The surge in demand for high-energy-density lithium-ion batteries has led to the exploration of high-C (high current draw) discharges in various applications. However, these high-C discharges introduce significant challenges related to battery performance and safety. This exploratory study aims to investigate early current interrupt device failure detection mechanisms in 18650 lithium-ion batteries subjected to discharges up to 16C. Our controlled experimental setup induces a 40 amp discharge to a single lithium nickel cobalt aluminum oxide 18650 cell. Employing digital image correlation techniques, the structural changes in the battery are monitored during discharge, pinpointing subtle deformations and strain patterns as potential precursors to failure. This data, coupled with voltage and temperature measurements, offer a more comprehensive understanding of the battery performance under extreme conditions, allowing for future methods to further enhance safety protocols for high-C discharge.
Acoustic emission (AE) monitoring technique is a well-known approach in the field of NDE/SHM. AE monitoring from the defect formation and failure in the materials were well studied by the researchers. However, conventional AE monitoring techniques are predominantly based on statistical analysis. In this study we focus on understanding the AE waveforms from the fatigue crack growth using physics based approach. The growth of the fatigue crack causes the acoustic emission in the material that propagates in the structure. One of the main challenges of this approach is to develop the physics based understanding of the AE source itself. The acoustic emission happens not only from the crack growth but also from the interaction of the crack lips during fatigue loading of the materials. As the waveforms are generated from the AE event, they propagate and create local vibration modes along the crack faces. Fatigue experiments were performed to generate the fatigue cracks. Several test specimens were used in the fatigue experiments and corresponding AE waveforms were captured. The AE waveforms were analyzed and distinguished into different groups based on the similar nature on both time domain and frequency domain. The experimental results are explained based on the physical observation of the specimen.
The increasing number, size, and complexity of nuclear facilities deployed worldwide are increasing the need to maintain readiness and develop innovative sensing materials to monitor important to safety structures (ITS). In the past two decades, an extensive sensor technology development has been used for structural health monitoring (SHM). Technologies for the diagnosis and prognosis of a nuclear system, such as dry cask storage system (DCSS), can improve verification of the health of the structure that can eventually reduce the likelihood of inadvertently failure of a component. Fiber optical sensors have emerged as one of the major SHM technologies developed particularly for temperature and strain measurements. This paper presents the development of optical equipment that is suitable for ultrasonic guided wave detection for active SHM in the MHz range. An experimental study of using fiber Bragg grating (FBG) as acoustic emission (AE) sensors was performed on steel blocks. FBG have the advantage of being durable, lightweight, and easily embeddable into composite structures as well as being immune to electromagnetic interference and optically multiplexed. The temperature effect on the FBG sensors was also studied. A multi-channel FBG system was developed and compared with piezoelectric based AE system. The paper ends with conclusions and suggestions for further work.
Acoustic emission (AE) caused by the growth of fatigue crack were well studied by researchers. Conventional approaches predominantly are based on statistical analysis. In this study we focus on identifying geometric features of the crack from the AE signals using physics based approach. One of the main challenges of this approach is to develop a physics of materials based understanding of the generation and propagation of acoustic emissions due to the growth of a fatigue crack. As the geometry changes due to the crack growth, so does the local vibration modes around the crack. Our aim is to understand these changing local vibration modes and find possible relation between the AE signal features and the geometric features of the crack. Finite element (FE) analysis was used to model AE events due to fatigue crack growth. This was done using dipole excitation at the crack tips. Harmonic analysis was also performed on these FE models to understand the local vibration modes. Experimental study was carried out to verify these results. Piezoelectric wafer active sensors (PWAS) were used to excite cracked specimen and the local vibration modes were captured using laser Doppler vibrometry. The preliminary results show that the AE signals do carry the information related to the crack geometry.
A mechanical resonant piezo-optical ring sensor was studied, designed to selectively enhance the response of piezoelectric wafer active sensors (PWAS) and fiber Bragg grating (FBG) sensors. The frequency characteristics of the ring sensor were modeled through modal and harmonic analyses. The models were used to guide the experimentation, serving as a basis for comparison and implementation. Pitch-catch, resonance, and acoustic emission (AE) experiments were performed to compare the performance of the ring sensor to plate-mounted PWAS and FBG. Factors relating to optimal in-service implementation, particularly symmetric placement of FBG and PWAS, were investigated. It was found that the ring sensor was capable of amplifying an incoming Lamb wave signal. This was applied to AE experiments, where selective frequencies were amplified such that the time-domain signal had a larger amplitude response.
