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Modeling Particle Transport In Biomedical Flows Using Implicit Geometry Representations
Biorxiv : the Preprint Server for Biology
|June 22, 2026
Summary
This study introduces an efficient particle dynamics model using signed distance fields for faster, accurate simulations of particle-wall contact in biomedical applications like drug delivery and disease modeling.
Area of Science:
- Computational fluid dynamics
- Biomedical engineering
- Scientific computing
Background:
- Particle-based Lagrangian methods are crucial for modeling physiological and biomedical systems, offering insights into fluid dynamics.
- Current methods face significant computational costs, particularly in resolving particle-wall contact in complex anatomical geometries.
- Efficient geometry representation is vital for high-fidelity patient-specific or device-specific computational models.
Purpose of the Study:
- To develop an efficient particle dynamics model for resolving particle-wall contact in complex anatomical structures.
- To reduce the computational expense associated with particle simulations in biomedical applications.
- To maintain high accuracy and geometric fidelity while improving simulation speed.
Main Methods:
- Developed a particle dynamics model utilizing a signed distance field for implicit representation of anatomical features.
- Transformed Lagrangian contact detection into an equivalent Eulerian operation.
- Validated the approach using simplified geometries and a representative simulation of embolic particles in a human vascular segment.
Main Results:
- The signed distance field approach significantly speeds up bulk particle dynamics computations.
- The method efficiently resolves particle-wall contact without substantial impact on accuracy or geometric fidelity.
- Comparison against classical mesh-based contact detection showed improved performance.
Conclusions:
- The developed signed distance field-based model offers a computationally efficient and accurate solution for particle dynamics simulations in biomedical applications.
- This approach enhances the feasibility of high-fidelity modeling for diseases like stroke and for targeted drug delivery systems.
- The transformation of contact detection from Lagrangian to Eulerian operations represents a significant advancement in computational efficiency.
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