In this research, three types of super austenitic stainless steel (SASS) with nitrogen levels of 0.2, 0.28, and 0.38 wt.% were developed. By maintaining a relatively high manganese content of approximately 1.5 wt.% and nickel content of around 18 wt.%, nitrogen was fully incorporated into the austenite, resulting in alloy samples designated as 0.2N, 0.3N and 0.4N. The hot-rolled plates of these alloys underwent a solution treatment at 1180°C for 30 min, followed by aging at 950°C for durations of 30 min, 2 h and 6 h, respectively. The influence of nitrogen content on phase precipitation behaviour was examined through various micro-structure characterisation techniques. The corrosion resistance of the samples was further evaluated using potentiodynamic polarisation experiments, Electrochemical impedance spectroscopy (EIS) and double loop-electrochemical potentiokinetic reactivation (DL-EPR) method. The results indicated that with an increase in nitrogen (N) addition, the quantity of σ phases decreased while and the formation of Cr₂N phases increased during the same aging period. Furthermore, at the same aging duration, the 0.3N SASS exhibited the highest pitting resistance and the lowest degree of sensitization (DOS) value compared to all other steels, attributed to the minimal precipitates present along the grain boundaries.

With the increasing size of floating offshore wind turbines (FOWTs), stall-induced aeroelastic instability has become a critical issue. This study numerically investigates this instability for FOWTs at stand-still conditions using time and frequency domain approaches. A nonlinear aeroelastic model based on quasi-steady theory and a linearized version are used for time and frequency domain simulations, respectively. Hydrodynamic damping considers both radiation and viscous drag effects. The aeroelastic instability of a stand-still NREL OC3-Hywind 5MW FOWT is analyzed for various inflow yaw misalignment angles. Frequency domain simulation shows rotor edgewise and tower side-side modes exhibit stall-induced instability due to aerodynamic negative damping at specific yaw misalignment and azimuth angles. The platform's yaw mode also shows small negative damping, despite large hydrodynamic damping, while other platform modes remain dynamically stable. Safety margins of FOWTs are analyzed for multi-mode stability, and an active control strategy is proposed to prevent stall-induced instability in all unstable modes. Limit cycle oscillations in the rotor's in-out plane are observed from time domain simulation. Instability regions predicted by both analyses highly overlap, but frequency domain results are more conservative. Blade instability may cause high-frequency vibrations in platform movements with limited amplitudes and severe oscillations in tower structures.

Explosion-induced domino accidents in the chemical industry, such as the 2005 Buncefield and 2019 Xiangshui accidents, can lead to catastrophic losses. Recent studies commonly use probit models (simplified linear regression models) to predict the probability of accident escalation caused by equipment failure due to overpressure conditions but necessitate distinct equations for different equipment types. In order to simplify the number of models and improve their accuracy, this study introduced three machine learning models (random forest model, convolutional neural network model, and deep neural network model), addressing complex nonlinear relationships that conventional regression models may not fully capture. By model training, the DNN model has the highest accuracy (99 %), followed by CNN (94 %) and random RF (95 %). The DNN model was selected as the optimal data-driven model for equipment vulnerability assessment due to their feedforward mechanism's capability to dynamically align parameters with evolving data distributions. The approach developed can not only predict the probability of equipment damage by integrating values related to peak overpressure and equipment type but also effectively address the accuracy validation issues associated with traditional regression models. Besides, this approach can be considered open source model and more explosion data may be used in the future to further improve the model.

Inspired by Vortex Generators' success in delaying airfoil stall, this study explores the potential of using Vortex Generators to mitigate stall-induced instability in floating offshore wind turbines at parked and skewed inflow conditions for the first time. Significant improvements are achieved by strategically installing Vortex Generators in the outboard sections of turbine blades and optimizing their parameters (normalized height, length, inflow angle, and chordwise positions) using the particle swarm optimization algorithm and fast optimization method. Numerical results, including both linear and nonlinear stall instability analyses, consistently demonstrate that Vortex Generator arrays effectively mitigate stall-induced instability in the edgewise motion of wind turbines. The yaw misalignment angle range corresponding to the occurrence of edgewise instability is reduced by 29.69% (for NREL 5 MW wind turbine) and 22.95% (for IEA 15 MW wind turbine) while also decreasing limit cycle oscillation amplitudes. Additionally, azimuth angle does not influence optimization results, and implementing Vortex Generators can increase the onset wind speed of stall-induced instability without negatively affecting operating conditions.