Impacts of wind conditions on hydrogen leakage during refilling hydrogen-powered vehicles

Although hydrogen leakage at hydrogen refueling stations has been a concern,less efforts have been devoted to hydrogen leakage during the refueling of hydrogen-powered vehicles.In this study,hydrogen leakage and dilution from the hydrogen dispenser during the refueling of hydrogen-powered vehicles were numerically investigated under different wind configurations.The shape,size,and distribution of flammable gas clouds(FGC)during the leakage and dilution processes were analyzed.The results showed that the presence of hydrogen-powered vehicles resulted in irregular FGC shapes.Greater wind speeds(v wv)were associated with longer FGC propagation distances.At v_(wv)=2 m·s^(−1)and 10 m·s^(−1),the FGC lengths at the end of the leakage were 7.9 m and 20.4 m,respectively.Under downwind conditions,higher wind speeds corresponded to lower FGC heights.The FGC height was larger under upwind conditions and was slightly affected by the magnitude of the wind speed.In the dilution process,the existence of a region with a high hydrogen concentration led to the FGC volume first increasing and then gradually decreasing.Wind promoted the mixing of hydrogen and air,accelerated FGC dilution,inhibited hydrogen uplifting,and augmented the horizontal movement of the FGC.At higher wind speeds,the low-altitude FGC movements could induce potential safety hazards.

Mechanistic Drifting Forecast Model for A Small Semi-Submersible Drifter Under Tide–Wind–Wave Conditions

Understanding the drifting motion of a small semi-submersible drifter is of vital importance regarding monitoring surface currents and the floating pollutants in coastal regions. This work addresses this issue by establishing a mechanistic drifting forecast model based on kinetic analysis. Taking tide–wind–wave into consideration, the forecast model is validated against in situ drifting experiment in the Radial Sand Ridges. Model results show good performance with respect to the measured drifting features, characterized by migrating back and forth twice a day with daily downwind displacements. Trajectory models are used to evaluate the influence of the individual hydrodynamic forcing. The tidal current is the fundamental dynamic condition in the Radial Sand Ridges and has the greatest impact on the drifting distance. However, it loses its leading position in the field of the daily displacement of the used drifter. The simulations reveal that different hydrodynamic forces dominate the daily displacement of the used drifter at different wind scales. The wave-induced mass transport has the greatest influence on the daily displacement at Beaufort wind scale 5–6; while wind drag contributes mostly at wind scale 2–4.

Model-based and Fuzzy Logic Approaches to Condition Monitoring of Operational Wind Turbines

It is common for wind turbines to be installed in remote locations on land or offshore, leading to difficulties in routine inspection and maintenance. Further, wind turbines in these locations are often subject to harsh operating conditions. These challenges mean there is a requirement for a high degree of maintenance. The data generated by monitoring systems can be used to obtain models of wind turbines operating under different conditions, and hence predict output signals based on known inputs. A model-based condition monitoring system can be implemented by comparing output data obtained from operational turbines with those predicted by the models, so as to detect changes that could be due to the presence of faults. This paper discusses several techniques for model-based condition monitoring systems: linear models, artificial neural networks, and state dependent parameter "pseudo" transfer functions.The models are identified using supervisory control and data acquisition(SCADA) data acquired from an operational wind firm. It is found that the multiple-input single-output state dependent parameter method outperforms both multivariate linear and artificial neural network-based approaches. Subsequently, state dependent parameter models are used to develop adaptive thresholds for critical output signals. In order to provide an early warning of a developing fault, it is necessary to interpret the amount by which the threshold is exceeded, together with the period of time over which this occurs. In this regard, a fuzzy logic-based inference system is proposed and demonstrated to be practically feasible.