![]() ![]() ![]() Our research interests include, but are not limited to:ĭeveloping capabilities for robust CFD models to couple new physics for colloidal dynamics to account for macroscale effects for the translation from small-scale to large-scale applications. The Multi-Physics Modelling Research Group focuses on capabilities which integrate complex physics with CFD models by developing advanced numerical algorithms to tackle the challenging, industrially-relevant, multi-physics problems which cannot be addressed by traditional CFD alone. The MOR-MPC approach achieves about 500 times faster than the original process model, while the relative error is below 0.1%. The MOR-MPC (Fig 3) approach is applied to control the transformation of synthesis of 3-piperidinopionic acid ethyl ester by a Michael addition scheme from piperidine and ethyl acrylate with water effect. The example below shows a schematic diagram of the experimental system (the shaded box is our MOR-MPC control implementation) for a chemical reaction process. As such, in applications, MPC helps to improve efficiency, productivity and product quality, whilst reducing operational cost and time. In addition, the concepts of online machine learning for control are also implemented to update the control policies in real-time to enable the power of adaptive control for highly nonlinear dynamic process systems. In our development, state-of-art model order reduction (MOR) methods, such as PBROM, are integrated into MPC to enable accurate and real-time controls of linear/nonlinear processes. ![]() MPC utilises a dynamic model within a robust optimisation algorithm for a process to optimise the control variables at every point in time while anticipating future events. ![]() In FCPO, IHPC develops a cutting-edge model predictive control (MPC) platform with powerful optimisation engines for advanced manufacturing processes. As the result, the pressure measurement data can represent the level of intensity for real-time tracking to support optimal control. The measurement results (Fig B) indicate that the pressure sensors and their location can accurately measure the kinetic energy transferred from the airflow to peen velocity at the impact, and kinetic energy from the peens to the treated component surface. In particular, the pressure sensor locations are chosen at the inlet and on the nozzle based on the CFD simulation results (Fig A). The example below shows the outcome of the sensor down-selection process for the model predictive control system to automate the shot peening machine. Thus, the framework can help to couple the numerical world with the physical world for decision-making. For data fusion, suitable data assimilation technique(s) is selected for different application (e.g., real-time optimal control, reconstruction, digital-twin, monitoring, etc.). For a specific process flow/system, the framework starts with a relevant model(s) to search for suitable sensor and their location based on the most information extracted using the low dimension and optimisation algorithms. In SDFT, IHPC focuses on the development of a framework that covers (1) automatically determines the number of sensors with their optimal locations and (2) the relevant data fusion technologies for fluid flow processes to enable the capabilities in prediction, monitoring and control. B) in a few seconds, while the full model (Fig. A), a combined Physics-Based Reduced-Order Model (PBROM) and Neural Networks (NN) method achieve very good results (Fig. The example below demonstrates a high-Re flow simulation. We also aim to further develop this framework for inverse modelling of specific engineering designs and discover hidden parameters in physical systems, such as the source leak location in a dispersion problem. Depending on specific applications, a suitable combination of the models will be chosen based on the models’ strength, validity and applicability. The framework is built on combinations of physics-based models, reduced-order models, machine learning models, and data assimilation models. Our development is leveraging the accuracy of physics-based approaches and the speed of physics-informed data-driven approaches. In APMC, IHPC aims to develop an AI-assisted robust modelling framework for real-time simulations to serve the growing demands to accelerate modelling, design, operating, and control in engineering domains such as accelerating design, real-time controls, digital twining, optimal operation, etc. ![]()
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