KEYWORDS: Metamaterials, Logic devices, Circuit switching, Digital electronics, Design and modelling, Data processing, Energy harvesting, Computing systems, Materials properties, Signal processing
Active mechanical metamaterials have shown a glimpse of their capacity to create the foundation for intelligent matter. This study presents the concept of mechanical metamaterial electronics (meta-mechanotronics) to design intelligent matter with information processing capability. This advanced functionality is achieved by fusing the mechanical metamaterials, digital electronics and nano energy harvesting technologies. Electronic mechanical metamaterials explored under the meta-mechanotronics paradigm rely merely on their constituent components to perform self-powered mechanical-electrical-logic operations. A proof-of-concept digital unit cell is presented as the 2-bit building block for electronic mechanical metamaterials. The digital unit cell is rationally designed as a monostable origami-inspired metamaterial with twist buckling behavior and specific multi-motion properties to synthesize discrete mechanical configurations and realize digital logic gates. Experimental studies are performed to evaluate the digital computing performance of the designed mechanical metamaterial logic gate.
In recent years, significant research efforts have been dedicated to the development and application of functionally graded materials (FGMs) in the control and manipulation of engineered materials and structures. This study proposes an analytical investigation of the postbuckling behavior of a multi-direction anisotropic FGMs beam subjected to bilateral constraints. The FGMs beam consists of two isotopic layers and is assumed to be graded in the x, y and z directions. Theoretical models are developed to examine the force-displacement relations and the postbuckling shape configurations of the FGMs. Two elastic moduli (i.e., following polynomial and trigonometric functions) are considered to obtain the desired stored potential energy under static axial compressive loading. Here, the FGMs beam’s behavior is represented by a fourth order nonlinear partial differential equation, while the energy minimization technique is employed to solve the governing equation of the mathematical model. Furthermore, the Nelder-mead algorithm and parallel Kernel configuration are used to determine the minimum energy paths of the deformed elastic beam, along with the corresponding snap through events. We compared the proposed model to existing studies in literature, and satisfactory agreements were obtained. Moreover, parametric studies are carried out to assess the influence of varying the material properties (i.e., volume fraction) on the tunable FGMs beam. The results revealed that the material distribution function has a significant effect on the postbuckling response of FGMs beam. Also, the results showed that optimizing material functions lead to better controllability over the FGM beams. The approach presented in this study provides a promising strategy to exploit the performance of FGMs, predicting and maneuvering the postbuckling response for advanced technological devices.
In this study, we explore the postbuckling instability of piezoelectric-integrated cylinders under axial displacement for energy harvest applications. Experiments are conducted using 3D printed cylinders with piezoelectric transducers bonded on their outer and inner surfaces. The local and global postbuckling responses of the cylinders are triggered based on their design and geometry. Numerical simulations are carried out to study the effect of varying cylinder geometries on the harvested energy. A comparative study is performed between the numerical and experimental results. Furthermore, a corrugated design is proposed to tailor the postbuckling response of the cylinders from local buckling to global buckling. The results shows that the new corrugated designs for the cylinders improves the energy harvesting efficiency.
Triboelectric nanogenerators have received significant research attention in recent years. The energy harvesting performance of triboelectric nanogenerator can be enhanced via more efficient structural and material designs. Here, we develop novel magnetic capsulate triboelectric nanogenerator (MC-TENG) devices to harvest electrical energy under various external excitations. MC-TENG uses a magnetic oscillation system to guide oscillating dielectric capsules within a conductive shield. Steel spring connectors are then utilized to maximize the oscillations and higher power density. Experimental studies is conducted to investigate the electrical performance of MC-TENG under cyclic loading. The output power of the developed nanogenerators reached 400 μW. The proposed MC-TENG concept provides an effective method to harvest electrical energy from low-frequency and low-amplitude oscillations.
