Engineering Fluid Mechanics (Advanced Modelling with Data Science)

Build the foundational knowledge in fluid mechanics that supports the study of complex air and water flows.

Develop fundamental skills in statistical modelling from both theoretical and applied perspectives.

This module covers essential theory, methodology, and computational techniques for constructing statistical models to represent real-world scenarios, supported by practical training in R and RStudio. 

Gain comprehensive expertise in machine learning tailored to civil engineering applications.

Key areas include supervised learning (regression, classification), unsupervised learning (clustering), large language models, and reinforcement learning.

Hands-on training in Python using NumPy, scikit-learn, and PyTorch will enhance your practical skills. It will help you put theory into practice across a set of case studies in the civil engineering sector. 

Analyse the computational methods used for the simulation and analysis of engineering fluid mechanics and solve practical numerical modelling problems.

Learn to set-up and run fluid flow simulations using computational fluid dynamics (CFD) tools, building on theory and understanding the required modelling choices.

Develop practical experience constructing, validating, and deploying machine learning models and algorithms in Python.

This module covers key areas including reinforcement learning, computer vision, developer tools, version control, cloud computing, visual analytics, data modelling, database management, and advanced data manipulation and visualisation techniques. 

Liaise with an industrial client in groups to develop, design, and assess a solution for a real-world project, related to energy (e.g., offshore renewables), or built environment (e.g., data centres, air quality or thermal comfort).

You'll apply interdisciplinary knowledge, develop practical skills for design, cultivate teamwork and project management skills, and communicate complex design proposals effectively.

Learn to estimate turbulent mass transport for tracers, contaminants and sediments in atmosphere, rivers, lakes and oceans.

Derive and apply linear and non-linear theories explaining wave motion and propagation needed in fundamental engineering and oceanographic applications. 

Obtain a detailed understanding of fluid motion driven by density differences and its relevance to engineering calculation and design.

This module equips you with a strong grounding in the flow and sediment processes that govern coastal and fluvial environments. It covers non-uniform flow in rivers, together with the morphological processes that drive the evolution of river channels and floodplains. The module also examines the nearshore behaviour of waves, currents, and tides, and provides the analytical tools required to quantify hydrodynamic processes in the coastal zone, including compound river–coastal flood hazards. 

Deepen your knowledge of the coupled atmosphere ocean system, wave spectra, and the role of the Earth’s rotation in geophysical fluid dynamics.

The module provides a general overview of energy physics and systems, covering multiple renewable energy generation principles and technologies such as solar (thermal and photovoltaic), waves, wind, hydropower, geothermal (shallow and deep) and hydrogen.

The content covers a range of technical considerations, calculations and techniques, especially related to fluid mechanics design but offering a holistic perspective on their strengths and fits within energy systems. 

Explore current thinking on modelling the built environment and discover the tools required for both internal and external fluid flows inside and around buildings.