Dynamic Behavior of Sandwich Beams With Internal Resonators

Dynamic behavior of sandwich beams with internal resonators was investigated. The effect of inserting spring-mass resonators into the sandwich core was theoretically analyzed and it was shown that a wave attenuation bandgap exists due to local resonance. Steady state experiments were used to demonstrate such an attenuation bandgap. Frequency response functions were obtained for a beam with resonators and without resonators. It was shown that insertion of resonators into the core causes a wave attenuation bandgap to open up. The experimental results were verified using finite element simulations. Further experiments were carried out by tuning the resonators at 12 Hz and it was demonstrated that a wave attenuation bandgap can be created at low frequencies which would help attenuate low frequency periodic loads such as those associated with hull slamming. The antiresonance phenomenon was experimentally demonstrated. By inserting local resonators tuned at the first flexural resonance frequency of the beam, it was shown that the excessive vibrations associated with resonance modes can be attenuated by inserting local resonators tuned at the global beam resonance frequency. The behavior of such sandwich beams under impact loads was also considered. Using finite element simulations, the effect of a chosen local resonance frequency on attenuating impact loads was analyzed. The behavior of a chosen internal resonator under different impact loads was also considered. By performing transverse impact experiments, the finite element models were verified and the advantage of using internal resonators in impact loading conditions was demonstrated. The effect of resonator periodicity was analyzed using a phased array method. The propagation constant for a sandwich beam with internal resonators was obtained by treating the resonators as an array of phase shifted forces. It was shown that the resonator periodicity causes Bragg gaps in addition to the local resonance gaps. The effect of resonator parameters on these bandgaps was analyzed and the relationship between the bounding frequencies and the unit cell mode shapes was obtained. The interaction between the local resonance bandgap and the periodicity induced bandgaps was studied. It was shown that a wider combined gap, with a very narrow passband in between, can be obtained by tuning the local resonators at the Bragg gap cut-on frequency

Fiber Length and Orientation in Long Carbon Fiber Thermoplastic Composites

Carbon fiber composites have become popular in aerospace applications because of their lightweight yet strong material properties. The injection molding process can be used to produce discontinuous fiber composites using less time and resources than traditional methods, thereby broadening carbon fiber composites’ applications in different industries. Utilization of longer fibers offers more load carrying capability and superior strength properties for injected molded composites. Since the fiber length and the orientation distribution in Long Fiber Thermoplastics (LFTs) directly affects LFT composites’ material properties, there is a need to study the microstructure of LFTs and characterize fiber length and orientation distributions. Therefore, this work aims to experimentally measure fiber length and orientation in pre-manufactured carbon fiber LFT composites in order to validate computer simulations of the injection molding process, and to therefore better predict mechanical properties. Fiber orientation distribution was measured by the optimization of several grinding and polishing steps followed by microscopic imaging of a sample’s cross-section. On the other hand, fiber length distribution was measured through the development of epoxy burn-off, down-selection, and fiber separation procedures, followed by microscopic imaging and manual fiber length measurements. By specifically optimizing these procedures for the analysis of carbon fiber LFTs, a detailed method has been developed to analyze the fiber length and orientation distributions and quantify any bias in the characterization techniques. Using the methods developed in this work, computer simulations can be validated and microstructure properties can be analyzed, allowing for better material strength predictions and industry implementation of LFTs

Acoustical Properties of Anisotropic Spinodoid Structures

Spinodoid structures, also called spinodoid metamaterials, are non-periodic cellular structures that mimic the spinodal topologies that are observed during diffusion-driven phase separation processes. Spinodoid structures are computationally efficient to model and they can be fabricated using additive techniques; as a result, they offer an attractive way to design multifunctional structures that can simultaneously provide mechanical stiffness and noise reduction performance. In recent work, the normal incidence sound absorption behavior of four distinct spinodoid topologies was studied: isotropic, cubic, columnar, and lamellar. The isotropic and cubic topologies are essentially isotropic in terms of acoustical behavior, and they can be successfully modeled by using the rigid JCA approach, for example. However, in contrast, the columnar and lamellar materials are inherently strongly anisotropic. In the present work, the acoustical properties of printed examples of the latter materials were measured in two different orientations to determine their direction-dependent properties. Those properties were used as input to a transversely isotropic implementation of the Biot theory, which successfully models the two materials. The columnar material, in particular, is inherently locally reacting and may perform well in duct lining applications. In contrast, the lamellar material is non-locally reacting and its surface impedance will therefore be strongly angularly dependent

