Wrigley, P., Hall, R., Wood, P. et al. (3 more authors) (2019) Automated design techniques for new nuclear power plant design : knowledge based engineering, generative design and optimisation. In: The 27th International Conference on Nuclear Engineering (ICONE27). The 27th International Conference on Nuclear Engineering (ICONE27), 19-24 May 2019, Tsukuba, Ibaraki, Japan. The Japan Society of Mechanical Engineers ISBN 9784888983051
Abstract
Ensuring a nuclear power plant is designed on time can be crucial to minimising delays and delivering the project on budget. (ETI, 2018) found that many of today's large nuclear reactors normally start construction without a completed final design which can have a major impact on total capital cost. Quick and efficient design is therefore crucial to designing new power plants. This paper assesses the concept of speeding up the nuclear power plant design process using Automated Design (AD) techniques through branches of Artificial Intelligence (AI) such as Knowledge Based Engineering (KBE) and Generative Design. KBE is “the process of capturing and use (and reuse) of product and process engineering knowledge by automating parts of the design process”. Knowledge Based Engineering can be applied at all stages of the design process, from analysing concept designs, to speeding up detailed design tasks and can be applied to components, products or systems. This wide definition creates many applications of KBE. Many of today's engineering design programs offer some form of KBE capability and there are even dedicated applications for KBE. While KBE has been utilised in a variety of industries such as: automotive, aerospace, chemical process plant, oil and gas, ship and submarine design. It has been highlighted that the technique has had little use within nuclear engineering and design academia. This paper reviews different types of KBE and presents possible directions for research utilising KBE in nuclear. It also gives examples of where KBE can be useful in Nuclear Engineering and Design.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Japan Society of Mechanical Engineers. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Advanced Manufacturing Institute (Sheffield) > Nuclear Advanced Manufacturing Research Centre |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Mar 2021 08:15 |
Last Modified: | 04 Mar 2021 08:15 |
Status: | Published |
Publisher: | The Japan Society of Mechanical Engineers |
Refereed: | Yes |
Identification Number: | 10.1299/jsmeicone.2019.27.1314 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:171671 |