Bismuth ferrite (BiFeO₃, BFO) submicron cubes and 3D BFO/graphene composite materials were synthesized by a simple hydrothermal process. The crystallization processes of the 3D BFO/graphene composites with different graphene oxide (GO) concentrations were studied for their visible light photocatalytic properties. Compared to the single BFO submicron cubes, 3D BFO/graphene composites have greatly improved photocatalytic activity. A high photocatalytic performance is obtained at a GO concentration of 3 mg/mL, with the degradation rate of methylene blue (MB) dye reaching up to 92% in 140 min. The enhancement of photocatalytic activity can be attributed to the large specific surface area and 3D architecture of 3D composites, which provide more transport paths to effectively improve the separation rate of photo-generated electrons and holes. Therefore, 3D BFO/graphene composites have a broad prospect of application in the field of photocatalysis.

The pressure drop increases along with the decrease in the flow maldistribution in the two-stage distributing inlet headers of a plate-fin heat exchanger. To solve this issue, we proposed a new inlet header configuration with splitter plates to decrease the flow maldistribution and pressure drop simultaneously. We used computational fluid dynamic technology to analyze the flow properties in the inlet header configuration and calculated the flow maldistribution degree of the outlet channels and the pressure drop in the inlet header. We performed a sensitivity analysis between the structural parameters, the flow maldistribution degree, and the pressure drop. We determined the optimum values of the structural parameters (the construction of transition duct, the number of splitter plates, the inclined angle of outermost plates, the height of splitter plates, the height of inlet header, etc.), and acquired the optimum configuration of the improved inlet header. Compared with the traditional inlet header, the flow maldistribution degree and pressure drop decreased by 91.5% and 40.9%, respectively, using the proposed improved inlet header with splitter plates, which performed better than the conventional two-stage distribution inlet header.

Temperature greatly influences the mechanical response of acrylonitrile butadiene styrene (ABS). The tensile behavior of ABS was explored in this study. The tensile experiments were conducted at a wide range of temperatures (from 40^°C to 130^°C). A model was established to reveal the quantitative relationship between temperature and tensile behavior of ABS. The results of tensile experiments showed that tensile behavior of ABS exhibited glassy state and high-elastics state. The model was also divided into two parts that rely on the boundary of glass transition temperature, in which the parameters of the model were calculated by the fitting method. The model predictions showed a good agreement with the results of the experimental tensile test. This study provides the quantitative relationship between temperature and tensile behavior of ABS, which saves time and experimental costs.

Warpage is a typical defect for injection-molded parts, especially for crystalline parts molded by rapid heat cycle molding (RHCM). In this paper, a prediction method is proposed for predicting the warpage of crystalline parts molded by the RHCM process. Multi-layer models were established to predict warpage with the same thicknesses as the skin-core structures in the molded parts. Warpages were defined as the deformations calculated by the multi-layer models. The deformations were solved using the classical laminated plate theory by Abaqus. A model was introduced to describe the elastic modulus with the influence of temperature and crystallinity. The simulation process was divided into two procedures, before ejection and after ejection. Thermal stresses and thermal strains were simulated, respectively, in the procedure before ejection and after ejection. The prediction results were compared with the experimental results, which showed that the average errors between predicted warpage and average experimental warpage are, respectively, 7.0%, 3.5%, and 4.4% in conventional injection molding (CIM), in RHCM under a 60 °C heating mold (RHCM60), and in RHCM under a 90 °C heating mold (RHCM90).

In the early stages of the product design, multiple principle solutions are obtained through function solving, and a large number of conceptual schemes are generated by combination. Therefore, scheme decisions are important factors in the concept design. The existing decision methods primarily focus on the satisfaction of economic needs, and the impact of technical indicators on the technical performance of the scheme, while ignoring the conflict of needs between the two subject objectives in the decision process. Actual decisions need to be weighed against each other’s expectations. In addition, the qualitative interactive objectives will affect the decision direction of the conceptual scheme. Herein, we propose a relative equilibrium decision approach for concept design based on the fuzzy decision-making trial and evaluation laboratory-cooperative game model. This model is primarily divided into two parts. One is to solve the impact relationship between the objectives, and the objectives’ weights are obtained through fuzzy decision-making trial and evaluation laboratory (FDEMATEL). The second is to incorporate the objectives’ weights and impact utility into the cooperative game model, to reasonably weigh the relative interests of the two subjects to meet the corresponding interactions, and to obtain the scheme with the largest overall design desirability. Finally, the case study proves that this decision model can identify the optimal scheme. This model is proven to be robust by comparison with other methods.

The critical problem of reliability design is how to obtain a more accurate failure probability with a smaller number of evaluations of actual complex and nonlinear performance function. To achieve this objective, an adaptive subset simulation method with a deep neural network (DNN) is proposed for accurate estimation of small failure probability. A determinate criterion for threshold values is developed, and the subset number is adaptively quantified according to the initial estimated value of small failure probability. Therefore, the estimation of small failure probability is converted to estimation problem of multiple large conditional probabilities. An adaptive deep neural network model is constructed in every subset to predict the conditional probability with a smaller number of evaluations of the actual performance function. Furthermore, the sampling points for the next subset can be adaptively selected according to the constructed DNN model, which can decrease the number of invalid sampling points and evaluations of actual performance function, then the computational efficiency for estimating the conditional probability in every subset is increased. The sampling points with high probability density functions are recalculated with actual performance function values to replace the predicted values of the DNN model, which can verify the accuracy of DNN model and increase the estimation accuracy of small failure probability. By analyzing a nonlinear problem, a multiple failure domain problem and two engineering examples, the effectiveness and accuracy of the proposed methodology for estimating small failure probability are verified.