Abstract
Micro-turbojet experiments frequently provide local, software-referenced measurements
that must be reconciled before they can be used as thermodynamic cycle states. This
study develops and experimentally demonstrates a measured-pressure-constrained inverse
thermal-cycle reconstruction for a stationary Swiwin SW190B micro-turbojet. Total/static
pressure pairs were measured at the intake and the compressor exit as well as immediately
downstream of the final nozzle outlet, while local temperatures, thrust, fuel flow, and rotor
speed were recorded independently. Three quasi-steady states at 38.01 × 103, 67.29 × 103,
and 93.22×103 min−1 were retained. Software-zeroed pressure increments were restored to
absolute pressure using a common ambient reference, and the measured compressor totalpressure
ratio increased from 1.0718 to 1.5298. A two-anchor observation model linked the
pressure-constrained compressor total temperature to the local compressor thermocouple
and achieved a threefold leave-one-state-out RMSE of 4.10 °C and MAE of 3.75 °C. The
reduced model was full rank, with condition number 1.63 and parameter correlation −0.309.
Conditional Monte Carlo reconstruction yielded cycle-admissibility probabilities of 0.885
and 0.985 at intermediate and high power. Thrust and fuel flow were withheld from
the primary inversion and used as independent cross-sensor checks. A one-parameter
soft-constraint diagnostic, in which only the existing aggregate compressor-exit-to-turbineinlet
pressure-loss term was adjusted globally across the two non-idle states, quantified
the influence of those auxiliary outputs without over-parameterizing the available data;
the pressure-constrained solution was retained as the primary reconstruction. The novel
aspect of this study is the explicit separation of measured signals, installation-dependent
observations, latent cycle states, common-mode pressure uncertainty, and auxiliary-output
information within one uncertainty-aware inverse framework. The method provides a
transferable basis for thermodynamic reconstruction, measurement consistency assessment,
and sensor-information prioritization in compact gas-turbine experiments.
that must be reconciled before they can be used as thermodynamic cycle states. This
study develops and experimentally demonstrates a measured-pressure-constrained inverse
thermal-cycle reconstruction for a stationary Swiwin SW190B micro-turbojet. Total/static
pressure pairs were measured at the intake and the compressor exit as well as immediately
downstream of the final nozzle outlet, while local temperatures, thrust, fuel flow, and rotor
speed were recorded independently. Three quasi-steady states at 38.01 × 103, 67.29 × 103,
and 93.22×103 min−1 were retained. Software-zeroed pressure increments were restored to
absolute pressure using a common ambient reference, and the measured compressor totalpressure
ratio increased from 1.0718 to 1.5298. A two-anchor observation model linked the
pressure-constrained compressor total temperature to the local compressor thermocouple
and achieved a threefold leave-one-state-out RMSE of 4.10 °C and MAE of 3.75 °C. The
reduced model was full rank, with condition number 1.63 and parameter correlation −0.309.
Conditional Monte Carlo reconstruction yielded cycle-admissibility probabilities of 0.885
and 0.985 at intermediate and high power. Thrust and fuel flow were withheld from
the primary inversion and used as independent cross-sensor checks. A one-parameter
soft-constraint diagnostic, in which only the existing aggregate compressor-exit-to-turbineinlet
pressure-loss term was adjusted globally across the two non-idle states, quantified
the influence of those auxiliary outputs without over-parameterizing the available data;
the pressure-constrained solution was retained as the primary reconstruction. The novel
aspect of this study is the explicit separation of measured signals, installation-dependent
observations, latent cycle states, common-mode pressure uncertainty, and auxiliary-output
information within one uncertainty-aware inverse framework. The method provides a
transferable basis for thermodynamic reconstruction, measurement consistency assessment,
and sensor-information prioritization in compact gas-turbine experiments.