Adam Ziomek

2025-01

AI-assisted acoustic data processing — built a Python pipeline that processes noise certification flight test data (microphone array, flight path GPS, meteorological data) and computes EPNL corrections automatically.

Challenge: EPNL correction algorithm per ICAO Annex 16 requires atmospheric absorption correction based on meteorological profile at each microphone — integrating variable-altitude atmospheric data was not in the original flight test data processing pipeline.

Learned: ICAO Annex 16 EPNL atmospheric absorption correction requires the full meteorological profile (temperature, humidity, pressure vs. altitude) along the sound propagation path, not just ground-level meteorological station values. Build the altitude-resolved met data integration into the data processing pipeline before flight test.

2024-05

Community noise footprint prediction for takeoff and approach — combined fan, jet, and airframe noise components to predict EPNL at flyover, sideline, and approach measurement points per ICAO Annex 16, Chapter 14.

Challenge: Chapter 14 cumulative noise margin computation requires summing EPNL margins at all 3 measurement points — our preliminary predictions showed only 1.2 EPNdB cumulative margin against the Chapter 14 limit. Required a coordinated fan noise reduction and liner optimization effort to achieve the required 2.0 EPNdB margin.

Learned: ICAO Chapter 14 cumulative noise margin must be tracked from the beginning of the aircraft design cycle, not just at certification. A 0.8 EPNdB shortfall discovered late in development requires concurrent fan aerodynamic, liner, and airframe noise reduction efforts — very expensive compared to early noise budget management.

2023-10

Acoustic liner design for nacelle inlet — defined liner impedance targets for minimum fan tonal noise at approach power. Ran ACTRAN duct acoustic analysis to evaluate liner coverage area and cell depth trade space.

Challenge: Liner impedance targets are specified at the fan noise frequencies — but the liner impedance is a function of grazing flow Mach number and sound pressure level, which are only known after the duct aero analysis. Required an iterative coupling between duct aero and liner acoustic analysis to converge on achievable impedance.

Learned: Acoustic liner design is an iterative loop between impedance target setting (acoustic analysis) and impedance achievability assessment (liner aeroacoustic characterization). Do not specify liner impedance targets without first verifying that the target is achievable at the flow conditions in the nacelle duct.

2023-04

Fan tonal noise prediction for a new turbofan — computed Tyler-Sofrin cut-on/cut-off mode orders for the fan stage, identified dominant modes at approach and sideline conditions, and predicted Effective Perceived Noise Level (EPNL) contribution from fan inlet and exhaust.

Challenge: Fan stage blade count ratio (18 rotor / 40 stator) generated 5 cut-on modes at approach power — more than expected from design intent. Root cause: rotor-stator spacing was reduced by 12mm from the acoustic design intent to meet a mechanical packaging constraint, increasing the number of cut-on modes.

Learned: Rotor-stator axial spacing is a primary acoustic design variable that must be protected from mechanical packaging compromises. A 12mm spacing reduction changed the acoustic modal cut-off behavior significantly — establish an acoustic design freeze gate for the rotor-stator gap before mechanical layout is completed.