Predicting the three-dimensional (3D) transport processes of reservoir temperature and pollutants is essential for water environmental protection and restoration, and introducing the lattice Boltzmann (LB) method into this prediction is necessary because of its simple algorithm, straightforward implementation of boundary conditions, and high computation efficiency. In this paper, a triple-distribution function (TDF) LB model for flow-temperature-concentration coupling simulations is introduced. Some essential techniques for implementing this method in 3D reservoirs are also described, including the treatment of water surface fluctuation, the consideration of surface heat exchange, and the hardware acceleration using the graphics processing unit (GPU). Two cases verified the proposed model, and then, the temporal-spatial variations of flow, temperature, and pollutants in the upper reservoir of a pumped-storage power station during both pumping and generating modes were analyzed to demonstrate its applicability. In the reservoir, the water forms several circulations, the cold water from the inlet flows as an undercurrent firstly, and then spread laterally, and the spreading of pollutants directly relates to the flow velocity. The results of flow, temperature, and concentration fields in different working conditions are consistent with model tests and physical laws, which shows good prospects of the proposed LB model.
A Bayesian representation of the analysis of variance by A. Gelman is introduced with ecological examples. These examples demonstrate typical situations encountered in ecological studies. Compared to conventional methods, the multilevel approach is more flexible in model formulation, easier to set up, and easier to present. Because the emphasis is on estimation, multilevel models are more informative than the results from a significance test. The improved capacity is largely due to the changed computation methods. In our examples, we show that (1) the multilevel model is able to discern a treatment effect that is smaller than the conventional approach can detect, (2) the graphical presentation associated with the multilevel method is more informative, and (3) the multilevel model can incorporate all sources of uncertainty to accurately describe the true relationship between the outcome and potential predictors. 相似文献