Abstract:An idealized 1:2 scale demonstrator and a numerical parameter optimization algorithm are proposed to closely reproduce the deformation shape and, thus, spatial strain directions of a real aerodynamically loaded civil aircraft spoiler using only four concentrated loads. Cost-efficient experimental studies on demonstrators of increasing complexity are required to transfer knowledge from coupons to full-scale structures and to build up confidence in novel structural health monitoring (SHM) technologies. Especiall… Show more
“…As shown in previous studies by Winklberger et al, 38,39 the deformation and strains of aircraft spoiler due to aerodynamic forces can be well approximated by an idealized model and a little number of optimized locally introduced forces. It was shown that two symmetrically applied local forces F 1 , F 2 (i.e., four forces) are adequate to imitate the landing load case of a real spoiler (the idealized model/ spoiler is assumed to be symmetrical).…”
Section: Global Modelmentioning
confidence: 72%
“…Thus, the utilized test rig was developed to approximate this important load case with respect to deformation and strain at the pressurized spoiler surface by only four local loads distributed via the whiffle tree. 38,39 Figure 6.Image of the experimental set-up of the idealized spoiler model.…”
Section: Methodsmentioning
confidence: 99%
“…Thus, the utilized test rig was developed to approximate this important load case with respect to deformation and strain at the pressurized spoiler surface by only four local loads distributed via the whiffle tree. 38,39 As schematically depicted in Figure 5, N n = 9 strain gauges (HBM RY93-6/120) with a mesh size of 55 mm were attached to the surface of the composite structure to monitor the strain distribution over the monitored face layer area (equivalent to the FE submodel). The measurement signal acquisition is done by two HBM Quantum X MX840 A.…”
In recent years, structural health monitoring has been increasingly applied to composite sandwich structures, as typically used in aerospace applications. In addition, machine learning approaches are increasingly popular for damage detection, localization and size estimation, due to their great advantages in pattern recognition and anomaly detection. However, a major disadvantage of machine learning techniques is that these algorithms generally require large amounts of realistic data. In general, these data are expensive or even impossible to obtain within a feasible time. In order to overcome this hindrance, this work introduces a computationally inexpensive framework for physics-driven feature generation of strain data for the training of ML-based SHM methods using sub-structuring and the concept of reanalysis. First, the global FE model is subdivided into a monitored part, i.e., a smaller submodel, and a global model. Second, the stiffness matrix of the submodel is extracted from the finite element software. Then, static condensation is performed to further reduce the computational effort. Afterwards, selected eigenvectors are derived in terms of displacements of master nodes and the corresponding strains are calculated. Finally, a statistically varied linear combination between the different characteristic eigenvector load cases is performed based on the superposition principle. This procedure enables the efficient generation of a large number of different physics-driven determined strain solutions for a subsequent training of a ML algorithms. The proposed framework is evaluated by means of a damage detection approach, based on an artificial neuronal network classifier algorithm. The applied approach utilizes strain measurements from selected positions as physical quantity and is demonstrated using a composite sandwich structure imitating an aircraft spoiler. The key principle of the damage detection algorithm is based on the fact that a change in the relationship between sensors indicates the presence of damage. Additionally, to the numerical healthy strains resulting from the framework, synthetically generated damage data are used for training the neuronal network classifier. The synthetic data are obtained by statistical modifications of the healthy strains, to avoid time-consuming and expensive damage simulations. The feature generation framework and health monitoring approach are validated using experiments and numerical simulations of a glass fiber reinforced polymer sandwich structure with a hole considered as damage. The presented numerical and the experimental results clearly show the high potential for the efficient approach for damage detection in a sandwich structure.
“…As shown in previous studies by Winklberger et al, 38,39 the deformation and strains of aircraft spoiler due to aerodynamic forces can be well approximated by an idealized model and a little number of optimized locally introduced forces. It was shown that two symmetrically applied local forces F 1 , F 2 (i.e., four forces) are adequate to imitate the landing load case of a real spoiler (the idealized model/ spoiler is assumed to be symmetrical).…”
Section: Global Modelmentioning
confidence: 72%
“…Thus, the utilized test rig was developed to approximate this important load case with respect to deformation and strain at the pressurized spoiler surface by only four local loads distributed via the whiffle tree. 38,39 Figure 6.Image of the experimental set-up of the idealized spoiler model.…”
Section: Methodsmentioning
confidence: 99%
“…Thus, the utilized test rig was developed to approximate this important load case with respect to deformation and strain at the pressurized spoiler surface by only four local loads distributed via the whiffle tree. 38,39 As schematically depicted in Figure 5, N n = 9 strain gauges (HBM RY93-6/120) with a mesh size of 55 mm were attached to the surface of the composite structure to monitor the strain distribution over the monitored face layer area (equivalent to the FE submodel). The measurement signal acquisition is done by two HBM Quantum X MX840 A.…”
In recent years, structural health monitoring has been increasingly applied to composite sandwich structures, as typically used in aerospace applications. In addition, machine learning approaches are increasingly popular for damage detection, localization and size estimation, due to their great advantages in pattern recognition and anomaly detection. However, a major disadvantage of machine learning techniques is that these algorithms generally require large amounts of realistic data. In general, these data are expensive or even impossible to obtain within a feasible time. In order to overcome this hindrance, this work introduces a computationally inexpensive framework for physics-driven feature generation of strain data for the training of ML-based SHM methods using sub-structuring and the concept of reanalysis. First, the global FE model is subdivided into a monitored part, i.e., a smaller submodel, and a global model. Second, the stiffness matrix of the submodel is extracted from the finite element software. Then, static condensation is performed to further reduce the computational effort. Afterwards, selected eigenvectors are derived in terms of displacements of master nodes and the corresponding strains are calculated. Finally, a statistically varied linear combination between the different characteristic eigenvector load cases is performed based on the superposition principle. This procedure enables the efficient generation of a large number of different physics-driven determined strain solutions for a subsequent training of a ML algorithms. The proposed framework is evaluated by means of a damage detection approach, based on an artificial neuronal network classifier algorithm. The applied approach utilizes strain measurements from selected positions as physical quantity and is demonstrated using a composite sandwich structure imitating an aircraft spoiler. The key principle of the damage detection algorithm is based on the fact that a change in the relationship between sensors indicates the presence of damage. Additionally, to the numerical healthy strains resulting from the framework, synthetically generated damage data are used for training the neuronal network classifier. The synthetic data are obtained by statistical modifications of the healthy strains, to avoid time-consuming and expensive damage simulations. The feature generation framework and health monitoring approach are validated using experiments and numerical simulations of a glass fiber reinforced polymer sandwich structure with a hole considered as damage. The presented numerical and the experimental results clearly show the high potential for the efficient approach for damage detection in a sandwich structure.
