0.01, 0.02 and 0.03.Physical simulation tests were carried out in two different series to investigate the bed changes during the time(in 4 runs) as well as the effects of different heights and periods of waves on ripples (in 8 runs). Results showed that increasing in wave height and period will caused increase of ripples height and wave length. Also increase of wave heights, will caused more amounts of transported sediments. As well as, bed ripples will be become more asymmetrical with increasing in wave period.we suggested relations for predicting ripple's dimension less height and wavelength by using physical model data together with a large existent set of experimental and field data. Calculating statistical indexes shows these relations have more precious in predicting ripples geometry towards existed relations.

However recognizing the weaknesses and strengths of these models in simulation indicates the importance of conducting a comparison between numerical models since they are used in critical marine districts and projects. In this article the wind-generated waves in the southern Caspian are simulated using SWAN and MIKE21 models. Than the present study then analyzed the results obtained from the application of these two models and comparing them together during one year. The results show that although the fluctuations of the two models are relatively alike, in analyzing the relative deviation of the two models, the MIKE21 SW showed a smaller size of relative deviation than the SWAN model and provided results that were more close to the reality. The maximum size of deviation in both models of SWAN and MIKE21 SW is in the range of 16.7 to 26.2 percent. The results shows that the SWAN model has a slower decrease of fluctuation and in contrast the MIKE 21 SW shows a slower increase of fluctuation, that makes the MIKE 21 model more sensitive to the changes in the input wind details.