Analysis of HEFA’s impact on micro gas turbine emissions and performances by nonlinear vibrational signatures

• An experimental analysis of a micro gas turbine burning alternative fuels has been coupled with a novel chaos theory approach, correlating emissions and vibrational signals. • The technique applied for the interpretation of vibration signal has shown interesting potential for detecting the correlation between emissions and performances. • The innovative methodology allows to identify a kind of “signature” of the particular operating condition with a non-invasive technique. The adoption of sustainable aviation fuels, such as Hydroprocessed Esters and Fatty Acids (HEFA), is a key strategy for reducing emissions in aviation and distributed power systems. However, detailed investigations into the impact of HEFA on both pollutant emissions and combustion dynamics in micro gas turbines (MGTs) are still limited. This study presents an integrated analysis of emissions and vibrational behavior of an MGT operating with JP8/HEFA blends ranging from 0% to 100% HEFA under fixed load conditions. Emissions were measured using a heated and cyclone-filtered ELPI™ sensor, while vibrational signals were assessed using a novel nonlinear parameter, Kω, to capture chaotic dynamics. The results show that replacing JP8 with HEFA leads to a reduction in CO and particulate emissions by up to two orders of magnitude. At the same time, the nonlinear analysis of vibrations identified a critical frequency region (ω* ≈ 1.977 rad) where signal clustering distinctly correlated with fuel composition. Notably, both diagnostics revealed a transitional behavior in the 20–40 % HEFA range. These findings highlight the complementary value of combining emissions and nonlinear vibrational analysis and point toward real-time diagnostic tools for clean and stable combustion with sustainable fuels.