The next generation of materials needs to be adaptive, multifunctional and tunable. This goal can be achieved by metamaterials that enable development of advanced artificial materials with novel functionalities. There is arguably a critical shortage in research needed to engineer new aspects of intelligence into the texture of metamaterials for multifunctional applications. The goal of this study is to create a new generation of multifunctional composite mechanical metamaterials called self-aware composite mechanical metamaterial (SCMM) with complex internal structures toward achieving self-sensing and self-powering functionalities. We develop finely tailored and seamlessly integrated microstructures composed of topologically different topologically materials to form a hybrid sensor and nanogenerator mechanical metamaterial system. Experimental studies are conducted to understand the mechanical and electrical behavior of the multifunctional SCMM systems. We highlight how introducing the self-sensing and self-powering functionality into the material design could in theory lay the foundation for living engineered materials and structures that can sense, empower and program themselves using their constituent components.
LIDAR on a silicon chip holds strong potentials for LIDAR system solutions featuring low cost, small size, and high robustness. In line with this effort, on-chip circulators are of great interest as they bring significant benefit for system complexity reduction and SNR improvement by enabling the LIDAR transmitter and receiver to share a single common aperture. Here, we present our recent study on passive silicon photonics nonlinear switches as conditional circulators for LIDAR applications. We propose a device implementation to address the nonlinear switch working principle by controlling waveguide nonlinear coefficient using sub-wavelength gratings. This implementation is foundry-compatible using only regular passive silicon waveguide components and are fully demonstrated in the experiment. In addition, we propose a sub-splitting coupler-based switch potentially can achieve a better fabrication tolerance than sub-wavelength grating-based switch. This work builds up signal processing functions in silicon photonics technology for optical communication and sensing applications. In particular, for LIDAR applications, this work contributes to the critical components of important use, and the easy integration with other existing functions such as optical phased arrays and spectral filters pronounces the potential for LIDAR on a silicon chip.
Artificial intelligence has the capacity to open new opportunities and potentials for engineering education. Artificial intelligence in education has undergone several paradigmatic shifts in its brief history. This study investigates the advent, development and future trends of artificial intelligence-based smart engineering education (AIED-Eng). Particular focus is placed on major paradigms in AIED-Eng including leaner-receiver, learner-partner and learner-center. The artificial intelligence techniques applied to these paradigms are evaluated. Computer-based tools enabling the engineering education paradigms are summarized. Further discussion is presented about the key role of artificial intelligence in improving smart engineering education as a guidance for future learning, teaching and design processes
Next generation of smart infrastructure is heavily dependent on distributed sensing technology to monitor the state of urban infrastructure. The smart sensor networks should react in time, establish automated control, and collect information for intelligent decision making. In this paper, we highlight our interdisciplinary research to address three main technical challenges related to smart infrastructure: (1) development of smart wireless sensors for civil infrastructure monitoring, (2) finding an innovative, cost-effective and sustainable energy resource for empowering heterogeneous, wireless sensor networks, and (3) designing advanced data analysis frameworks for the interpretation of the information provided by these emerging monitoring systems. More specifically, we focus on development of a self-powered piezo-floating-gate (PFG) sensor that uses only self-generated electrical energy harvested by piezoelectric transducers directly from a structure under vibration. The performance of this sensing technology is discussed for different civil infrastructure systems with complex behavior. Subsequently, the proposed data interpretation systems integrating deterministic, machine learning and statistical methods are reviewed. We outline our thoughtful vision for the proposed framework to serve as an integral part of future smart civil infrastructure, which will be capable of self-charging and the self-diagnosis of damage well in advance of the occurrence of failure.
