Catalytic Combustion of Lean Methane Assisted by Electric Field over Pd/Co<Subscript>3</Subscript>O<Subscript>4</Subscript> Catalysts at Low Temperature

A series of Pd/Co₃O₄ catalysts were prepared by Self-Propagating High-Temperature Synthesis (SHS) method in this study, and electric field was applied for catalytic combustion of lean methane over Pd/Co₃O₄ catalysts at low temperature. When electric field was applied, the catalytic combustion performance of Pd/Co₃O₄ catalysts was greatly improved, and the application of electric field could reduce the load of active element Pd to some extent while maintaining the same efficiency. Based on experimental tests and the analysis results of X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), H₂-temperature-programmed reduction (H₂-TPR) and in-situ diffuse reflectance infrared Fourier transform spectroscopy (in-situ DRIFTS), the mechanism of catalytic oxidation of CH₄ over Pd/Co₃O₄ catalysts in electric field was proposed. The catalytic combustion of CH₄ occurs only when the temperature is higher than 250 °C normally, but when electric field was applied, the whole process of CH₄ oxidation was promoted significantly and the reaction temperature was reduced. Electric field could promote the reduction of the support Co₃O₄ to release the lattice oxygen, resulting in the increase of PdO_x and the surface chemisorbed oxygen, which could provide more active sites for the low-temperature oxidation of CH₄. Furthermore, electric field could accelerate the dehydroxylation of CoOOH to further enhance the activity of the catalysts.     

Fault diagnosis for wind turbine based on improved extreme learning machine

A fault diagnosis method based on improved extreme learning machine (IELM) is proposed to solve the weakness (weak generalization ability, low diagnostic rate) of traditional fault diagnosis with feedforward neural network algorithm. This method fuses signal feature vectors, extracts six parameters as the principal component analysis (PCA) variables, and calculates correlation coefficient matrix among the variables. The weight values of control parameters in the extreme learning model are dynamically adjusted according to the test samples’ constantly changing. Consequently, the weight fixed drawback in the original model can be remedied. A fault simulation experiment platform for wind turbine drive system is built, eight kinds of fault modes are diagnosed by the improved extreme learning model, and the result is compared with that of other machine learning methods. The experiment indicates that the method can enhance the accuracy and generalization ability of diagnosis, and increase the computing speed. It is convenient for engineering application.     

Techno-economic study of LNG diesel power (dual fuel) ship

This article mainly proposed three technically effective alternatives to comply with the emission control regulations and laws in shipping. Liquefied natural gas (LNG)-diesel dual fuel power technology was introduced through feasibility study on several aspects including research development, retrofitting methods, vessel type, safety issues, and other technical characteristics. Based on sample ship and route, economic evaluation was conducted on these three alternatives. Cost-effectiveness of each project was detailed in the calculation of net present value (NPV) and payback time via discount cash flow method. The findings show that LNG-diesel dual fuel power technology performs best among three alternatives. Due to the impact of fuel price, two scenarios were carried out in sensitivity analysis which witnessed a variation of NPV with the fluctuation of fuel price. Further study shows the turning point between project (i) and project (iii) with different discount rate and the interaction between discount rate and fuel price, left project (ii) the least cost-effective solution in three alternatives.     

Rapid first-stage tests of on-ship infection

Protection against on-ship infectious disease—whether due to mishap or to harmful purpose—faces special situational problems. Sometimes, when infection levels on board have reached threshold levels, emergency actions are required. Often, the most thorough strategies for responding to threat are not feasible. A rapid first-stage test (RFT) is a fast, minimally invasive procedure used to rule out from possible infection a large percentage of an infection-threatened group. Prevention and control of on-ship infection need to combine various interconnected tactics. When timely criterion tests are not possible, the medical team must adopt fast alternative measures. The methods used to summarize protection against on-ship infectious agents included a scientific literature review and a web search. The fields of the review were maritime, health, and technology sources. Special attention was paid to material dealing with risks and threats of on-ship penetration by infectious agents, on-ship infection prevalence thresholds, and rapid diagnostic screens. The Bayes rule and the law of large numbers were applied to the analysis, for large on-ship populations, of RFT indications of crossing of an infection prevalence threshold. The increasing risk of serious on-ship infection—either accidental or purposeful—calls for a multi-layered protection approach. RFTs are a key part of the outer layer of such a defense. Well-designed and well-administered RFTs provide several advantages for defense against on-ship infection: low-cost, non-invasive, fast, and focuses on a drastically smaller number of infection possibilities.     

