Gaurav Makkar, Cameron Smith, George Drakoulas, F. Kopsaftopoulos, F. Gandhi
Computational mechanics is a useful tool in the structural health monitoring community for accurately predicting the mechanical performance of various components. However, high-fidelity models simulated through the finite element analysis (FEA) necessitate a large amount of computing power. This paper presents a new approach to develop a multi-fidelity model using artificial neural networks for health monitoring purposes. The proposed framework provides significant savings in computational time compared to a model trained only using high-fidelity data, while maintaining an acceptable level of accuracy. The analysis is conducted using two finite element models, of different fidelity, of an unmanned aerial vehicle (UAV) wing, with damage modeled at six locations, and varying severity. The damage is modeled by changing the stiffness properties of the materials at these locations. The algorithm developed aims at minimizing the number of high-fidelity data points for correcting the outputs of the low-fidelity model. It was observed that the low-fidelity model requires 8 high-fidelity data points to meet the desired error tolerance. This corrected low-fidelity model is then used for locating and quantifying the damage given the strains and frequency by expanding the previously trained network to output damage diagnosis results. The model with applied correction is able to locate the damage with an accuracy of ∼ 94% and quantify the damage with an accuracy of 93%. The performance of the corrected low-fidelity model is compared with a network trained only with high-fidelity datasets and it was observed that the corrected model requires 54% fewer data points as compared to the high-fidelity trained network.
{"title":"A Machine Learning Framework for Physics-Based Multi-Fidelity Modeling and Health Monitoring for a Composite Wing","authors":"Gaurav Makkar, Cameron Smith, George Drakoulas, F. Kopsaftopoulos, F. Gandhi","doi":"10.1115/imece2022-94850","DOIUrl":"https://doi.org/10.1115/imece2022-94850","url":null,"abstract":"\u0000 Computational mechanics is a useful tool in the structural health monitoring community for accurately predicting the mechanical performance of various components. However, high-fidelity models simulated through the finite element analysis (FEA) necessitate a large amount of computing power. This paper presents a new approach to develop a multi-fidelity model using artificial neural networks for health monitoring purposes. The proposed framework provides significant savings in computational time compared to a model trained only using high-fidelity data, while maintaining an acceptable level of accuracy. The analysis is conducted using two finite element models, of different fidelity, of an unmanned aerial vehicle (UAV) wing, with damage modeled at six locations, and varying severity. The damage is modeled by changing the stiffness properties of the materials at these locations. The algorithm developed aims at minimizing the number of high-fidelity data points for correcting the outputs of the low-fidelity model. It was observed that the low-fidelity model requires 8 high-fidelity data points to meet the desired error tolerance. This corrected low-fidelity model is then used for locating and quantifying the damage given the strains and frequency by expanding the previously trained network to output damage diagnosis results. The model with applied correction is able to locate the damage with an accuracy of ∼ 94% and quantify the damage with an accuracy of 93%. The performance of the corrected low-fidelity model is compared with a network trained only with high-fidelity datasets and it was observed that the corrected model requires 54% fewer data points as compared to the high-fidelity trained network.","PeriodicalId":23648,"journal":{"name":"Volume 1: Acoustics, Vibration, and Phononics","volume":"7 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72598929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The nondestructive evaluation of stress using guided waves through the acoustoelastic effect has significant importance for the safety of the structure. In this paper, a Semi-Analytical Finite Element (SAFE) method is used to develop the acoustoelastic theory of guided waves propagating in plate-like structures with arbitrarily shaped cross-sections. Based on the anisotropy of the material induced by the axial force, a method for in-situ detection of biaxial stress through multi-angle dispersion change was developed. The inversion algorithm was validated by data of the SAFE method and the Sweeping Frequency Finite Element Modeling (SFFEM) method, respectively. The inversion results of S0 mode under this method are mainly studied, which can achieve accurate stress in-situ detection of the plate-like structure, and the Root Mean Square Error (RMS) can reach below 1%.
