The Discrete-Continuous, Global Optimisation of an Axial Flow Blood Pump - Publication - Bridge of Knowledge

Search

The Discrete-Continuous, Global Optimisation of an Axial Flow Blood Pump

Abstract

This paper presents the results of the discrete-continuous optimisation of an axial flow blood pump. Differential evolution (DE) is used as a global optimisation method in order to localise the optimal solution in a relatively short time. The whole optimisation process is fully automated. This also applies to geometry modelling. Numerical simulations of the flow inside the pump are performed by means of the Reynolds-Average Navier-Stokes approach. All equations are discretised by means of the finite volume method, and the corresponding algebraic equation systems are solved by the open source software for CFD, namely Open-FOAM. Finally, the optimisation results are presented and discussed. The objective function to be maximised is simply pressure increase. The higher pressure increase the lower angular velocities required. This makes it possible to minimise the effect of haemolysis because it is mainly caused by high shear stresses which are related, among others, to angular velocities.

Citations

  • 4

    CrossRef

  • 0

    Web of Science

  • 5

    Scopus

Cite as

Full text

download paper
downloaded 65 times
Publication version
Accepted or Published Version
License
Creative Commons: CC-BY open in new tab

Keywords

Details

Category:
Articles
Type:
artykuły w czasopismach
Published in:
FLOW TURBULENCE AND COMBUSTION pages 1 - 17,
ISSN: 1386-6184
Language:
English
Publication year:
2019
Bibliographic description:
Tesch K., Kaczorowska-Ditrich K.: The Discrete-Continuous, Global Optimisation of an Axial Flow Blood Pump// FLOW TURBULENCE AND COMBUSTION -, (2019), s.1-17
DOI:
Digital Object Identifier (open in new tab) 10.1007/s10494-019-00100-5
Bibliography: test
  1. Kafagy, D.H., Dwyer, T.W., McKenna, K.L., Mulles, J.P., Chopski, S.G., Moskowitz, W.B., Throckmor- ton, A.L.: Design of axial blood pumps for patients with dysfunctional fontan physiology: computational studies and performance testing. Artif. Organs 39(1), 34-42 (2015) open in new tab
  2. Carr, C.M., Jacob, J., Park, S.J., Karon, B.L., Williamson, E.E., Araoz, P.A.: CT of left ventricular assist devices. RadioGraphics 30(2), 429-444 (2010) open in new tab
  3. Aaronson, K.D. et al.: Use of an intrapericardial, continuous-flow, centrifugal pump in patients awaiting heart transplantation. Circulation 125(25), 3191-3200 (2012) open in new tab
  4. Rogers, J. et al.: Intrapericardial left ventricular assist device for advanced heart failure. N. Engl. J. Med. 376, 451-460 (2017) open in new tab
  5. Slaughter, M. et al.: HeartWare ventricular assist system for bridge to transplant: combined results of the bridge to transplant and continued access protocol trial. J. Heart Lung Transplant. 32(7), 675-683 (2013) open in new tab
  6. Behbahani, M., Behr, M., Hormes, M., Steinseifer, U., Arora, D., Coronado, O., Pasquali, M.: A review of computational fluid dynamics analysis of blood pumps. Eur. J. Appl. Math. 20, 363-397 (2009) open in new tab
  7. Yu, H., Janiga, G., Thévenin, D.: Computational fluid dynamics-based design optimization method for Archimedes screw blood pumps. Artif. Organs 40(4), 341-352 (2016) open in new tab
  8. Zhu, L., Zhang, X., Yao, Z.: Shape optimization of the diffuser blade of an axial blood pump by computational fluid dynamics. Artif. Organs 34, 185-192 (2010) open in new tab
  9. Derakhshan, S., Pourmahdavi, M., Abdolahnejad, E., Reihani, A., Ojaghi, A.: Numerical shape optimiza- tion of a centrifugal pump impeller using artificial bee colony algorithm. Comput. Fluids 81, 145-151 (2013) open in new tab
  10. Zhang, Y., Zhan, Z., Gui, X.M., Sun, H.S., Zhang, H., Zheng, Z., Zhou, J.Y., Zhu, X.D., Li, G.R., Hu, S.S., Jin, D.H.: Design optimization of an axial blood pump with computational fluid dynamics. ASAIO J. 54, 150-155 (2008) open in new tab
  11. Gouskov, A.M., Lomakin, V.O., Banin, E.P., Kuleshova, M.S.: Minimization of hemolysis and improve- ment of the hydrodynamic efficiency of a circulatory support pump by optimizing the pump flowpath. Biomed. Eng. 51(4), 229-233 (2017) open in new tab
  12. Frazier, O.H., Khalil, H.A., Benkowski, R.J., Cohn, W.E.: Optimization of axial-pump pressure sensitivity for a continuous-flow total artificial heart. J. Heart Lung Transplant. 29(6), 687-691 (2010) open in new tab
  13. Korakianitis, T., Rezaienia, M.A., Paul, G.M., Avital, E.J., Rothman, M.T., Mozafari, S.: Optimization of axial pump characteristic dimensions and induced hemolysis for mechanical circulatory support devices. ASAIO J. 64(6), 727-734 (2018) open in new tab
  14. Tesch, K., Kaczorowska, K.: Arterial cannula shape optimization by means of the rotational firefly algorithm. Eng. Optim. 48(3), 497-518 (2016) open in new tab
  15. Eaton, J.W., et al.: GNU Octave version 4.2.1 manual: a high-level interactive language for numerical computations, https://www.gnu.org/software/octave/doc/v4.2.1/ (2017)
  16. Tesch, K.: Continuous optimisation algorithms. GUT Publishers, Gdansk (2016)
  17. Kaczorowska, K., Tesch, K.: A short review of blood modelling methods: from macro-to microscales. Task Quarterly 22(1), 5-16 (2017) open in new tab
  18. Wilcox, D.C.: Turbulence modeling for CFD, DCW Industries, California (1994)
  19. Menter, F.R.: Two-equations eddy-viscosity turbulence models for engineering applications. AIAA-J. 32(8), 1598-1605 (1994) open in new tab
  20. OpenFOAM user guide 2015, OpenFOAM Foundation Ltd open in new tab
  21. Issa, R.I.: Solution of the implicitly discretised fluid flow equations by operator-splitting. J. Comput. Phys. 62(1), 40-65 (1986) open in new tab
  22. Tesch, K., Kludzinska, K., Doerffer, P.: Investigation of the aerodynamics of an innovative vertical-axis wind turbine. Flow Turbulence and Combustion 95, 739-754 (2015) open in new tab
  23. Price, K.V., Storn, R., Lampinen, J.: Differential evolution: A practical approach to global optimization. Springer-Verlag, Berlin (2005)
  24. Storn, R., Price, K.: Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341-359 (1997) open in new tab
  25. Lacasse, D., Garon, A., Pelletier, D.: Mechanical hemolysis in blood flow: User-independent predictions with the solution of a partial differential equation. Comput. Methods Biomech. Biomed. Engin. 10(1), 1-12 (2007) open in new tab
  26. Yu, H., Engel, S., Janiga, G., Thévenin, D.: A Review of hemolysis prediction models for computational fluid dynamics. Artif. Organs 41(7), 603-621 (2017) open in new tab
Verified by:
Gdańsk University of Technology

seen 104 times

Recommended for you

Meta Tags