Validation of a Multidimensional Smouldering Model
Bibliographic record
Abstract
Smouldering is a flameless form of combustion driven by exothermic oxidation surface reactions within a porous medium. Smouldering is being harnessed by engineers to remediate liquid hydrocarbon and Per- and Polyfluoroalkyl substances (PFAS) contaminated soils, drive waste-to-energy processes, and to provide off-grid sanitation solutions in the developing world. In all applications, initial heat is supplied to a small ignition region and air is injected to support self-sustaining smouldering. However, engineers and researchers have only a few tools to utilize and study smouldering, and this is a key limitation. This work addresses this limitation via developing a novel multidimensional, thermodynamic-based smouldering model. This model is valuable for both engineers and researchers to gain a deeper understanding into key physical (e.g., temperature, air flow, and oxygen distribution), chemical (e.g., a non-uniform oxidation reaction), and operational processes in smouldering systems (e.g., the effects of radial heat losses on energy efficiency). As smouldering gains popularity as a novel technology, there is a growing need for robust smouldering models. This presentation highlights both the model development and validation from highly instrumented experiments. These results highlight the processes that govern key operational characteristics, such as peak temperature and air flow distributions (critical for PFAS remediation) and overall energy efficiency (critical for waste-to-energy and sanitation purposes). Altogether, this work is anticipated to support investigating, designing, and optimizing the future smouldering systems for a range of applications such as PFAS remediation, waste-to-energy, and improving sanitation in the developing world.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".