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.
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.Optimisation
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.
Fig. A: POD, proper orthogonal decomposition, modes used for load prediction on pre-swirl duct
Fig. B: Optimisation of duct geometry to achieve maximum efficiency
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).