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Computational Modeling and Bifurcation Analysis of Fluidization Processes
Bing Zhu
Institute for Neural Computation, UC San Diego
Abstract:
Fluidization processes have many important applications in industry, in particular, in chemical, fossil, and petrochemical industries where good gas-solid mixing is required. Such mixing is commonly achieved through bubbles which are formed spontaneously and whose time-evolution appears to be governed by low-dimensional deterministic dynamics. A low-dimensional, computational agent-based bubble model is used to study the changes in the global bubble dynamics in response to changes in the frequency of the rising bubbles. A computationally-based bifurcation analysis shows that the collective bubble dynamics undergoes a series of transitions from equilibrium points to highly periodic orbits, chaotic attractors, and even intermittent behavior between periodic orbits and chaotic sets. Using ideas and methods from nonlinear dynamics and time-series analysis, long-term predictions for the purpose of developing control algorithms is possible through model fitting.
Tuesday, April 28, 2009
11:00AM AP&M 2402
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Directors:
Randolph E. Bank
Philip E. Gill
Michael Holst
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Randolph E. Bank
Philip E. Gill
Michael Holst
Administrative Contact:
[ Click to Send Email ]
Bing Zhu
Institute for Neural Computation, UC San Diego
Abstract:
Fluidization processes have many important applications in industry, in particular, in chemical, fossil, and petrochemical industries where good gas-solid mixing is required. Such mixing is commonly achieved through bubbles which are formed spontaneously and whose time-evolution appears to be governed by low-dimensional deterministic dynamics. A low-dimensional, computational agent-based bubble model is used to study the changes in the global bubble dynamics in response to changes in the frequency of the rising bubbles. A computationally-based bifurcation analysis shows that the collective bubble dynamics undergoes a series of transitions from equilibrium points to highly periodic orbits, chaotic attractors, and even intermittent behavior between periodic orbits and chaotic sets. Using ideas and methods from nonlinear dynamics and time-series analysis, long-term predictions for the purpose of developing control algorithms is possible through model fitting.
Tuesday, April 28, 2009
11:00AM AP&M 2402