Bibliographic record
Abstract
High temperature pressurized bleed air generated in the engine compressor is used for air conditioning in a jetliner. The engine bleed air must be regulated to a target temperature in the bleed air temperature control system using cold ram air before it can be used in the cabin or other low temperature area. The bleed air system is a control system that consists of sensors, and valve actuators. Potential faults associated with these components need to be considered in the design of the control system. This dissertation focuses on fault detection and diagnosis of the bleed air temperature control system. The faults in temperature sensors and valve actuators are detected using two unscented Kalman filters. The source of a fault is identified using the squared residuals. Nonlinear governing equations for the engine bleed air temperature control system are derived in state space form. Convergence analysis of the proposed fault detection and diagnosis methods is conducted. Heat exchanger is another core component in the bleed air temperature control system. A common fault associated with a heat exchanger is fouling which reduces the heat transfer efficiency, introduces additional resistance to flow, and increases fuel consumption. This dissertation presents a heat exchanger fouling detection method based on the valve control command of an engine bleed air temperature control system. Heat exchanger fouling is monitored by estimating the deviation of valve control command. A simulated bleed air temperature control system has been designed and built. Experiments have been conducted for temperature sensor fault, valve actuator fault, and heat exchanger fouling. Computer simulations for each of these cases are conducted and compared to experimental results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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".