On-line fouling detection of aircraft environmental control system cross flow heat exchanger
Bibliographic record
Abstract
A Diagnostics, Prognostics and Health Management (DPHM) solution is proposed for the aircraft environmental control system (ECS) cross flow heat exchanger. In particular, a dynamic model is derived and applied to on-line detection of fouling in the aircraft ECS crossflow plate-and-fin heat exchanger. Predictive maintenance actions can be scheduled as per the on-line detected fouling status of the specific component, supporting condition based maintenance. The heat exchanger model is of the lumped state space form where the state is represented by the core and fin temperatures. The ratios of the thermal capacities of the masses of the two air streams to the thermal capacity of the core itself are negligibly small, and hence can be equated to zero. The model parameters' functional dependency of mass flow rate and inclusion of secondary surfaces (fins) accurately describes the dynamic behavior of the heat exchanger. Since the parameters are functions of mass flow rate as are the core and fin temperatures, and the model is nonlinear in the state variables, the extended Kalman filtering (EKF) algorithm is applied to estimate the state dependent parameters. The model's formulation is justified by the quality of the predicted results, as validated via experimental tests.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".