Simultaneous Heat and Moisture Transport in 3D Printed Walls
Bibliographic record
Abstract
The national and international building stock -representing one of the most intensive energy-consuming sectors worldwide -is characterized by a large share of old constructions, designed without following any energy criteria. This scenario has promoted the rising of powerful technologies, e.g., Additive Manufacturing (AM), which despite its recent rise is leading the innovation process involving both the industrial and civil sectors. 3D printing techniques are going to outperform current production techniques because of their various advantages, i.e., design of complex forms, uniform materials, reduced production steps and costs. The aim of the present work is to combine the accuracy of computer-aided design (CAD) for AM structures with the benefits of the computational thermo-fluid dynamic simulation (CFD) to perform thermal and moisture performance analysis of innovative building walls. Natural convection and radiation problem -involving buoyancy-driven flow in a cavity -is investigated and solved under appropriate boundary conditions defined in a finite element commercial code. After validation with international guidelines and literature data, the model is simulated in Napoli (Italy) under winter design conditions. Moreover, this work provides a comparison between a simplified procedure for the condensation risk detection, i.e., the Glaser method, and an advanced one -based on the steady-state diffusion theory -which considers latent heat effect and capillary transport of moisture liquid. The results show that the radiative heat transfer mechanism has a significant influence on thermal transmittance. On the other hand -with reference to the case study -here we present the discrepancy between the prediction of the condensation effect during the winter months by adopting the present method with respect to the Glaser one.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".