Jarosław Jędrzejewski
Aircraft Performance Engineer · Flight Dynamics & Aeroelasticity
Warsaw, Poland
Aircraft Performance Engineer responsible for fuel burn analysis, payload-range characterization, and airport performance certification. Integrates engine performance decks with aircraft aerodynamic data to produce mission analysis and flight manual performance data. Has led CS-25 performance certification activities including takeoff field length, climb gradient, and missed approach climb performance compliance.
Expertise
- aircraft fuel burn and payload-range analysis
- climb and cruise performance optimization
- airport performance (takeoff and landing distances)
- performance certification per CS-25
- mission analysis and route planning simulation
Technologies
Work History
2025-01
Performance AI assistant — provided labeled performance calculation examples to AI team for building a natural language performance query tool. Validated AI-generated performance summaries for accuracy.
Challenge: AI-generated performance summaries frequently confused OEI (one engine inoperative) and AEI (all engines inoperative) scenarios — a safety-critical distinction. Required implementing a mandatory classification check: any performance summary must explicitly state the engine failure assumption.
Learned: AI performance tools for aviation must have explicit output validation for safety-critical numerical distinctions. OEI vs. AEI confusion is a foreseeable AI error mode — build deterministic validators that check output text for mandatory safety qualifiers before any AI performance output is presented to users.
2024-05
Mission analysis automation pipeline — built Python-based mission analysis tool that integrates engine performance decks, aerodynamic polars, and atmospheric models to compute block fuel for arbitrary city-pair missions.
Challenge: Atmospheric data interpolation at arbitrary lat/lon/altitude required ERA5 reanalysis data integration — the ERA5 grid resolution (0.25°) was insufficient for route deviation analyses near mountainous terrain. Required custom bilinear interpolation with terrain-aware pressure altitude correction.
Learned: Mission analysis tools using ERA5 atmospheric data need explicit resolution validation for the route types being analyzed. ERA5 resolution is adequate for oceanic routes but too coarse for analysis of terrain-following routes or mountain wave avoidance.
2023-10
CS-25.121 takeoff climb gradient compliance analysis — computed 2nd segment and final segment climb gradients for all engine failure scenarios and gross weight/altitude/temperature combinations required for AFM certification.
Challenge: One engine inoperative (OEI) climb gradient at maximum structural takeoff weight and ISA+20 at 5,000ft field elevation was below CS-25.121 minimum of 2.4% by 0.15%. Required a maximum takeoff weight (MTOW) reduction of 2,200 kg for those conditions — a significant payload penalty.
Learned: OEI climb gradient is a critical constraint at hot-and-high conditions that can impose significant MTOW penalties. Verify OEI climb gradient compliance early in design — after landing gear, engine nacelle, and trim drag are all characterized — to allow design trades to address the constraint without impacting the overall configuration.
2023-03
Fuel burn optimization study — evaluated step climb vs. optimal cruise altitude trajectory for a 4,000nm mission. Quantified 2.3% fuel burn saving from optimal altitude vs. fixed FL350 cruise.
Challenge: Piano-X optimal cruise trajectory analysis requires the full aerodynamic polar (CL vs. CD) at each altitude — the aerodynamic database was missing data at FL430 and FL450 needed for the optimization. Required requesting additional CFD cases before the study could be completed.
Learned: Performance analysis tool aerodynamic database gaps are often discovered mid-analysis rather than at setup. Audit the performance analysis tool database coverage against the required flight envelope before starting a campaign — database gaps discovered during analysis cause significant schedule delays.