In this study, the effect of bow shape on resistance acting on a hull in regular head waves was investigated by applying a commercial Computational Fluid Dynamics (CFD) code. For this purpose, the hydrodynamic performance as well as the resistance of ships with blunt and bulbous bows were simulated. By analyzing the obtained CFD simulation results, the effects of the bow shape on the hydrodynamic performance and resistance of the ships were found. A new bulbous bow shape with drastically reduced added resistance acting on the hull in waves is proposed. Finally, the obtained CFD results for the hydrodynamic performance of ships are presented.

The aerospace industry needs to be provided with system solutions to technologically challenging and mission-critical problems. Based on the industrial control point of view, development engineers must take costs and existing standards into account in order to effectively design, implement, and deploy control systems with reasonable costs. The customization and reusability are important factors associated with the production of new applications in order to reduce their costs, resources, and development time. In this work, the Model-Driven Architecture (MDA)/Model-Based Systems Engineering (MBSE) approach combined with the real-time Unified Modeling Language (UML)/Systems Modeling Language (SysML), Unscented Kalman Filter (UKF) algorithm, and hybrid automata is specialized to obtain a hybrid control model in order to conveniently deploy controllers of Quadrotor Unmanned Aerial Vehicles (Q-UAVs). This hybrid control model also provides a real-time capsule pattern, which allows the designed elements to be customizable and reusable in new applications of various multirotor UAVs of the Vertical Take-Off and Landing (VTOL) type. The Q-UAV dynamics and control architecture are combined with the MDA/MBSE specialization as follows: the Computation Independent Model (CIM) is defined by specifying the use-case model together with the UKF algorithm and hybrid automata to intensively gather the control requirements. The Platform Independent Model (PIM) is then designed by specializing the real-time UML/SysML’s features to obtain the main control capsules, ports, and protocols, together with their dynamic evolution. The detailed PIM is subsequently transformed into the PSM by open-source platforms to rapidly implement and deploy the Q-UAV controller. The paper ends with trial flights and deployment results that show good feasibility to be used for a trajectory-tracking controller of a low-cost Q-UAV. In this case study, the Q-UAV controller is implemented with the simulation model in the OpenModelica tool. The obtained simulation results then allow the main control elements and their properties to be defined, as well as building the implementation libraries in the Arduino environment based on C++ language to quickly perform the realization model in the ATMEGA32-U2 and STM32 Cortex-M4 microcontrollers.

This paper addresses a formation tracking problem of multiple low-cost underwater drones by implementing distributed adaptive neural network control (DANNC). It is based on a leader-follower architecture to operate in hazardous environments. First, unknown parameters of underwater vehicle dynamics, which are important requirements for real-world applications, are approximated by a neural network using a radial basis function. More specifically, those parameters are only calculated by local information, which can be obtained by an on-board camera without using an external positioning system. Secondly, a potential function is employed to ensure there is no collision between the underwater drones. We then propose a desired configuration of a group of unmanned underwater vehicles (UUVs) as a time-variant function so that they can quickly change their shape between them to facilitate the crossing in a narrow area. Finally, three UUVs, based on a robot operating system (ROS) platform, are used to emphasize the realistic low-cost aspect of underwater drones. The proposed approach is validated by evaluating in different experimental scenarios.