The bounds of the elastic properties of an E-glass particle reinforced BISGMA/TEGDMA composite were predicted by a random unit cell model. Two means of tensile loading were used: iso-displacement loading and iso-stress loading. The iso-displacement loading predicts the upper bound of Young’s modulus, while iso-stress loading predicts the practical lower bound of Young’s modulus. The results showed that Young’s modulus increases, while Poisson’s ratio decreases with increasing filler content for both loading conditions. For comparative purposes, the upper and practical lower bounds were also calculated by Hashin’s method, a periodic three-dimensional single-particle, and two-particle unit cells. Analytical solutions using the Mori–Tanaka model and experiments were also conducted for verification purpose. The results showed that the random unit cell predicts better overall bounds for elastic properties. 相似文献
In this paper, we doped p-type conductivity dehydrated nanotubed titanic acid (DNTA) into insulator polymer poly(methyl methacrylate) (PMMA). The electric properties of this nanocomposite were investigated. The photoluminescence efficiency of fluorescent dye 4-(dicyanomethylene)-2-t-butyl-6(1,1,7,7-tetramethyljulolidyl-9-enyl)-4H-pyran (DCJTB) in PMMA matrix was enhanced by doping with DNTA. Moreover, screen effect by DNTA with high permittivity caused the emission from DCJTB to be blue-shifted. 相似文献
Extended Graetz problem in microchannel is analyzed by using eigenfunction expansion to solve the energy equation. The hydrodynamically developed flow is assumed to enter the microchannel with uniform temperature or uniform heat flux boundary condition. The effects of velocity and temperature jump boundary condition on the microchannel wall, streamwise conduction and viscous dissipation are all included. From the temperature field obtained, the local Nusselt number distributions are shown as the dimensionless parameters (Peclet number, Knudsen number, Brinkman number) vary. The fully developed Nusselt number for each boundary condition is obtained also in terms of these parameters. 相似文献
Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine-learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a (Chl-a) itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1–2 weeks. 相似文献
Lake water resources operation and water quality management come up with higher challenges due to climate change. The frequency and intensity of extreme hydrological events are increasing under global warming, which may directly lead to more uncertainty and complexity for hydrodynamic and water-quality conditions in large shallow lake. However, studies about effects of climate change on lake hydrodynamic and water-quality conditions are not enough. Thus, a coupled model is es-tablished to investigate the potential responses of lake water level, flow field and pollutant migra-tion to the changing climatic factors. The results imply that water flow capacity and self-purification in the Hongze Lake can be improved by west, northwest, north, south and southeast winds indi-cating wind filed change has a great effect on the hydrodynamic and water-quality conditions in large shallow lake. It is further observed that both hydrodynamics and water quality are more sensitive to rainfall change than to temperature change; compared to the effect from temperature and rainfall, the effect from wind field appear to be more pronounced. Moreover, the results verify the feasibility of coupling basin hydrological model with lake hydrodynamic and water quality model. To the best of knowledge, the coupled model should not be used until independent calibra-tions and verifications for hydrodynamics and water quality modeling, the hydrological model and the coupled model.
Heuristic algorithms (HAs) are widely used in multi-objective reservoir optimal operation (MOROO) due to the rapidity of the calculation and simplicity of their design. The literature usually focuses on one or two categories of HAs and simply reviews the state of the art. To provide an overall understanding and a specific comparison of HAs in MOROO, differential evolution (DE), particle swarm optimisation (PSO), and artificial physics optimisation (APO), which serve as typical examples of the three categories of HAs, are compared in terms of the development and applications using a designed experiment. Besides, the general model with constraints and fitness function, and the solution process using a hybrid feasible domain restoration method and penalty function method are also presented. Taking a designed experiment with multiple scenarios, the mean average of the optimal objective function values, the standard deviation of optimal objective function values, the mean average of the computational time, and population diversity are used for comparisons. Results of the comparisons show that (a) the problem of optimal multipurpose reservoir long-term operation is a mathematic programming problem with narrow feasible region and monotonic objective function; (b) it is easy to obtain the same optimal objective function value, but different optimal solutions using HAs; and (c) comparisons do not result in a clear winner, but DE can be more appropriate for MOROO.