Fluidic Topology Optimization with an Anisotropic Mixture Model
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Abstract (arXiv)
Fluidic devices are crucial components in many industrial applications involving fluid mechanics. Computational design of a high-performance fluidic system faces multifaceted challenges regarding its geometric representation and physical accuracy. We present a novel topology optimization method to design fluidic devices in a Stokes flow context. Our approach is featured by its capability in accommodating a broad spectrum of boundary conditions at the solid-fluid interface. Our key contribution is an anisotropic and differentiable constitutive model that unifies the representation of different phases and boundary conditions in a Stokes model, enabling a topology optimization method that can synthesize novel structures with accurate boundary conditions from a background grid discretization. We demonstrate the efficacy of our approach by conducting several fluidic system design tasks with over four million design parameters.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Sep 2022.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 2.
5 papers.
- 2023DiffAvatar: Simulation-Ready Garment Optimization with Differentiable Simulationcited by 40
- 2025EmbodiedOcc++: Boosting Embodied 3D Occupancy Prediction with Plane Regularization and Uncertainty Samplercited by 26
- 2024NeuralFluid: Neural Fluidic System Design and Control with Differentiable Simulationcited by 10
- 2025DualMS: Implicit Dual-Channel Minimal Surface Optimization for Heat Exchanger Designcited by 1
- 2023Automatic Truss Design with Reinforcement Learningcited by 0
- 2025EmbodiedOcc++: Boosting Embodied 3D Occupancy Prediction with Plane Regularization and Uncertainty Sampler13 Apr 2025
- 2025DualMS: Implicit Dual-Channel Minimal Surface Optimization for Heat Exchanger Design2 Mar 2025
- 2024NeuralFluid: Neural Fluidic System Design and Control with Differentiable Simulation22 May 2024
- 2023DiffAvatar: Simulation-Ready Garment Optimization with Differentiable Simulation20 Nov 2023
- 2023Automatic Truss Design with Reinforcement Learning27 Jun 2023