Mathematical modelling and simulation of stochastic collisional exchange processes - PhDData

Access database of worldwide thesis




Mathematical modelling and simulation of stochastic collisional exchange processes

The thesis was published by Huang, Chunbing, in September 2022, UCL (University College London).

Abstract:

Collision-exchange processes play a prominent role in a variety of natural systems where system members interact to engender the change of quantities, material transfer and information exchange over a population, driving a macroscopic evolution of system state in time. Mathematical modelling provides a useful approach to the quantitative characterisation of collision-exchange processes in order to assist the identification of change of system state. The established mathematical model that involves state variables and model parameters is expected to be identified based on the relevant experimental observations.

Although collision-exchange processes have been extensively studied in many systems, especially in particulate systems, by formulating models based on discrete element methods, these models still suffer from several limitations, in particular the significant computational intensity required by simulations that restrict the further research into the models, leading to the difficulty using these models in model-based tasks, including design of experiments and optimisation.

This project focuses on the investigation of a stochastic modelling approach for collision-exchange processes and the development of identification strategies for stochastic models. The work addresses the following challenges: i) the development of a stochastic model to simulate the collision-exchange process and predict the dynamical evolution of system state within tractable computational time; ii) the design and execution of experiments in an industrial seed coating process for the verification of the established stochastic model; iii) the development of parameter estimation and model-based design of experiments techniques suitable for stochastic models, i.e. the model outputs with uncertainty.

The work presented in the Thesis facilitates an alternative modelling approach for collision-exchange processes, providing a systematic methodology for the identification and optimisation of stochastic systems with higher accuracy in prediction and less computational intensity.



Read the last PhD tips