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[Optimization of centrifugal artificial heart pump blade parameters based on back propagation neural network and grey
Lulu Mu1, Huanhuan Duan1,2, Yuan Xiao1
1Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China.
Summary
Optimizing artificial heart pump impellers reduced maximum shear stress by 16%, significantly improving hemolytic performance. This novel approach enhances artificial heart pump design by minimizing blood damage.
Area of Science:
- Biomedical Engineering
- Fluid Dynamics
Context:
- Artificial heart pumps are crucial for patients with end-stage heart failure.
- Impeller design directly impacts pump efficiency and blood compatibility.
- High shear stress generated by impellers can cause hemolysis, damaging red blood cells.
Purpose:
- To optimize impeller blade parameters for enhanced hemolytic performance in artificial heart pumps.
- To identify the ideal combination of blade number, outlet angle, and thickness.
- To minimize maximum shear stress within the pump.
Summary:
- An optimization design was performed using a back propagation neural network and grey wolf optimization algorithm.
- Design variables included impeller blade number, outlet angle, and thickness.
- Optimized parameters (7 blades, 25° outlet angle, 1.2 mm thickness) reduced maximum shear stress by 16% (to 377 Pa).
Impact:
- Significantly reduced high shear stress regions in the impeller, improving hemolytic performance.
- The coupled algorithm reduced modeling and simulation workload.
- Provides a novel, advantageous approach for centrifugal artificial heart pump parameter optimization.
