Large-scale Composite Structures Research Articles

A global–local approach for progressive damage analysis of fiber-reinforced composite laminates

The present work applies the global–local technique to the progressive damage analysis of fiber-reinforced composite laminates. A one-way, loosely-coupled global–local approach is developed as a combination of a low-fidelity linear global analysis and a high-fidelity local nonlinear analysis of specific regions within the structure, where damage is expected to occur. The local model is based on higher-order structural theories derived using the Carrera Unified Formulation (CUF), and specifically, Lagrange polynomials are used to model each ply through its thickness, leading to a layer-wise model. Composite damage is described using the CODAM2 material model, which is based on continuum damage mechanics. Initial assessments compare the relative performance of 3D finite elements (FE), 1D-CUF, and the proposed global–local approach via the free-edge stress analysis of a stiffened composite plate. The proposed technique is then used to predict the tensile strength of an open-hole specimen. The last assessment simulates damage progression within an over-height compact tension specimen using the global–local approach. Verification and validation of results are carried out via refined models and experiments from literature. The results demonstrate the accurate evaluation of 3D stress fields and composite laminates’ mechanical response in the progressive damage regime. A multi-fold improvement in the computational cost is shown when compared to full-scale CUF analyses and indicates this technique’s strong potential towards the computationally-efficient high-fidelity analysis of complex and large-scale composite structures.

Generic stacks and application of composite rules for the detailed sizing of laminated structures

Two-stage approaches are commonly applied to optimise the stacking sequence of large-scale composite structures. The two stages consist of a gradient and non-gradient based optimisation addressing the mixed nature of continuous and discrete constraints and design variables of the detailed sizing of laminated structures. For the two-stage process to be successful, all constraints from both stages should be fulfilled eventually. This study employs generic stacks to model the thickness and stiffness distribution of the structure. A generic stack is comprised of a collection of layers whose orientations are fixed a priori, but thicknesses can vary independently enabling exploration of the design space. To achieve a ‘right first time’ implementation and avoid burdensome iterations over both stages, a maximum amount of discrete design and manufacturing constraints should be considered in the gradient-based optimisation stage. A special attention is paid to the continuity or blending constraint which can be formulated precisely using generic stacks. The results for a benchmark case show that the introduced two-stage approach can, not only satisfy all imposed constraints in a single iteration of the overall two-stage process but also yield a lower structural mass when compared to equivalent previously proposed approaches.

Mechanical properties of large-scale composite structures according to process conditions

The purpose of this study is to determine the correct estimation of the concept design and laminating pattern for composites. In this study, the effect of carbon tow prepreg on the composite structure has been investigated. Also, this work evaluated the structural safety according to the laminating pattern and process technique by computer simulation. The laminating pattern of three kinds was designed through the normal shape. Also, process technique of two kinds were carried out in dry and wet method to produce composites structure. The result of FEA showed that the structural safety of carbon tow prepreg is not significantly superior to that of the structure with wet method material. However, considering the benefits of working environment and the reduction of the number of process, the possibility of replacing materials and process technique will be sufficient for composite structure.

A general optimization strategy for composite sandwich structures

An original strategy for the design of large-scale composite sandwich structures is developed. Such structures are typical of launcher and spacecraft structures, but the methodology remains applicable to any thin-walled sandwich structure (such as aircraft fuselage panels or watercraft hulls). The method hinges on multi-step strategies devised for variable-stiffness laminates. Indeed, laminate optimization problems correspond to large combinatorial design spaces and their efficient approximate solutions often combine continuous relaxation and metaheuristics. Typically, the first step consists of a gradient-based optimization that handles the large number of variables with a continuous representation of the composite mechanical behavior. In our work, this continuous representation is adapted to the specific features of laminated sandwich composites. In particular, we modeled the dependency of the material transverse shear stiffness on the design variables (composite skins and core) by employing a machine learning–based approximation. The face sheet laminates are then determined in a second step using an evolutionary algorithm with stacking sequence tables to represent thickness variations. Finally, we introduce a third optimization step to overcome the design feasibility issues related to the use of a stiffness matching method for the determination of the layups of the face sheet laminates. The method is applied to the minimization of the overall weight of a load-carrying structure similar to the Ariane 5 dual-launch system, under several launcher-specific mechanical constraints.

