Carbon nanotube-based composites have been deeply investigated in recent years. CNTs due to their unique physical properties have been employed for various applications in different disciplines of science and engineering. Due to the remarkable electrical conductivity of CNTs, one of practical applications is related to the development of strain sensing smart coatings. Up to now, high sensitivity strain sensors for micro- and macro-scale applications were proposed. However, controlling electro-mechanical properties of those devices is still a challenging issue.<p> </p>In order to facilitate the design process and to investigate multi-domain relationships between sensor's parameters and its properties, numerical models and simulations of CNT-based structures have been carried out with the primary focus on investigation of electrical conductivity for various concentrations of CNTs within the composite material. More accurate and detailed studies include analysis of the influence of deformation on changes in conductivity. However, due to significant microstructural complexity of the system (i.e. large number of CNTs within the structure) multiscale modeling and analysis approach must be employed.<p> </p>The main objective of this paper is to outline an relationships between micro- and macro-structural properties of CNT-based strain sensors and to discuss guidelines for a multiscale electro-mechanical model based on the Representative Volume Element (RVE) concept. The device employs the change in electrical conductivity of a CNT-based nanocomposite under applied deformation. The study investigates the impact of various micro-scale model parameters (e.g. size of an RVE, CNTs parameters etc.), on the macro scale model. Model parameters convergence studies are performed for different geometrical properties of CNTs and for various sizes of RVEs - revealing their critical mutual relationships. Also, the impact of boundary conditions at the micro-scale RVE structure is discussed.
Remarkable electrical properties of carbon nanotubes (CNT) have lead to increased interest in studying CNT- based devices. Many of current researches are devoted to using all kinds of carbon nanomaterials in the con- struction of sensory elements. One of the most common applications is the development of high performance, large scale sensors. Due to the remarkable conductivity of CNT's such devices represent very high sensitivity. However, there are no sufficient tools for studying and designing such sensors. The main objective of this paper is to develop and validate a multiscale numerical model for a carbon nanotubes based sensor. The device utilises the change of electrical conductivity of a nanocomposite material under applied deformation. The nanocomposite consists of a number of CNTs dispersed in polymer matrix. The paper is devoted to the analysis of the impact of spatial distribution of carbon nanotubes in polymer matrix on electrical conductivity of the sensor. One of key elements is also to examine the impact of strain on electric charge ow in such anisotropic composite structures. In the following work a multiscale electro-mechanical model for CNT - based nanocomposites is proposed. The model comprises of two length scales, namely the meso- and the macro-scale for mechanical and electrical domains. The approach allows for evaluation of macro-scale mechanical response of a strain sensor. Electrical properties of polymeric material with certain CNT fractions were derived considering electrical properties of CNTs, their contact and the tunnelling effect.
Acoustic emission is a vital non-destructive testing technique and is widely used in industry for damage detection, localisation and characterization. The latter two aspects are particularly challenging, as AE data are typically noisy. What is more, elastic waves generated by an AE event, propagate through a structural path and are significantly distorted. This effect is particularly prominent for thin elastic plates. In these media the dispersion phenomenon results in severe localisation and characterization issues. Traditional Time Difference of Arrival methods for localisation techniques typically fail when signals are highly dispersive. Hence, algorithms capable of dispersion compensation are sought. This paper presents a method based on the Time - Distance Domain Transform for an accurate AE event localisation. The source localisation is found through a minimization problem. The proposed technique focuses on transforming the time signal to the distance domain response, which would be recorded at the source. Only, basic elastic material properties and plate thickness are used in the approach, avoiding arbitrary parameters tuning.
Acoustic Emission phenomenon is of great importance for analyzing and monitoring health status of critical structural components. In acoustic emission, elastic waves generated by sources propagate through the structure and are acquired by networks of sensors. Ability to accurately locate the event strongly depends on the type of medium (e.g. geometrical features) and material properties, that result in wave signals distortion. These effects manifest themselves particularly in plate structures due to intrinsic dispersive nature of Lamb waves. In this paper two techniques for acoustic emission source localization in elastic plates are compared: one based on a time-domain distance transform and the second one is a two-step hybrid technique. A time-distance domain transform approach, transforms the time-domain waveforms into the distance domain by using wavenumber-frequency mapping. The transform reconstructs the source signal removing distortions resulting from dispersion effects. The method requires input of approximate material properties and geometrical features of the structure that are relatively easy to estimate prior to measurement. Hence, the method is of high practical interest. Subsequently, a two-step hybrid technique, which does not require <i>apriori</i> knowledge of material parameters, is employed. The method requires a setup of two predefined clusters of three sensors in each. The Lamb wave source is localized from the intersection point of the predicted wave propagation directions for the two clusters. The second step of the two-step hybrid technique improves the prediction by minimizing an objective function. The two methods are compared for analytic, simulated and experimental signals.