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A Mini Review on Fluid Topology Optimization
He Li1, Cong Wang1, Xuyu Zhang1
1School of Engineering, RMIT University, Melbourne 3001, Australia.
Topology optimization advances fluid dynamics designs, reviewing density-based methods for diverse flow conditions. Future directions include isogeometric analysis and machine learning for enhanced performance and validation.
Area of Science:
- Engineering
- Computational Fluid Dynamics
- Materials Science
Background:
- Topology optimization is crucial for high-performance fluid applications like aircraft components and microfluidic mixers.
- The density-based approach is widely used for its simplicity, robustness, and ease of implementation in structural design.
Purpose of the Study:
- To comprehensively review technical progress in topology optimization for fluid-related applications over the last decade.
- To analyze advancements from the perspective of structural expression, focusing on boundary smoothness and computational efficiency.
Main Methods:
- Review of density-based topology optimization for various fluid flows: Stokes, laminar Navier-Stokes, turbulent, non-Newtonian, and unsteady-state.
- Discussion of isogeometric analysis (IGA) and moving morphable components/voids (MMC/MMV) methods for CAD integration and reduced computational cost.
- Concentration on level set and spline expression methods for achieving smoother boundaries and lower energy dissipation.
Main Results:
- Density-based methods are effective across a wide range of fluid flow regimes.
- IGA and MMC/MMV methods offer significant advantages in design integration and computational efficiency.
- Smoother boundaries, achieved through level set and spline methods, show potential for further reducing energy dissipation.
Conclusions:
- Topology optimization, particularly density-based methods, has seen substantial progress in fluid applications.
- Isogeometric analysis and machine learning are identified as key future directions for the field.
- Challenges remain in accurate fluid model construction and experimental validation of optimized designs.
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