An Abaqus plugin for efficient damage initiation hotspot identification in large-scale composite structures with repeated features

Identifying the hotspots for damage initiation in large-scale composite structure designs presents a significant challenge due to the high modelling cost. For most industrial applications, the finite element (FE) models are often coarsely meshed with shell elements and used to predict the global stiffness and internal loads. Because of the lack of detailed descriptions for the composite materials and 3D stress states, most of the established failure criteria are not applicable. In this work we present an Abaqus plugin tool which implements a framework to identify the hotspots by using a pre-computed database generated for specific, heavily-repeated feature types based on a given structural model. Developed with an object-oriented implementation in Python, this software is split into two main parts, specifically for feature generation and structural analysis. The pre-computed model presents a full 3D description for the considered feature and works as a submodel to the coarse structure model driven by a one-way transfer of the boundary conditions. The presented framework is an analysis tool for efficient sizing of large-scale composite structures, as it enables 3D damage analysis of the structures in critical zones with significant savings of the modelling and computational cost. The results are compared with conventional FE modelling and satisfactory agreement is observed. In addition, the software also enables the pre-computed database to be stored in an HDF5 data file for further reuse on new structures with the same feature.

An experimental implementation of inverse finite element method for real-time shape and strain sensing of composite and sandwich structures

In this study, the inverse finite element method (iFEM) is experimentally applied to real-time displacement reconstruction of a moderately thick wing-shaped sandwich structure via a network of strain sensors. For this purpose, the iFEM algorithm is incorporated to the kinematic relations of refined zigzag theory (RZT) by considering laminate mechanics of the woven-fabric reinforcement. After a twill-woven wing-shaped structure is manufactured with embedded fiber Bragg grating sensors and surface mounted strain gauges/rosettes, the discrete real-time experimental strains are acquired from these sensors concurrently during a flexural test of the structure. This data is then processed by iFEM algorithm for full-field displacement and strain monitoring. Moreover, the displacement fields at the one edge of the sandwich structure is monitored by digital image correlation (DIC) system simultaneously. Furthermore, the reference displacement solutions are established by performing high-fidelity FEM analysis. Finally, the three-dimensional real-time deformations and strains obtained through iFEM approach show very good consistency when compared to the results of DIC/FEM analysis and experimental strains, respectively. Overall, the present study serves as a comprehensive experimental guidance of iFEM-based shape and strain sensing for its realistic implementation on large-scale composite structures and notably increases technology readiness level of the iFEM methodology.

In-situ monitoring of liquid composite molding process using piezoelectric sensor network

The excellent properties of advanced composite materials provide great opportunities for making industrial structures large-scale and intelligent. Liquid composite molding process is suitable for manufacturing complex large-scale composite structures and has the potential for low cost and mass production. In present work, the concept of Networked Elements for Resin Visualization and Evaluation network was developed to measure and monitor the manufacturing process in-situ. This paper investigates the capability of piezoelectric lead-zirconate-titanate sensors in the Networked Elements for Resin Visualization and Evaluation network to monitor two important parameters in liquid composite molding process, including the resin flow front and the progress of the reaction. The piezoelectric lead-zirconate-titanate sensor network can be integrated with a composite structure either installed on the interface between the mold and laminates or embedded inside the laminates during the liquid composite molding process. Experimental results demonstrated that the liquid composite molding process can be effectively monitored by the embedded Networked Elements for Resin Visualization and Evaluation network with a piezoelectric lead-zirconate-titanate sensor network.

Experimental study on shaped steel shear connectors used in large-scale composite structures

Steel–concrete–steel (SCS) composite structures are used in immersed tunnels and are superior in capacity, constructional facility, etc. in comparison with traditional structures. L- and T-shaped shear connectors are two typical types of shear connectors used in the composite structures. To investigate the mechanical performances of the connectors, 12 groups of push-out tests were conducted. Interfacial slip, bending deformation of the connector web, and strains on the concrete, were measured. Failure modes of concrete crushing were observed in all tested specimens. Evoked outcomes indicated that the performances of the L- and T-shaped connectors were controlled by the non-reinforced concrete. The strain measured in the tests indicated that stress concentration and concrete crushing were mainly located at the root regions. The shear-force–slip relationship, strain distribution, etc., were further analyzed and discussed. Accordingly, the currently available methods used to evaluate the ultimate strength are discussed. These methods were found to be accurate in the case of the L-shaped shear connectors but too conservative for T-shaped shear connectors. A scaling factor was proposed for modification. Finally, 122 groups testing data was collected for validation, which proved that the modified method has adequate accuracy and can be used in various design applications. The experiment data of the L-shape and T-shape shear connectors with different experimental methods were studied which shows the push-out specimens and beam specimens have similar performance. The results of the experimental study and theoretical analyses indicate that the L-shape and T-shape shear connectors have similar mechanical properties while the T-shape shear connectors have better performance concerning the shear stiffness and the ultimate shear strength.

