Fluid Dynamics

To develop cutting edge modelling, simulation and data intelligent solutions for fluid flow, thermal/mass transfer and fluid related multi-physics applications. IHPC focuses on insight of fluid phonomena, new numerical algorithms, and advanced simulation approaches. To support urban sustainability and industry innovation through model simulation and design optimisation.

Coupled & Complex Flow

IHPC develop capabilities in modeling and simulation for a wide spectrum of complex flow phenomena, ranging from oil and gas flow, mass/heat transfer dynamics in additive manufacturing, micro-/nano-fluidics, to urban climate environment. These complex flows contain multiple physics at different length scales. These solutions require accurate and efficient modelling techniques. 

Our foci areas include:

  • Multiscale Flow: Aims to connect molecular dynamics models, mesoscopic models and continuum models to solve complex flow problems efficiently.
  • Multiphase Flow: The objective is to develop new algorithms and models to solve multiphase / interfacial flow dynamics for natural phenomena and industrial processes.
  • Multiscale Flow: Aims to connect molecular dynamics models, mesoscopic models and continuum models to solve complex flow problems efficiently.

  • Recently we established an Immersed Boundary – Lattice Boltzmann Method approach to study mass/heat transfer and phase change phenomenon in additive manufacturing. Through such simulations, we gained a better physics insight of melting pool and thermal history in additive manufacturing process, which improved the quality and functionality of the built product.

    Laser Beam Melting and Re-solidification
    Simulation of laser beam melting and re-solidification processes in additive manufacturing using Lattice-Boltzmann method

    Through the collaboration of IHPC, Institute for Infocomm Research (I2R), and Housing & Development Board (HDB), we developed an Integrated Environmental Modeller (IEM). IEM was created based on open source and in-house codes with combined multiple environmental factors such as solar, wind, air temperature and traffic noise. IEM is a first-of-its-kind tool that provides complex multi-physics coupling, system integration, high scalability, with ground truth sensor validation, and successfully adopted by end users for urban environment design and planning.

    Integrated Environmental Modeller (IEM)
    Integrated Environmental Modeller (IEM) platform and its applications

    Computation for Optimisation and Design of Engineering Systems (CODES)

    Digital design has become an important thread in many engineering applications. The CODES research encompasses formulating optimisation problems in design of engineering systems; developing methodologies and algorithms for optimisation of multi-objective, multi-physics problems. The key focus is on reducing computation cost in an optimisation cycle through surrogate models and advanced numerical algorithms.

    For effective exploration and discovery of optimal design space, the group further develop capabilities in geometrical modelling and meshing coupled with efficient sampling techniques and fast searching algorithms. The research also addresses challenges in introducing uncertainties to models and optimisation process.

    This research initiative broadens the existing core expertise and strong underlying capabilities in the area of computational methods and numerical analysis in geometrical modelling, fluid dynamics and coupled physics. The ultimate objective is to build robust and comprehensive computational design tools for a wide range of applications in marine offshore, aerospace, manufacturing and urban sustainability solutions.

    design of engineering systems
    Key research areas in computation for design and optimization of engineering systems

    To streamline geometrical model generation and preparation for design exploration through efficient sampling and fast searching algorithms. To integrate mesh parametrisation and mesh manipulation (morphing, free form deformation) into optimisation loop for any design change and variation.

    To advance methodologies including using adjoint solvers and surrogate models (see image below) for shape optimisation and multiphysics complex problems. We explore multitudes of reduced order modelling techniques such as data driven surrogate models, low order models based on high fidelity simulations enabling fast computation in any optimization cycle.

    Uncertainty Quantification
    To develop methods for quantifying errors and uncertainties inherently embedded in numerical models; thus, providing confidence to simulations. We aim at incorporating uncertainties into design through stochastic optimisation.

