The effect of buoyancy orientation on turbulent channel flow has been investigated using DNS (direct numerical simulation). Grashof number is kept at 9.6 $\times 10^{5}$ while changing the orientation of the buoyancy vector to be parallel or perpendicular to the channel walls. Four study cases can be distinguished during this research namely; streamwise, wall-normal unstable stratification, wall-normal stable stratification and spanwise oriented buoyancy. The driving mean pressure gradient used in all cases is adjusted to keep mass flow rate constant while friction Reynolds number is around 150. At this Grashof number, the skin friction shows decrement in the unstable and stable stratification and increment in the other two cases. Analyses of the changes of flow structure for the four cases are presented highlighting on the mean quantities and second order statistics.

This paper addresses details of wave load nonlinearity effect on stress RAO and damage ratio using component stochastic fatigue analysis. Traditional spectral fatigue analysis for ship structure is based on linear theory; however, there are a number of nonlinearity sources. Especially loading nonlinearity, such as hydrodynamic pressure applying to ship side and gravity changes due to roll and pitch motion, is thought to critically violate the linearity assumption of spectral fatigue analysis, which involves stress RAO as linear parameter. The main focus is placed on how to idealize complicated characteristics of loading nonlinearity and how to implement the nonlinear bias to linear spectral fatigue analysis.

Increased usage of AUVs has led to the development of alternative navigational methods that use the acoustic beacons and dead reckoning. This paper describes a concurrent mapping and localization (CML) scheme that uses range sonars mounted on SNUUV­I, which is a small test AUV developed by Seoul National University. The CML is one of such alternative navigation methods for measuring the environment that the vehicle is passing through. In addition, it is intended to provide relative position of AUV by processing the data from sonar measurements. A technique for CML algorithm which uses several ranging sonars is presented. This technique utilizes an extended Kalman filter to estimate the location of the AUV. In order for the algorithm to work efficiently, the nearest neighbor standard filter is introduced as the algorithm of data association in the CML for associating the stored targets the sonar returns at each time step. The proposed CML algorithm is tested by simulations under various conditions. Experiments in a towing tank for one dimensional navigation are conducted and the results are presented. The results of the simulation and experiment show that the proposed CML algorithm is capable of estimating the position of the vehicle and the object and demonstrates that the algorithm will perform well in the real environment.

The performance of optimisation methods, based on penalty functions, is highly problem- dependent and many methods require additional tuning of some variables. This additional tuning is the influences of penalty coefficient, which depend strongly on the degree of constraint violation. Moreover, Binary-coded Genetic Algorithm (BGA) meets certain difficulties when dealing with continuous and/or discrete search spaces with large dimensions. With the above reasons, Real-coded Micro-Genetic Algorithm (R$\mu$GA) is proposed to find the global optimum of continuous and/or discrete nonlinear constrained engineering problems without handling any of penalty functions. R$\mu$GA can help in avoiding the premature convergence and search for global solution-spaces, because of its wide spread applicability, global perspective and inherent parallelism. The proposed R$\mu$GA approach has been demonstrated by solving three different engineering design problems. From the simulation results, it has been concluded that R$\mu$GA is an effective global optimisation tool for solving continuous and/or discrete nonlinear constrained real­world optimisation problems.