Bridging Scales in Modelling and Simulation of Reacting Flows, Part B, Volume 53, presents key methods used to bridge scales in the simulation of reacting multiphase flows. It looks at the different aspects of such flows (transport phenomena, reactions) and includes illustrations of the methods on a variety of applications, along with the contribution of key groups in the field. Sections in this new release include multi-scale methods for fluidized bed reactors, a discussion of advances in coarse-grained discrete particle methods with industrial applications, and spatial filtering for scale bridging and its application to transport in dense bidisperse particle beds, and more.

Bridging Scales in Modelling and Simulating Reacting Flows, Part I , Volume 52 presents key methods to bridge scales in the simulation of reacting single phase flows. New sections in the updated release include topics such as quadrature-based moment methods for multiphase chemically reacting flows, the collaboration of experiments and simulations for the development of predictive models, a simulation of turbulent coalescence and breakage of bubbles and droplets in the presence of surfactants, a section on salts and contaminants, and information on the numerical simulation of reactive flows. - Contains reviews by leading authorities in their respective areas - Presents up-to-date reviews of the latest techniques in the modeling of catalytic processes - Includes a broad mix of US and European authors, as well as academic, industrial and research institute perspectives - Provides discussions on the connections between computational and experimental methods

Bridging Scales in Modelling and Simulation of Reacting Flows, Part B, Volume 53, presents key methods used to bridge scales in the simulation of reacting multiphase flows. It looks at the different aspects of such flows (transport phenomena, reactions) and includes illustrations of the methods on a variety of applications, along with the contribution of key groups in the field. Sections in this new release include multi-scale methods for fluidized bed reactors, a discussion of advances in coarse-grained discrete particle methods with industrial applications, and spatial filtering for scale bridging and its application to transport in dense bidisperse particle beds, and more. - Contains reviews by leading authorities in their respective areas - Presents up-to-date reviews of the latest techniques in the modeling of catalytic processes - Includes a broad mix of US and European authors, as well as academic, industrial and research institute perspectives - Provides discussions on the connections between computation and experimental methods

Table of contents

The simulation of technological and environmental flows is very important for many industrial developments. A major challenge related to their modeling is to involve the characteristic turbulence that appears in most of these flows. The traditional way to tackle this question is to use deterministic equations where the effects of turbulence are directly parametrized, i. e. , assumed as functions of the variables considered. However, this approach often becomes problematic, in particular if reacting flows have to be simulated. In many cases, it turns out that appropriate approximations for the closure of deterministic equations are simply unavailable. The alternative to the traditional way of modeling turbulence is to construct stochastic models which explain the random nature of turbulence. The application of such models is very attractive: one can overcome the closure problems that are inherent to deterministic methods on the basis of relatively simple and physically consistent models. Thus, from a general point of view, the use of stochastic methods for turbulence simulations seems to be the optimal way to solve most of the problems related to industrial flow simulations. However, it turns out that this is not as simple as it looks at first glance. The first question concerns the numerical solution of stochastic equations for flows of environmental and technological interest. To calculate industrial flows, 3 one often has to consider a number of grid cells that is of the order of 100 .

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