Topological optimization for tailored designs of advection-diffusion-reaction porous reactors based on pore scale modeling and simulation
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it