Surface–Volume–Surface EFIE Formulation for Fast Direct Solution of Scattering Problems on General 3-D Composite Metal–Dielectric Objects

A new formulation of the surface–volume–surface electric field integral equation (SVS-EFIE) combined with the regular EFIE is introduced for the electromagnetic analysis of the 3-D general composite structures of impenetrable metal and penetrable homogeneous dielectric objects. The proposed method introduces independent surface electric current densities on the boundary of each region which brings the benefit of the independent discretization of the regions according to their respective material properties and straightforward handling of the material junctions. This makes the algorithm more efficient and flexible for analysis of multiscale and large-scale composite structures in comparison with the traditional Poggio–Miller–Chang–Harrington–Wu–Tsai (PMCHWT) integral equation combined with the EFIE. The method of moment (MoM) solution of the new formulation is accelerated by the hierarchical ( $\mathcal H$ )-matrix framework. The numerical results are given to validate the new formulation. Also, the $\mathcal H$ -matrix acceleration of MoM solution of the new combined EFIE and SVS-EFIE formulation is shown to produce accurate results using only the small fraction of memory and total CPU time needed by conventional MoM.

An efficient multiscale surrogate modelling framework for composite materials considering progressive damage based on artificial neural networks

Modelling of the progressive damage behaviour of large-scale composite structures presents a significant challenge in terms of computational cost. This is due to the detailed description in finite element (FE) models for the materials, i.e., with each unidirectional layer defined as required by the applicability of laminate failure criteria, and numerous iterations required to capture the highly nonlinear behaviour after damage initiation. In this work, we propose a method to accelerate the nonlinear FE analysis by using a pre-computed surrogate model which acts as a general material database representing the nonlinear effective stress-strain relationship and the possible failure information. Developed using artificial neural network algorithms, the framework is separated into an offline training phase and an online application phase. The surrogate model is first trained with a vast number of sampling data obtained from mesoscale unit cell models offline, and then used for online predictions on a macroscale FE model. The prediction accuracy of the surrogate model was examined by comparing the results with conventional FE modelling and good agreement was observed. The presented method enables progressive damage analysis of composite structures with significant savings of the online computational cost. Lastly, the surrogate model is only based on material designs and is reusable for other structures with the same material.

An orthotropic non-local approach to modeling intra-laminar damage progression in laminated composites

A non-local macroscopic constitutive model is presented to simulate the progression of intra-laminar damage in laminated composite structures. A novel feature of the model is an orthotropic, non-local averaging scheme that is specially designed to capture the preferred path of damage / fracture propagation in laminated structures consisting of strongly orthotropic layers. In addition to maintaining the objectivity of the numerical results, through rendering them mesh size and mesh orientation invariant, the orthotropic nonlocal regularization results in a more realistic prediction of damage pattern and damage growth trajectory in orthotropic laminates. The new model, CODAM2, has been implemented in the open source finite element (FE) code, OOFEM, as well as the commercial explicit FE code, LS-DYNA. Numerical examples are presented to demonstrate the capabilities of CODAM2 in predicting the overall response and extent of damage in quasi-statically loaded, notched laminated specimens with various root notch radii ranging from sharp to blunt geometries. The developed methodology serves as a useful tool for robust and efficient inelastic analysis of large-scale composite structures leading to their damage tolerant design.

A constraint satisfaction problem algorithm for large-scale multi-panel composite structures

This paper proposes a constraint satisfaction problem algorithm based on implicit decision trees and dynamic programming for the design of multiple composite panels subjected to a set of blending rules and external forces. This decision tree is built incrementally on the ‘fly’, adding plies from a particular set, and does not require any guiding laminate or stacking sequence table for blending purposes. The upper bound algorithm complexity is provided in this work. The technique is applied to the design of a well-known composite multi-panel problem addressed in previous studies; the novel procedure exhibits high-quality solutions (in terms of structure weight) and low computational cost (in terms of laminate evaluations). The novel algorithm scalability is also checked showing that the node tree expansion is independent of the number of panels to be designed. This algorithm could therefore be a suitable choice for large-scale multi-panel design problems.

