TY  - JOUR
AU  - Raza, Kumail
AU  - Hussain, Sayed Akif
AU  - Ali, Saqib
AU  - Hussain, Syed Amer
AU  - Hussain, Syed Atif
PY  - 2026
DA  - 2026/02/08
TI  - Topological Optimization of a 2D Microfluidic Channel for Particle Separation
JO  - ICCK Transactions on Advanced Computing and Systems
T2  - ICCK Transactions on Advanced Computing and Systems
JF  - ICCK Transactions on Advanced Computing and Systems
VL  - 2
IS  - 2
SP  - 74
EP  - 84
DO  - 10.62762/TACS.2025.192275
UR  - https://www.icck.org/article/abs/TACS.2025.192275
KW  - topology optimization
KW  - advanced computing framework
KW  - SIMP penalization
KW  - Navier-Stokes solver
KW  - Lagrangian particle advection
KW  - physics-driven geometric optimization
AB  - This study presents an advanced computational framework that integrates a finite-difference Navier--Stokes solver, a SIMP-based topology optimization engine, and a Lagrangian particle advection module into a unified, iteratively coupled pipeline for physics-driven geometric design. The framework autonomously evolves the internal material distribution of a 2D microchannel by minimizing an objective function that directly quantifies particle mis-sorting, eliminating reliance on manual heuristic design and external actuation forces. Applied to the problem of passive microfluidic particle separation, the computational approach generated manufacturable, binary-material topologies across five optimization iterations, achieving a peak sorting efficiency of $\Phi = 0.6667$ (66.67%) at iteration~2 and stabilizing at $\Phi = 0.6111$ (61.11%) in subsequent iterations. The results demonstrate that physics-based iterative optimization can autonomously discover non-intuitive channel geometries that outperform conventional heuristic designs in adaptability and separation robustness. This work contributes a modular, extensible computing methodology applicable to a broad class of constrained geometric optimization problems in engineering systems.
SN  - 3068-7969
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Raza2026Topologica,
  author = {Kumail Raza and Sayed Akif Hussain and Saqib Ali and Syed Amer Hussain and Syed Atif Hussain},
  title = {Topological Optimization of a 2D Microfluidic Channel for Particle Separation},
  journal = {ICCK Transactions on Advanced Computing and Systems},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {74-84},
  doi = {10.62762/TACS.2025.192275},
  url = {https://www.icck.org/article/abs/TACS.2025.192275},
  abstract = {This study presents an advanced computational framework that integrates a finite-difference Navier--Stokes solver, a SIMP-based topology optimization engine, and a Lagrangian particle advection module into a unified, iteratively coupled pipeline for physics-driven geometric design. The framework autonomously evolves the internal material distribution of a 2D microchannel by minimizing an objective function that directly quantifies particle mis-sorting, eliminating reliance on manual heuristic design and external actuation forces. Applied to the problem of passive microfluidic particle separation, the computational approach generated manufacturable, binary-material topologies across five optimization iterations, achieving a peak sorting efficiency of \$\Phi = 0.6667\$ (66.67\%) at iteration~2 and stabilizing at \$\Phi = 0.6111\$ (61.11\%) in subsequent iterations. The results demonstrate that physics-based iterative optimization can autonomously discover non-intuitive channel geometries that outperform conventional heuristic designs in adaptability and separation robustness. This work contributes a modular, extensible computing methodology applicable to a broad class of constrained geometric optimization problems in engineering systems.},
  keywords = {topology optimization, advanced computing framework, SIMP penalization, Navier-Stokes solver, Lagrangian particle advection, physics-driven geometric optimization},
  issn = {3068-7969},
  publisher = {Institute of Central Computation and Knowledge}
}