Search

The waves and currents acting near a monopile foundation will potentially lead to scour, which may affect the stability of the wind turbine. The design of scour protection against the seabed lowering around a wind turbine monopile foundation is an important issue for wind energy industries. Many laboratory tests have been carried out to investigate the relationship between the hydrodynamic conditions and the monopile foundation scour protection layer damage, and various design criteria have been proposed. However, the experimental uncertainty of the underlying test results has not been discussed in detail. In the present research, small scale wave flume experiments of a 5m diameter monopile foundation scour protection under waves combined with currents in shallow water are described. Two groups of repetitive experiments are completed under the same wave and current conditions. The erosion development of the scour protection armor layer is measured by using a laser profiler and is evaluated based on three dimensional damage numbers. Together with visualization of the damage pattern, the damage analysis discusses the erosion in different subareas and the variances of the subarea damage number. The analysis of the uncertainty of the erosion results based on two sets of repetitive tests has been carried out. Using the uncertainty analysis methodology stated in ISO GUM standard: JCGM 100-2008, the Type A uncertainty, calibration uncertainty and combined uncertainty of the experiment are evaluated separately. The Type A uncertainty gives an overall uncertainty level and it shows that higher uncertainty occurs in the regions where stronger vortices exist. The combined uncertainty is analyzed based on scour protection dynamic stability design formula. Analysis result shows that the uncertainty due to modelling is a major source of the total uncertainty. The study gives a preliminary result of uncertainty level in wave flume test of monopile scour protection and provides a reference for future experimental research.

The meshless method called Smoothed Particle Hydrodynamics (SPH) is here proposed to simulate floating Oscillating Water Column (OWC) Wave Energy Converters (WECs). The SPH-based DualSPHysics code is coupled with MoorDyn, an open-source dynamic mooring line model. The coupled model is first validated using laboratory tests of a floating solid box moored to the wave flume bottom using four mooring lines interacting with regular waves. The numerical free-surface elevation at different locations, the motions of the floating solid box (heave, surge and pitch) and the tensions in the mooring lines are compared with the experimental data. Secondly, the coupled model is employed to simulate a floating OWC WEC moored to the sea bottom, while numerical results are also validated using data from physical modelling. The numerical results are promising to simulate floating OWC WECs. However, some discrepancies are noticed since the simulations presented in this work only consider a single-phase (water) so the full OWC WEC behaviour is only partially reproduced. Nevertheless, considering the aforementioned limitations, DualSPHysics can be used at this stage as complementary tool to physical modelling for a preliminary design of floating wave energy converters.

Based on the function level of a gas turbine generator, this paper presents a method for the quantitative assessment of the health condition of a gas turbine generator by using fuzzy comprehensive assessment (Fuzzy) and an analysis hierarchy process (AHP). First, the failure mode, effects and criticality analysis method (FMECA) is used to construct the assessment framework and obtain the indicator set for the health assessment. The membership degree of each indicator is realized by using triangular and trapezoidal membership functions. Second, the fuzzy comprehensive assessment model with analysis hierarchy process (Fuzzy-AHP) is established to implement the quantitative assessment of the health condition of the gas turbine generator, and the man-made influence on the weight assignment is decreased. Finally, an example of a three-shaft gas turbine generator health assessment is provided to validate and prove that the method is effective and feasible.

When a gas turbine operates in a marine environment, gradual performance degradation occurs due to salt fog in the compressor and turbine. Regular water washing of a gas turbine can effectively restore the performance loss caused by compressor salt fog fouling on the flow passage. However, inappropriate washing will increase maintenance costs, cause unnecessary down time and premature erosion of leaf surfaces. In this paper, a coefficient matching method for a three shaft marine gas turbine salt fog fouling degradation factor model is proposed, which can establish a model of salt fog fouling degradation factor according to a change in operating time and exhaust temperature in the washing cycle. The influences of load, environment temperature, inlet pressure loss and salt fog fouling rate on the performance degradation of the gas turbine are simulated and analyzed; then, the degradation regularity of the performance parameters of the gas turbine under different operating conditions and fouling degrees is obtained. Finally, a method of operating cost estimation for marine gas turbines is proposed that can estimate the cost of transient change and cumulative change in the cleaning cycle caused by the salt fog fouling, which can help the operator to determine the cleaning strategy and reduce the operation cost of the gas turbine.

A vast amount of operating data, control data and log data are generated in the monitoring process of a gas turbine. This massive amount of data includes not only spatial distance features, but also related features. Currently, the analysis of data-related features based on data-driven diagnosis methods is challenging. In response to this limitation, a gas turbine fault diagnosis method based on network community detection is proposed in this paper. First, a gas turbine topology network model is established based on complex network theory. In the network model, gas turbine sampled-data are defined as the nodes; the similarity degree between the sampled-data is defined as the edge of the network; and the reciprocal function is selected as the similarity criterion function. Second, to reflect the community characteristics of the network, the topology network is converted to a training network by a threshold criterion that is designed based on the variation of the average path length and the average clustering coefficient. This method can establish a training network without prior knowledge of the number of clusters. Third, a fault detection algorithm is proposed based on community modularity, and fault reasoning is presented by calculating the probability based on the change in the community modularity which demonstrates the occurrence possibility of multiple faults. The effectiveness of the algorithm is verified by a three-shaft gas turbine fault simulation data-set. The results indicate that the proposed algorithm can be used to identify known faults, unknown faults and the graphical representation of a network can reflect the spatial distance and relationship among sampled-data.