{"title":"Biaxial Stress Inversion in Plate-Like Structures Based on Acoustoelastic Guided Waves","authors":"Chunyu Zhao, Xin Chen, Jian Li, Yang Liu","doi":"10.1115/imece2022-96718","DOIUrl":"https://doi.org/10.1115/imece2022-96718","url":null,"abstract":"\u0000 The nondestructive evaluation of stress using guided waves through the acoustoelastic effect has significant importance for the safety of the structure. In this paper, a Semi-Analytical Finite Element (SAFE) method is used to develop the acoustoelastic theory of guided waves propagating in plate-like structures with arbitrarily shaped cross-sections. Based on the anisotropy of the material induced by the axial force, a method for in-situ detection of biaxial stress through multi-angle dispersion change was developed. The inversion algorithm was validated by data of the SAFE method and the Sweeping Frequency Finite Element Modeling (SFFEM) method, respectively. The inversion results of S0 mode under this method are mainly studied, which can achieve accurate stress in-situ detection of the plate-like structure, and the Root Mean Square Error (RMS) can reach below 1%.","PeriodicalId":23648,"journal":{"name":"Volume 1: Acoustics, Vibration, and Phononics","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82811799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents the numerical investigation of ultrasonic guided waves for bolted lap joint Structural Health Monitoring (SHM), considering temperature variations. It first systematically discusses the mechanisms behind the linear and nonlinear ultrasonic techniques for bolt loosening detection. Afterwards, a general Finite Element Model (FEM) of a bolted structure was established to observe and verify the influence of the change of bolt pre-tightening force on the linear and nonlinear characteristics of ultrasonic guided waves. In order to further study the effect of temperature variation on the sensing signals, the structure is subjected to various levels of thermal loads. This study examines the temperature influence on both linear and nonlinear signal features, such as the transmitted wave energy and the nonlinearity of waveforms. Simulation results show that an increment in temperature can cause partial detachment of the interface between two lap joint components in the structure, resulting in a decrease in both the linear energy and the degree of nonlinear higher-order harmonics. The paper finishes with concluding remarks and suggestions for future work.
{"title":"Bolted Lap Joint Monitoring Using Ultrasonic Guided Waves Considering Temperature Variations","authors":"Xue Peng, Yanfeng Shen","doi":"10.1115/imece2022-94922","DOIUrl":"https://doi.org/10.1115/imece2022-94922","url":null,"abstract":"\u0000 This paper presents the numerical investigation of ultrasonic guided waves for bolted lap joint Structural Health Monitoring (SHM), considering temperature variations. It first systematically discusses the mechanisms behind the linear and nonlinear ultrasonic techniques for bolt loosening detection. Afterwards, a general Finite Element Model (FEM) of a bolted structure was established to observe and verify the influence of the change of bolt pre-tightening force on the linear and nonlinear characteristics of ultrasonic guided waves. In order to further study the effect of temperature variation on the sensing signals, the structure is subjected to various levels of thermal loads. This study examines the temperature influence on both linear and nonlinear signal features, such as the transmitted wave energy and the nonlinearity of waveforms. Simulation results show that an increment in temperature can cause partial detachment of the interface between two lap joint components in the structure, resulting in a decrease in both the linear energy and the degree of nonlinear higher-order harmonics. The paper finishes with concluding remarks and suggestions for future work.","PeriodicalId":23648,"journal":{"name":"Volume 1: Acoustics, Vibration, and Phononics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84780369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Junjiao Zhang, G. Shen, Daqing Chen, Yilin Yuan, Yongna Shen
The good consistency of acoustic emission sensor sensitivity is the premise to ensure accurate and reliable acoustic emission detection. To facilitate the sensitivity consistency test of acoustic emission sensors, a set of acoustic emission sensor sensitivity consistency test platform was built in this paper. The key parameters of the test device and test conditions are studied through simulation analysis and actual test. The research results show that the pulse signal excitation of the transducer can provide a stable excitation source for the test. The propagation of the acoustic signal in the test block is affected by its diameter and thickness. The acoustic signal propagation is relatively stable in a steel plate with a diameter of 600 mm and a thickness of 8 mm. The test position is related to the size of the test block. This study provides a convenient device and specific method for the sensitivity consistency test of acoustic emission sensors.
