Thomas J. O'Brien

Simulation of granular layer inversion in liquid fluidized beds

Abstract A hydrodynamic computer model for describing multiparticle fluidization has been developed. Each group of particles, identical in density and in diameter, is treated as a particulate phase in this model. The computer code solves the mass and momentum balance equations for the fluidizing fluid and for the required number of particulate phases. The model has been used to simulate granular layer inversion in a liquid fluidized bed. This phenomenon occurs during the fluidization of a binary mixture of particles in which the denser particles are smaller. In such a system at low fluid velocities, the larger particles segregate into a top layer; at higher fluid velocities, they sink to form a bottom layer. At intermediate fluid velocities, the extent to which the particles mix is determined by the fluid velocity. The simulation results using the multiparticle code are in good agreement with experimental data on granular layer inversion. It is also shown that under some conditions, a radial segregation pattern exists in addition to the experimentally observed axial segregation pattern.

Acceleration techniques for reduced-order models based on proper orthogonal decomposition

This paper presents several acceleration techniques for reduced-order models based on the proper orthogonal decomposition (POD) method. The techniques proposed herein are: (i) an algorithm for splitting the database of snapshots generated by the full-order model; (ii) a method for solving quasi-symmetrical matrices; (iii) a strategy for reducing the frequency of the projection. The acceleration techniques were applied to a POD-based reduced-order model of the two-phase flows in fluidized beds. This reduced-order model was developed using numerical results from a full-order computational fluid dynamics model of a two-dimensional fluidized bed. Using these acceleration techniques the computational time of the POD model was two orders of magnitude shorter than the full-order model.

Open-source software in computational research: a case study

A case study of open-source (OS) development of the computational research software MFIX, used for multiphase computational fluid dynamics simulations, is presented here. The verification and validation steps required for constructing modern computational software and the advantages of OS development in those steps are discussed. The infrastructure used for enabling the OS development of MFIX is described. The impact of OS development on computational research and education in gas-solids flow, as well as the dissemination of information to other areas such as geophysical and volcanology research, is demonstrated. This study shows that the advantages of OS development were realized in the case of MFIX: verification by many users, which enhances software quality; the use of software as a means for accumulating and exchanging information; the facilitation of peer review of the results of computational research.

Comparison of frameworks for a next‐generation multiphase flow solver, MFIX: a group decision‐making exercise

Computational fluid dynamics (CFD) simulations have emerged as a powerful tool for understanding the multiphase flows that occur in a wide range of engineering applications and natural processes. A multiphase CFD code called Multiphase Flow with Interphase eXchanges (MFIX) has been under development at the National Energy Technology Laboratory (NETL) since the 1980s for modeling the multiphase flows that occur in fossil fuel reactors. CFD codes such as MFIX are equipped with a number of numerical algorithms to solve a large set of coupled partial differential equations over three‐dimensional grids consisting of hundreds of thousands of cells on parallel computers. Currently the next‐generation version of MFIX is under development with the goal of building a multiphase problem‐solving environment (PSE) that would facilitate the simple reuse of modern software components by application scientists. Several open‐source frameworks were evaluated to identify the best‐suited framework for the multiphase PSE. There are many requirements for the multiphase PSE and each of these open‐source frameworks offers functionalities that satisfy the requirements to varying extents. Therefore, matching the requirements and the functionalities is not a simple task and requires a systematic and quantitative decision‐making procedure. We present a multi‐criteria decision‐making approach for determining a major system design decision and demonstrate its application on the framework‐selection problem. Copyright

Practical Aspects of the Implementation of Proper Orthogonal Decomposition

This paper discusses two practical aspects of the implementation of reduced-order models based on proper orthogonal decomposition (POD). The POD method calculates basis functions used in a reduced-order representation of two-phase flow in fluidized beds by calculating the eigenvectors of an autocorrelation matrix composed of snapshots of the flow. The aspects discussed are: (i) the time sampling of snapshots that minimize error in the POD reconstruction of the flowfield, and (ii) the form of the autocorrelation matrix that minimizes error in the POD reconstruction of the flowfield. Two regions in the flow are identified, a transient region and a quasi-steady region. Two methods are then proposed for time sampling the flow to retain additional snapshots in the transient region. Both methods are shown to produce less error than the case where snapshots are sampled a constant frequency. A time sampling rate based on a logarithmic distribution with 200 snapshots is shown to produce error on the same order as an evenly spaced snapshot database with 800 snapshots. The composition of the autocorrelation matrix is also considered. An approach treating field variables entirely separately is shown to produce less error than a coupled approach when the field variables are reconstructed.