Vocational and academic approaches to maritime education and training (MET): Trends,challenges and opportunities

Traditional seafarer training has always focused on the acquisition and use of practical skills. The prevailing view is that, while this approach addresses a degree of cognitive skills, it focuses on and gives much more emphasis to the acquisition of hands-on practical skills for the performance of specific tasks. On the other hand, academic education has been seen to be much more focused on the development of in-depth analytical and critical thinking skills; cognitive skills that are less reliant on hands-on task-oriented training, but stress critical reading and discussion. The global trend in maritime education and training is increasingly to link an essentially vocational education that provides specific and restricted competence outcomes with more general or deeper academic components leading to an academic qualification. This trend has led to some dilemmas for curriculum development, for training legislation in a global industry, and for achieving desired learning outcomes in a professional setting (in the shipping industry). This paper discusses some of the challenges arising from this trend and the opportunities the trend offers.     

A numerical study on piezoelectric energy harvesting by combining transverse galloping and parametric instability phenomena

This paper aims to numerically investigate the effects of parametric instability on piezoelectric energy harvesting from the transverse galloping of a square prism. A two degrees-of-freedom reduced-order model for this problem is proposed and numerically integrated. A usual quasi-steady galloping model is applied, where the transverse force coefficient is adopted as a cubic polynomial function with respect to the angle of attack. Time-histories of nondimensional prism displacement, electric voltage and power dissipated at both the dashpot and the electrical resistance are obtained as functions of the reduced velocity. Both, oscillation amplitude and electric voltage, increased with the reduced velocity for all parametric excitation conditions tested. For low values of reduced velocity, 2:1 parametric excitation enhances the electric voltage. On the other hand, for higher reduced velocities, a 1:1 parametric excitation (i.e., the same as the natural frequency) enhances both oscillation amplitude and electric voltage. It has been also found that, depending on the parametric excitation frequency, the harvested electrical power can be amplified in 70% when compared to the case under no parametric excitation.     

Vortex-induced vibration of a tension leg platform tendon: Multi-mode limit cycle oscillations

This paper studies the application of mathematical models to analyze the vortex-induced vibrations of the tendons of a given TLP along the Indian coastline, by using an analytical approach, analyzed using MATLAB. The tendon is subjected to a steady current load, which causes vortex-shedding downstream, leading to cross-flow vibrations. The magnitude of the excitation (lift and drag coefficients) depends on the vortex-shedding frequency. The resulting vibration is studied for possible resonant behavior. The excitation force is quantified empirically, the added mass by potential flow hydrodynamics, and the vibration by normal mode summation method. Non-linear viscous damping of the water is considered. The non-linear oscillations are studied by the phase-plane method, investigating the limit-cycle oscillations. The stable/unstable regions of the dynamic behavior are demarcated. The modal contribution to the total deflection is studied to establish the possibility of resonance of one of the wet modes with the vortex-shedding frequency.     

CT reconstruction with priori MRI images through multi-group datasets expansion

Computed tomography (CT) reconstruction with a well-registered priori magnetic resonance imaging (MRI) image can improve reconstruction results with low-dose CT, because well-registered CT and MRI images have similar structures. However, in clinical settings, the CT image of patients does not always match the priori MRI image because of breathing and movement of patients during CT scanning. To improve the image quality in this case, multi-group datasets expansion is proposed in this paper. In our method, multi-group CT-MRI datasets are formed by expanding CT-MRI datasets. These expanded datasets can also be used by most existing CT-MRI algorithms and improve the reconstructed image quality when the CT image of a patient is not registered with the priori MRI image. In the experiments, we evaluate the performance of the algorithm by using multi-group CT-MRI datasets in several unregistered situations. Experiments show that when the CT and priori MRI images are not registered, the reconstruction results of using multi-group dataset expansion are better than those obtained without using the expansion.