Bridging theory and practice: CFD simulation and interactive VR for conduction heat transfer learning
Bibliographic record
Abstract
Abstract Software simulation programs and virtual reality (VR) have become powerful tools for several educational purposes, and recently, they were used in a wide range of applications. In cases of inaccessibility to labs, workshops, or industries, as happened before in the coronavirus disease 2019 pandemic, these tools could be effectively integrated with practical lab experiments. In this study, a computational fluid dynamics (CFD) simulation and a VR module were utilized to simulate heat transfer by conduction through various experimental cases. The CFD simulation results were obtained using ANSYS/FLUENT software. Meantime, the experimental data were obtained by carrying out three experiments of heat conduction with different heat transfer rates through simple, composite, and different cross‐sectional area bars. At last, the experimental procedure and devices used were virtually constructed using SolidWorks software as three‐dimensional models, which were then extruded into VR and augmented reality models. It was found that the simulation results closely align with the experimental ones, and the temperature profile in both cases has the same behavior with small differences, which indicates the validity of the developed module to be used as a simulation of the actual experiments. In addition to improving knowledge of heat transfer principles, this combination of simulation and VR technology advances many Sustainable Development Goals (SDGs), including advancing quality education (SDG 4) and innovation in higher education (SDG 9). Additionally, this method assists in achieving the course learning objectives by mimicking real‐world lab experiments, guaranteeing that students graduate from the course with the required information and abilities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".