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.
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.
Many signals of interest in the assessment of structural systems lie in the quasi-static range (frequency << 1Hz). This
poses a significant challenge for the development of self-powered sensors that are required not only to monitor these
events but also to harvest the energy for sensing, computation and storage from the signal being monitored. This paper
combines the use of mechanically-equivalent frequency modulators and piezo-powered threshold detection modules
capable of computation and data storage with a total current less than 10nA. The system is able to achieve events
counting for input deformations at frequencies lower than 0.1Hz. The used mechanically-equivalent frequency
modulators allow the transformation of the low-amplitude and low-rate quasi-static deformations into an amplified input
to a piezoelectric transducer. The sudden transitions in unstable mode branch switching, during the elastic postbuckling
response of slender columns and plates, are used to generate high-rate deformations. Experimental results show that an
oscillating semi-crystalline plastic polyvinylidene fluoride (PVDF), attached to the up-converting modules, is able to
generate a harvestable energy at levels between 0.8μJ to 2μJ. In this work, we show that a linear injection response of
our combined frequency up-converter / piezo-floating-gate sensing system can be used for self-powered measurement
and recording of quasi-static deformations levels. The experimental results demonstrate that a sensor fabricated in a 0.5-
μm CMOS technology can count and record the number of quasi-static input events, while operating at a power level
significantly lower than 1μW.
Harvested vibration energy is typically considered as an alternative power source for sensors' networks for health and
usage monitoring in civil and mechanical structures. The longevity, and hence the efficacy, of these sensors is severely
limited by the levels of generated power. Piezoelectric vibration harvesters have been widely used given their energy
conversion ability and relatively high mechanical to electrical coupling properties. Several techniques can be applied to
improve these properties and to cancel external environmental effects such as temperature variations. In this paper, the
temperature compensation of the response characteristics of a bimorph cantilever Lead zirconate titanate (PZT)
piezoelectric beam, through a combination with shape memory alloys, is studied. A mathematical model, based on onedimensional
linear piezoelectricity equations and one dimensional constitutive behavior of shape memory alloys, is
derived. The model describes the effect of temperature deviations on the theoretical harvestable energy levels as well as
the compensation methodology. Proof of concept experimental results are also presented. The voltage response transfer
functions are measured at different temperatures to show the induced effect by shape memory alloys.
Many signals of interest in structural engineering, for example seismic activity, lie in the infrasonic range
(frequency less than 20Hz). This poses a significant challenge for developing self-powered structural health
monitoring sensors that are required not only to monitor rare infrasonic events but also to harvest the energy
for sensing, computation and storage from the signal being monitored. In this paper, we show that a linear
injection response of our previously reported piezo-floating-gate sensor is ideal for self-powered sensing and
computation of infrasonic signals. Our experimental results demonstrate that the sensor fabricated in a 0.5-
μm CMOS technology can compute and record level crossing statistics of an input infrasonic event with total
current less than 10nA.
Fatigue and overload of mechanical, civil and aerospace structures remains a major problem that can lead to costly repair
and catastrophic failure. Long term monitoring of mechanical loading for these structures could reduce maintenance
cost, improve longevity and enhance safety. However, the powering of these sensors during the lifetime of the
monitored structure remains a major problem. In this paper we describe an implementation of a novel self-powered
fatigue monitoring sensor. The sensor is based on the integration of piezoelectric transduction with floating gate
avalanche injection. The miniaturized sensor enables self-powered continuous battery free monitoring and time-to-failure
predictions of mechanical and civil structures. Measured results from a fabricated prototype in a 0.5&mgr;m CMOS
process indicate that the device can compute cumulative statistics of electrical signals generated by the piezoelectric
transducer, while consuming less that one microwatt of power. Furthermore, the sensor is capable of storing this
information in non-volatile memory which makes it an attractive alternative when the converted electrical energy levels
are low due to small mechanical force inputs. The current microchip is less than 2 square millimeters in area. The non
volatile memory storage is coupled to a radio frequency (RF) identification microchip which allows the sensor to be
interrogated asynchronously through a RF reader. We are currently developing a state vector machine (SVM), neural
network based hardware to be included on the microchip. The SVM hardware will enable low-power processing and
computation of the incoming mechanical loading cycle data.
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