Devices supporting work in multi-physical environments present new challenges for material design.Due to the wavelength difference,waves from multi-field are difficult to modulate simultaneously,limiting the multi-fie...Devices supporting work in multi-physical environments present new challenges for material design.Due to the wavelength difference,waves from multi-field are difficult to modulate simultaneously,limiting the multi-field functions integration.Inspired by characteristic scale analysis,in this work,a devisable metasurface with characteristic scale compatibility is proposed.Under the reduced characteristic scale,waves in microwave,infrared,and acoustic fields can be modulated simultaneously,which can realize the multi-physics functions compatibility.In the microwave field,the far-field performance can be modulated by designing wavefront phase distribution.In the infrared field,the infrared radiation characteristic can be spatially modulated through noninvasive insetting of infrared devices in the microwave layer.In the acoustic field,the sound wave entering the metasurface can realize high-efficiency loss under the action of the Helmholtz cavity.To verify the design method,a functional sample is simulated and experimented.Three typical functions are effectively verified,which can realize 10 dB backward scattering reduction at 8-10 GHz,digital infrared camouflage with infrared emissivity modulation from 0.4 to 0.8 at 3-14μm,and sound absorptivity of more than 60%at 160-410 Hz,respectively.The comparable characteristic scale design method paves a new way for individually devisable metasurfaces in multi-physical field integration.展开更多
Numerous influence factors will lead to the inaccurate prediction of fatigue crack propagation(FCP)life of the metal structure based on the existing FCP model,while the prediction method based on machine learning(ML)a...Numerous influence factors will lead to the inaccurate prediction of fatigue crack propagation(FCP)life of the metal structure based on the existing FCP model,while the prediction method based on machine learning(ML)and data-driven approach can provide a new idea for accurately predicting the FCP life of the metal structure.In response to the inconvenience of the online prediction method and the inaccu-racy of the offline prediction method,an improved offline prediction method based on data feedback is presented in this paper.FCP tests of reduced scale models of balcony opening corners in a cruise ship are conducted to obtain experimental data with respect to the a-N curves.The crack length corresponding to the cycle is trained using a support vector regression(SVR)and back propagation neural network(BP NN)algorithms.FCP prediction lives of test specimens are performed according to the online,offline,and improved offline prediction methods.Effects of the number of feedback data,the sequence length(SL)in the input set,and the cycle interval on prediction accuracy are discussed.The generalization ability of the proposed method is validated by comparing the prediction results with the experimental data in the literature.The larger the number of feedback data,the higher the prediction accuracy.The results show that 1/5 and 1/2 feedback data are needed in the SVR and BP NN algorithm with SL is 5,respectively.Furthermore,the SVR algorithm and SL=5 are recommended for FCP life prediction using the improved offline prediction method.展开更多
基金Natural Science Foundation for Young Scientists of Shaanxi Province(2024JC-YBMS-504)Ministry of Science and Technology of the People's Republic of China(2022YFB3806200)+1 种基金Shaanxi Key Science and Technology Innovation Team Project(2023-CX-TD-48)National Natural Science Foundation of China(62401614)。
文摘Devices supporting work in multi-physical environments present new challenges for material design.Due to the wavelength difference,waves from multi-field are difficult to modulate simultaneously,limiting the multi-field functions integration.Inspired by characteristic scale analysis,in this work,a devisable metasurface with characteristic scale compatibility is proposed.Under the reduced characteristic scale,waves in microwave,infrared,and acoustic fields can be modulated simultaneously,which can realize the multi-physics functions compatibility.In the microwave field,the far-field performance can be modulated by designing wavefront phase distribution.In the infrared field,the infrared radiation characteristic can be spatially modulated through noninvasive insetting of infrared devices in the microwave layer.In the acoustic field,the sound wave entering the metasurface can realize high-efficiency loss under the action of the Helmholtz cavity.To verify the design method,a functional sample is simulated and experimented.Three typical functions are effectively verified,which can realize 10 dB backward scattering reduction at 8-10 GHz,digital infrared camouflage with infrared emissivity modulation from 0.4 to 0.8 at 3-14μm,and sound absorptivity of more than 60%at 160-410 Hz,respectively.The comparable characteristic scale design method paves a new way for individually devisable metasurfaces in multi-physical field integration.
文摘Numerous influence factors will lead to the inaccurate prediction of fatigue crack propagation(FCP)life of the metal structure based on the existing FCP model,while the prediction method based on machine learning(ML)and data-driven approach can provide a new idea for accurately predicting the FCP life of the metal structure.In response to the inconvenience of the online prediction method and the inaccu-racy of the offline prediction method,an improved offline prediction method based on data feedback is presented in this paper.FCP tests of reduced scale models of balcony opening corners in a cruise ship are conducted to obtain experimental data with respect to the a-N curves.The crack length corresponding to the cycle is trained using a support vector regression(SVR)and back propagation neural network(BP NN)algorithms.FCP prediction lives of test specimens are performed according to the online,offline,and improved offline prediction methods.Effects of the number of feedback data,the sequence length(SL)in the input set,and the cycle interval on prediction accuracy are discussed.The generalization ability of the proposed method is validated by comparing the prediction results with the experimental data in the literature.The larger the number of feedback data,the higher the prediction accuracy.The results show that 1/5 and 1/2 feedback data are needed in the SVR and BP NN algorithm with SL is 5,respectively.Furthermore,the SVR algorithm and SL=5 are recommended for FCP life prediction using the improved offline prediction method.
