One of the main problems in controlling the shape of active structures (AS) is to determine the actuations that drive the structure from the current state to the target state. Model-based methods such as stochastic search require a known type of load and relatively long computational time, which limits the practical use of AS in civil engineering. Moreover, additive errors may be produced because of the discrepancy between analytic models and real structures. To overcome these limitations, this paper presents a compound system called WAS, which combines AS with a wireless sensor and actuator network (WSAN). A bio-inspired control framework imitating the activity of the nervous systems of animals is proposed for WAS. A typical example is tested for verification. In the example, a triangular tensegrity prism that aims to maintain its original height is integrated with a WSAN that consists of a central controller, three actuators, and three sensors. The result demonstrates the feasibility of the proposed concept and control framework in cases of unknown loads that include different types, distributions, magnitudes, and directions. The proposed control framework can also act as a supplementary means to improve the efficiency and accuracy of control frameworks based on a common stochastic search.

Thin-walled spread foundations are used in coastal projects where the soil strength is relatively low. Developing a predictive model of bearing capacity for this kind of foundation is of interest due to the fact that the famous bearing capacity equations are proposed for conventional footings. Many studies underlined the applicability of artificial neural networks (ANNs) in predicting the bearing capacity of foundations. However, the majority of these models are built using conventional ANNs, which suffer from slow rate of learning as well as getting trapped in local minima. Moreover, they are mainly developed for conventional footings. The prime objective of this study is to propose an improved ANN-based predictive model of bearing capacity for thin-walled shallow foundations. In this regard, a relatively large dataset comprising 145 recorded cases of related footing load tests was compiled from the literature. The dataset includes bearing capacity (Q_u), friction angle, unit weight of sand, footing width, and thin-wall length to footing width ratio (L_w/B). Apart from Q_u, other parameters were set as model inputs. To enhance the diversity of the data, four more related laboratory footing load tests were conducted on the Johor Bahru sand, and results were added to the dataset. Experimental findings suggest an almost 0.5 times increase in the bearing capacity in loose and dense sands when L_w/B is increased from 0.5 to 1.12. Overall, findings show the feasibility of the ANN-based predictive model improved with particle swarm optimization (PSO). The correlation coefficient was 0.98 for testing data, suggesting that the model serves as a reliable tool in predicting the bearing capacity.

Conceptual design plays an important role in product life cycle, which requires engineers to use sound design theory, cross-disciplinary knowledge, and complex technical support to acquire design concepts. However, the lack of sufficient computational tools makes it difficult for designers to fully explore in the wide design solution spaces. Therefore, this paper proposes an integrated cognitive computing approach to formalize the cognitive activities of conceptual design. A cognitive computing model composed of concept associative memory, concept generation, and decision-making process is established based on the integration of cognitive psychology and engineering design. First of all, the Hopfield neural network is used to acquire similar concept solutions for specific subfunctions from a knowledge base. Then, morphological matrix and genetic algorithm are introduced to produce a set of feasible candidate solutions in the concept generation process. Furthermore, a technique for order preference by similarity to an ideal solution is applied to evaluate the generated concept solutions and obtain the optimal solution automatically. Finally, a case study is given to demonstrate the effectiveness and efficiency of the proposed approach.

This paper is concerned with the free vibration analysis of open circular cylindrical shells with either the two straight edges or the two curved edges simply supported and the remaining two edges supported by arbitrary classical boundary conditions. Based on the Donnell-Mushtari-Vlasov thin shell theory, an analytical solution of the traveling wave form along the simply supported edges and the modal wave form along the remaining two edges is obtained. With such a unidirectional traveling wave form solution, the method of the reverberation-ray matrix is introduced to derive the equation of natural frequencies of the shell with different classical boundary conditions. The exact solutions for natural frequencies of the open circular cylindrical shell are obtained with the employment of a golden section search algorithm. The calculation results are compared with those obtained by the finite element method and the methods in the available literature. The influence of length, thickness, radius, included angle, and the boundary conditions of the open circular cylindrical shell on the natural frequencies is investigated. The exact calculation results can be used as benchmark values for researchers to check their numerical methods and for engineers to design structures with thin shell components.

The poppet valve is a fundamental component in fluid power systems. Under particular conditions, annoying “squeal” noises may be generated in hydraulic poppet valves. In the present study, the frequency spectrum of the squeal noise is obtained by analyzing the sampling data from the accelerometer mounted on the valve body. It is found that the flow velocity, pressure, and structural parameters have crucial effects on the properties of squeal noise, especially frequency. Larger valve chamber volume or lower backpressure leads to lower fundamental frequency of the squeal noise. An explanation for the squeal noise, as a result of Helmholtz resonance, is suggested and proved by experimental results.

Diesel emission fluid (DEF) soaking and urea deposits on selective catalytic reduction (SCR) catalysts are critical issues for real diesel engine NH₃-SCR systems. To investigate the impact of DEF soaking and urea deposits on SCR catalyst performance, fresh Cu-zeolite catalyst samples were drilled from a full-size SCR catalyst. Those samples were impregnated with DEF solutions and subsequently hydrothermally treated to simulate DEF soaking and urea deposits on real SCR catalysts during diesel engine operations. Their SCR performance was then evaluated in a flow reactor with a four-step test protocol. Test results show that the DEF soaking leached some Cu from the SCR catalysts and slightly reduced their Cu loadings. The loss of Cu and associated metal sites on the catalysts weakened their catalytic oxidation abilities and caused lower NO/NH₃ oxidation and lower high-temperature N₂O selectivity. Lower Cu loading also made the catalysts less active to the decomposition of surface ammonium nitrates and decreased low-temperature N₂O selectivity. Cu loss during DEF impregnation released more acid sites on the surface of the catalysts and increased their acidities, and more NH₃ was able to be adsorbed and involved in SCR reactions at medium and high temperatures. Due to lower NH₃ oxidation and higher NH₃ storage, the DEF-impregnated SCR catalyst samples showed higher NO_x conversion above 400 °C compared with the non-soaked one. The negative impact of urea deposits during DEF impregnation was not clearly observed, because the high-temperature hydrothermal treatment helped to remove the urea deposits.