Use of CFD in the Analysis of Heat Transfer Related Problems That Arise in Building Energy Studies
Bibliographic record
Abstract
Energy simulation (ES) computer programs have been and still are widely used in the design and analysis of building energy systems. However, most ES programs assume that the air in the indoor building space is well mixed. As a result such programs cannot accurately predict building energy consumption for buildings with non-uniform air temperature distributions in the indoor space. They also cannot predict variations in thermal comfort levels in different parts of the building. Computational Fluid Dynamics (CFD), as a result, has become quite widely used in the design and evaluation of buildings energy systems in recent years. CFD can be used, for example, to predict the thermal comfort, natural lighting, natural ventilation, spread of smoke and contaminants in the building, and indoor air quality in a building. As a result it is proving to be an extremely valuable tool in the design of buildings and building systems. This, together with the fact that today’s commercial CFD software packages are relatively easy to use, has led to this quite widespread adoption of CFD methods in building energy analysis. Energy usage in buildings can be decreased by, for example, the use of daylighting (use of solar illumination in place of artificial lighting), by the use of natural ventilation, and by solar heating. CFD analysis provides a means of relatively accurately studying the effect of building design on the effectiveness of daylighting, natural ventilation, and solar heating. Another example of the use of CFD is in the study of the effect of various window blind arrangements on the building performance. In order for a CFD package to be used effectively in building energy analysis it should allow the use of a wide range of turbulence models, it should allow the incident solar radiation on the building to be found and used in the calculation of the indoor flow and temperature fields, it should allow the radiant heat exchange in the building to be incorporated into the calculation, and it should allow the effects of the thermal masses of the walls, floors, etc. to be easily incorporated into the calculation when they are deemed to be important. In this paper, the use of CFD methods in building energy analysis will be discussed as will some applications of CFD in building design. The use of CFD methods in developing design guidelines for particular types of buildings will also be briefly discussed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".