Recently, the development of hydrophobic nanoporous technologies has drawn increased attention, especially for the applications of energy absorption and impact protection. Although significant amount of research has been conducted to synthesis and characterize materials to protect structures from impact damage, the tradition methods focused on converting kinetic energy to other forms, such as heat and cell buckling. Due to their high energy absorption efficiency, hydrophobic nanoporous particle liquids (NPLs) are one of the most attractive impact mitigation materials. During impact, such particles directly trap liquid molecules inside the non-wetting surface of nanopores in the particles. The captured impact energy is simply stored temporarily and isolated from the original energy transmission path. In this paper we will investigate the energy absorption efficiency of combinations of silica nanoporous particles and with multiple liquids. Inorganic particles, such as nanoporous silica, are characterized using scanning electron microscopy. Small molecule promoters, such as methanol and ethanol, are introduced to the prepared NPLs. Their effects on the energy absorption efficiency are studied in this paper. NPLs are prepared by dispersing the studied materials in deionized water. Energy absorption efficiency of these liquids are experimentally characterized using an Instron mechanical testing frame and in-house develop stainless steel hydraulic cylinder system.
Polymer matrix composites (PMCs) are ubiquitous in engineering applications due to their superior mechanical properties at low weight. However, they are susceptible to damage due to their low interlaminar mechanical properties and poor heat and charge transport in the transverse direction to the laminate. Moreover, methods to inspect and ensure the reliability of composites are expensive and labor intensive. Recently, mechanophore-based smart polymer has attracted significant attention, especially for self-sensing of matrix damage in PMCs. A cyclobutane-based self-sensing approach using 1,1,1-tris (cinnamoyloxymethyl) ethane (TCE) and poly (vinyl cinnamate) (PVCi) has been studied in this paper. The self-sensing function was investigated at both the polymer level and composite laminate level. Fluorescence emissions were observed on PMC specimens subjected to low cycle fatigue load, indicating the presence of matrix cracks. Results are presented for graphite fiber reinforced composites.
Impact damage has been identified as a critical form of defect that constantly threatens the reliability of composite structures, such as those used in aircrafts and naval vessels. Low energy impacts can introduce barely visible damage and cause structural degradation. Therefore, efficient damage detection and risk assessment methods, which can accurately detect, quantify, and localize impact damage in complex composite structures, are required. In this paper a novel damage detection methodology is demonstrated for monitoring and quantifying the impact damage propagation. Statistical outlier analysis, composed of features extracted from the time and frequency domains, are developed. Autoregression with exogenous is used to classify the statistical feature and estimate the structural risk. The developed methodology has been validated using low velocity impact experiments with a sandwich composite wing.
Carbon fiber reinforced composites are used in a wide range of applications in aerospace, mechanical, and civil structures. Due to the nature of material, most damage in composites, such as delaminations, are always barely visible to the naked eye, which makes it difficult to detect and repair. The investigation of biological systems has inspired the development and characterization of self-healing composites. This paper presents the development of a new type of self-healing material in order to impede damage progression and conduct in-situ damage repair in composite structures. Carbon nanotubes, which are highly conductive materials, are mixed with shape memory polymer to develop self-healing capability. The developed polymeric material is applied to carbon fiber reinforced composites to automatically heal the delamination between different layers. The carbon fiber reinforced composite laminates are manufactured using high pressure molding techniques. Tensile loading is applied to double cantilever beam specimens using an MTS hydraulic test frame. A direct current power source is used to generate heat within the damaged area. The application of thermal energy leads to re-crosslinking in shape memory polymers. Experimental results showed that the developed composite materials are capable of healing the matrix cracks and delaminations in the bonded areas of the test specimens. The developed self-healing material has the potential to be used as a novel structural material in mechanical, civil, aerospace applications.
The development of structural health monitoring techniques leads to the integration of sensing capability within
engineering structures. This study investigates the application of multi walled carbon nanotubes in polymer matrix
composites for autonomous damage detection through changes in electrical resistance. The autonomous sensing
capabilities of fiber reinforced nanocomposites are studied under multiple loading conditions including tension loads.
