Assessment of natural ventilation design and efficacy in a net-zero energy house
Bibliographic record
Abstract
Achieving acceptable indoor environmental quality and thermal comfort in buildings can be difficult without relying on energy intensive mechanical equipment. When the climate conditions permit, natural ventilation could potentially help minimize the reliance on mechanically conditioned air; however, natural ventilation is rarely engineered. Houses are typically designed as fully enclosed climate systems in which the connection with the outdoor environment is rarely planned. Unlike in commercial or specialized buildings, houses are not designed with many energy conservation measures in mind. Reconnecting them with the outdoors has a great potential to increase thermal comfort and reduce reliance on mechanical systems. With such a connection to the dynamic weather conditions of the outdoors, it is difficult for architects to choose beneficial design elements to be included in the construction of their houses. Knowing which elements work and to what extent under particular conditions can potentially achieve increased thermal comfort using little or no energy. This research aims to offer a thorough assessment of a case study house and determine the effects of the design choices made by the architect of the house. This research may help architects know the risk factors affecting natural ventilation design in a systematic manner; and in doing so, enable quantifying the benefits of natural ventilation to meet the design goals of maintaining satisfactory indoor conditions without the use of air conditioning, particularly in the summer. A constructed net-zero case study house located in the Pacific marine climate of Canada was used to develop the proposed research. The house had been designed by an architect to rely solely on natural ventilation for cooling during the summer and much of the spring and fall. The house was instrumented and its indoor environment was monitored for a period of several months in 2014 to collect data to evaluate the effectiveness of design choices made, including the effect of a large atrium and the air flow characteristics of the windows intended by the architect to deliver most of the ventilation. Recorded data showed the house performed commendably and this was confirmed through evidence from the home owners. To aid in the understanding of the dynamics of the Harmony House, whole-building, multizone air flow network modeling and computational fluid dynamics (CFD) modeling of the house was developed and calibrated with monitored data and testing. The models were used to assess the indoor air quality and further quantify the natural ventilation of the house, as well as test hypothetical situations that were once considered for the house. Simulations revealed some additional insight into the design choices that were implemented in the house and showed that further technologies intended to increase ventilation were unnecessary and some instead, reduced ventilation through the house.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".