Programable Sound Absorption Performance Enabled by 3D Printing

Design of acoustic materials can be achieved through the connection between their geometry and acoustical performance. Here, we propose 3D-printing as an enabling technology that allows us to precisely control an acoustic material’s micro-geometry and orientation, which then eliminates microscopic geometry bias due to conventional manufacturing process, thus realizing precise material characterization at the 3D-printing CAD programming stage. This concept was practiced in the current study focusing on 3D-printing fibrous sound absorbing layers. A fused deposition modeling (FDM) method was applied to produce the fibers. A Tarnow-based airflow resistivity model was implemented together with Johnson-Champoux-Allard and Biot theories for modeling the geometry-performance connection for the fibers. The sound absorption prediction accuracy of the model was validated by E-1050 standing wave tube measurements on the printed sample

Acoustical Investigation of Aerogel Granules Modeled as a Layer of Poroelastic Material

Aerogels are defined as mesoporous materials obtained by replacing the liquid phase within a gel by a gaseous phase, typically air. This underlying mesoporous structure provides aerogels unique macrostructural properties such as ultralow density, high transparency, and low thermal conductivity. Given their ultralow density, aerogels are also an attractive, lightweight solution for noise control applications. Recent studies have shown that the acoustical properties of granular aerogels are very different from those observed for other granular media. In this presentation, we present results from our recent investigation of the acoustical behavior of Enova IC3100 aerogel granules. Manufactured and commercially sold by Cabot Corporation, IC3100 aerogels are characterized by their comparatively smaller granule dimensions. Our previous experimental measurements show that the acoustical performance of the IC 3100 aerogel granules differs from conventionally used sound absorption materials; multiple, lightly damped depth resonances with large peak values of absorption coefficients are observed low frequencies. Here, we present results from our attempt to model the acoustical behavior of IC3100 aerogels. The acoustical-related bulk properties required for the Johnson-Champoux-Allard (JCA) model are calculated using an inverse characterization approach. These properties are then used to model the acoustical behavior of the granular aerogel layer using the Biot theory for porous media

Reliable and Congestion Control Protocols for Wireless Sensor Networks

The objective of this paper is to analyze review and different congestion control protocols that are employed at the transport layer and some of them working at the medium access control layer in wireless sensor networks. Firstly, a brief introduction is given about wireless sensor networks and how congestion occurs in such networks. Secondly, the concept of congestion is discussed. Thirdly, the reason of occurrence of congestion in wireless sensor networks is analyzed. After that, congestion control and why it is needed in the wireless sensor networks is discussed. Then, a brief review of different congestion control and reliable data transport mechanisms are discussed. Finally, a comparative analysis of different protocols is made depending on their performance on various parameters such as - traffic direction, energy conservation characteristic, efficiency etc. and the paper is concluded

Hybrid SFNet Model for Bone Fracture Detection and Classification Using ML/DL

An expert performs bone fracture diagnosis using an X-ray image manually, which is a time-consuming process. The development of machine learning (ML), as well as deep learning (DL), has set a new path in medical image diagnosis. In this study, we proposed a novel multi-scale feature fusion of a convolution neural network (CNN) and an improved canny edge algorithm that segregate fracture and healthy bone image. The hybrid scale fracture network (SFNet) is a novel two-scale sequential DL model. This model is highly efficient for bone fracture diagnosis and takes less computation time compared to other state-of-the-art deep CNN models. The innovation behind this research is that it works with an improved canny edge algorithm to obtain edges in the images that localize the fracture region. After that, grey images and their corresponding canny edge images are fed to the proposed hybrid SFNet for training and evaluation. Furthermore, the performance is also compared with the state-of-the-art deep CNN models on a bone image dataset. Our results showed that SFNet with canny (SFNet + canny) achieved the highest accuracy, F1-score and recall of 99.12%, 99% and 100%, respectively, for bone fracture diagnosis. It showed that using a canny edge algorithm improves the performance of CNN