Fuel mobility dynamics and their influence on applied smouldering systems
Bibliographic record
Abstract
Many recent environmentally beneficial applications of smouldering treat hazardous organic liquid fuels in inert porous media. In these applications, organic liquid mobilization can affect the treatment process, and the dynamics are poorly understood. Organic liquid mobilization is therefore a key knowledge gap that hinders the optimization of applied smouldering. This is especially the case in large scales where mobilization appears to be more significant. Liquid mobilization inside a porous medium cannot be easily measured directly, therefore numerical modelling is essential to understand the fundamental processes and to clarify the effects and dynamics of the fuel mobilization on the smouldering reaction. Contrasting numerical models with experimental temperature measurements have revealed many aspects of smouldering that cannot be measured. In this study, a previously developed 1D smouldering model was equipped with multiphase flow equations and compared against laboratory column experiments. The combination of model and experiments has served to quantify the dynamics of organic liquid fuel mobility by simulating high (i.e., non-mobile) and low (i.e., mobile) viscous fuels. The findings from this study shed light on the complicated interplay between multiphase flow, heat and mass transfer, and smoulder chemistry common to many applied smouldering systems. Numerical results confirmed that increasing the viscosity results in fuel remaining in the reaction zone and led to an increase in the peak temperature and smouldering front velocities. Lower viscosity fuels mobilized away from the reaction zone, thereby accumulating fuel in the pre-heating zone of the reactor. The fundamental understanding generated from this research will improve the design, implementation, and optimization of smouldering-based technologies for environmentally beneficial applications worldwide.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".