Automated shape optimisation of a plane asymmetric diffuser using combined Computational Fluid Dynamic simulations and multi-objective Bayesian methodology
- Submitting institution
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University of Exeter
- Unit of assessment
- 12 - Engineering
- Output identifier
- 6418
- Type
- D - Journal article
- DOI
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10.1080/10618562.2019.1683165
- Title of journal
- International Journal of Computational Fluid Dynamics
- Article number
- -
- First page
- 256
- Volume
- 33
- Issue
- 6-7
- ISSN
- 1061-8562
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
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C - Water and Environment
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is an invited paper for the IJCFD Special Issue on 'CFD-enabled Design Optimisation of Industrial Flows'. The work was developed under EPSRC Grant No. EP/M017915/1 (value £554,616). This led to a KTP project with Hydro International who have patented designs generated by the machine learning algorithm described here (Patent # GB1816265.1; contact Dr Dan Jarman: djarman@hydro-int.com). Several other companies are evaluating this work in their product development process, including Torin-Sifan, who are funding a second KTP, as well as Teignbridge Propellers and AUDI. I also presented this work at a keynote presentation for the 13th OpenFOAM Workshop (2018).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -