Adapting data processing to compare model and experiment accurately: A discrete element model and magnetic resonance measurements of a 3d cylindrical fluidized bed

Discrete element modeling is being used increasingly to simulate flow in fluidized beds. These models require complex measurement techniques to provide validation for the approximations inherent in the model. This paper introduces the idea of modeling the experiment to ensure that the validation is accurate. Specifically, a 3D, cylindrical gas-fluidized bed was simulated using a discrete element model (DEM) for particle motion coupled with computational fluid dynamics (CFD) to describe the flow of gas. The results for time-averaged, axial velocity during bubbling fluidization were compared with those from magnetic resonance (MR) experiments made on the bed. The DEM-CFD data were postprocessed with various methods to produce time-averaged velocity maps for comparison with the MR results, including a method which closely matched the pulse sequence and data processing procedure used in the MR experiments. The DEM-CFD results processed with the MR-type time-averaging closely matched experimental MR results, validating the DEM-CFD model. Analysis of different averaging procedures confirmed that MR time-averages of dynamic systems correspond to particle-weighted averaging, rather than frame-weighted averaging, and also demonstrated that the use of Gaussian slices in MR imaging of dynamic systems is valid. © 2013 American Chemical Society.

Wastage of low alloy steels in a fluidized bed environment

The wastage behaviour of four low alloy steels, suitable for use as evaporator tubing in industrial atmospheric fluidized bed combustors (AFBCs), was examined in a laboratory-scale test rig. Specimens exposed in the test apparatus experienced a high flux of impacts at low particle velocities similar to conditions in a FBC boiler. The influence of time, velocity and temperature on the wastage behaviour was examined and incubation times and velocity exponents were determined and their values discussed. Since high-temperature oxidation played an important role in this process, the short-term oxidation rate of each of the steels was measured. The mechanisms of material loss across the temperature range were discussed and the behaviour of the low alloy steels in the current work was compared with that of high alloy and stainless steels in earlier studies. © 1995.

Laboratory-scale fluidized bed rig for high-temperature tube wastage studies

As part of a study of the wear of candidate heat exchanger tube materials for use in fluidized bed combustors, two similar laboratory-scale rigs have been built and characterized. Specimens of selected alloys are carried on counter-rotating rotors immersed in a fluidized bed, and are exposed to particle impact velocities of up to approximately 3 ms-1 at temperatures up to 1000°C. The performance of this design of apparatus has been investigated in detail. The effects of several experimental variables have been studied, including angle of particle impact, specimen speed, position of the rotor within the fluidized bed, duration of exposure, bed material particle size, degradation of the bed material, degree of fluidization of the bed, and size of specimen. In many cases the results obtained with steel specimens at elevated temperatures are similar to those observed with polymeric specimens at low temperatures.

Rigorous finite-time guarantees for optimization on continuous domains by simulated annealing

Simulated annealing is a popular method for approaching the solution of a global optimization problem. Existing results on its performance apply to discrete combinatorial optimization where the optimization variables can assume only a finite set of possible values. We introduce a new general formulation of simulated annealing which allows one to guarantee finite-time performance in the optimization of functions of continuous variables. The results hold universally for any optimization problem on a bounded domain and establish a connection between simulated annealing and up-to-date theory of convergence of Markov chain Monte Carlo methods on continuous domains. This work is inspired by the concept of finite-time learning with known accuracy and confidence developed in statistical learning theory.