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dc.contributor.authorMayo, Michaelen_NZ
dc.contributor.authorWakes, Sarahen_NZ
dc.contributor.authorAnderson, Chrisen_NZ
dc.contributor.editorWu, X.en_NZ
dc.contributor.editorSoon, O.Y.en_NZ
dc.contributor.editorAggarwal, C.en_NZ
dc.contributor.editorChen, H.en_NZ
dc.coverage.spatialSingaporeen_NZ
dc.date.accessioned2019-02-11T01:16:55Z
dc.date.available2018en_NZ
dc.date.available2019-02-11T01:16:55Z
dc.date.issued2018en_NZ
dc.identifier.citationMayo, M., Wakes, S., & Anderson, C. (2018). Neural networks for predicting the output of wind flow simulations over complex topographies. In X. Wu, O. Y. Soon, C. Aggarwal, & H. Chen (Eds.), Proceedings of 2018 IEEE International Conference on Big Knowledge (ICBK) (pp. 184–191). Washington, DC, USA: IEEE. https://doi.org/10.1109/ICBK.2018.00032en
dc.identifier.isbn978-1-5386-9125-0en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/12319
dc.description.abstractWe use deep learning techniques to model computational fluid dynamics (CFD) simulations of wind flow over a complex topography. Our motivation is to "speed up" the optimisation of CFD-based simulations (such as the 3D wind farm layout optimisation problem) by developing surrogate models capable of predicting the output of a simulation at any given point in 3D space, given output from a set of training simulations that have already been run. Our promising results using TensorFlow show that deep neural networks can be learned to model CFD outputs with an error of as low as 2.5 meters per second.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherIEEEen_NZ
dc.rightsThis is an author’s accepted version of an article published in the Proceedings of 2018 IEEE International Conference on Big Knowledge (ICBK). © 2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.sourceICBK 2018en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectwind farm layout optimisationen_NZ
dc.subjectdeep learningen_NZ
dc.subjectcomputational fluid dynamicsen_NZ
dc.subjectwind flow modellingen_NZ
dc.subjectcomplex topographyen_NZ
dc.subjectMachine learning
dc.titleNeural networks for predicting the output of wind flow simulations over complex topographiesen_NZ
dc.typeConference Contribution
dc.identifier.doi10.1109/ICBK.2018.00032en_NZ
dc.relation.isPartOfProceedings of 2018 IEEE International Conference on Big Knowledge (ICBK)en_NZ
pubs.begin-page184
pubs.elements-id234142
pubs.end-page191
pubs.finish-date2018-11-18en_NZ
pubs.place-of-publicationWashington, DC, USA
pubs.start-date2018-11-17en_NZ


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