“…[61][62][63][64][65] Methods for damage identifications based on strains recorded from zero-strain trajectories are also employed. 35,66,67 Lately, strain-based SHM methodologies utilizing the inverse FE method are emerging. [68][69][70] As previously mentioned, aerospace structures are designed under the principles of damage tolerance, operating under the existence and development of damage, intrinsic or induced, within their volume.…”
In this study, a multi-level Structural Health Monitoring methodology for stiffened composite panels is introduced. A digital twin (DT), that is, a three-dimensional finite element (FE) model, representing the pristine state baseline of the test article, is developed and verified for compressive loading in the post-buckling regime. The detailed FE model is utilized to train a surrogate model with respect to exogenous input, that is, axial load magnitude. The surrogate assists the DT concept that would allow prediction of the load acting on the structure based on an influx of strain data, acquired from fiber Bragg grating sensors permanently attached along the stringer feet. For this purpose, we leverage on the observation that remote from the damage, the strain field remains virtually unaltered with regard to the pristine state. The load is estimated by a sensor placed far from the damage whilst the diagnostic actions are performed by exploiting measurements from the remaining sensing locations. A health indicator, which compares the experimentally received strains with those from the surrogate representing the pristine state, is utilized to (1) detect, (2) localize, and (3) characterize the damage. As damage, we consider either skin-to-stringer disbond or initial impact damage propagation as well as overall stiffness degradation during thousands or millions of fatigue cycles. The sensors that have detected a disbond are dedicated to evaluating the potential propagation of it, while the remaining sensors evaluate the overall stiffness degradation. The proposed methodology is tested for one artificially disbonded and two impacted single-stringer panels subjected to block loading compression-compression fatigue.
“…Recent studies have focused on the development of novel systems for structural health monitoring (SHM), based on the continuous and distributed monitoring of low-impact occurrences, with the aim of achieving early detection of potential structural damage during aircraft operating time [ 5 , 6 , 7 ]. This would prevent the late detection of more serious damage, which requires more expensive repairing interventions when possible, or the use of expensive techniques for non-destructive testing and evaluation (NDT&E) [ 8 , 9 , 10 ]. The use of SHM technologies in the aviation market will have a commercial impact, possibly reducing maintenance costs by up to 40% and also modifying the repairing process, leading to a move from time-based maintenance (TbM) to condition-based maintenance (CbM) [ 11 , 12 , 13 ].…”
Within the paradigm of smart mobility, the development of innovative materials aimed at improving resilience against structural failure in lightweight vehicles and electromagnetic interferences (EMI) due to wireless communications in guidance systems is of crucial relevance to improve safety, sustainability, and reliability in both aeronautical and automotive applications. In particular, the integration of intelligent structural health monitoring and electromagnetic (EM) shielding systems with radio frequency absorbing properties into a polymer composite laminate is still a challenge. In this paper, we present an innovative system consisting of a multi-layered thin panel which integrates nanostructured coatings to combine EM disturbance suppression and low-energy impact monitoring ability. Specifically, it is composed of a stack of dielectric and conductive layers constituting the sensing and EM-absorbing laminate (SEAL). The conductive layers are made of a polyurethane paint filled with graphene nanoplatelets (GNPs) at different concentrations to tailor the effective electrical conductivity and the functionality of the material. Basically, the panel includes a piezoresistive grid, obtained by selectively spraying onto mylar a low-conductive paint with 4.5 wt.% of GNPs and an EM-absorbing lossy sheet made of the same polyurethane paint but properly modified with a higher weight fraction (8 wt.%) of graphene. The responses of the grid’s strain sensors were analyzed through quasi-static mechanical bending tests, whereas the absorbing properties were evaluated through free-space and waveguide-based measurement techniques in the X, Ku, K, and Ka bands. The experimental results were also validated by numerical simulations.
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