Computational Engineering Design option - MSc in Computational and Software Techniques in Engineering

• Dr Irene Moulitsas

The module aims to provide an understanding of a variety of computational methods for integration, solution of differential equations and solution of linear systems of equations.

The module explores numerical integration methods; the numerical solution of differential equations using finite difference approximations including formulation, accuracy and stability; matrices and types of linear systems, direct elimination methods, conditioning and stability of solutions, iterative methods for the solution of linear systems.

• Dr Irene Moulitsas

Object oriented programming (OOP) is the standard programming methodology used in nearly all fields of major software construction today, including engineering and science and C++ is one of the most heavily employed languages. This module aims to answer the question ‘what is OOP’ and to provide the student with the understanding and skills necessary to write well designed and robust OO programs in C++. Students will learn how to write C++ code that solves problems in the field of computational engineering, particularly focusing on techniques for constructing and solving linear systems and differential equations. Hands-on programming sessions and assignment series of exercises form an essential part of the course.
An introduction to the Python language is also provided.

• The OOP methodology and method, Classes, abstraction and encapsulation;
• Destructors and memory management, Function and operator overloading, Inheritance and aggregation, Polymorphism and virtual functions, Stream input and output;
• Templates, Exception handling, The C++ Standard Library and STL.

On successful completion of this module a student should be able to:

1. Apply the principles of the object oriented programming methodology - abstraction, encapsulation, inheritance and aggregation - when writing C++ programs.
2. Create robust C++ programs of simple to moderate complexity given a suitable specification.
3. Use the Standard Template Library and other third party class libraries to assist in the development of C++ programs.
4. Solve a range of numerical problems in computational engineering using C++.
5. Use development environments and associated software engineering tools to assist in the construction of robust C++ programs.
6. Evaluate existing C++ programs and assess their adherence to good OOP principles and practice.

• Dr Richard Adams

The importance of technology leadership in driving the technical aspects of an organisation’s products, innovation, programmes, operations and strategy is paramount, especially in today’s turbulent commercial environment with its unprecedented pace of technological development. Demand for ever more complex products and services has become the norm. The challenge for today’s manager is to deal with uncertainty, to allow technological innovation and change to flourish but also to remain within planned parameters of performance. Many organisations engaged with technological innovation struggle to find engineers with the right skills. Specifically, engineers have extensive subject/discipline knowledge but do not understand management processes in organisational context. In addition, STEM graduates often lack interpersonal skills.

• Engineers and Technologists in organisations:
  • the role of organisations and the challenges facing engineers and technologies,
• People management:
  • understanding you, understanding other people, working in teams and dealing with conflicts.
• The Business Environment:
  • understanding the business environment; identifying key trends and their implications for the organisation.
• Strategy and Marketing:
  • developing effective strategies, focusing on the customer, building competitive advantage, the role of strategic assets.
• Finance:
  • profit and loss accounts, balance sheets, cash flow forecasting, project appraisal.
• New product development:
  • commercialising technology, market drivers, time to market, focusing technology, concerns.
• Business game:
  • Working in teams (companies), you will set up and run a technology company and make decisions on investment, R&D funding, operations, marketing and sales strategy,
• Negotiation:
  • preparation for negotiations, negotiation process, win-win solutions.
• Presentation skills:
  • understanding your audience, focusing your message, successful presentations, getting your message across.

On successful completion of this module you should be able to:

• Recognise the importance of teamwork in the performance and success of organisations with particular reference to commercialising technological innovation,
• Operate as an effective team member, recognising the contribution of individuals within the team, and capable of developing team working skills in yourself and others to improve the overall performance of a team,
• Compare and evaluate the impact of the key functional areas (strategy, marketing and finance) on the commercial performance of an organisation, relevant to the manufacture of a product or provision of a technical service,
• Design and deliver an effective presentation that justifies and supports any decisions or recommendations made,
• Argue and defend your judgements through constructive communication and negotiating skills.

• Professor Karl Jenkins

The aim of this module is to provide the student with the knowledge and practice of the mathematical techniques and the principal algorithms used for the construction and implementation of parametric curve, surface and solid geometry. The material covered here forms the basis of free-form modelling as used in all major CAD/CAM systems and, more generally, in the fields of visualisation and computer graphics. Hands-on programming exercises and a modelling assignment form part of the course.

