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Sorry for the delay in responding.
The same snapshot numbers represent similar, but NOT the same operating conditions. The snapshot numbers are indeed ordered in time, nominally at:
1. Takeoff, Mach 0
2. Takeoff, Rotation
3. End of takeoff power
4. Climb, approx. 20K ft
5. Climb, approx. 30K ft
6. Top of Climb (beginning of cruise)
7. Mid Cruise
8. End of Cruise
Depending on the flight profile, not all flights many have all eight snapshots, although most should.One more thing — engine health parameters are assumed to be constant for a single flight, so that they are the same for all eight snapshots!
Ambient Conditions:
Pt2 — Total pressure at fan inlet, also known as dynamic pressure (ambient pressure with Mach number effect)
TAT — Total air temperature at fan inlet, again higher than ambient temperature for non-zero Mach number
Actuator Positions:
WFuel — Fuel flow to combustor
VAFN — Variable area fan nozzle
VBV — Variable Bleed ValveThese parameters are measured using sensors on the engine, except Pt2 which is provided by an aircraft sensor.
You are welcome to use any method. This is the PHM Society challenge, not a Machine Learning challenge!
- Yes, the sensors are at different axial locations from the front to the back of the engine. T25 is the temperature at the HPC inlet; T3 is the temperature at the HPC exit; T45 is the temperature at the LPT inlet (HPT exit); T5 is the temperature at the LPT exit.
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Not sure what you mean by “Is the dataset intended to be modeled with any spatial structure?” As pointed out above each sensor is at the different axial location. It would be nice if we measured temperature and pressure at the inlet and exit of each of the five components — Fan, LPC, HPC, HPT, and LPT. But, we only have six of these sensors. You can estimate efficiency and flow changes to each of these five components individually or all together, your choice. If this did not answer your question, please let me know.
Shreeder Adibhatla
