Topological optimization for tailored designs of advection-diffusion-reaction porous reactors based on pore scale modeling and simulation
Bibliographic record
Abstract
Reactive transport within porous reactors is crucial to many diverse applications, and the efficacy of these reactors hinges on their microstructure. Mathematical modeling and optimization play a pivotal role in the exploration of efficient designs, enabling the generation of structures that may not be achievable through random realizations of packings. In this study, we propose a framework for high-resolution topological optimization of porous flow-through reactors based on pore-scale simulations using a non-dominated sorting genetic algorithm II. A pore network model for an advection-diffusion-reaction system is developed to simulate reactor performance. This model is integrated with a mathematical optimization algorithm, incorporating a background grid and Delaunay tessellation. The optimization framework generates enhanced porous structures, simultaneously maximizing conversion rates while minimizing pumping costs. Striking a balance between permeability and reactive surface area, the final designs yield a set of Pareto optimal solutions, encompassing diverse non-dominated designs with varying reaction rates and hydraulic requirements. The results demonstrate that optimal pore configurations lead to a 280% increase in conversion rates and a 6% reduction in pumping costs at one end, while on the opposite end of the Pareto front, a 15.2% increase in reaction rates and an 11.3% reduction in pumping costs are observed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".