Modeling and Analysis of Cooling Coil for Control System Design Using Gray Box Approach
Bibliographic record
Abstract
The purpose of the study is to design a method analyzing dynamic behavior of cooling coil in order to be applicable in online simulations. Coils account for the highest energy consumers among other components in an Air Handling Unit (AHU). Essential data as input-output of neural network is provided using energy and mass conservation equations. An implicit numerical method is used to solve dynamic equations of coil. The results of mathematical methods are applied in the output of neural network to design an online model. The proposed model is based on an active coil used in Heating, Ventilation and Air Conditioning (HVAC) systems of clean rooms in Iran Pasteur Institute. Since in active air handlers, input and outputs are not measured, here we model air conditioning systems generally. The results in comparison with actual system data indicate an acceptable performance of the proposed method, so that combination of numerical results with a nonlinear autoregressive exogenous model (NARX) makes it possible to control system effectively by saving a significant amount of time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".