ENM7005-B Modelling and Optimisation Assignment - University of Bradford, UK

Module Aims - To establish an appreciation for the role of modelling and optimisation within modern (science and) engineering practice and to provide evidence that modelling and optimisation is an integrated tool kit (that includes analytical, simulation, and statistical methods met at earlier FHEQs) for addressing, evaluating, and improving multiple solutions to complex science and engineering-based problems.

Coursework Learning Outcomes -

1. Demonstrate a critical understanding of design of experiments and response surface methodology in theory and practice as applied to engineering problem-solving, problem prevention and product development.

2. a. Plan and run statistically based experiments appropriate to a wide variety of engineering scenarios. b. Fit and validate empirical transfer functions to the resulting data. c. Use transfer functions to understand the impact of variation on system performance.

3. Demonstrate advanced statistical experimentation skills, use of specialised packages for DoE analysis, communicate effectively in a project team and contribute to teamwork facilitation.

4. Collate and manage data, and apply scientific method, IT skills and complex systematic problem-solving strategies.

(1) Tutorial assessments - where students are given an engineering tasks to analyse using either Minitab and Matlab, and submit a brief report through Canvas, within one week of the task being set up; the tasks are similar to the class tutorials. The purpose of the tutorial assessment tasks is to consolidate the skills. This is largely a formative assessment - in that students will be given guidance, example solution (discussed in class), and feedback on their submission. The tutorial assessment mark will be considered in the overall coursework assessment with a weight of 0.1 of the total coursework component assessment. An example of the tutorial assessment is given in Appendix 1.

(2) Independent student let experiment and analysis - as described below.

Task brief -

You are expected to submit a technical report of about 2,500 words length (circa 10-12 pages of text plus any diagrams, data, calculations, etc), based on an engineering modelling / metamodelling Case Study (chosen by the students). The report is expected to cover the analysis of the technical problem identifying factors (as design of modelling parameters that have a likely significant influence on the response of interest); critical evaluation of the modelling strategy - justifying the choice of the experimentation strategy (screening experiments followed by detailed optimal experiments, or space filling design of experiments); evaluation of the DoE; collection of data; fit a response surface model - arguing the model choice, and evaluate the model quality using statistical indicators - with interpretation; use the model to derive an optimal solution against a set of criteria, using software of choice.

A typical experiment would be based on a computer simulation, but physical experiments (either lab based or classroom based - paper helicopters or catapults) are also welcome. The students can use parametric CAE models that they have previously developed, or can use simulation models available in Matlab or Simulink - for which the task is to develop metamodels by applying the tools from the course. Tutorial examples and case studies provided during tutorial sessions can be expanded for the purpose of the coursework. Students can work in pairs (or small groups) to carry out the experiments - but the reports must be individual. Where students have worked in a small group the reports must be submitted together / collaboration must be acknowledged (i.e. the list of students that have collaborated for the experiment must be provided).

Software used: Minitab and / or Matlab (the Model Based Calibration toolbox).

Report Structure -

1. Executive summary

2. Contents list

3. Introduction and Objectives

4. Methodology

5. Results and analysis

6. Discussion

7. Conclusions and recommendation

8. References

9. Appendices.

Attachment:- Modelling and Optimisation Assignment & Sample File.rar