KEYWORDS: Bridges, Measurement devices, Structural health monitoring, Signal generators, Temperature metrology, Transducers, 3D modeling, Thermal effects, Sensors
This study proposes a novel multistable mechanism to detect thermal limits though harvesting energy from thermally induced deformation. A detecting device is developed consisting of a bilaterally constrained beam equipped with a piezoelectric polyvinylidene fluoride (PVDF) transducer. Under thermally induced displacement, the bilaterally confined beam is buckled. The post-buckling response is deployed to convert low-rate and low-frequency excitations into high-rate motions. The attached PVDF transducer harvests the induced energy and converts it to electrical signals, which are later used to measure the thermal limits. The efficiency of the proposed method was verified through a numerical study on a prestressed concrete bridge. To this aim, finite element simulations were conducted to obtain the thermally induced deformation of the bridge members between the deck and girder. In addition, an experimental study was carried out on a 3D printed measuring device to simulate the thermal loading of bridge. In this phase, the correlation between the electrical signals generated by the PVDF film and the corresponding deck-girder displacement was investigated. Based on the results, the proposed method effectively measures the mechanical response of concrete bridges under thermal loading.
KEYWORDS: Energy harvesting, Beam shaping, Beam analyzers, Systems modeling, Energy efficiency, Transducers, Sensors, Energy conversion efficiency, Ferroelectric polymers, Structural health monitoring
Systems based on post-buckled structural elements have been extensively used in many applications such as actuation, remote sensing and energy harvesting thanks to their efficiency enhancement. The post-buckling snap- through behavior of bilaterally constrained beams has been used to create an efficient energy harvesting mechanism under quasi-static excitations. The conversion mechanism has been used to transform low-rate and low-frequency excitations into high-rate motions. Electric energy can be generated from such high-rate motions using piezoelectric transducers. However, lack of control over the post-buckling behavior severely limits the mechanism’s efficiency. This study aims to maximize the levels of the harvestable power by controlling the location of the snapping point along the beam at different buckling transitions. Since the snap-through location cannot be controlled by tuning the geometry properties of a uniform cross-section beam, non-uniform cross sections are examined. An energy-based theoretical model is herein developed to predict the post-buckling response of non-uniform cross-section beams. The total potential energy is minimized under constraints that represent the physical confinement of the beam between the lateral boundaries. Experimentally validated results show that changing the shape and geometry dimensions of non- uniform cross-section beams allows for the accurate control of the snap-through location at different buckling transitions. A 78.59% increase in harvested energy levels is achieved by optimizing the beam’s shape.
Development of fatigue cracking is affecting the structural performance of many of welded steel bridges in the United States. This paper presents a support vector machine (SVM) method for the detection of distortion-induced fatigue cracking in steel bridge girders based on the data provided by self-powered wireless sensors (SWS). The sensors have a series of memory gates that can cumulatively record the duration of the applied strain at a specific threshold level. Each sensor output has been characterized by a Gaussian cumulative density function. For the analysis, extensive finite element simulations were carried out to obtain the structural response of an existing highway steel bridge girder (I-96/M- 52) in Webberville, Michigan. The damage states were defined based on the length of the crack. Initial damage indicator features were extracted from the sensor output distribution at different data acquisition nodes. Subsequently, the SVM classifier was developed to identify multiple damage states. A data fusion model was proposed to increase the classification performance. The results indicate that the models have acceptable detection performance, specific ally for cracks larger than 10 mm. The best classification performance was obtained using the information from a group of sensors located near the damage zone.
This paper presents a structural damage identification approach based on the analysis of the data from a hybrid network of self-powered accelerometer and strain sensors. Numerical and experimental studies are conducted on a plate with bolted connections to verify the method. Piezoelectric ceramic Lead Zirconate Titanate (PZT)-5A ceramic discs and PZT-5H bimorph accelerometers are placed on the surface of the plate to measure the voltage changes due to damage progression. Damage is defined by loosening or removing one bolt at a time from the plate. The results show that the PZT accelerometers provide a fairly more consistent behavior than the PZT strain sensors. While some of the PZT strain sensors are not sensitive to the changes of the boundary condition, the bimorph accelerometers capture the mode changes from undamaged to missing bolt conditions. The results corresponding to the strain sensors are better indicator to the location of damage compared to the accelerometers. The characteristics of the overall structure can be monitored with even one accelerometer. On the other hand, several PZT strain sensors might be needed to localize the damage.
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