    Proper orthogonal decomposition

    Fig. A: POD, proper orthogonal decomposition, modes used for load prediction on pre-swirl duct

    Duct geometry
    Fig. B: Optimisation of duct geometry to achieve maximum efficiency

    Pre-swirl duct in a bulk carrier

    Fig. C. Duct design optimization for a marine ship propeller

    Example in application of surrogate models for design of pre-swirl duct to maximize propeller operations in interactions with ship hull. We optimized a pre-swirl duct to achieve the best propulsion efficiency by considering duct, hull and propeller interaction (Fig. C). A surrogate model based on proper-orthogonal decomposition (POD) interpolation approach was developed to predict load on a duct under different design parameters (Fig. A). The model is built on POD modes and validated with full order model. Based on POD results, we conduct a duct geometrical optimization using a global maximization approach. The optimum configuration (right) showed marked improvement in propeller efficiency (Fig. B).

    Physics-Based Data-Driven Modelling

    Our research focuses are on two main areas:

    • Physics-Based Data-Driven Models (PBDDM)
    • Advanced model-based control (Model Predictive Control, MPC)

    In PBDDM, IHPC aims to develop a robust modelling framework for near-real time simulations to serve the growing demands for accelerating modelling & design in engineering domain such as accelerating design, real-time controls, digital twining, etc. Our development is leveraging on the accuracy of physics-based approaches and speed of physics-informed data-driven approaches.

    The framework is built on combinations of physics-based models, reduced-order models, machine learning models, data assimilation models. Dependent on specific applications, suitable combinations of models will be chosen based on the models’ strength, validity and applicability. We also aim to further develop this framework for inverse modelling of specific engineering designs and discovery of hidden parameters in physical systems, such as the source leak location in a dispersion problem.

    The example below demonstrates a high-Re flow simulation. Compared to full model result (Fig. A), a combined Physics-Based Reduced-Order Model (PBROM) and Neural Networks (NN) method achieves very good results (Fig. B) in a few seconds, while the full model (Fig. A) simulation takes a few hours. 

    Physics-Based Reduced-Order Model (PBROM) and Neural Networks (NN)
    High-Re flow simulation

    In MPC, IHPC develops a platform for advanced model predictive control (MPC). MPC utilises a dynamic model for a process to optimize the control variables at every point in time while anticipating future events. In practical applications, MPC helps to improve efficiency, productivity and product quality, whilst reduces operational cost and time. 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. 

    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. The MOR-MPC 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 MOR-MPC approach achieves about 500 times faster than the original process model, while the relative error is below 0.1%.

    MOR-MPC control implemention
    MOR-MPC control implementation

    Engineering Fluid Flow

    Describing flow phenomena arising in many industrial processes is challenging due the multiplicity of the underlying physics compounded by the typically complex behaviour of the flowing medium itself. To bridge gaps in modelling and simulation in disparate areas of industrial interest, our research comprises of three areas:

    • Rheologically Complex Flows addresses the challenges of obtaining stable numerical solutions in the simulation of highly viscous and rheologically complex flows.
    • Particulate Flows covers high Mach number and highly turbulent subsonic and supersonic compressible flows, propelling particles with a size distribution. 
    • Component and System Level Modelling seeks fit-for-purpose solutions vis-à-vis computationally expensive high fidelity simulations.  

    Abrasive flow machining of nozzle guide vanes

    Modelling and simulation of abrasive flow machining of nozzle guide vanes (L to R): nozzle guide vane geometry, computational domain grid mesh, predicted media velocity and material removal distribution on NGV blades

    This study was undertaken to find a new reliable design model  for an additive manufactured component considering material removal compensation by an abrasive flow machining process. For this extremely high Weissenberg number problem with the abrasive media modelled as a PTT non-linear viscoelastic fluid, the log-conformation tensor representation technique was embedded in the OpenFoam solver to stabilise the numerical solution. Based on this modelling framework, a practical engineering design tool has been developed and adopted by our collaborator.