Parent Nested Optimizing Structure for Vibration Reduction in Floating Wind Turbine Structures

A tuned mass damper (TMD) is a system that effectively reduces the vibrations of floating offshore wind turbines (FOWTs). To maximize the performance of TMDs, it is necessary to optimize their design parameters (i.e., stiffness, damping, and installation location). However, this optimization process is challenging because of the existence of multiple local minima. Although various methods have been proposed to determine the global minimum (e.g., exhaustive search, genetic algorithms, and artificial fish swarm algorithms), they are computationally intensive. To address this issue, a novel optimization approach based on a parent nested optimizing structure and approximative search is proposed in this paper. The approximative search determines an initial parameter set (close to the optimal set) with fewer calculations. Then, the global minimum can be rapidly determined using the nested and parent optimizers. The effectiveness of this approach was verified with an FOWT exposed to stochastic winds. The results show that this approach is 30–55 times faster than a conventional global optimization method

Area-Efficient Universal Code Generator for GPS L1C and BDS B1C Signals

Recently, multi-frequency multi-constellation receivers have been actively studied, which are single receivers that process multiple global navigation satellite system (GNSS) signals for high accuracy and reliability. However, in order for a single receiver to process multiple GNSS signals, it requires as many code generators as the number of supported GNSS signals, and this is one of the problems that must be solved in implementing an efficient multi-frequency multi-constellation receiver. This paper proposes an area-efficient universal code generator that can support both GPS L1C signals and BDS B1C signals. The proposed architecture alleviates the area problem by sharing common hardware in a time-multiplex mode without degrading the overall system performance. According to the result of the synthesis using the CMOS 65 nm process, the proposed universal code generator has an area reduced by 98%, 93%, and 60% compared to the previous memory-based universal code generator (MB UCG), the Legendre-generation universal code generator (LG UCG), and the Weil-generation universal code generator (WG UCG), respectively. Furthermore, the proposed generator is applicable to all Legendre sequence-based codes

Natural Frequency Degradation Prediction for Offshore Wind Turbine Structures

The scale of offshore wind turbines (OWTs) has increased in order to enhance their energy generation. However, strong aero/hydrodynamic loads can degrade the dynamic characteristics of OWTs because they are installed on soft seabeds. This degradation can shorten the structural life of the system; repetitive loads lead to seabed softening, reducing the natural frequency of the structure close to the excitation frequency. Most of the previous studies on degradation trained prediction algorithms with actual sensor signals. However, there are no actual sensor data on the dynamic response of OWTs over their lifespan (approximately 20 years). In order to address this data issue, this study proposes a new prediction platform combining a dynamic OWT model and a neural network-based degradation prediction model. Specifically, a virtual dynamic response was generated using a three-dimensional OWT and a seabed finite element model. Then, the LSTM model was trained to predict the natural frequency degradation using the dynamic response as the model input. The results show that the developed model can accurately predict natural frequencies over the next several years using past and present accelerations and strains. In practice, this LSTM model could be used to predict future natural frequencies using the dynamic response of the structure, which can be measured using actual sensors (accelerometers and strain gauges)

Numerical Determination of the Frictional Coefficients of a Fluid Film Journal Bearing Considering the Elastohydrodynamic Lubrication and the Asperity Contact Force

This paper proposes a numerical method to investigate the frictional characteristics of a fluid film journal bearing considering the elastohydrodynamic lubrication and the asperity contact force. We solved the average Reynolds equation by utilizing the FEM to determine the hydrodynamic force developed by the lubricant of the journal bearing. We also used a modified GT model (Greenwood–Tripp model) developed by Greenwood and Tripp to decompose the asperity contact force into normal and tangential directions. Once we applied those forces to a rotor, we solved the equations of motion of a flexible shaft to determine the friction coefficient. We verified the proposed method by comparing the calculated friction coefficient with the measured one of journal bearings conducted by prior researchers. Then, the proposed method was applied to investigate the frictional characteristics of a journal bearing of a scroll compressor on which dynamic loads are applied. This paper can contribute to developing robust rotor systems supported by journal bearings

Natural Frequency Degradation Prediction for Offshore Wind Turbine Structures

The scale of offshore wind turbines (OWTs) has increased in order to enhance their energy generation. However, strong aero/hydrodynamic loads can degrade the dynamic characteristics of OWTs because they are installed on soft seabeds. This degradation can shorten the structural life of the system; repetitive loads lead to seabed softening, reducing the natural frequency of the structure close to the excitation frequency. Most of the previous studies on degradation trained prediction algorithms with actual sensor signals. However, there are no actual sensor data on the dynamic response of OWTs over their lifespan (approximately 20 years). In order to address this data issue, this study proposes a new prediction platform combining a dynamic OWT model and a neural network-based degradation prediction model. Specifically, a virtual dynamic response was generated using a three-dimensional OWT and a seabed finite element model. Then, the LSTM model was trained to predict the natural frequency degradation using the dynamic response as the model input. The results show that the developed model can accurately predict natural frequencies over the next several years using past and present accelerations and strains. In practice, this LSTM model could be used to predict future natural frequencies using the dynamic response of the structure, which can be measured using actual sensors (accelerometers and strain gauges)