The Use of CFD to Understand Thermal Environments Inside Roman Baths: A Transdisciplinary Approach
Bibliographic record
Abstract
Computational fluid dynamics or CFD can provide another tool for the classical archaeologist to use when analyzing ancient structures. It is an engineering technique used to solve problems involving fluid flow and heat transfer. CFD can create an approximation of the temperature distribution, the velocity profiles, and other properties based on the geometry, materials, and environmental conditions that are supplied by archaeological research. Scholars have already applied CFD to the study of aqueducts in the Roman Empire, but there are many other possibilities for its use. This paper presents the benefits and problems of one of these possibilities – the caldaria of Imperial thermae – and provides some preliminary results from an initial case study. The caldarium , despite being the largest heated room in most complexes, has many unanswered questions surrounding its thermal atmosphere. For example, did the hot air rise and gather near the roof? Was the humidity localized above the pools? What air speed was created? The answers to these questions have many implications to the operation of the baths. This project uses CFD in conjunction with classical archaeology to address these questions in a case study of the Baths of Caracalla. CFD itself, however, is complex because multiple variables that affect the results and, as such, many analyses begin with a validation study of a documented example of similar geometry and purpose. For this project, the validation study is modelling the reconstructed bath in Turkey built for the television series NOVA, for which we have the exact dimensions, thereby eliminating archaeological uncertainty. The results from this model are extremely promising. The temperatures match those of presented by Yegul and Couch and velocities are low. Furthermore, they show that humidity did not affect the temperature drastically though it did create new air currents. Finally, the results prove the importance of an aspect often overlooked in previous thermal analyses – the doorway. The exchange with the next room is arguably the most important driving force for the environment inside the caldarium. References: Yegul, F. K., & Couch, T. (2003). Building a Roman Bath for the Cameras. Journal of Roman Archaeology. 16 , 153-177.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".