Single-lap joints with different joint lengths are tested. Acoustic emission sensing is used to validate the matrix
crack propagation. A digital image correlation system is used to measure the shear strain field of the joint area. The
joints with 1.5 inch length have better autonomous sensing capabilities than those with 0.5 inch length. The
autonomous sensing capabilities of nanocomposites are found to be sensitive to crack propagation and can
revolutionize the research on composite structural health management in the near future.
A methodology based on Lamb wave analysis and time-frequency signal processing has been developed for
damage detection and structural health monitoring of composite structures. Because the Lamb wave signals
are complex in nature, robust signal processing techniques are required to extract damage features. In this
paper, Lamb wave mode conversion is used to detect the damage in composite structures. Matching pursuit decomposition algorithm is used to represent each Lamb wave mode in the time-frequency domain. Results from numerical Lamb wave propagation simulations and experiments using orthotropic composite plate structures are presented. The capability of the proposed algorithm is demonstrated by detecting seeded delaminations in the composite plate samples. The advantages of the methodology include accurate time-frequency resolution, robustness to noise, high computational efficiency and ease of post-processing.
Prognostic algorithms indicate the remaining useful life based on fault detection and diagnosis through condition
monitoring framework. Due to the wide-spread applications of advanced composite materials in industry, the
importance of prognosis on composite materials is being acknowledged by the research community. Prognosis has
the potential to significantly enhance structural monitoring and maintenance planning. In this paper, a Gaussian
process based prognostics framework is presented. Both off-line and on-line methods combined state estimation and
life prediction of composite beam subject to fatigue loading. The framework consists of three main steps: 1) data
acquisition, 2) feature extraction, 3) damage state prediction and remaining useful life estimation. Active
piezoelectric and acoustic emission (AE) sensing techniques are applied to monitor the damage states. Wavelet
transform is used to extract the piezoelectric sensing features. The number of counts from AE system was used as a
feature. Piezoelectric or AE sensing features are used to build the input and output space of the Gaussian process.
The future damage states and remaining useful life are predicted by Gaussian process based off-line and on-line
algorithms. Accuracy of the Gaussian process based prognosis method is improved by including more training sets.
Piezoelectric and AE features are also used for the state prediction. In the test cases presented, the piezoelectric
features lead to better prognosis results. On-line prognosis is completed sequentially by combining experimental and
predicted features. On-line damage state prediction and remaining useful life estimation shows good correlation with
experimental data at later stages of fatigue life.
The use of monolithic piezoceramic materials in sensing and actuation applications has become quite common over the
past decade. However, these materials have several properties that limit their application in practical systems. These
materials are very brittle due to the ceramic nature of the monolithic material, making them vulnerable to accidental
breakage during handling and bonding procedures. In addition, they have very poor ability to conform to curved
surfaces and result in large add-on mass associated with using a typically lead-based ceramic. These limitations have
motivated the development of alternative methods of applying the piezoceramic material, including piezoceramic fiber
composites (PFCs), and piezoelectric paints. Piezoelectric paint is desirable because it can be spayed or painted on and
can be used with abnormal surfaces. The ease at which the active composite can be applied allows for far larger surfaces
to be used for energy harvesting than can be achieved with typical materials. Developments in piezoelectric
nanocomposites for energy harvesting will also allow for the development of compliant materials with electromechanical
coupling greater than available through existing piezoelectric polymers such as polyvinylidene floride (PVDF).
Furthermore, the application of PVDF is limited to thin films due to the straining process required to obtain piezoelectric
phase of the material. However, active nanocomposites can be molded into geometries that could not be obtained using
currently available materials. The present study will characterize a variety of piezoelectric nanocomposite materials to
determine how the properties of the polymer matrix and the piezoelectric inclusion affect the energy harvesting
performance. The resulting active nanocomposites will be compared to existing piezo-polymers for power harvesting.