Modeling of a Rotary Blood Pump. Issue 3 (1st August 2013)
- Record Type:
- Journal Article
- Title:
- Modeling of a Rotary Blood Pump. Issue 3 (1st August 2013)
- Main Title:
- Modeling of a Rotary Blood Pump
- Authors:
- Nestler, Frank
Bradley, Andrew P.
Wilson, Stephen J.
Timms, Daniel L. - Abstract:
- <abstract abstract-type="main"> <title>Abstract</title> <p>The accurate representation of rotary blood pumps in a numerical environment is important for meaningful investigation of pump–cardiovascular system interactions. Although numerous models for ventricular assist devices (VADs) have been developed, modeling methods for rotary total artificial hearts (rTAHs) are still required. Therefore, an rTAH prototype was characterized in a steady flow, hydraulic test bench over a wide operational range for pump and hydraulic parameters. In order to develop a generic modeling method, a data‐driven modeling approach was chosen. k‐Nearest‐neighbors, artificial neural networks, and support vector machines (SVMs) were the machine learning approaches evaluated. The best performing parameters for each algorithm were determined via optimization. The resulting multiple‐input–multiple‐output models were subsequently assessed under identical conditions, and a SVM with a radial basis function kernel was identified as the best performing. The achieved root mean squared errors were 0.03 L/min, 0.06 L/min, and 0.18 W for left and right flow and motor power consumption, respectively. In comparison with existing models for VADs, the flow errors are more than 70% lower. Further advantages of the SVM model are the robustness to measurement noise and the capability to operate outside of the trained parameter range. This proposed modeling method will accelerate further device refinements by providing<abstract abstract-type="main"> <title>Abstract</title> <p>The accurate representation of rotary blood pumps in a numerical environment is important for meaningful investigation of pump–cardiovascular system interactions. Although numerous models for ventricular assist devices (VADs) have been developed, modeling methods for rotary total artificial hearts (rTAHs) are still required. Therefore, an rTAH prototype was characterized in a steady flow, hydraulic test bench over a wide operational range for pump and hydraulic parameters. In order to develop a generic modeling method, a data‐driven modeling approach was chosen. k‐Nearest‐neighbors, artificial neural networks, and support vector machines (SVMs) were the machine learning approaches evaluated. The best performing parameters for each algorithm were determined via optimization. The resulting multiple‐input–multiple‐output models were subsequently assessed under identical conditions, and a SVM with a radial basis function kernel was identified as the best performing. The achieved root mean squared errors were 0.03 L/min, 0.06 L/min, and 0.18 W for left and right flow and motor power consumption, respectively. In comparison with existing models for VADs, the flow errors are more than 70% lower. Further advantages of the SVM model are the robustness to measurement noise and the capability to operate outside of the trained parameter range. This proposed modeling method will accelerate further device refinements by providing a more appropriate numerical environment in which to evaluate the pump–cardiovascular system interaction.</p> </abstract> … (more)
- Is Part Of:
- Artificial organs. Volume 38:Issue 3(2014:Mar.)
- Journal:
- Artificial organs
- Issue:
- Volume 38:Issue 3(2014:Mar.)
- Issue Display:
- Volume 38, Issue 3 (2014)
- Year:
- 2014
- Volume:
- 38
- Issue:
- 3
- Issue Sort Value:
- 2014-0038-0003-0000
- Page Start:
- 182
- Page End:
- 190
- Publication Date:
- 2013-08-01
- Subjects:
- Artificial organs -- Periodicals
617.956 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1525-1594 ↗
http://www.blackwell-synergy.com/member/institutions/issuelist.asp?journal=aor ↗
http://onlinelibrary.wiley.com/ ↗
http://firstsearch.oclc.org ↗ - DOI:
- 10.1111/aor.12142 ↗
- Languages:
- English
- ISSNs:
- 0160-564X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 1735.052000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 4108.xml