Projects
Projects: Projects for Investigator |
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| Reference Number | EP/N005740/1 | |
| Title | Design Mining: A Microbial Fuel Cell Pilot Study | |
| Status | Completed | |
| Energy Categories | Not Energy Related 50%; Hydrogen and Fuel Cells(Fuel Cells) 50%; |
|
| Research Types | Basic and strategic applied research 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 50%; ENGINEERING AND TECHNOLOGY (General Engineering and Mineral & Mining Engineering) 50%; |
|
| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Professor L Bull No email address given Computing Engineering and Maths Science University of the West of England |
|
| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 01 September 2015 | |
| End Date | 07 November 2017 | |
| Duration | 27 months | |
| Total Grant Value | £298,433 | |
| Industrial Sectors | Energy; Manufacturing | |
| Region | South West | |
| Programme | Manufacturing : Manufacturing | |
| Investigators | Principal Investigator | Professor L Bull , Computing Engineering and Maths Science, University of the West of England (99.998%) |
| Other Investigator | Dr IA Ieropoulos , Computing Engineering and Maths Science, University of the West of England (0.001%) Professor J Greenman , Applied Sciences, University of the West of England (0.001%) |
|
| Web Site | ||
| Objectives | ||
| Abstract | Design Mining is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototyping technology to meet given objectives. It enables the exploitation of novel materials and processes without formal models or complex simulation, whilst harnessing the creativity of both computational and human design methods. The traditional engineering design process and the data mining process share many similarities, and the proposed project will seek to exploit this fact and embed data mining within design. Models which enable what-if testing of the characteristics of the object design space are created throughout. A sample-model-search-sample loop creates an agile/flexible approach, ie, primarily test-driven, enabling a continuing process of prototype design consideration and criteria refinement by both producers and users. Parallel/sub-design scenarios will also be explored, considering the effects of the degree of prototype and data/model synchronisation in the concurrent tasks upon the utility of the approach. In particular, machine learning techniques will be used to iteratively search and model the object design space informed by the performance metrics of microbial fuel cells whose electrodes are fabricated using 3D printing, both as individual units and as collectives in cascades. | |
| Publications | (none) |
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| Final Report | (none) |
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| Added to Database | 15/07/15 | |
