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. Local mesh refinement controls can resolve geometry details appropriately, and poor CAD models can be improved in SpaceClaim/Discovery, or you can use Fluent's Fault Tolerant meshing workflow, which is robust to CAD faults.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. Poor mesh quality can also be caused by insufficient mesh resolution of geometry details or by poor underlying CAD. When selected, you will then enter the percentage of the total cells to be improved, and the number of iterations of smoothing sweeps performed. If you are in Fluent Solution mode, you can improve the mesh quality by selecting the improve the mesh option on the Quality button under the Domain tab. To improve the surface mesh, you will have to enter a face quality limit to target, and when improving the volume mesh, you will have to enter a cell quality limit value to target. If you are still in the process of generating your mesh in Fluent Meshing mode, you can insert a task after both the 'Generate the Surface Mesh' task and 'Generate the Volume Mesh' task by right-clicking on either one and inserting a new 'Improve Mesh' task. Mesh quality can be improved in a variety of places in Ansys Fluent.
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