Particle model resources

Wu, S.-C., and P. M. Gschwend, Numerical modeling of sorption kinetics of organic compounds to soil and sediment particles , Water Resources Res., 24, 1373-1383 (1988). 

An activity which illustrates this general point in relation to particle theory (the topic of Chapter 2) is included in a publication available from SEP (the Science Enhancement Programme, details of which are given in the Other resources section at the end of this introduction). The first of two group-work tasks in this activity Judging models in science asks students to consider two types of particle models - particles like tiny hard billiard balls particles as molecules with soft electron clouds - and to consider which model better explains a range of evidence based on the observable properties of matter. Students will find that each model is useful for explaining some phenomena, but neither fits all the evidence - and of course both models are stiU found useful in science. 

A modeling approach following the trajectories of all of these molecules would typically require computational resources exceeding the capabilities of most of today s computers. For this reason, each particle in a DSMC simulation represents a whole ensemble of molecules. Specifically, a DSMC algorithm comprises a repeated sequence of the following steps ... 

It must be noted here that most industrial fluidized bed reactors operate in a turbulent flow regime. Trajectory simulations of individual particles in a turbulent field may become quite complicated and time consuming. Details of models used to account for the influence of turbulence on particle trajectories are discussed in Chapter 4. These complications and constraints on available computational resources may restrict the number of particles considered in DPM simulations. Eulerian-Eulerian approaches based on the kinetic theory of granular flows may be more suitable to model such cases. Application of this approach to simulations of fluidized beds is discussed below. 

Another field where dielectric continuum models are extensively used is the statistical mechanical study of many particle systems. In the past decades, computer simulations have become the most popular statistical mechanical tool. With the increasing power of computers, simulation of full atomistic models became possible. However, creating models of full atomic detail is still problematic from many reasons (1) computer resources are still unsatisfactory to obtain simulation results for macroscopic quantities that can be related to experiments (2) unknown microscopic structures (3) uncertainties in developing intermolecular potentials (many-body correlations, quantum-corrections, potential parameter estimations). Therefore, creating continuum models, which process is sometimes called coarse graining in this field, is still necessary. 

For dispersion in flows with significant variation in direction and speed at different heights and different times, the only reliable modelling method is to track individual fluid particles or track many clouds of particles from the source. The former method is now used for regional and synoptic scale dispersion prediction from localised sources, such as nuclear accidents and volcanoes, e.g. Maryon and Buckland, 1995 [396], Assumptions have to be made about how atmospheric turbulence on scales less than 3Ax diffuses particles as they are advected by the resolved flow field on scale Ax. This method requires large computer resources and then can be computed in minutes. For studying critical events in UK urban areas this method should be considered. 

Draxler, R.R. and Rolph, G.D., 2003. HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) Model access via NOAA ARL READY Website (http //www.arl.noaa.gov/ready/hysplit4.html). NOAA Air Resources Laboratory, Silver Spring, MD. 

We set the radius of the constituent particles equal to a = 0.1 pm, the same radius as was inferred in [66] from the arguments for cometary dust temperature and has long been used for modeling cometary dust [67]. We refer the reader to [68] for a discussion of the CP s size as well as for details of the computational techniques. The number N of the CPs is A = 64, 128, or 256 the larger numbers of N fall outside of the limitation of our computational resources for the selected refractive index, radius, and configuration of CPs. As a result, the aggregate with a = 0.1 pm has a radius of a volume-equivalent sphere Oy = 0.400, 0.504, or 0.635 pm. 

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