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 Academic Year: | 2018/9 |
| Owning Department/School: | Department of Mechanical Engineering |
| Credits: | 6 [equivalent to 12 CATS credits] |
| Notional Study Hours: | 120 |
| Level: | Masters UG & PG (FHEQ level 7) |
| Period: |
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| Assessment Summary: | CW 60%, OR 40% |
| Assessment Detail: |
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| Supplementary Assessment: |
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| Requisites: | |
| Description:
| Aims: This unit aims to: * Develop expertise in design optimisation theory, including problem description techniques, design and solution spaces, optimisation using (measurement) data, optimisation solvers and design trade-offs. * Introduce the theory of Generative Design and its relationship with design optimisation. * Develop a student's ability to strip down a product/part and identify its key performance metrics, which will subsequently be used in optimisation. * Develop a student's ability to use tools and techniques to measure the performance of a design using simulation or experimentation, such that they can optimise a design through iteration. Learning Outcomes: After successfully completing this unit a student will be able to: * Propose a design optimisation project by identifying, in quantifiable terms, the performance parameters of a product/part, taking into account factors such as budget and timescale. * Identify constraints and freedoms, objectives and performance indices that may be used to describe and then optimise a design. * Identify appropriate experimentation or optimisation techniques to use in conjunction with a design problem, comparing the merits and drawbacks of each feasible method. * Evaluate (quantitatively) by experimentation or practical testing the effect of the approach they have selected, demonstrating their product's/part's performance compared to a previously established baseline. * Reflect on design trade-offs that have been identified and report on how this new knowledge would influence further design iterations. Skills: Problem solving and data analysis (T/F/A); Design Optimisation theory, tools and methods (T); Product specification construction (F/A); team working (F/A); practical product improvement skills (F/A); presentation skills (T/A); written communication (A). Content: The unit will include taught content on Design Optimisation theory (simulation-based optimisation, experimental optimisation, Design of Experiments, generative design, etc.) and introductions to specific tools. Computer aided optimisation tools include: FEA/topology optimisation, materials selection software, general purpose optimisation toolboxes (MATLAB), etc. Practical approaches for the optimisation task include: instrumentation and data analysis techniques using Labview or Matlab. Students will be given a product and tasked with improving an aspect of its performance. In teams of two, students will use simulation to rapidly iterate through design changes to improve the performance of their product. Students will then verify their predicted product performance by building suitable physical models for testing. |
| Programme availability: |
ME40358 is Compulsory on the following programmes:Department of Mechanical Engineering
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Notes:
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