Identification of inlet total-pressure distortion patterns using a parallel compressor-based engine performance model

Inlet distortion is an important external disturbance during aero-engine operation. It changes the flow matching relationship of the compression system and affects engine performance and stability. Since inlet distortion is difficult to measure directly, its induced variations in overall performance parameters may overlap with the responses caused by gas-path faults or component performance degradation. This makes it difficult to distinguish the contributions of internal degradation and external distortion to performance deviations, thereby affecting the accurate assessment of component and overall engine operating states under complex inlet conditions. To address this problem, a zero-dimensional engine performance analysis model considering circumferential inlet total-pressure distortion is established based on parallel compressor theory. The inlet total-pressure distribution is parameterized using a first-order circumferential mode, and the separability between inlet distortion and typical gas-path faults is analyzed in the overall performance measurement parameter space. An inverse identification framework is then developed using the distortion intensity α ₁, mean total-pressure recovery coefficient σ_mean , and phase angle ϕ ₁ as the identification variables, with a Genetic Algorithm employed for parameter optimization. The results show that inlet distortion and fan-related faults exhibit strong response similarities in the overall performance parameter space, indicating the need to treat external inlet distortion as an independent deviation source in overall performance analysis and condition identification under complex inlet conditions. For the ideal first-order distortion samples, after candidate solution screening, the mean relative errors of the reconstructed circumferential total-pressure distributions for the nine samples range from 0.313% to 1.340%, while the maximum local relative errors range from 0.476% to 2.040%. Further validation is conducted using combined first- and second-order distortion samples and samples constructed from experimentally measured distortion fields. When the target distributions deviate from a strict first-order modal form, the maximum mean reconstruction error of the circumferential total-pressure distribution does not exceed 1.980%, and the maximum local error does not exceed 4.293%. The primary single-peak and single-trough characteristics and the general location of the low-pressure region can still be recovered, indicating that the identified parameters can provide a first-order equivalent representation of actual non-ideal distortion fields. Additional robustness validation shows that measurement noise reduces identification accuracy, whereas repeated measurements and averaging effectively mitigate its influence. The identified circumferential total-pressure distribution can also be used as the inlet boundary condition of the parallel compressor model to analyze sub-fan operating-point migration and surge-margin variation in different circumferential channels. The proposed method provides a feasible approach for inlet distortion state inversion in the absence of direct inlet flow-field measurements and for interpreting engine performance deviation sources and assessing operating states under complex inlet conditions.