$\mathcal {H}$-Matrix Accelerated Solution of Surface–Volume–Surface EFIE for Fast Electromagnetic Analysis on 3-D Composite Dielectric Objects

An efficient fast direct algorithm based on the hierarchical ( $\mathcal {H}$ -) matrices is presented for solution of the radiation problems on piecewise homogeneous dielectric objects using Method of Moment (MoM) discretization of the surface–volume–surface electric field integral equation (SVS-EFIE). The SVS-EFIE for the composite objects introduces independent surface electric current density on the boundary of each region. Therefore, different from the traditional Poggio–Miller–Chang–Harrington–Wu–Tsai formulation, in the SVS-EFIE, the object regions can be meshing independently according to their local properties which improves the flexibility and efficiency of the proposed method. It also makes the proposed algorithms appropriate for the analysis of both multiscale and large-scale composite structures. The numerical results from the proposed fast method are provided for the high-loss biological tissues from bioelectromagnetics applications and agree well with the analytical Mie series solution and commercial software. The CPU time and memory cost of the required $\mathcal {H}$ -matrix operations are analyzed in details and verified through several numerical experiments. The new computational framework allows for fast direct solution of 3-D radiation and scattering problems of moderate electrical size with $O(P^{\alpha } \log ^2 P)$ CPU time and $O(P^{\alpha } \log P)$ memory complexity, $P$ being the number of surface unknowns produced by the MoM discretization, and $1\leq \alpha \leq 1.5$ being a geometry-dependent parameter.

Impact Monitoring for Aircraft Smart Composite Skins Based on a Lightweight Sensor Network and Characteristic Digital Sequences.

Due to the growing use of composite materials in aircraft structures, Aircraft Smart Composite Skins (ASCSs) which have the capability of impact monitoring for large-scale composite structures need to be developed. However, the impact of an aircraft composite structure is a random transient event that needs to be monitored on-line continuously. Therefore, the sensor network of an ASCS and the corresponding impact monitoring system which needs to be installed on the aircraft as an on-board device must meet the requirements of light weight, low power consumption and high reliability. To achieve this point, an Impact Region Monitor (IRM) based on piezoelectric sensors and guided wave has been proposed and developed. It converts the impact response signals output from piezoelectric sensors into Characteristic Digital Sequences (CDSs), and then uses a simple but efficient impact region localization algorithm to achieve impact monitoring with light weight and low power consumption. However, due to the large number of sensors of ASCS, the realization of lightweight sensor network is still a key problem to realize an applicable ASCS for on-line and continuous impact monitoring. In this paper, three kinds of lightweight piezoelectric sensor networks including continuous series sensor network, continuous parallel sensor network and continuous heterogeneous sensor network are proposed. They can greatly reduce the lead wires of the piezoelectric sensors of ASCS and they can also greatly reduce the monitoring channels of the IRM. Furthermore, the impact region localization methods, which are based on the CDSs and the lightweight sensor networks, are proposed as well so that the lightweight sensor networks can be applied to on-line and continuous impact monitoring of ASCS with a large number of piezoelectric sensors. The lightweight piezoelectric sensor networks and the corresponding impact region localization methods are validated on the composite wing box of an unmanned aerial vehicle. The accuracy rate of impact region localization is higher than 92%.

A feature extraction method for deformation analysis of large-scale composite structures based on TLS measurement

How to obtain a three-dimensional (3D) model efficiently and extract the feature information of larger-scale composite structures, such as tunnels, accurately is a significant issue in the field of health monitoring. Therefore, an effective method based on TLS measurement is proposed and developed using surface-based non-destructive technology.In this paper, terrestrial laser scanning (TLS) technology is adopted to investigate the tunnel structure, focusing on the extraction of the characteristic section and central curve, which could be applied in deformation monitoring. Point cloud data from TLS measurement is processed in four steps: section extraction, section projection, calculation of central points and curve approximation.The innovation of this paper lies in the projection and iterative filtering of the ring data and rasterization of the point clouds for vertical and horizontal lines. The random sample consensus (RANSAC) algorithm is implemented to approximate the vertical and horizontal lines. The central curve, approximated from the central points, agrees with the general design model and the accuracy falls within the millimeter range.