Wireframe, surface and solid geometry, Polynomial and spline interpolation, B-spline curve and surface interpolation and approximation, Some advanced modelling techniques, Solid model representation schemes, Boundary representation models.

• Dr Laszlo Konozsy

This course covers more advanced aspects of CAE, the aim being to introduce students to key concepts and techniques in the use of CAE application software tools. Use is made of structured computer based workshops which employ industry standard systems for CAD through to Engineering Analysis.

• Introduction to I-DEAS CAE Finite Element Analysis (FEA) Simulation software,
• CAE FEA Pre- and Post-Processing, Free mesh and Mapped mesh techniques,
• Quality checks on nodes and elements, Finite element and geometry based
• boundary conditions, Utilising solids based modelling geometry for downstream
• CAE FEA, CAE linear statics analysis using the I-DEAS CAE FEA Simulation
• software, Case Studies.

• Professor Karl Jenkins

The aim of the CAE Solid Modelling module is to introduce students to key concepts, techniques and applications of a 3D Solid Modelling system. Use is made of structured computer based workshops which employ an industry standard system (CATIA) for 3D Solid Modelling. Introductory lectures are reinforced by the ‘hands-on’ approach through a series of part, assembly and surface modelling exercises covering the major workbenches available in the CAD system.

• Introduction to CATIA CAE Solid Modelling Software,
• Some benefits of using solid modelling and the CAE approach,
• Different construction methods for 3D geometrical models,
• Parametric and variational design,
• Production of drafting setup details from 3D geometrical parts,
• Modifying parts and features,
• Part, Assembly and Surface Modelling workbenches

• Professor Karl Jenkins

To introduce the techniques and tools for modelling, simulating and analysing realistic computational engineering problems for industrial applications with practical hands on experience of commercial software packages used in industry.

• Introduction to Computational Engineering
• Fundamental equations
• The Computational Engineering Process
• Fluid Simulation for Computer Graphics
• Modelling techniques
• Practical sessions

• Professor Karl Jenkins

Computer graphics is a key element in the effective presentation and manipulation of data in engineering software.  The aim of this module is to provide an in depth practical understanding of the mathematical and software principles behind 2D and 3D visualisation using the widely used OpenGL (desktop) and WebGL (web based) graphic libraries. Representative GUI based 2D and 3D OpenGL/WebGL applications using both Javascript/HTML5 and the Qt development environment are employed. The module will also cover some of the more advanced rendering techniques including lighting, texturing and other image mapping methods used to enhance visual interpretation of data. An introduction to the implementation and use of Virtual Reality in engineering completes the module. Hands-on exercises and an assignment supplement the learning process.

• Mathematical principles behind 2D and 3D visualisation, The graphic and coordinate pipelines, Matrix transformations, Modelling, viewing and projection, OpenGL and WebGL libraries, GLSL shader programming.for the graphic pipeline and GPU
• Development of interactive CG applications using OpenGL, WebGL, GLSL and Qt,
• Advanced rendering techniques, lighting, texturing and image mapping
• Introduction to virtual reality.

On successful completion of this module a student should be able to:

1. Apply the principles underlying the graphic and coordinate pipelines to display and manipulate 2D and 3D models. .
2. Use the mathematical basis behind 2D/3D modelling and viewing to solve visualisation problems in OpenGL and WebGL.
3. Understand, implement and use GLSL shader programs forimplementing the graphic pipeline
4. Create interactive visualisation applications using OpenGL/ WebGL, GLSL and Qt.
5. Evaluate the major differences between the different versions of OpenGL.
6. Evaluate the use of VRi and other libraries used in engineering visualisation.

• Professor Karl Jenkins

The numerical solutions of partial differential equations are used for simulating physical systems and phenomena and for the investigation of a wide range engineering applications. These numerical solutions may used in engineering design optimisation to explore the implications of design changes. The aim of this course is to provide the student with the mathematical background to the discretisation of partial differential equations using finite element and finite difference approaches, and an insight into methods for their solution along with the vital numerical techniques for the analysis of the solution and numerical errors.

• Introduction to Simulation, Finite Element Methods, Finite Difference Methods,
• Numerical Solution to Partial Differential Equations: Parabolic, Elliptic, Hyperbolic
• Stability Analysis and Truncation Errors, Case Studies