Sustainable Built Environment module details
Engineering Business Environment and Research Methods
The engineering business part of this module is to enable students to understand and reflect upon the role of business in a rapidly changing, globalised world. It identifies opportunities and threats for industry arising from environmental policy, legislation and societal change, and explores how businesses respond to future environmental challenges: for example, through supply chain management, logistics, life-cycle analysis, green accounting and carbon trading. Challenging questions are asked such as: can industry be a positive force for good? How do businesses learn and adapt to new challenges and economic models? This module benefits practitioners in industry, and future academics exploring the sustainability of engineering businesses.
The module also teaches students self-direction, and originality in problem solving. The research methods and associated study skills parts of the module provide students with the skills to successfully complete a research project.
Data Analytics for Sustainable Energy Systems
As energy systems become smarter, their data footprint increases drastically. It is imperative to be able to manage these large datasets, for the sustainability of the global energy system. Data management, as used here, includes data acquisition, cleaning, manipulation, processing, and storage. This module teaches students the key concepts of data analytics and its application to energy system design and operation. It starts with a revision of the fundamentals of scientific programming in Python, to provide students with the requisite skills for advanced topics later in the module. The Python programming language has been chosen by virtue of its popularity in industry and its plethora of open-source Data Science libraries. Students are further introduced to Statistics, Machine Learning, and Optimisation to equip them with the skills required for solving moderately advanced problems in, but not limited to, uncertainty analysis; supervised and unsupervised machine learning; reinforcement learning; mixed-integer linear programming; model-predictive control; operation management; and decision making under uncertainty.
The second part of the module is activity based, and applies the concepts studied in the first part to carefully selected real-world case studies from all stages of the energy value chain. Case studies could be drawn from: demand forecasting in multi-vector energy systems, renewable energy generation prediction, electric vehicle charge scheduling, model-predictive control of distributed energy systems, outage management in electricity grids, load management, energy theft detection, economic dispatch of power systems, consumer profiling, and energy market analysis.
Sustainable Building Design and Performance Modelling
In this module, students will learn about the physics of energy flows, airflows and lighting in buildings, as well as the computer modelling techniques for predicting how buildings respond to the external environment, the occupancy and how these affects their energy consumption and the comfort and wellbeing of their occupants.
The students will gain a detailed understanding of the key features of climatic datasets and how they reveal the local meteorological conditions. A comprehensive understanding of the interaction between fabric/form, airflow, solar irradiation, and building thermal performance and how local environmental conditions and the design of a building affect the thermal comfort of its occupants will be developed. This will enable students to understand why and how building simulation methods can be used to analyse building performance in the design process. Students will be able to make appropriate selection of simulation methods, evaluate results and give coherent recommendations.
A number of different airflow modelling techniques are included in the module. The students will gain a comprehensive understanding of the physical processes of airflow and the design principles of natural ventilation and gain a practical understanding of how natural ventilation has been employed in real building designs. The computer modelling with complement this learning towards mixed-mode sustainable buildings, while also enabling students to carry out simulation-based studies to predict solar control and daylighting quantities and to test their sensitivity to building parameters.
The module will enable students to carry out Dynamic Thermal modelling to analyse the thermal performance of buildings in the design process and to understand why and how building energy simulation methods can be used to estimate the benefits, in terms of energy and emissions, of a sustainable approach to building design.
Built Environment Advanced Modelling
In the prerequisite module, students learn about sustainable buildings and modelling of single buildings. This module develops modelling to a more advanced level, at the single building level (such as detailed airflow modelling), and at the wider community level of areas with multiple buildings and the interactions between them of solar gain, shading, airflow, vegetation, and shared energy systems. It uses a range of state-of-the-art software.
Students will develop technical assessments of building and communities of buildings, and propose improvements and innovations, presenting these as reports and in presentations. This responds to a growing need for modelling and design at larger scales, where for example buildings share renewable energy sources rather than having individual fossil fuel systems.
This module merges two previously distinct modules, Dissertation (for non-engineering courses) and Individual Project (for engineering courses). As it will cover a great diversity of courses, it will be delivered as a team effort.
The module aims to introduce the student to the discipline of independent research carried out in a restricted timeframe. It will involve self-organisation, application, analysis and presentation of work. The topic will be chosen from a list provided by staff, grouped by discipline, or chosen by the student and agreed with the dissertation supervisor. It must be relevant to the course being taken. The project may involve practical work, or be entirely desktop based. An ethics form will be required with approval but is not marked. The Report should be approximately 10,000 – 15,000 words, reflecting the amount of practical work and the nature of the topic.