Igor Izdebski
Flight Dynamics Engineer — Stability & Control · Flight Dynamics & Aeroelasticity
Warsaw, Poland
Flight Dynamics Engineer focused on aircraft stability and control analysis, with particular expertise in stall and post-stall regime modeling. Develops aerodynamic derivative databases from both CFD data and flight test measurements. Uses JSBSim for open-source flight dynamics simulation and Python for extensive flight data analysis automation.
Expertise
- stability and control analysis
- aerodynamic derivative estimation
- stall and post-stall flight dynamics
- flight test data reduction
- JSBSim open-source flight simulation
Technologies
Work History
2025-02
Flight dynamics model uncertainty quantification — applied Monte Carlo analysis to propagate aerodynamic derivative uncertainties through the flight dynamics model to quantify handling qualities compliance confidence.
Challenge: Monte Carlo with 5,000 samples and the full 6-DOF model took 48 hours to run. Implemented surrogate-accelerated Monte Carlo: trained a polynomial surrogate on 200 full model evaluations, then ran 5,000 Monte Carlo samples on the surrogate — total time reduced to 3 hours.
Learned: Monte Carlo uncertainty quantification on flight dynamics models is prohibitively slow with direct simulation. Surrogate-accelerated Monte Carlo (train a fast surrogate, run MC on the surrogate) achieves the same statistical coverage in a fraction of the time — a practical necessity for production analysis workflows.
2024-06
Spin susceptibility analysis — assessed susceptibility to developed spin for the new variant using CS-25.221 criteria. Built spin entry model from high-AoA aerodynamic database and computed incipient spin characteristics.
Challenge: The aerodynamic database had no data for combined high-AoA and high sideslip — a corner of the envelope needed for spin analysis. Requesting additional CFD for 10 combined AoA/sideslip conditions required scheduling and resource negotiation with the CFD team, delaying the spin analysis by 6 weeks.
Learned: Spin susceptibility analysis requires aerodynamic data at combined high-AoA and high-sideslip conditions that are rarely included in standard CFD or wind tunnel databases. Identify these requirements at the start of the aerodynamic database planning — not when the spin analysis begins.
2023-11
Post-stall flight dynamics modeling — incorporated high-AoA aerodynamic behavior (above 25°) into the JSBSim model using NASA stall model blending and controllability prediction at extreme attitudes.
Challenge: High-AoA aerodynamics are extremely geometry-specific and wind tunnel data was only available to 35°. Beyond 35°, used blended Newtonian/CFD estimates. Validation against upset recovery test flights showed 20% overprediction of maximum roll rate at 40° AoA — the high-AoA lateral aerodynamics required additional test.
Learned: Post-stall flight dynamics models require validation data specifically at high-AoA conditions. Panel method or Newtonian estimates at extreme AoA have large uncertainty — if the certification program includes upset recovery, dedicated high-AoA wind tunnel or flight test is necessary.
2023-04
Aerodynamic derivative estimation from oscillatory flight test maneuvers — extracted pitch damping (Cmq+Cmalphadot) and yaw damping (Cnr) derivatives from 50+ flight test maneuvers using output-error parameter estimation.
Challenge: Output-error parameter estimation required high-quality air data and inertial measurements with known systematic errors. The pitot-static system had a position error that biased the estimated derivatives by 8% at low speeds. Required a position error correction step before running the estimation.
Learned: Flight test parameter estimation is extremely sensitive to instrumentation systematic errors. Airspeed position error calibration must be completed and applied before any aerodynamic derivative estimation — biased air data produces biased derivatives that cascade into incorrect flight dynamics model predictions.