The objective of this study was to evaluate the foundation structure of a 3.6-MW wind turbine generator (WTG) installed offshore in Western Korea. The ultimate limit state (ULS) and fatigue limit state (FLS) of the multi-pile steel foundation (MSF) installed at the Saemangeum offshore wind farm were structurally investigated using the finite element (FE) software, ANSYS Workbench 19.0. According to the ULS analysis, no plastic deformation was found in any of the components constituting the substructure. At the same time, the maximal stress value reached the calculation limit of 335 MPa. According to the FLS results, the stress concentration factor (SCF) ranged from 1.00 to 1.88 in all components. The results of this study can be applied to determine the optimal design for MSFs.

In this paper, finite element modeling of the laser ultrasonics (LU) process in ablation regime is of interest. The momentum resulting from the removal of material from the specimen surface by the laser beam radiation in ablation regime is modeled as a pressure pulse. To model this pressure pulse, two equations are required: one for the spatial distribution and one for the temporal distribution of the pulse. Previous researchers have proposed various equations for the spatial and temporal distributions of the pressure pulse in different laser applications. All available equations are examined and the best combination of the temporal and spatial distributions of the pressure pulse that provides the most accurate results is identified. This combination of temporal and spatial distributions has never been used for modeling laser ultrasonics before. Then by using this new model, the effects of variations in pulse duration and laser spot radius on the shape, amplitude, and frequency spectrum of ultrasonic waves are studied. Furthermore, the LU in thermoelastic regime is simulated by this model and compared with LU in ablation regime. The interaction of ultrasonic waves with a defect is also investigated in the LU process in ablation regime. Good agreement of the results obtained from the new finite element model and available experimental data confirms the accuracy of the proposed model.

Torque ripple content and variable switching frequency operation of conventional direct torque control (DTC) are reduced by the integration of space vector modulation (SVM) into DTC. Integration of space vector modulation to conventional direct torque control known as SVM-DTC. It had been more frequently used method in renewable energy and machine drive systems. In this paper, SVM-DTC is used to control the rotor side converter (RSC) of a wind driven doubly-fed induction generator (DFIG) because of its advantages such as reduction of torque ripples and constant switching frequency operation. However, flux and torque ripples are still dominant due to distorted current waveforms at different operations of the wind turbine. Therefore, to smoothen the torque profile a Neural Network Controller (NNC) based SVM-DTC has been proposed by replacing the PI controller in the speed control loop of the wind turbine controller. Also, stability analysis and simulation study of DFIG using process reaction curve method (RRCM) are presented. Validation of simulation study in MATLAB/SIMULINK environment of proposed wind driven DFIG system has been performed by laboratory developed prototype model. The proposed NNC based SVM-DTC yields superior torque response and ripple reduction compared to other methods.

The gear drive of a combat aircraft engine is responsible for power transmission to the different accessories necessary for the engine's operation. Incorrect power transmission can occur due to the presence of failure modes in the gears like bending fatigue, pitting, adhesive wear, scuffing, abrasive wear and polished wear etc. Fault diagnosis of the gear drive is necessary to get an early indication of failure of the gears. The present research is to develop an algorithm using different vibration signal processing techniques on industrial vibration acquisition systems to establish gear fault diagnosis architecture. The signal processing techniques have been used to extract various feature vectors in the development of the fault diagnosis architecture. An open-source dataset of other gear fault conditions is used to validate the developed architecture. The results is a basis for development of artificial intelligence based expert systems for gear fault diagnosis of a combat aircraft engine.