{"title":"Research on Testing Method and Device of Sensitivity Consistency of Acoustic Emission Sensors","authors":"Junjiao Zhang, G. Shen, Daqing Chen, Yilin Yuan, Yongna Shen","doi":"10.1115/imece2022-96832","DOIUrl":"https://doi.org/10.1115/imece2022-96832","url":null,"abstract":"\u0000 The good consistency of acoustic emission sensor sensitivity is the premise to ensure accurate and reliable acoustic emission detection. To facilitate the sensitivity consistency test of acoustic emission sensors, a set of acoustic emission sensor sensitivity consistency test platform was built in this paper. The key parameters of the test device and test conditions are studied through simulation analysis and actual test. The research results show that the pulse signal excitation of the transducer can provide a stable excitation source for the test. The propagation of the acoustic signal in the test block is affected by its diameter and thickness. The acoustic signal propagation is relatively stable in a steel plate with a diameter of 600 mm and a thickness of 8 mm. The test position is related to the size of the test block. This study provides a convenient device and specific method for the sensitivity consistency test of acoustic emission sensors.","PeriodicalId":23648,"journal":{"name":"Volume 1: Acoustics, Vibration, and Phononics","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74202560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
David B. Maupin, C. Dumm, G. Klinzing, Carey D. Balaban, J. Vipperman
Optical acoustic sensors provide a potential means for making accurate intracranial pressure measurements. Complex cranial geometries consisting of bone, tissue, and fluid filled spaces pose problematic conditions for the use of conventional acoustic sensors. This research investigates the potential limitations of previously devised optical acoustic sensors in addition to introducing a novel procedure utilizing micro-scale additive manufacturing to fabricate such sensors with a bandwidth on the order of 20kHz to 200kHz. The significance of individual parameters describing the sensor geometry are discussed as a basis for developing sensors with desired characteristics. Results are obtained through finite element modeling comparing mechanical sensitivities and frequency response arising from diaphragm geometric design and optical fiber positioning within a sensor body. Fabrication techniques and sensor performance are reported.
{"title":"Microscopic Optical Acoustic Sensors for Intracranial Measurements","authors":"David B. Maupin, C. Dumm, G. Klinzing, Carey D. Balaban, J. Vipperman","doi":"10.1115/imece2022-96139","DOIUrl":"https://doi.org/10.1115/imece2022-96139","url":null,"abstract":"\u0000 Optical acoustic sensors provide a potential means for making accurate intracranial pressure measurements. Complex cranial geometries consisting of bone, tissue, and fluid filled spaces pose problematic conditions for the use of conventional acoustic sensors. This research investigates the potential limitations of previously devised optical acoustic sensors in addition to introducing a novel procedure utilizing micro-scale additive manufacturing to fabricate such sensors with a bandwidth on the order of 20kHz to 200kHz. The significance of individual parameters describing the sensor geometry are discussed as a basis for developing sensors with desired characteristics. Results are obtained through finite element modeling comparing mechanical sensitivities and frequency response arising from diaphragm geometric design and optical fiber positioning within a sensor body. Fabrication techniques and sensor performance are reported.","PeriodicalId":23648,"journal":{"name":"Volume 1: Acoustics, Vibration, and Phononics","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81245391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaocen Wang, Min Lin, Jian Li, Dingpeng Wang, Yang Liu
Ultrasonic guided wave (UGW) imaging quality is limited by the large number of sensors. In this paper, a sparse data recovery algorithm based on back forward (BP) neural network is proposed to solve the problem that the image quality deteriorates with the decrease of the number of sensors. The sparse data from sparse sensor array is up-sampled preprocessing by compressive sensing and then input to the BP neural network to further reduce the recovery error. Numerical results show that the recovery errors reduce from 10−3 and 10−2 to 10−6 for 32 and 16 sensors. After sparse data recovery, the recovered dense data is used for imaging. The average correlation coefficient related to the imaging quality of 32 sensors is improved to the level with 64 sensors. For 16 sensors imaging, the average correlation coefficient is also improved, but the image quality is still slightly reduced compared with 64 sensors.
