Shuxia Tian

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.

To address the defect detection problem of composite laminate, this study proposes a defect defection detection method based on ultrasonic Lamb waves and deep learning algorithms. Taking an anisotropic healthy composite laminate as the base model, simulation models with different defects were generated through batch modifications via code, and the displacement-time data of ultrasonic guided wave propagation in these structures were obtained through simulation analysis. The data were resampled to construct a training dataset with consistent compression lengths while preserving the waveform features of different parameters. Based on this dataset, an active learning strategy was employed to iteratively train an artificial neural network, thereby establishing a surrogate model capable of predicting the corresponding defect waveform from the input defect parameters. By combining this surrogate model with approximate Bayesian inference and subset simulation algorithms, the posterior distribution of defect parameters was inverted from the real waveform signals, and a set of possible defect parameters was obtained through posterior distribution sampling. Analysis of multiple groups of defect inversion data and comparative verification with existing literature methods demonstrate that the proposed method can efficiently and accurately predict the defect parameters of composite laminate, and exhibit significant advantages over other methods in terms of the portability of measurement operations and detection accuracy.

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.

The traditional magnesium reduction process consumes a significant amount of energy, which contradicts China's green and low-carbon development goals. Therefore, exploring more energy-efficient methods is crucial for environmental protection. The reduction rate of magnesium is influenced by several factors, including gas flow rate, briquetting pressure, ferrosilicon content, reduction temperature, and reduction time. In this manuscript, data analysis utilizing a machine learning algorithm: support vector machine (SVM)-was employed to predict the magnesium reduction rate. Given that energy-saving processes are a primary objective for enterprises, the processing was optimized using the particle swarm optimization (PSO) algorithm based on the SVM model, while maintaining a constant magnesium reduction rate. This optimization aims to reduce energy and gas consumption during the magnesium smelting process. Experimental verification of the magnesium reduction rate under the optimized processing conditions demonstrated that the application of machine learning algorithms can lead to resource savings in the magnesium reduction process. To further evaluate the environmental benefits of the optimized process, a Life Cycle Assessment (LCA) focusing on energy consumption and carbon dioxide (CO2) emissions was conducted. The LCA results indicate that the optimized process significantly reduces the life cycle energy consumption (reduced by 5.33%) and CO2 emissions (reduced by 3.63%) compared with the initial process, providing precise environmental performance data for the promotion and application of magnesium alloys in lightweight structures.

In order to solve the problem of damage identification of composite laminates during processing and service, a quantitative damage detection method based on swarm intelligence optimization was proposed for structural damage detection of curved composite laminates. Firstly, the structural damage element was defined by the method of reducing the elastic modulus of the element, and the modal parameters of the numerical model of the laminate under different damage conditions were obtained by analyzing the structural vibration characteristics. Secondly, the objective function was constructed from the vibration data, and the precise location and degree of damage were quantitatively calculated by the swarm intelligence optimization algorithm. In order to prevent the particles from falling into the local optimal, the boundary rebound strategy was used to process the boundary, and the MS operator was introduced to greatly accelerate the convergence speed of the algorithm. The numerical results indicate that without the influence of noise, the algorithm was not affected by the quantity, location or size of the damage and could effectively detect damage in curved fiber-reinforced composites, with the detection error rates being within 0.5%. After adding 1% and 5% noise to the frequency and vibration mode, respectively, the convergence speed of the algorithm slowed down, and the convergence times obviously increased. However, it could still accurately locate the damage, and the calculation error of the damage degree was less than 6%. Finally, the effectiveness of the proposed algorithm was verified through experimental tests.

Nb ₃ Sn cable-in-conduit conductor (CICC) technology is widely applied to the large-scale superconducting magnets. Due to the multi-level composite structure, it is easy to cause partial damage or even strand breakage of the superconducting cable in the complex deformation mode. To evaluate the cable breakage of Nb ₃ Sn CICC, direct current potential drop (DCPD) technique is proposed in this study. Serious of benchmark samples of cable with stage 3 were prepared and different breakage rates were designed in the cable samples. Potential difference (PD) obtained from the both side of the cable breakage was measured and analyzed. The experimental results indicated that direct current potential drop (DCPD) method is effective to qualitative measurement of cable breakage.

This paper presents microstructural topology optimization of viscoelastic materials for the plates with constrained layer damping (CLD) treatments. The design objective is to maximize modal loss factor of macrostructures, which is obtained by using the Modal Strain Energy (MSE) method. The microstructure of the viscoelastic damping layer is composed of 3D periodic unit cells. The effective elastic properties of the unit cell are obtained through the strain energy-based method. The density-based topology optimization is adopted to find optimal microstructures of viscoelastic materials. The design sensitivities of modal loss factor with respect to the design variables are analyzed and the design variables are updated by Method of Moving Asymptotes (MMA). Numerical examples are given to demonstrate the validity of the proposed optimization method. The effectiveness of the optimal design method is illustrated by comparing a solid and an optimized cellular viscoelastic material as applied to the plates with CLD treatments.

Superconducting cable is an important component of CICC conductor. The quality of the cable is the guarantee to ensure safe operation of the superconductor, and a key step to reduce the degeneration of superconductor performance. The cables will sustain extremely complex stress during the manufacturing process, which easily causes damage on strands and influent conductor performance. However, the spiral structure caused by irregular cabling with multiple materials, cores and layers strengthen the complexity of CICC, which also brings more difficulties on R&D of non-destructive examination (NDE) of cable inspection. On the basis of electromagnetic NDE, this paper proposes a method based on an inversion of electromagnetic property and explores relative characteristics of cable damage signal and magnetic leakage signal, as well as the synchronous NDE quantitative evaluation method through the theoretical models.

In this paper, a method based on structural vibration characteristic was proposed to detect the debonding defects of the lattice sandwich plate. At first, based on the finite element method, the vibration behavior of the sandwich plates were simulated for delamination damages of different locations and damage degree. Then, based on the Uniform Load Surface (ULS) curvature parameter, the debonding defects were identified by using the difference method. At last, the wavelet analysis method was applied to overcome the dependence on the unflawed model. The validity and efficiency of the proposed methods was demonstrated through numerical simulations and preliminary experiments.