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Employing the laser ultrasonic system, information on fatigue evolution can be captured and explicitly stored in the frequency‐wavenumber wavefield of the guided wave. This paper presents a deep‐learning architecture for processing the frequency‐wavenumber wavefield, which comprises two distinct steps: fatigue characterization and evolution prediction. Firstly, the fatigue characterization step employs a convolutional autoencoder (CAE) for compressing the frequency‐wavenumber wavefield and a fully connected network (FCN) for obtaining the fusion fatigue characteristic. Due to the high cost of experimental samples, extensive simulation‐generated wavefields are used to pre‐train the network. Subsequently, the fatigue evolution prediction model based on the latent ordinary differential equation (Latent‐ODE) is trained for the step‐by‐step prediction of fatigue evolution with a small amount of composite fatigue test data. The results validate the effectiveness of the deep‐learning architecture in characterizing fatigue and predicting its evolution, as well as the feasibility of the frequency‐wavenumber wavefield compression of guided wave. Highlights: The deep‐learning architecture for fatigue evaluation and prediction is proposed.The frequency‐wavenumber wavefield of guided wave is fused for fatigue evaluation.The fatigue evolution prediction is achieved using the Latent‐ODE model.The fatigue life of the composite specimen is predicted. [ABSTRACT FROM AUTHOR]

Carbon fiber reinforced polymer (CFRP) is widely used in modern industries, and damages may occur from manufacturing to utilization, highlighting the importance of CFRP detection. As being a multi-phase structural material, the CFRP's mesostructural features contained in eddy current signals can obscure macroscopic defect characteristics. Phase rotation is a common method for eddy current signal processing, but the specific criteria for evaluation of processing effect are lacking. This paper proposes three reliable criteria for assessment of the phase rotation processing effects of Transmitter-Receiver (T-R) probe signals, namely: signal overlap ratio (SOR), signal-to-noise ratio (SNR), and image average gradient (IAG). To begin, the SOR serves as a crucial indicator for assessing signal similarity and the degree of overlap, with a low SOR indicating the method's effectiveness in distinguishing defect characteristics. Subsequently, by comparing the SNRs of images, the clarity of defects under different rotation angles can be effectively quantified. Finally, a novel criterion, i.e. the IAG, is introduced. What distinguishes this criterion is its higher degree of automation, facilitating a comprehensive evaluation of processing effects without excessive subjective intervention. After analyzing these criteria, it is clear that the IAG criterion excels in automation and objectivity for evaluating eddy current signal processing effects. [ABSTRACT FROM AUTHOR]

Macro fiber composites (MFCs) are widely used in various fields due to their excellent piezoelectric performance. This paper presents a novel method that combines analytical and finite element analysis to accurately model MFCs. The method iteratively calculates the nonlinear poling field generated by interdigital electrodes (IDEs), leading to reliable estimates of the local orientation and properties of the piezoceramic. The local poling state is then incorporated into a 3D representative volume element (RVE) model to determine the strain response of the device under realistic loading conditions. The influence of the poling voltages, piezoceramic fiber thickness, and IDE geometries on the poling and actuating performance of the device is studied. Furthermore, considering the complexity of the modeling the entire MFC device, a simplified method is further presented that is much more efficient and maintains the same level of accuracy. [ABSTRACT FROM AUTHOR]

• A defect model modification method with surface heat flux identification is proposed for thermal protection system. • Levenberg–Marquardt method is adopted to identify surface heat flux, and whether the defect has occurred is determined. • A defect model modification method is proposed from the knowledge of the identified heat flux. • An experiment is designed to prove good accuracy of the proposed method. Thermal protection systems (TPS) have become a key issue in ensuring the reliability of hypersonic flights. Aimed at monitoring the thermal loads and possible defects for TPS, a defect model modification method with surface heat flux identification is proposed in this paper. Firstly, by the measured temperature from a distributed sensor network and the initial TPS model, the surface heat flux was identified with the Levenberg–Marquardt Method (LMM). If there existed outliers in the identified heat flux distribution, it can be visually indicated that the defect occurred. Then, defects were represented by shape parameters and can be determined by the model modification from the knowledge of the identified heat flux and the measured temperature. The effectiveness of the proposed method was verified numerically using TPS containing a surface defect. Meanwhile, the effects of various factors on the identified results were investigated in detail, such as defect sizes, defect shapes, sensor locations, and real aerodynamic heating. Finally, the proposed method was successfully applied to an experiment, and results demonstrated that the proposed method can achieve good identification results by experimentally measured temperature. [ABSTRACT FROM AUTHOR]

• A new method to suppress the panel flutter by using add-on acoustic black hole (AABH). • Numerical simulations investigate the interaction between the panel and supersonic flow. • The energy flow between panel and AABH are calculated. • AABH can increase the critical flutter velocity and reduce flutter amplitude validly. • AABH can transfer and dissipate energy from the supersonic flow effectively. Panel flutter is a critical challenge for supersonic aircraft, impacting their structural integrity and safety. This study introduces a new method to suppress panel flutter in supersonic flow using an add-on acoustic black hole (AABH). Numerical simulations investigate the interaction between the panel and supersonic flow, comparing the panel only, panel with AABH, and panel with equivalent mass. Results show that the AABH increases the critical flutter velocity by 33.9%, outperforming the equivalent mass configuration (18.7%). The AABH acts as a linear energy sink with high damping and rich modal characteristics, transferring and dissipating energy from the supersonic flow effectively. This capability stabilizes the panel and reduces flutter amplitude, demonstrating the AABH's potential for supersonic flutter suppression. [ABSTRACT FROM AUTHOR]