{"title":"Sparse Data Recovery Algorithm Based on BP Neural Network for Ultrasonic Guided Wave Imaging","authors":"Xiaocen Wang, Min Lin, Jian Li, Dingpeng Wang, Yang Liu","doi":"10.1115/imece2022-96700","DOIUrl":"https://doi.org/10.1115/imece2022-96700","url":null,"abstract":"\u0000 Ultrasonic guided wave (UGW) imaging quality is limited by the large number of sensors. In this paper, a sparse data recovery algorithm based on back forward (BP) neural network is proposed to solve the problem that the image quality deteriorates with the decrease of the number of sensors. The sparse data from sparse sensor array is up-sampled preprocessing by compressive sensing and then input to the BP neural network to further reduce the recovery error. Numerical results show that the recovery errors reduce from 10−3 and 10−2 to 10−6 for 32 and 16 sensors. After sparse data recovery, the recovered dense data is used for imaging. The average correlation coefficient related to the imaging quality of 32 sensors is improved to the level with 64 sensors. For 16 sensors imaging, the average correlation coefficient is also improved, but the image quality is still slightly reduced compared with 64 sensors.","PeriodicalId":23648,"journal":{"name":"Volume 1: Acoustics, Vibration, and Phononics","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87724211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To comprehensively quantitative assessment of delamination location in CFRP composite plates, a Lamb wave spatial frequency wavenumber imaging method based on laser ultrasonic full-wavefield scanning inspection is proposed in this paper. For a CFRP composite plate specimen containing a delamination, a piezoelectric sensor is arranged to excite a sinusoidal modulation tone-burst signal. A laser transducer is used for pointwise reception to obtain Lamb waves full-wavefield data. Frequency domain filtering were performed on the wavefield signal to obtain single-mode wavefield. Short-space Fourier transform and instantaneous wavenumber analysis were applied to single-mode wavefield signal to obtain a distribution image of Lamb wave spatial wavenumber respectively. At the same time, the Lamb wave dispersion relation in CFRP composite plate is analyzed, and the delamination location is calculated based on this relationship. Finally, it can be seen from the imaging results that instantaneous wavenumber analysis can accurately locate the distance between the delamination and the laser scanning detection surface, but the short-space Fourier transform technology cannot identify the location of defects under the experimental parameters set in this paper.
