A simulation-based investigation of the robustness of sequences of operation to zone-level faults in single-duct multi-zone variable air volume air handling unit systems
Bibliographic record
Abstract
High-performance sequences of operation for variable air volume (VAV) air handling units (AHUs) respond to requests from zones, leaving these sequences vulnerable to faults that occur at the zone level. While prior research has shown that ASHRAE Guideline 36 reduces energy consumption, its ability to withstand zone-level faults is understudied. This paper investigates the fault tolerance of supply air temperature (SAT) setpoint, duct static pressure (DSP) setpoint and mode of operation (MOP) in single-duct multi-zone VAV AHUs. First, customized sequences were defined for different scenarios in EnergyPlus in this study. Then, common actuator/sensor faults were injected into one zone to identify the most efficient fault-tolerant scenario. The results indicate that trim and respond reset logic is the most fault-tolerant scenario for this paper's case study, with a 12% energy use intensity (EUI) increase for the SAT setpoint and an 11% increase for the DSP setpoint. Moreover, implementing an average temperature-based control for MOP significantly reduces the setup/setback mode activation, resulting in a lower EUI.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".