Reduced-order urban wind interference. (September 2015)
- Record Type:
- Journal Article
- Title:
- Reduced-order urban wind interference. (September 2015)
- Main Title:
- Reduced-order urban wind interference
- Authors:
- Wilkinson, Samuel
Bradbury, Gwyneth
Hanna, Sean - Other Names:
- Gerber David Jason guest-editor.
Goldstein Rhys guest-editor. - Abstract:
- A novel approach is demonstrated to approximate the effects of complex urban interference on the wind-induced surface pressure of tall buildings. This is achieved by decomposition of the domain into two components: the obstruction model (OM) of the static large-scale urban context, for which a single computational fluid dynamics (CFD) simulation is run; and the principal model (PM) of the isolated tall building under design, for which repeatable reduced-order model (ROM) predictions can be made. The ROM is generated with an artificial neural network (ANN), using a set of feature vectors comprising an input of local shape descriptors and a range of wind speeds from a training geometry, and an output response of pressure. For testing, the OM CFD simulation provides the flow boundary condition wind speeds to the PM ROM prediction. The result is vertex-resolution surface pressure data for the PM mesh, intended for use within generative design exploration and optimisation. It is found that the mean absolute prediction error is around 5.0% ( σ : 7.8%) with an on-line process time of 390 s, 27 times faster than conventional CFD simulation; considering full process time, only 3.2 design iterations are required for the ROM time to match CFD. Existing work in the literature focuses solely on creating generalised rules relating global configuration parameters and a global interference factor (IF). The work presented here is therefore a significantly alternative approach, with theA novel approach is demonstrated to approximate the effects of complex urban interference on the wind-induced surface pressure of tall buildings. This is achieved by decomposition of the domain into two components: the obstruction model (OM) of the static large-scale urban context, for which a single computational fluid dynamics (CFD) simulation is run; and the principal model (PM) of the isolated tall building under design, for which repeatable reduced-order model (ROM) predictions can be made. The ROM is generated with an artificial neural network (ANN), using a set of feature vectors comprising an input of local shape descriptors and a range of wind speeds from a training geometry, and an output response of pressure. For testing, the OM CFD simulation provides the flow boundary condition wind speeds to the PM ROM prediction. The result is vertex-resolution surface pressure data for the PM mesh, intended for use within generative design exploration and optimisation. It is found that the mean absolute prediction error is around 5.0% ( σ : 7.8%) with an on-line process time of 390 s, 27 times faster than conventional CFD simulation; considering full process time, only 3.2 design iterations are required for the ROM time to match CFD. Existing work in the literature focuses solely on creating generalised rules relating global configuration parameters and a global interference factor (IF). The work presented here is therefore a significantly alternative approach, with the advantages of increased geometric flexibility, output resolution, speed, and accuracy. … (more)
- Is Part Of:
- Simulation. Volume 91:Number 9(2015:Sep.)
- Journal:
- Simulation
- Issue:
- Volume 91:Number 9(2015:Sep.)
- Issue Display:
- Volume 91, Issue 9 (2015)
- Year:
- 2015
- Volume:
- 91
- Issue:
- 9
- Issue Sort Value:
- 2015-0091-0009-0000
- Page Start:
- 809
- Page End:
- 824
- Publication Date:
- 2015-09
- Subjects:
- wind interference -- machine learning -- computational fluid dynamics
Computer simulation -- Periodicals
003.3 - Journal URLs:
- http://SIM.sagepub.com/ ↗
http://fidelio.ingentaselect.com/vl=3713861/cl=37/nw=1/rpsv/ij/sage/00375497/contp1.htm ↗
http://firstsearch.oclc.org ↗
http://www.uk.sagepub.com/home.nav ↗ - DOI:
- 10.1177/0037549715595135 ↗
- Languages:
- English
- ISSNs:
- 0037-5497
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 6525.xml