{"title":"Quantitative Detection of Delaminations in CFRP Composite Plate by Spatial-Frequency-Wavenumber Analysis Based on Laser Ultrasonic Guided Waves","authors":"Zenghua Liu, Xiaoyu Liu, Jiuzhou Tian","doi":"10.1115/imece2022-95421","DOIUrl":"https://doi.org/10.1115/imece2022-95421","url":null,"abstract":"\u0000 To comprehensively quantitative assessment of delamination location in CFRP composite plates, a Lamb wave spatial frequency wavenumber imaging method based on laser ultrasonic full-wavefield scanning inspection is proposed in this paper. For a CFRP composite plate specimen containing a delamination, a piezoelectric sensor is arranged to excite a sinusoidal modulation tone-burst signal. A laser transducer is used for pointwise reception to obtain Lamb waves full-wavefield data. Frequency domain filtering were performed on the wavefield signal to obtain single-mode wavefield. Short-space Fourier transform and instantaneous wavenumber analysis were applied to single-mode wavefield signal to obtain a distribution image of Lamb wave spatial wavenumber respectively. At the same time, the Lamb wave dispersion relation in CFRP composite plate is analyzed, and the delamination location is calculated based on this relationship. Finally, it can be seen from the imaging results that instantaneous wavenumber analysis can accurately locate the distance between the delamination and the laser scanning detection surface, but the short-space Fourier transform technology cannot identify the location of defects under the experimental parameters set in this paper.","PeriodicalId":23648,"journal":{"name":"Volume 1: Acoustics, Vibration, and Phononics","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84384185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Some structures have different natural frequencies in the two lateral X and Y-directions. Tuned damping of such structures require using either a) the less attractive option of two regular PTMDs each tuned to one of the natural frequencies of the structures’ lateral modes or b) one pendulum TMD with two different tuning frequencies (one for each lateral directions), necessitating two different swinging lengths. Pendulum TMDs with two different tuning frequencies in the two lateral X and Y directions, are realized by constraining the swinging length of the pendulum in one direction but not in the other direction. Such two degree-of-freedom pendulum tuned mass damper, is called Bi-PTMD. In this work, the dynamics of a two degree-of-freedom pendulum tuned mass damper (Bi-TMD) appended to a structure with two low-frequency, lateral degrees of freedom (representing the first two modes of a tall structure) is studied and the nonlinear differential equations of motion are derived using the Lagrangian mechanics approach. The equations of motion are simplified using small angle and slow motion assumptions. The system of nonlinear differential equations are numerically simulated in Matlab/Simulink environment and the responses of the structure without and with the pendulum Bi-TMD to a number of different perturbations in the lateral directions are evaluated. The numerical model is verified by comparing its simulation results with the outcomes of SimScape Multibody physical model of the same system.
{"title":"Spatial Pendulum TMD With Two Tuning Frequencies","authors":"Waled T. A. Mohamed, A. Kashani","doi":"10.1115/imece2022-96610","DOIUrl":"https://doi.org/10.1115/imece2022-96610","url":null,"abstract":"\u0000 Some structures have different natural frequencies in the two lateral X and Y-directions. Tuned damping of such structures require using either a) the less attractive option of two regular PTMDs each tuned to one of the natural frequencies of the structures’ lateral modes or b) one pendulum TMD with two different tuning frequencies (one for each lateral directions), necessitating two different swinging lengths. Pendulum TMDs with two different tuning frequencies in the two lateral X and Y directions, are realized by constraining the swinging length of the pendulum in one direction but not in the other direction. Such two degree-of-freedom pendulum tuned mass damper, is called Bi-PTMD.\u0000 In this work, the dynamics of a two degree-of-freedom pendulum tuned mass damper (Bi-TMD) appended to a structure with two low-frequency, lateral degrees of freedom (representing the first two modes of a tall structure) is studied and the nonlinear differential equations of motion are derived using the Lagrangian mechanics approach. The equations of motion are simplified using small angle and slow motion assumptions.\u0000 The system of nonlinear differential equations are numerically simulated in Matlab/Simulink environment and the responses of the structure without and with the pendulum Bi-TMD to a number of different perturbations in the lateral directions are evaluated. The numerical model is verified by comparing its simulation results with the outcomes of SimScape Multibody physical model of the same system.","PeriodicalId":23648,"journal":{"name":"Volume 1: Acoustics, Vibration, and Phononics","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90939559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marianne E. Cites, C. Dumm, Anna C. Hiers, G. Klinzing, Carey D. Balaban, J. Vipperman
The geometric complexity of the contents of the head confounds mechanical analysis of intracranial structures. Conventional models for computational analysis are typically created through a laborious segmentation and reconstruction process highly dependent on expert labor and anatomical insight. This study explores a deterministic process for construction of a simplified, anatomically-relevant head model appropriate for acoustical modeling. Various key anatomical features with acoustical significance are reviewed. Models of increasing complexity are generated, spanning a range from coupled concentric spheres to more advanced geometries incorporating ventricles, brain structures, and other anatomical landmarks. Geometric relevance of the models is assessed by comparison to a high-fidelity computational geometry derived from medical imagery. These techniques and models are useful for a variety of studies investigating phenomena such as traumatic brain injury mechanics and industrial safety.
