dc.contributor.author |
Bogaers, Alfred EJ
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|
dc.contributor.author |
Kok, S
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|
dc.contributor.author |
Reddy, BD
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|
dc.contributor.author |
Fran, T
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dc.date.accessioned |
2012-08-02T09:42:50Z |
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dc.date.available |
2012-08-02T09:42:50Z |
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dc.date.issued |
2012-07 |
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dc.identifier.citation |
Bogaers, AEJ, Kok, S, Reddy, BD and Fran, T. Inverse parameter identification for a branching 1D arterial network. EngOpt 2012 - International Conference on Engineering Optimization Rio de Janeiro, Brazil, 1-5 July 2012 |
en_US |
dc.identifier.isbn |
978-85-7650-344-6 |
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dc.identifier.isbn |
9788576503439 |
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dc.identifier.uri |
http://www.engopt.org/paper/340.pdf
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dc.identifier.uri |
http://hdl.handle.net/10204/6035
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|
dc.description |
EngOpt 2012 - International Conference on Engineering Optimization Rio de Janeiro, Brazil, 1-5 July 2012 |
en_US |
dc.description.abstract |
In this paper we investigate the invertability of a branching 1D arterial blood flow network. We limit our investigation to a single bifurcating vessel, where the material properties, unloaded areas and variables characterizing the input and output boundary conditions are included as free parameters. The synthetic time data used for the optimization problem, as well as the blood flow analysis is performed using a 1D finite volume vascular network model. We pose and investigate four different problem formulations based on synthetic data which could hypothetically be measured experimentally. We will demonstrate the invertibality of the problem based on synthetic time data at a single location within the bifurcation as well as demonstrate the influance of the number of data points included within these time signals. Lastly, we will show how the addition of increasing levels of noise to the synthetic data influences the ability of obtaining the correct system parameters. For purposes of the inverse optimization we make use of a bounded BFGS algorithm where the gradients are approximated using the complex step method. |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.ispartofseries |
Workflow;9364 |
|
dc.subject |
Inverse parameter identification |
en_US |
dc.subject |
1D branching blood flow |
en_US |
dc.subject |
Complex step method |
en_US |
dc.title |
Inverse parameter identification for a branching 1D arterial network |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Bogaers, A. E., Kok, S., Reddy, B., & Fran, T. (2012). Inverse parameter identification for a branching 1D arterial network. http://hdl.handle.net/10204/6035 |
en_ZA |
dc.identifier.chicagocitation |
Bogaers, Alfred EJ, S Kok, BD Reddy, and T Fran. "Inverse parameter identification for a branching 1D arterial network." (2012): http://hdl.handle.net/10204/6035 |
en_ZA |
dc.identifier.vancouvercitation |
Bogaers AE, Kok S, Reddy B, Fran T, Inverse parameter identification for a branching 1D arterial network; 2012. http://hdl.handle.net/10204/6035 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Bogaers, Alfred EJ
AU - Kok, S
AU - Reddy, BD
AU - Fran, T
AB - In this paper we investigate the invertability of a branching 1D arterial blood flow network. We limit our investigation to a single bifurcating vessel, where the material properties, unloaded areas and variables characterizing the input and output boundary conditions are included as free parameters. The synthetic time data used for the optimization problem, as well as the blood flow analysis is performed using a 1D finite volume vascular network model. We pose and investigate four different problem formulations based on synthetic data which could hypothetically be measured experimentally. We will demonstrate the invertibality of the problem based on synthetic time data at a single location within the bifurcation as well as demonstrate the influance of the number of data points included within these time signals. Lastly, we will show how the addition of increasing levels of noise to the synthetic data influences the ability of obtaining the correct system parameters. For purposes of the inverse optimization we make use of a bounded BFGS algorithm where the gradients are approximated using the complex step method.
DA - 2012-07
DB - ResearchSpace
DP - CSIR
KW - Inverse parameter identification
KW - 1D branching blood flow
KW - Complex step method
LK - https://researchspace.csir.co.za
PY - 2012
SM - 978-85-7650-344-6
SM - 9788576503439
T1 - Inverse parameter identification for a branching 1D arterial network
TI - Inverse parameter identification for a branching 1D arterial network
UR - http://hdl.handle.net/10204/6035
ER -
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en_ZA |