Setup of a 3D printed wind tunnel: application for calibrating bi-directional velocity probes used in Fire Engineering Applications
Bibliographic record
Abstract
The research presented here focuses on the development of a 3D printed wind tunnel and the relevant equipment to be used for calibrating bi-directional velocity probes (BDVP). BDVP are equipment to be used for measuring velocity flow by determining the pressure difference of hot gases generated during fires. The manufactured probes require calibration to determine the calibration factor to achieve precise measurement. The calibration is usually performed in wind tunnels which can be difficult to access due to costs, complexity and the various pieces of equipment required. The aim of the current study is to develop and assemble an inexpensive and easy-to-build bench-scale wind tunnel, with a data-logging system and fan control functionalities for fast and effective calibration of BDVP. A 3D printer with a PET-G filament is used, able to produce parts for the wind tunnel system which are durable and easy to handle and assemble. The system additionally includes an Arduino-based measuring unit with a hot-wire anemometer and temperature correction: Rev. P. This takes precise measurements; continuously logging data on a computer through a USB interface and capable of saving data on an SD card. This design provides users with parameters of velocity flow up to 4 m/s with standard deviation of 1.2 % and turbulence intensity of 1 %. The main advantages of this wind tunnel are its simplicity to build and portability. The dataset contains design files for 3D printing of the wind tunnel, BOM and wiring of electronic components. The project is prototype under development and authors are not responsible for any damages or injuries caused by inappropriate construction or operation. This source is distributed WITHOUT ANY EXPRESS OR IMPLIED WARRANTY, INCLUDING OF MERCHANTABILITY, SATISFACTORY QUALITY AND FITNESS FOR A PARTICULAR PURPOSE.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".