{"title":"Simplified Geometries for Intracranial Acoustic Modeling","authors":"Marianne E. Cites, C. Dumm, Anna C. Hiers, G. Klinzing, Carey D. Balaban, J. Vipperman","doi":"10.1115/imece2022-96161","DOIUrl":"https://doi.org/10.1115/imece2022-96161","url":null,"abstract":"\u0000 The geometric complexity of the contents of the head confounds mechanical analysis of intracranial structures. Conventional models for computational analysis are typically created through a laborious segmentation and reconstruction process highly dependent on expert labor and anatomical insight. This study explores a deterministic process for construction of a simplified, anatomically-relevant head model appropriate for acoustical modeling. Various key anatomical features with acoustical significance are reviewed. Models of increasing complexity are generated, spanning a range from coupled concentric spheres to more advanced geometries incorporating ventricles, brain structures, and other anatomical landmarks. Geometric relevance of the models is assessed by comparison to a high-fidelity computational geometry derived from medical imagery. These techniques and models are useful for a variety of studies investigating phenomena such as traumatic brain injury mechanics and industrial safety.","PeriodicalId":23648,"journal":{"name":"Volume 1: Acoustics, Vibration, and Phononics","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75361317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As an important part of long-range nondestructive testing and structural health monitoring technology, ultrasonic guided wave technology has been used in a wide range of applications in aerospace, petrochemical, transportation, and other fields. This paper extends the previously developed wave finite element method by introducing the prestressing effect in Murnaghan hyperelastic materials and solving the dispersion curves of prestressed waveguide structures. Furthermore, this paper proposes a mode-tracking algorithm based on image sequential alignment that can achieve the multi-mode classification of guided wave dispersion curves and compare the changes in propagation characteristics of different guided wave modes. The results reveal that the change in guided wave phase velocity produced by prestressing is related to the applied stress, frequency-thickness product, and propagation direction and that the susceptibility of different guided wave modes to prestress varies. Finally, the model approach is validated by comparing its predictions to theoretical results from the literature, which match remarkably well. This study is an important guideline for the preferential selection of environmentally insensitive guided wave modes and excitation frequencies, correction of detection signals, and accurate assessment of engineering structure damage information in ultrasonic guided wave technology engineering applications.
{"title":"Investigating the Effect of Uniaxial Stress on Guided Wave Propagation in Plates by Wave Finite Element Method","authors":"Xu Zhang, Gang Liu, Lei Chen","doi":"10.1115/imece2022-89912","DOIUrl":"https://doi.org/10.1115/imece2022-89912","url":null,"abstract":"\u0000 As an important part of long-range nondestructive testing and structural health monitoring technology, ultrasonic guided wave technology has been used in a wide range of applications in aerospace, petrochemical, transportation, and other fields. This paper extends the previously developed wave finite element method by introducing the prestressing effect in Murnaghan hyperelastic materials and solving the dispersion curves of prestressed waveguide structures. Furthermore, this paper proposes a mode-tracking algorithm based on image sequential alignment that can achieve the multi-mode classification of guided wave dispersion curves and compare the changes in propagation characteristics of different guided wave modes. The results reveal that the change in guided wave phase velocity produced by prestressing is related to the applied stress, frequency-thickness product, and propagation direction and that the susceptibility of different guided wave modes to prestress varies. Finally, the model approach is validated by comparing its predictions to theoretical results from the literature, which match remarkably well. This study is an important guideline for the preferential selection of environmentally insensitive guided wave modes and excitation frequencies, correction of detection signals, and accurate assessment of engineering structure damage information in ultrasonic guided wave technology engineering applications.","PeriodicalId":23648,"journal":{"name":"Volume 1: Acoustics, Vibration, and Phononics","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82303948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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