Structural health monitoring (SHM) is a relevant topic for civil systems and involves the monitoring, data processing and interpretation to evaluate the condition of a structure, in order to detect damage. In real structures, two or more sites or types of damage can be present at the same time. It has been shown that one kind of damaged condition can interfere with the detection of another kind of damage, leading to an incorrect assessment about the structure condition. Identifying combined damage on structures still represents a challenge for condition monitoring, because the reliable identification of a combined damaged condition is a difficult task. Thus, this work presents a fusion of methodologies, where a single wavelet-packet and the empirical mode decomposition (EMD) method are combined with artificial neural networks (ANNs) for the automated and online identification-location of single or multiple-combined damage in a scaled model of a five-bay truss-type structure. Results showed that the proposed methodology is very efficient and reliable for identifying and locating the three kinds of damage, as well as their combinations. Therefore, this methodology could be applied to detection-location of damage in real truss-type structures, which would help to improve the characteristics and life span of real structures.

With the continuous improvement of the train speed, the dynamic environment of trains turns out to be aerodynamic domination. Solving the aerodynamic problems has become one of the key factors of the high-speed train head design. Given that the aerodynamic drag is a significant factor that restrains train speed and energy conservation, reducing the aerodynamic drag is thus an important consideration of the high-speed train head design. However, the reduction of the aerodynamic drag may increase other aerodynamic forces (moments), possibly deteriorating the operational safety of the train. The multi-objective optimization design method of the high-speed train head was proposed in this paper, and the aerodynamic drag and load reduction factor were set to be optimization objectives. The automatic multi-objective optimization design of the high-speed train head can be achieved by integrating a series of procedures into the multi-objective optimization algorithm, such as the establishment of 3D parametric model, the aerodynamic mesh generation, the calculation of the flow field around the train, and the vehicle system dynamics. The correlation between the optimization objectives and optimization variables was analyzed to obtain the most important optimization variables, and a further analysis of the nonlinear relationship between the key optimization variables and the optimization objectives was obtained. After optimization, the aerodynamic drag of optimized train was reduced by up to 4.15%, and the load reduction factor was reduced by up to 1.72%.

From the professional perspective of journal publishing, whether a bilingual (multilingual) publication is suspicious from an ethical point of view has been a topic of debate. Indeed this problem has been long puzzling authors who are preparing for submission. Although the worldwide publishing industry has had some discussions about it, so far in China no normative text or guide is available for authors and journal editors as reference. In fact, it is not easy to sum up the rights and wrongs of duplicate publication and bilingual (multilingual) publication. From a professional perspective, a few highlights for discussion on the criteria for bilingual (multilingual) publication and substantial duplicate publication are proposed and could be a reference for authors and editors.

Journal misalignment is common in journal bearings. When severe journal misalignment takes place, it affects nearly all aspects of bearing performance. This paper provided a comprehensive analysis of misaligned journal bearings based on two different mass-conservative models which appropriately took into account film rupture and reformation. The lubrication characteristics and performance parameters including the cavitation zones, pressure distribution, density distribution, oil leakage, load capacity, moment, and attitude angle were compared with the traditional lubrication model. The results showed that cavitation has great effect on bearing performances, especially when the surface roughness is large. Therefore, it is necessary to consider the effects of journal misalignment alongside inter-asperity cavitation theory in the design and analyses of journal bearings.

In this paper, a multi-objective particle swarm optimization (MOPSO) algorithm and a nondominated sorting genetic algorithm II (NSGA-II) are used to optimize the operating parameters of a 1.6 L, spark ignition (SI) gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NO_x), which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks (ANNs) were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms: NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NO_x, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NO_x, respectively.

In this study, a porous inserted regenerative thermal oxidizer (PRTO) system was developed for a 125 kW industrial copper-melting furnace, due to its advantages of low NO_x emissions and high radiant efficiency. Zirconium dioxide (ZrO₂) ceramic foams were placed into the combustion zone of a regenerative thermal oxidizer (RTO). Different performance characteristics of the RTO and PRTO systems, including pressure drop, temperature distribution, emissions, and energy efficiency, were evaluated to study the effects of the porous inserts on non-premixed CH₄ combustion. It was found that the PRTO system achieved a significant reduction in the NO_x emission level and a fuel saving of approximately 30% compared to the RTO system. It is most suitable for a lean combustion process at an equivalence ratio <0.4 with NO_x and CO emission levels within 0.002%–0.003% and 0.001%–0.002%, respectively.

This paper presented a control design methodology for a proton exchange membrane fuel cell (PEMFC) generation system for residential applications. The dynamic behavior of the generation system is complex in such applications. A comprehensive control design is very important for achieving a steady system operation and efficiency. The control strategy for a 60 kW generation system was proposed and tested based on the system dynamic model. A two-variable single neuron proportional-integral (PI) decoupling controller was developed for anode pressure and humidity by adjusting the hydrogen flow and water injection. A similar controller was developed for cathode pressure and humidity by adjusting the exhaust flow and water injection. The desired oxygen excess ratio was kept by a feedback controller based on the load current. An optimal seeking controller was used to trace the unique optimal power point. Two negative feedback controllers were used to provide AC power and a suitable voltage for residential loads by a power conditioning unit. Control simulation tests showed that 60 kW PEMFC generation system responded well for computer-simulated step changes in the load power demand. This control methodology for a 60 kW PEMFC generation system would be a competitive solution for system level designs such as parameter design, performance analysis, and online optimization.

The original version of this article unfortunately contained a mistake. In the second page, before Fig. 2, Eq. (7) was missing, which is given then.