Data reconciliation for a reliable diagnostics and characterisation of performance, elements and emissions of a microjet engine under stationary tests at ground level

The objective of this work is to demonstrate the robustness of an identification algorithm applied to key measurements in a micro-jet engine, including exhaust gas temperature, thrust, angular speed, volumetric flow rate, and emissions. This algorithm aims to provide reliable and coherent results on engine performance and individual components by evaluating the thermodynamic cycle. Initially, experimental tests were conducted across the entire engine load range, accompanied by thermographic imaging. From them, a preliminary study of the engine’s behaviour was performed by analysing trends across several loads and by assessing measurement deviations using statistical methods. Following this, several sensitivity analyses, including Monte Carlo simulations, were conducted to highlight inconsistencies that arise when evaluating the thermodynamic cycle directly from raw measurement data. Thereby justifying the use of the conciliating algorithm. This procedure was then implemented for each operating condition to determine the effective values for selected inputs. The resulting deviations were statistically evaluated and compared with previous assessments, revealing quite low differences. Starting at 2–3% due to uncertainties in measurement devices, these deviations reached a maximum of 7.2% after all estimates, indicating the algorithm’s ability to effectively constrain intermediate results. This methodology demonstrates significant consistency and serves as a valuable reference for future studies, such as evaluating different fuels and geometries or conducting focused analyses of individual elements. In addition, the reliability of thermography for characterising heat losses has been proven with notably low values observed.