Inverse Modal Noise Analysis for Unducted Fan Radiation

Centrale Innovation; Écully; Sur place; EUR 29 000 - 38 000; Plein temps

Résumé du poste

Centrale Innovation in collaboration with LMFA (École Centrale de Lyon) seeks a postdoctoral researcher to extend modal analysis to unducted fan configurations. The project uses near-field data from flight tests to identify radiated noise modes and assess generation mechanisms.

The candidate will develop eigenfunctions, apply Bayesian inverse methods, and validate on existing datasets before analyzing TAKE-OFF flight data, with a start date of 01/01/2027.

Qualifications

• PhD in acoustics, aeroacoustics or signal processing is required.
• Skills in array methods, inverse problems and modal analysis are essential.
• Scientific programming with Python/Matlab is expected.

Responsabilités

• Task 1: Model and inverse problem—develop eigenfunctions for unducted rotor-stator noise and embed in a Bayesian inverse framework.
• Task 2: Validation on the MAMBO database with multiple sensor arrays and a reference setup.
• Task 3: Apply methodology to flight-test fuselage data to identify modal content of Open Fan noise.

Connaissances

array methods

inverse problems

modal analysis

Python/Matlab

Formation

PhD in acoustics, aeroacoustics or signal processing

Outils

Python

Matlab

Description du poste

Centrale Innovation in collaboration with LMFA (École Centrale de Lyon) seeks a postdoctoral researcher to extend modal analysis to unducted fan configurations. The project uses near-field data from flight tests to identify radiated noise modes and assess generation mechanisms.

The candidate will develop eigenfunctions, apply Bayesian inverse methods, and validate on existing datasets before analyzing TAKE-OFF flight data, with a start date of 01/01/2027.