Wind Turbine Blade Damage Detection Using Supervised Machine Learning Algorithms

Wind turbine blades undergo high operational loads, experience variable environmental conditions, and are susceptible to failure due to defects, fatigue, and weather-induced damage. These large-scale composite structures are fundamentally enclosed acoustic cavities and currently have limited, if any, structural health monitoring (SHM) in place. A novel acoustics-based structural sensing and health monitoring technique is developed, requiring efficient algorithms for operational damage detection of cavity structures. This paper describes the selection of a set of statistical features for acoustics-based damage detection of enclosed cavities, such as wind turbine blades, as well as a systematic approach used in the identification of competent machine learning algorithms. Logistic regression (LR) and support vector machine (SVM) methods are identified and used with optimal feature selection for decision-making via binary classification algorithms. A laboratory-scale wind turbine with hollow composite blades was built for damage detection studies. This test rig allows for testing of stationary or rotating blades, of which time and frequency domain information can be collected to establish baseline characteristics. The test rig can then be used to observe any deviations from the baseline characteristics. An external microphone attached to the tower will be utilized to monitor blade health while blades are internally ensonified by wireless speakers. An initial test campaign with healthy and damaged blade specimens is carried out to arrive at several conclusions on the detectability and feature extraction capabilities required for damage detection.

Thermal Diffusivity Mapping of Graphene Based Polymer Nanocomposites

Nanoparticle dispersion is widely recognised as a challenge in polymer nanocomposites fabrication. The dispersion quality can affect the physical and thermomechanical properties of the material system. Qualitative transmission electronic microscopy, often cumbersome, remains as the ‘gold standard’ for dispersion characterisation. However, quantifying dispersion at macroscopic level remains a difficult task. This paper presents a quantitative dispersion characterisation method using non-contact infrared thermography mapping that measures the thermal diffusivity (α) of the graphene nanocomposite and relates α to a dispersion index. The main advantage of the proposed method is its ability to evaluate dispersion over a large area at reduced effort and cost, in addition to measuring the thermal properties of the system. The actual resolution of this thermal mapping reaches 200 µm per pixel giving an accurate picture of graphene nanoplatelets (GNP) dispersion. The post-dispersion treatment shows an improvement in directional thermal conductivity of the composite of up to 400% increase at 5 wt% of GNP. The Maxwell-Garnet effective medium approximation is proposed to estimate thermal conductivity that compare favourably to measured data. The development of a broadly applicable dispersion quantification method will provide a better understanding of reinforcement mechanisms and effect on performance of large scale composite structures.

A Multi-Response-Based Wireless Impact Monitoring Network for Aircraft Composite Structures

Composite structures may be subjected to internal damages caused by impact events, which can seriously degrade the property of the structures. Hence, impact monitoring is of great importance to ensure the applications of composite, especially in aerospace engineering. This paper puts forward, for the first time, a new multi-response-based wireless sensor network (WSN) to realize the large-scale impact monitoring with the weight and complexity of the monitoring system reduced greatly. A novel multi-response-based global impact localization method that can unite multiple leaf nodes to solve the problems of localization confliction and mid-region localization is proposed for the WSN. Evaluations performed on large-scale complex composite structures have shown the advantages of the presented new methods.

Impact localization by a multi-radio sink–based wireless sensor network for large-scale structures

With the rapid development of wireless sensor network technology, more and more researchers have been interested in taking advantages of wireless sensor network to reduce weight and cost and to solve the installing problem of the wired structural health monitoring systems. A number of wireless-based structural health monitoring methods have been developed over the years to ensure the safety of large-scale structures. However, little research has been reported on wireless impact monitoring of large-scale composite structures due to the limitations of ordinary monitoring methods and wireless sensor networks. In this article, a wireless multi-radio sink which can access multiple communication channels is developed and adopted to build an impact monitoring wireless sensor network. Besides, a corresponding network architecture with an energy-weighted factor–based localization method adopted is presented to enable impact localization within the whole monitoring scope of the network. To verify the performance of the impact monitoring wireless sensor network, the experiments are performed on complex aircraft composite structures and the experimental results prove the effectiveness of the proposed wireless sensor network.

A framework for design and optimization of tapered composite structures. Part I: From individual panel to global blending structure

A novel framework to design tapered composite structures is proposed for buckling analysis with multiple manufacturing constraints, with variables dealt with separately at different levels and manufacturing constraints divided and imposed in each step. The framework is based on two techniques – a sequential permutation table (SPT) method and a global shared-layer blending (GSLB) method; a design concept is extending from an individual panel to the overall structure by improving its blending property. A problem to design an 18-panel tapered composite structure is adopted to study and validate the proposed framework; a detailed design process is executed step by step to demonstrate the improvements of blending property. Multiple manufacturing constraints are analyzed for the obtained optimal solution, which is also compared with previous studies. The high efficiency of the proposed framework implies its potential for design of large-scale composite structures with complex manufacturing constraints.