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Tomasz Sadowski

Senior Thermal Engineer · Thermal Engineering

Warsaw, Poland

Senior Thermal Engineer with a decade of experience in gas turbine thermal management system design and analysis. Leads thermal architecture definition for new propulsion programs — lube system heat rejection, fuel-cooled oil cooler (FCOC) sizing, and ECS thermal integration. Builds system-level thermal network models in AMESim to support early design trade studies and hot day margin verification.

Expertise

  • gas turbine lube and fuel thermal systems
  • heat exchanger design and sizing
  • thermal network modeling
  • hot day thermal margin assessment
  • ECS and aircraft thermal management

Technologies

Flotherm AMESim (Simcenter Amesim) MATLAB Python ANSYS Thermal Excel (VBA) Git

Work History

2025-01

Digital twin for engine thermal state monitoring — provided AMESim model as the physics base for a real-time thermal state estimator. Worked with Digital Engineering team to convert the batch AMESim model to a real-time executable.

Challenge: Real-time execution requirement (update at 1 Hz) was incompatible with AMESim's default stiff solver — the full model solved in 8 seconds on the development PC. Required model order reduction: simplified bearing heat load maps and coarser oil network discretization to achieve <0.5s solve time.

Learned: System models designed for batch simulation typically cannot run in real-time without reduction. Plan for a 'real-time version' of the model from the architecture phase — a separate reduced-order model that is validated against the full model before deployment.

AMESim Python MATLAB

2024-04

Hot day thermal margin verification — simulated hot day (ISA+33°C) ground hold and takeoff thermal transient using the AMESim lube system model. Verified minimum oil temperature margin to coking limit across all engine operating points.

Challenge: Hot day taxi transient showed oil temperature exceeding the 180°C continuous operating limit for 90 seconds during ground idle following a high-power run. Required increasing FCOC bypass valve opening schedule — but this reduced fuel filter inlet temperature below the wax crystallization limit in cold day conditions.

Learned: Thermal margin design is a two-sided constraint problem — solutions to hot-day margin problems often create cold-day problems. Model both extreme conditions simultaneously and use parametric sweeps to find solutions that satisfy both margin requirements.

AMESim Python MATLAB

2023-09

Lube system thermal network model for the development engine — integrated bearing chamber heat loads, heat exchanger effectiveness maps, and oil cooler bypass valve logic into an AMESim system model.

Challenge: Bearing chamber heat load data from CFD was provided at 3 operating points. Interpolation between points for the transient lube system model required careful heat load scaling — incorrect interpolation caused the lube temperature transient to overshoot limits during acceleration simulations.

Learned: Bearing heat load scaling with power setting is non-linear — using linear interpolation between sparse CFD points in a transient thermal model produces erroneous transient temperatures. Extract enough CFD points to support the interpolation accuracy required, or use physics-based scaling laws.

AMESim Python MATLAB

2023-03

Thermal management system architecture trade study for a new turbofan — compared fuel-cooled oil cooler (FCOC), air-cooled oil cooler (ACOC), and combined FCOC+ACOC configurations for hot day ground idle and max climb conditions.

Challenge: FCOC sizing is constrained by fuel temperature at the FCOC exit — exceeding 200°C risks fuel coking and valve deposits. At high-power ground idle in hot ambient, fuel flow is low, increasing fuel temperature rise across the FCOC. The FCOC+ACOC split ratio required optimization for the multi-condition thermal margin.

Learned: Thermal architecture trades for oil cooling must be multi-condition — sizing for one design point (max climb) often fails the margin requirement at another (ground idle hot day). Set up the trade study as a simultaneous multi-condition optimization from the start.

AMESim MATLAB Python