Accounting for eccentricity in compressor performance prediction
- Submitting institution
-
The University of Bath
- Unit of assessment
- 12 - Engineering
- Output identifier
- 202401826
- Type
- D - Journal article
- DOI
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10.1115/1.4036201
- Title of journal
- Journal of Turbomachinery
- Article number
- 091008
- First page
- -
- Volume
- 139
- Issue
- 9
- ISSN
- 0889-504X
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2017
- 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
-
3
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work led to a collaborative Knowledge Transfer Fellowship with Rolls-Royce (£48k from Cambridge’s EPSRC Impact Acceleration Account, contact: AI/Horizon Scanning Lead in Future Technologies, Rolls-Royce) on machine learning and operability, which has so far resulted in a further journal publication (Taylor et al 2020 https://doi.org/10.1115/1.4046658). The impact of the work is further demonstrated by its selection as one of two case studies presented at the Rolls-Royce Aerothermal Excellence conference (21-22 July 2016) as an example of how realistic engine conditions can be incorporated into their design system.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -