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M.J. Martinez
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This article presents an experimental evaluation of a morphing leading edge demonstrator by investigating its morphed shape, the level of induced strains in the airfoil skin, the actuation force, and the morphing mechanism’s capability to lock and transfer the applied loads. In addition, a finite element model of the demonstrator is assembled comprising an elastic morphing skin and a kinematic morphing mechanism. The obtained results are used to assess whether the demonstrator performs according to the design objectives, such as the target shape, the character of the morphing deformation and the morphing mechanism locking, applied during the design process. The comparison between experimental and numerical results yielded a good agreement in terms of observed morphed shape and pertaining strains. The average difference in morphed shape was less than 0.08% chord at the maximum actuator extension. The observed difference in the respective strains was less than 400 micro-strains. A significant difference, up to 70%, was observed in the actuation force, which was attributed to the modelling assumptions and to the force measurement technique employed in the experiment. Nevertheless, both results show good qualitative agreement showing similar trends.
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This article presents an experimental evaluation of a morphing leading edge demonstrator by investigating its morphed shape, the level of induced strains in the airfoil skin, the actuation force, and the morphing mechanism’s capability to lock and transfer the applied loads. In addition, a finite element model of the demonstrator is assembled comprising an elastic morphing skin and a kinematic morphing mechanism. The obtained results are used to assess whether the demonstrator performs according to the design objectives, such as the target shape, the character of the morphing deformation and the morphing mechanism locking, applied during the design process. The comparison between experimental and numerical results yielded a good agreement in terms of observed morphed shape and pertaining strains. The average difference in morphed shape was less than 0.08% chord at the maximum actuator extension. The observed difference in the respective strains was less than 400 micro-strains. A significant difference, up to 70%, was observed in the actuation force, which was attributed to the modelling assumptions and to the force measurement technique employed in the experiment. Nevertheless, both results show good qualitative agreement showing similar trends.
Structural health monitoring (SHM) is a growing field of research, as it has the potential to simultaneously improve the reliability of structures and reduce their maintenance cost. SHM requires accurate stress and strain information, preferably for the entire structure. Unfortunately, it is often infeasible to instrument every part of the structure, making it necessary to estimate the stress and strain fields based on data from a limited number of sensors. One promising technique for making this estimate is the inverse finite element method (iFEM), which can be applied to any combination of geometry and loading conditions. In addition, it can also process several different types of sensor data. In this study, benchmark problems based on the MacNeal and Harder linear elastic problem set for FEM algorithms were extended to test the accuracy of iFEM algorithms. As the benchmarks use linear elastic materials, small displacements and strains, the iFEM implementation was also limited to these conditions. Accurate iFEM estimates can be obtained for the benchmark problems for which accurate FEM solutions can be obtained with solid elements, specifically 3-dimensional 20 node hexahedral elements with reduced integration (C3D20R), based on either displacement sensors, strain sensors, or both combined, and provided that a sufficient number of sensors is used. The iFEM algorithms generally produce more accurate estimates of displacements than of strains. The addition of Tikhonov regularization does not result in a significant increase in accuracy for either the displacement or strain distribution estimates and can even deteriorate the results in certain cases.
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Structural health monitoring (SHM) is a growing field of research, as it has the potential to simultaneously improve the reliability of structures and reduce their maintenance cost. SHM requires accurate stress and strain information, preferably for the entire structure. Unfortunately, it is often infeasible to instrument every part of the structure, making it necessary to estimate the stress and strain fields based on data from a limited number of sensors. One promising technique for making this estimate is the inverse finite element method (iFEM), which can be applied to any combination of geometry and loading conditions. In addition, it can also process several different types of sensor data. In this study, benchmark problems based on the MacNeal and Harder linear elastic problem set for FEM algorithms were extended to test the accuracy of iFEM algorithms. As the benchmarks use linear elastic materials, small displacements and strains, the iFEM implementation was also limited to these conditions. Accurate iFEM estimates can be obtained for the benchmark problems for which accurate FEM solutions can be obtained with solid elements, specifically 3-dimensional 20 node hexahedral elements with reduced integration (C3D20R), based on either displacement sensors, strain sensors, or both combined, and provided that a sufficient number of sensors is used. The iFEM algorithms generally produce more accurate estimates of displacements than of strains. The addition of Tikhonov regularization does not result in a significant increase in accuracy for either the displacement or strain distribution estimates and can even deteriorate the results in certain cases.
Conference paper
(2019)
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Nicolas Venturini, Marcias Martinez, Enrico Troiani, Maria Barroso-Romero, Francesco Falcetelli
In the Structural Health Monitoring (SHM) field, Acoustic Emissions (AE) is the process by which acoustic signals generated during the formation of damage are captured by sensors, analyzed and used for localization within the structure. In plate like structures, these signals lead to the formation of Lamb Waves (LW), which are broadband in nature. These LW are generally captured by Piezoelectric Titanum Zirconate (PZT) sensors. As such, the captured broadband signals are of difficult interpretation in part due to several phenomena such as dispersion or attenuation suffered by the waves during their propagation. In this study, we hypothesize that the nature of the emitted signal contains information on the damage type, as if the features of the emitted signal were a 'fingerprint' of the damage. Wing or fuselage panels are some of the aeronautical structures were LW can develop during the emission of an acoustic signal. In operational service environments, the damage type and size may lead to the generation of different signal sources. This study aims at the development, through experimental techniques, of a classification algorithm based on Artificial Intelligence (AI) for determining the source of the emission in addition to their location within a structure. It is envisioned that the AI algorithms will be capable of identifying specific features within the emitted signals and thus correlate them to a database of known signals and their corresponding associated damage types. In order to create an AE signal damage database, the captured signal cannot be used since it has been affected by its propagation through the structure. As such, a Time Reversal process will be implemented in order to reconstruct the original signal. This original signal will be the one utilized by the AI algorithm in order to identify its corresponding damage source.
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In the Structural Health Monitoring (SHM) field, Acoustic Emissions (AE) is the process by which acoustic signals generated during the formation of damage are captured by sensors, analyzed and used for localization within the structure. In plate like structures, these signals lead to the formation of Lamb Waves (LW), which are broadband in nature. These LW are generally captured by Piezoelectric Titanum Zirconate (PZT) sensors. As such, the captured broadband signals are of difficult interpretation in part due to several phenomena such as dispersion or attenuation suffered by the waves during their propagation. In this study, we hypothesize that the nature of the emitted signal contains information on the damage type, as if the features of the emitted signal were a 'fingerprint' of the damage. Wing or fuselage panels are some of the aeronautical structures were LW can develop during the emission of an acoustic signal. In operational service environments, the damage type and size may lead to the generation of different signal sources. This study aims at the development, through experimental techniques, of a classification algorithm based on Artificial Intelligence (AI) for determining the source of the emission in addition to their location within a structure. It is envisioned that the AI algorithms will be capable of identifying specific features within the emitted signals and thus correlate them to a database of known signals and their corresponding associated damage types. In order to create an AE signal damage database, the captured signal cannot be used since it has been affected by its propagation through the structure. As such, a Time Reversal process will be implemented in order to reconstruct the original signal. This original signal will be the one utilized by the AI algorithm in order to identify its corresponding damage source.
The lack of a widely-accepted test standard for characterizing the mode II fatigue disbond growth behavior of adhesively bonded interfaces is a challenge to the research community in terms of producing consistent and repeatable results. Typically, researchers apply the End Notch Flexure specimen, which is already used for static delamination studies. However, the needs for static and fatigue disbond growth characterization are not the same, resulting in some undesirable effects in such specimen. This study looks at a particular mode II test configuration known as the Central Cut Plies (CCP) specimen. A critical evaluation of the suitability of this specimen, including the influence of geometry, disbond measurement approaches and the stability of the disbond growth is carried out through a combination of numerical and experimental investigations. A distributed strain sensing system based on Rayleigh Backscattering provided a surface strain profile from which disbond growth rate data was obtained. A finite element model was used to verify the experimental results and determine the disbond length from the strain profiles. Results of this evaluation have shown that the CCP specimen is a promising specimen configuration for characterizing fatigue disbond growth; however, it also presents several challenges that require consideration in its application.
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The lack of a widely-accepted test standard for characterizing the mode II fatigue disbond growth behavior of adhesively bonded interfaces is a challenge to the research community in terms of producing consistent and repeatable results. Typically, researchers apply the End Notch Flexure specimen, which is already used for static delamination studies. However, the needs for static and fatigue disbond growth characterization are not the same, resulting in some undesirable effects in such specimen. This study looks at a particular mode II test configuration known as the Central Cut Plies (CCP) specimen. A critical evaluation of the suitability of this specimen, including the influence of geometry, disbond measurement approaches and the stability of the disbond growth is carried out through a combination of numerical and experimental investigations. A distributed strain sensing system based on Rayleigh Backscattering provided a surface strain profile from which disbond growth rate data was obtained. A finite element model was used to verify the experimental results and determine the disbond length from the strain profiles. Results of this evaluation have shown that the CCP specimen is a promising specimen configuration for characterizing fatigue disbond growth; however, it also presents several challenges that require consideration in its application.
Conference paper
(2018)
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Francesco Falcetelli, Maria Barroso Romero, Shashank Pant, Enrico Troiani, Marcias Martinez
In Acoustic Emissions (AE) Hsu-Nielsen Pencil-Lead Breaks (PLB) are used to generate sound waves enabling the characterization of acoustic wave speed in complex structures. The broadband signal of a PLB represents a repeatable emission, which can be applied at different regions of the structure, and therefore can be used to calibrate the localization algorithms of the AE system. In recent years, the use of Finite Element Method (FEM) has flourished for modelling acoustic Lamb wave propagation, which is present in thin plate-like structures. The primary challenge faced by the AE community is the lack of a well-known mathematical function of a PLB signal that can be applied in numerical simulations. This study makes use of a Time Reversal (TR) approach to identify the emission source of the PLB on a 7075-T651 aluminum plate. An ABAQUS CAE™ model with piezoelectric actuators and sensors was developed. In order to avoid edge reflections, absorbing boundaries based on the Stiffness Reduction Method (SRM) were considered. The captured PLB signals were used as input to the FEM and was time-reversed. Furthermore, a band-limited white noise signal was used to calibrate the contribution of the broadband frequencies found in the transmitted wave packet. Preliminary results indicate that the TR approach can be used to understand the shape and function of the original transmitted signal.
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In Acoustic Emissions (AE) Hsu-Nielsen Pencil-Lead Breaks (PLB) are used to generate sound waves enabling the characterization of acoustic wave speed in complex structures. The broadband signal of a PLB represents a repeatable emission, which can be applied at different regions of the structure, and therefore can be used to calibrate the localization algorithms of the AE system. In recent years, the use of Finite Element Method (FEM) has flourished for modelling acoustic Lamb wave propagation, which is present in thin plate-like structures. The primary challenge faced by the AE community is the lack of a well-known mathematical function of a PLB signal that can be applied in numerical simulations. This study makes use of a Time Reversal (TR) approach to identify the emission source of the PLB on a 7075-T651 aluminum plate. An ABAQUS CAE™ model with piezoelectric actuators and sensors was developed. In order to avoid edge reflections, absorbing boundaries based on the Stiffness Reduction Method (SRM) were considered. The captured PLB signals were used as input to the FEM and was time-reversed. Furthermore, a band-limited white noise signal was used to calibrate the contribution of the broadband frequencies found in the transmitted wave packet. Preliminary results indicate that the TR approach can be used to understand the shape and function of the original transmitted signal.
When Conservation Meets Engineering
Predicting the Damaging Effects of Vibrations on Pastel Paintings
Pastel paintings are one of the most fragile types of objects of art. When handling loan requests, conservators lack scientific data to assess the risk for damage in transport, and thus for making decisions whether they can be transported. A research project was initiated in 2014 to investigate the effect of vibrations on the condition of pastel paintings, and to determine under what conditions they can be transported with minimum risk for damage due to vibrations. The initial results of this work indicate that the vibration behaviour of pastel paintings is a cumulative one and can be dealt with as an issue of fatigue. If failure is defined as a given level of unacceptable visual loss of pastel, it has been shown that higher stress amplitudes lead to shorter lives to failure than lower stress amplitudes. The use of a fixative appears to prolong fatigue life. There also appears to be a fatigue limit for freshly drawn pastels without fixative. This study highlights the synergism between typically non associated fields of research, in this, art conservation and the fatigue failure of materials.
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Pastel paintings are one of the most fragile types of objects of art. When handling loan requests, conservators lack scientific data to assess the risk for damage in transport, and thus for making decisions whether they can be transported. A research project was initiated in 2014 to investigate the effect of vibrations on the condition of pastel paintings, and to determine under what conditions they can be transported with minimum risk for damage due to vibrations. The initial results of this work indicate that the vibration behaviour of pastel paintings is a cumulative one and can be dealt with as an issue of fatigue. If failure is defined as a given level of unacceptable visual loss of pastel, it has been shown that higher stress amplitudes lead to shorter lives to failure than lower stress amplitudes. The use of a fixative appears to prolong fatigue life. There also appears to be a fatigue limit for freshly drawn pastels without fixative. This study highlights the synergism between typically non associated fields of research, in this, art conservation and the fatigue failure of materials.
Structural health monitoring has focused on the use of computational models to capture the effect of crack-like discontinuities on the behaviour of acoustic-ultrasonic signals. However, few models have taken into account the effect of geometric complexity in combination with residual stresses generated during the fatigue crack growth (FCG) process. In this study, a finite element analysis model of a C-channel type aeronautical structure is evaluated under a pitch-catch scenario. Three different finite element model configurations were considered in order to understand the effects that residual stresses of a fatigue crack emanating from a through-hole have on the guided Lamb wave propagation behaviour. The results demonstrate that numerical modelling is able to capture the change in amplitude and the effect of a phase shift on the guided Lamb wave behaviour due to the presence of the discontinuity and the stress field generated during the FCG process.
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Structural health monitoring has focused on the use of computational models to capture the effect of crack-like discontinuities on the behaviour of acoustic-ultrasonic signals. However, few models have taken into account the effect of geometric complexity in combination with residual stresses generated during the fatigue crack growth (FCG) process. In this study, a finite element analysis model of a C-channel type aeronautical structure is evaluated under a pitch-catch scenario. Three different finite element model configurations were considered in order to understand the effects that residual stresses of a fatigue crack emanating from a through-hole have on the guided Lamb wave propagation behaviour. The results demonstrate that numerical modelling is able to capture the change in amplitude and the effect of a phase shift on the guided Lamb wave behaviour due to the presence of the discontinuity and the stress field generated during the FCG process.
This study focuses on the development of a source identification algorithm inspired by the SHAZAM music app. The algorithm makes use of a spectrogram analysis technique for distinguishing different Acoustic Emission (AE) events. The peaks of the spectrogram are used to obtain a constellation map generating a "fingerprint" like pattern for each acoustic emission source. The fingerprints are then used within an artificial intelligence algorithm as part of a Knowledge Discovery database. The database is then able to link the AE signal to a specific source type. An experimental program was developed to test the methodology. The results of this study demonstrate that signal sources can be classified and linked to specific emission types with a high level of accuracy.
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This study focuses on the development of a source identification algorithm inspired by the SHAZAM music app. The algorithm makes use of a spectrogram analysis technique for distinguishing different Acoustic Emission (AE) events. The peaks of the spectrogram are used to obtain a constellation map generating a "fingerprint" like pattern for each acoustic emission source. The fingerprints are then used within an artificial intelligence algorithm as part of a Knowledge Discovery database. The database is then able to link the AE signal to a specific source type. An experimental program was developed to test the methodology. The results of this study demonstrate that signal sources can be classified and linked to specific emission types with a high level of accuracy.
Mechanical behaviour of thermoplastic composites spot-welded and mechanically fastened joints
A preliminary comparison
Journal article
(2016)
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Tian Zhao, Genevieve Palardy, Irene Fernandez Villegas, Calvin Rans, Marcias Martinez, Rinze Benedictus
The in-plane and out-of-plane mechanical behaviour of both ultrasonically spot-welded and mechanically fastened joints was investigated by double-lap shear and pull-through tests, respectively. Spot-welded specimens showed comparable onset failure load and significantly higher joint stiffness compared to mechanical fasteners when carrying shear load. The failure modes and the damage within specimens were analysed after mechanical tests. Intralaminar failure and very limited damage on the out-most ply were found for welded specimens, whereas catastrophic through-the-thickness failure was observed for mechanically fastened joints. Based on the experimental outcomes, the mechanical performance and failure mechanisms of spot-welded joints were critically assessed in comparison to the mechanical fasteners .
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The in-plane and out-of-plane mechanical behaviour of both ultrasonically spot-welded and mechanically fastened joints was investigated by double-lap shear and pull-through tests, respectively. Spot-welded specimens showed comparable onset failure load and significantly higher joint stiffness compared to mechanical fasteners when carrying shear load. The failure modes and the damage within specimens were analysed after mechanical tests. Intralaminar failure and very limited damage on the out-most ply were found for welded specimens, whereas catastrophic through-the-thickness failure was observed for mechanically fastened joints. Based on the experimental outcomes, the mechanical performance and failure mechanisms of spot-welded joints were critically assessed in comparison to the mechanical fasteners .
The primary objective of this research study was to evaluate the capabilities for measuring strain of a composite UAV wing with an embedded optical fiber connected to a Rayleigh backscattering distributed sensing system. This research paper summarizes the manufacturing procedure used during the instrumentation of the composite UAV wing. In addition, a Finite Element Model was developed in order to verify the strain distribution of this complex structure under static and dynamic loading conditions. The use of strain gauge data as a means for verification is presented as part of this research. Finally, fatigue tests were carried out to determine the longevity of the embedded fiber during the design life of the structure. The results demonstrate the ability of a distributed sensing system to obtain complex and accurate strain distributions on a single non-grated fiber. In addition, the findings demonstrate current limitations of the system for capturing accurate strain profiles in dynamic loading test cases.
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The primary objective of this research study was to evaluate the capabilities for measuring strain of a composite UAV wing with an embedded optical fiber connected to a Rayleigh backscattering distributed sensing system. This research paper summarizes the manufacturing procedure used during the instrumentation of the composite UAV wing. In addition, a Finite Element Model was developed in order to verify the strain distribution of this complex structure under static and dynamic loading conditions. The use of strain gauge data as a means for verification is presented as part of this research. Finally, fatigue tests were carried out to determine the longevity of the embedded fiber during the design life of the structure. The results demonstrate the ability of a distributed sensing system to obtain complex and accurate strain distributions on a single non-grated fiber. In addition, the findings demonstrate current limitations of the system for capturing accurate strain profiles in dynamic loading test cases.
In this paper, a one-sided, in situ method based on the time of flight measurement of ultrasonic waves was described. The primary application of this technique was to non-destructively measure the stiffness properties of isotropic and transversely isotropic materials. The method consists of generating and receiving quasi-longitudinal and quasi-shear waves at different through-thickness propagation angles. First, analytical equations were provided to calculate the ultrasonic wave velocities. Then, an inverse method based on non-linear least square technique was used to calculate the stiffness constants using the ultrasonic wave velocities. Sensitivity analysis was performed by randomly perturbing the velocity data, thus observing the effects of perturbations on the calculated stiffness constants. An improved algorithm was proposed and tested to reduce the effects of random errors. Based on the sensitivity analysis, minimum number of angles required to inversely calculate the stiffness constants were suggested for isotropic and transversely isotropic material. The method was experimentally verified on an isotropic 7050-T7451 aluminum with two different thicknesses and a transversely isotropic composite laminate fabricated using 24 plies of CYCOM 977-2 12 k HTA unidirectional carbon fiber reinforced polymer (CFRP) prepregs. The results demonstrated that this technique is able to accurately measure the material properties of isotropic material. As for the transverselyisotropic material, this method was able to accurately measure the materialproperties if the experimental errors can be reduced to less than 1 %
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In this paper, a one-sided, in situ method based on the time of flight measurement of ultrasonic waves was described. The primary application of this technique was to non-destructively measure the stiffness properties of isotropic and transversely isotropic materials. The method consists of generating and receiving quasi-longitudinal and quasi-shear waves at different through-thickness propagation angles. First, analytical equations were provided to calculate the ultrasonic wave velocities. Then, an inverse method based on non-linear least square technique was used to calculate the stiffness constants using the ultrasonic wave velocities. Sensitivity analysis was performed by randomly perturbing the velocity data, thus observing the effects of perturbations on the calculated stiffness constants. An improved algorithm was proposed and tested to reduce the effects of random errors. Based on the sensitivity analysis, minimum number of angles required to inversely calculate the stiffness constants were suggested for isotropic and transversely isotropic material. The method was experimentally verified on an isotropic 7050-T7451 aluminum with two different thicknesses and a transversely isotropic composite laminate fabricated using 24 plies of CYCOM 977-2 12 k HTA unidirectional carbon fiber reinforced polymer (CFRP) prepregs. The results demonstrated that this technique is able to accurately measure the material properties of isotropic material. As for the transverselyisotropic material, this method was able to accurately measure the materialproperties if the experimental errors can be reduced to less than 1 %
Sensor Fusion for Shape Sensing
Theory and Numerical Simulation
Computing global strain distributions in complex aerospace and wind energy structures in quasi-real time is an important challenge for the aerospace and wind energy industry. Shape sensing is being considered as a potential means for obtaining global strain fields of complex structures. This is achieved through the use of inverse finite element methods (iFEM). Traditional shape sensing techniques use a variety of sensors in isolation. These isolated approaches lead to a number of drawbacks, including significant errors in the strain and/or displacement distributions that they determine. This study focuses on reducing these errors by combining data from various sensor types.
In order to achieve this objective, a new iFEM approach was developed to improve how shape sensing determines a structure’s deformation from distributed sensors. The method utilizes a procedure that minimizes a quadratic error functional, based on the difference between the theoretical and the measured strains and displacements. The analytical iFEM equations are discretized for use in a numerical model, which is used to analyze simulated sensor data. The simulated data is obtained from a FEM analysis of a structure loaded in bending, torsion and shear, in addition to experiencing combined loads. As with previous inverse methods, the error functional weights were employed to manage missing measurements and Tikhonov regularization was applied to guarantee smoothness of the numerical solution.
In this study, it has been shown that the new iFEM methodology can determine the structural deformations to within 1% of the FEM results for each of the load cases. In addition, the methodology is capable of achieving the same accuracy as single sensor type iFEM methods with a smaller total number of sensors. The single sensor type iFEM methods that were considered made use of only strain data or only displacement data. Finally, an experimental setup consisting of a simple cantilever plate structure was constructed in order to experimentally verify the newly developed iFEM algorithm. The experimental strain and displacement data were obtained utilizing a Rayleigh backscattering fiber optic distributed sensing system and MEMS sensors, respectively. Both of these sensor types were mounted on the structure and subjected to the load cases that were simulated in the FEM analysis.
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Computing global strain distributions in complex aerospace and wind energy structures in quasi-real time is an important challenge for the aerospace and wind energy industry. Shape sensing is being considered as a potential means for obtaining global strain fields of complex structures. This is achieved through the use of inverse finite element methods (iFEM). Traditional shape sensing techniques use a variety of sensors in isolation. These isolated approaches lead to a number of drawbacks, including significant errors in the strain and/or displacement distributions that they determine. This study focuses on reducing these errors by combining data from various sensor types.
In order to achieve this objective, a new iFEM approach was developed to improve how shape sensing determines a structure’s deformation from distributed sensors. The method utilizes a procedure that minimizes a quadratic error functional, based on the difference between the theoretical and the measured strains and displacements. The analytical iFEM equations are discretized for use in a numerical model, which is used to analyze simulated sensor data. The simulated data is obtained from a FEM analysis of a structure loaded in bending, torsion and shear, in addition to experiencing combined loads. As with previous inverse methods, the error functional weights were employed to manage missing measurements and Tikhonov regularization was applied to guarantee smoothness of the numerical solution.
In this study, it has been shown that the new iFEM methodology can determine the structural deformations to within 1% of the FEM results for each of the load cases. In addition, the methodology is capable of achieving the same accuracy as single sensor type iFEM methods with a smaller total number of sensors. The single sensor type iFEM methods that were considered made use of only strain data or only displacement data. Finally, an experimental setup consisting of a simple cantilever plate structure was constructed in order to experimentally verify the newly developed iFEM algorithm. The experimental strain and displacement data were obtained utilizing a Rayleigh backscattering fiber optic distributed sensing system and MEMS sensors, respectively. Both of these sensor types were mounted on the structure and subjected to the load cases that were simulated in the FEM analysis.
Sensor Fusion for Shape Sensing
Theory and Numerical Results
Shape sensing utilizing an inverse finite element method (iFEM) was considered for obtaining the displacement fields of two cantilever beams. Traditional iFEM techniques make use of only a single type of sensor, which can lead to errors in the strain and/or displacement distributions. This study has reduced these errors by combining data from multiple sensor types. A new iFEM algorithm was developed in order to minimize the error on computed strain profiles. The new iFEM algorithm incorporates Tikhonov smoothing in addition to compensating for the lack of sensors in areas of the structure where no sensors are present. The new iFEM algorithm was demonstrated for cantilever plates under various load cases. The preliminary results showed that the new iFEM algorithm is able to determine the structural deformations of two cantilever plates with greater accuracy than a traditional iFEM implementation that was found in the literature, using fewer sensors.
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Shape sensing utilizing an inverse finite element method (iFEM) was considered for obtaining the displacement fields of two cantilever beams. Traditional iFEM techniques make use of only a single type of sensor, which can lead to errors in the strain and/or displacement distributions. This study has reduced these errors by combining data from multiple sensor types. A new iFEM algorithm was developed in order to minimize the error on computed strain profiles. The new iFEM algorithm incorporates Tikhonov smoothing in addition to compensating for the lack of sensors in areas of the structure where no sensors are present. The new iFEM algorithm was demonstrated for cantilever plates under various load cases. The preliminary results showed that the new iFEM algorithm is able to determine the structural deformations of two cantilever plates with greater accuracy than a traditional iFEM implementation that was found in the literature, using fewer sensors.
Sensor Fusion for Shape Sensing
Theory and Numerical Simulation
Computing global strain distributions in complex aerospace and wind energy structures in quasi-real time is an important challenge for the aerospace and wind energy industry. Shape sensing is being considered as a potential means for obtaining global strain fields of complex structures. This is achieved through the use of inverse finite element methods (iFEM). Traditional shape sensing techniques use a variety of sensors in isolation. These isolated approaches lead to a number of drawbacks, including significant errors in the strain and/or displacement distributions that they determine. This study focuses on reducing these errors by combining data from various sensor types.
In order to achieve this objective, a new iFEM approach was developed to improve how shape sensing determines a structure’s deformation from distributed sensors. The method utilizes a procedure that minimizes a quadratic error functional, based on the difference between the theoretical and the measured strains and displacements. The analytical iFEM equations are discretized for use in a numerical model, which is used to analyze simulated sensor data. The simulated data is obtained from a FEM analysis of a structure loaded in bending, torsion and shear, in addition to experiencing combined loads. As with previous inverse methods, the error functional weights were employed to manage missing measurements and Tikhonov regularization was applied to guarantee smoothness of the numerical solution.
In this study, it has been shown that the new iFEM methodology can determine the structural deformations to within 1% of the FEM results for each of the load cases. In addition, the methodology is capable of achieving the same accuracy as single sensor type iFEM methods with a smaller total number of sensors. The single sensor type iFEM methods that were considered made use of only strain data or only displacement data. Finally, an experimental setup consisting of a simple cantilever plate structure was constructed in order to experimentally verify the newly developed iFEM algorithm. The experimental strain and displacement data were obtained utilizing a Rayleigh backscattering fiber optic distributed sensing system and MEMS sensors, respectively. Both of these sensor types were mounted on the structure and subjected to the load cases that were simulated in the FEM analysis.
...
Computing global strain distributions in complex aerospace and wind energy structures in quasi-real time is an important challenge for the aerospace and wind energy industry. Shape sensing is being considered as a potential means for obtaining global strain fields of complex structures. This is achieved through the use of inverse finite element methods (iFEM). Traditional shape sensing techniques use a variety of sensors in isolation. These isolated approaches lead to a number of drawbacks, including significant errors in the strain and/or displacement distributions that they determine. This study focuses on reducing these errors by combining data from various sensor types.
In order to achieve this objective, a new iFEM approach was developed to improve how shape sensing determines a structure’s deformation from distributed sensors. The method utilizes a procedure that minimizes a quadratic error functional, based on the difference between the theoretical and the measured strains and displacements. The analytical iFEM equations are discretized for use in a numerical model, which is used to analyze simulated sensor data. The simulated data is obtained from a FEM analysis of a structure loaded in bending, torsion and shear, in addition to experiencing combined loads. As with previous inverse methods, the error functional weights were employed to manage missing measurements and Tikhonov regularization was applied to guarantee smoothness of the numerical solution.
In this study, it has been shown that the new iFEM methodology can determine the structural deformations to within 1% of the FEM results for each of the load cases. In addition, the methodology is capable of achieving the same accuracy as single sensor type iFEM methods with a smaller total number of sensors. The single sensor type iFEM methods that were considered made use of only strain data or only displacement data. Finally, an experimental setup consisting of a simple cantilever plate structure was constructed in order to experimentally verify the newly developed iFEM algorithm. The experimental strain and displacement data were obtained utilizing a Rayleigh backscattering fiber optic distributed sensing system and MEMS sensors, respectively. Both of these sensor types were mounted on the structure and subjected to the load cases that were simulated in the FEM analysis.
Special Issue
25th International Conference on Adaptive Structures and Technologies (ICAST 2014)
The 25th International Conference on Adaptive Structures and Technologies (ICAST 2014) was held from 5 to 8 October in The Hague, The Netherlands. The 25th “silver” anniversary of this conference allowed for the technical interchange among the growing international community of researchers, leading international scholars, and specialists in the field of smart materials, structures, and devices. ICAST takes place annually and the venue rotates between the Americas, Asia, and Europe. The conference provides a forum for discussion of recent advances in the highly multidisciplinary field of smart materials and structures and encourages transfer of advanced scientific results from research to application. The main topics include multi-functional materials and devices, modeling and characterization, bio-inspired sensors and actuators, morphing technologies, structural health monitoring, and vibration damping.
The silver jubilee of ICAST was celebrated with more than 60 oral technical presentations, 30 posters, and over 100 participants from all over the world. In this Special Issue of the Journal of Intelligent Material Systems and Structures (JIMSS), we present a selection of 10 papers from ICAST 2014 that highlight research on morphing, vibration mitigation and control, biomechanical devices, structural health monitoring, and active material modeling.
We sincerely thank all the conference participants for their contributions and in particular those authors that augmented their work to prepare this Special Issue. We would also like to express our appreciation to the editors of JIMSS for the opportunity to prepare and publish this Special Issue.
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The 25th International Conference on Adaptive Structures and Technologies (ICAST 2014) was held from 5 to 8 October in The Hague, The Netherlands. The 25th “silver” anniversary of this conference allowed for the technical interchange among the growing international community of researchers, leading international scholars, and specialists in the field of smart materials, structures, and devices. ICAST takes place annually and the venue rotates between the Americas, Asia, and Europe. The conference provides a forum for discussion of recent advances in the highly multidisciplinary field of smart materials and structures and encourages transfer of advanced scientific results from research to application. The main topics include multi-functional materials and devices, modeling and characterization, bio-inspired sensors and actuators, morphing technologies, structural health monitoring, and vibration damping.
The silver jubilee of ICAST was celebrated with more than 60 oral technical presentations, 30 posters, and over 100 participants from all over the world. In this Special Issue of the Journal of Intelligent Material Systems and Structures (JIMSS), we present a selection of 10 papers from ICAST 2014 that highlight research on morphing, vibration mitigation and control, biomechanical devices, structural health monitoring, and active material modeling.
We sincerely thank all the conference participants for their contributions and in particular those authors that augmented their work to prepare this Special Issue. We would also like to express our appreciation to the editors of JIMSS for the opportunity to prepare and publish this Special Issue.