Computational fluid dynamics (CFD) and computational mechanics (CM) methodology expedited with Machine Learning (ML) techniques for a sustainable and secure coastal/urban environment.

The present Student Research Hub (SRH) teaches undergraduate/postgraduate students the CFD and CM methodology and provides practical skills with emphasis in three-dimensional (3D) design, MATLAB computer programming, computational setups and analyses of mechanical systems behaviour (fluid flow, turbulent with heat and mass transfer, mechanics, forces and stress analysis). It is envisaged that students will acquire skills for design, modelling, and flow and mechanics simulations and pertinent analyses. Practical use cases including automotive components’ simulations and road safety guardrails’ mechanics and impact simulations will be presented.

CFD and CM 3D investigations with design of parts and assembly, and practical setup of simulation data will be delivered with SolidWorks computer aided design (CAD) software. The activities will cover all the stages of design, modelling, simulation and parametric investigation for solving real engineering problems occurring at sea, coastal and urban environments. The methodology including the constitutive equations for CFD, CM and mechanics calculations and simulations will be presented, complemented with simple example analyses using MATLAB computer programming for results and data visualisation. Specialised software including the CFD code STAR-CD and the ANSYS software for mechanics investigations will be demonstrated, followed by design and simulation practice in SolidWorks, considered as an essential tool for undergraduate students and researchers.

During the activities of the present SRH, alternative options for open-source software and digital tools deployment, including machine learning (ML) and artificial intelligence (AI) specialised design and calculation tools will be illustrated and used, in order to facilitate design, modelling, simulation, prototyping and manufacturing of components and mechanical systems, as well as exploiting existing data from which patterns can be utilised for modelling and calculations expedited by ML. ML tools and data utilisation are considered complementary to the CFD and CM methodologies and CAD software, and can support engineering design and modelling for problem formulation, literature survey activities for project development and presentations by the students attending the present SRH.

General learning outcomes

• Develop multidisciplinary engineering skills for formulating problems and reaching practical solutions.
• Apply engineering design for problem geometry and boundary conditions definition.
• Comprehend computational approaches for modelling, calculation and simulation for tackling problems of energy, emissions and mechanics for transport safety.
• Acquire skills for data analysis, visualisation and presentation.
• Synthesise geometry and computational data for problem solution presentation, suggesting improvement points.
• Develop skills for project implementation, utilising CFD & CM methodology, CAD software and ML tools, for model development and data generation, reporting of results and presenting of solutions to engineering problems.