Two reconstruction methods for constraint and load parameters of aero-engine pipelines based on intelligent optimization algorithms are proposed in this paper. First, a simplified finite element model (FEM) of the aero-engine pipeline structure is established, and its reliability is validated by comparing simulation data with experimental data. Second, a reconstruction algorithm for spring constraint parameters and pipeline load parameters based on the improved particle swarm optimization (IPSO) algorithm is developed on the MATLAB data analysis and ANSYS simulation platforms, which completes the reconstruction calculation of parameters such as spring constraint stiffness and applied harmonic excitation. For harmonic excitation parameter reconstruction, the maximum error of this algorithm reaches 24.9%, revealing its significant inapplicability to load parameter reconstruction. To solve this problem, a load reconstruction method based on the conjugate gradient method (CGM) is further proposed to achieve accurate reconstruction of pipeline load parameters, which mitigates the large reconstruction error of the IPSO algorithm under working conditions with multiple loads. Under 5% noise interference, the maximum error of the CGM is merely 5.16%. Finally, experimental verification of harmonic excitation amplitude reconstruction is performed using the CGM with lower reconstruction errors. Experimental results indicate that the maximum error is 14.24% for harmonic excitation amplitude reconstruction, which verifies the high applicability of the conjugate gradient algorithm to load reconstruction of aero-engine pipelines.

Composite laminates possess excellent specific mechanical performance and are widely adopted in aerospace, rail transit and national defense industries, whereas interlayer delamination, as a typical concealed damage, seriously threatens structural safety. Most existing vibration-based delamination detection algorithms rely on intact structure baseline data, which restricts their practical engineering application. Aiming at this limitation, this paper investigates two baseline-free delamination identification approaches combining modal shape curvature and uniform load surface (ULS) curvature for eight-layer symmetric cross-ply composite laminates. Finite-element models containing boundary-layer and middle-layer delamination are established using the node-merging method. Modal shape curvature and ULS curvature are computed via the central difference method, and two baseline-free schemes, namely the surface smoothing algorithm and curvature mode change rate method, are implemented for damage feature extraction. Three quantitative evaluation indices, including Peak to Background Ratio (PBR), localization error and Signal to Noise Ratio (SNR), are introduced to objectively compare the identification performance of different curvature indicators and algorithms. Numerical results demonstrate that both methods can effectively locate delamination under 3% random measurement noise. Boundary-layer delamination yields more prominent damage features than middle-layer delamination; modal shape curvature outperforms ULS curvature with 1.29–1.53 times higher PBR and 1.23–1.52 times higher SNR, alongside reduced localization error. Under identical conditions, the curvature mode change rate method achieves 1.51–1.99 times higher PBR, 1.22–1.36 times higher SNR and localization error reduced to 0.50–0.79 of that obtained by the surface smoothing algorithm. Scanning laser Doppler vibration tests on carbon-fiber specimens with preset delamination defects further validate the proposed methods. Experimental results reveal that under actual measurement noise, the curvature mode change rate method attains maximum PBR of 12.56, localization error down to 6.46 mm and SNR up to 14.13 dB, exhibiting better comprehensive performance compared with the surface smoothing algorithm. This work provides quantitative reference for baseline-free delamination detection of composite laminates and offers a feasible technical route for practical non-destructive inspection.