Interim storage of spent nuclear fuel from reactor sites has gained additional importance and urgency for resolving waste-management-related technical issues. In total, there are over 1482 dry cask storage system (DCSS) in use at US plants, storing 57,807 fuel assemblies. Nondestructive material condition monitoring is in urgent need and must be integrated into the fuel cycle to quantify the “state of health”, and more importantly, to guarantee the safe operation of radioactive waste storage systems (RWSS) during their extended usage period. A state-of-the-art nuclear structural health monitoring (N-SHM) system based on in-situ sensing technologies that monitor material degradation and aging for nuclear spent fuel DCSS and similar structures is being developed. The N-SHM technology uses permanently installed low-profile piezoelectric wafer sensors to perform long-term health monitoring by strategically using a combined impedance (EMIS), acoustic emission (AE), and guided ultrasonic wave (GUW) approach, called "multimode sensing", which is conducted by the same network of installed sensors activated in a variety of ways. The system will detect AE events resulting from crack (case for study in this project) and evaluate the damage evolution; when significant AE is detected, the sensor network will switch to the GUW mode to perform damage localization, and quantification as well as probe "hot spots" that are prone to damage for material degradation evaluation using EMIS approach. The N-SHM is expected to eventually provide a systematic methodology for assessing and monitoring nuclear waste storage systems without incurring human radiation exposure.
Experimental results on the propagation of guided waves through a bolted joint under various bolt load values are presented. Piezoelectric wafer active sensor (PWAS) transducers are used for the generation and reception of the guided waves. Two specimen types are used, a strip lap joint and a plate lap joint. The signals measured under various bolt load values and frequency values are studied in order to identify relevant features that change drastically with bolt load. We found that some of the signals, especially those at S0 tuning frequency of 320 kHz, were very much simplified by the change from strip to plate conditions. The main contribution of this paper to the advancement of the state of the art consists in highlighting the need for removing the confounding effects of the strip side reflections on the correct interpretation of guided wave changes as they travel through a lap joint with various fastener loads.
The impregnated active carbon used in air purification systems degrades over time due to exposure to contamination and
mechanical effects (packing, settling, flow channeling, etc.). A novel approach is proposed to detect contamination in
active carbon filters by combining the electromechanical impedance spectroscopy (EMIS) and electrochemical
impedance spectroscopy (ECIS). ECIS is currently being used to evaluate active carbon filtration material; however, it
cannot differentiate the impedance changes due to chemical contamination from those due to mechanical changes. EMIS
can detect impedance changes due to mechanical changes. For the research work presented in this paper, Piezoelectric
wafer active sensor (PWAS) was used for the EMIS method. Some remarkable new phenomena were unveiled in the
detection of carbon filter status.
1. PWAS EMIS can detect the presence of contaminants, such as water and kerosene in the carbon bed
2. PWAS EMIS can monitor changes in mechanical pressure that may be associated with carbon bed packing,
settling and flow channeling
3. EMIS and ECIS measurements are consistent with each other and complimentary
A tentative simplified impedance model was created to simulate the PWAS-carbon bed system under increasing
pressure. Similar impedance change pattern was observed when comparing the simulation results with experimental
data.
Embedded ultrasonic structural radar (EUSR) algorithm is developed for using piezoelectric wafer active sensor (PWAS) array to detect defects within a large area of a thin-plate specimen. Signal processing techniques are used to extract the time of flight of the wave packages, and thereby to determine the location of the defects with the EUSR algorithm. In our research, the transient tone-burst wave propagation signals are generated and collected by the embedded PWAS. Then, with signal processing, the frequency contents of the signals and the time of flight of individual frequencies are determined. This paper starts with an introduction of embedded ultrasonic structural radar algorithm. Then we will describe the signal processing methods used to extract the time of flight of the wave packages. The signal processing methods being used include the wavelet denoising, the cross correlation, and Hilbert transform. Though hardware device can provide averaging function to eliminate the noise coming from the signal collection process, wavelet denoising is included to ensure better signal quality for the application in real severe environment. For better recognition of time of flight, cross correlation method is used. Hilbert transform is applied to the signals after cross correlation in order to extract the envelope of the signals. Signal processing and EUSR are both implemented by developing a graphical user-friendly interface program in LabView. We conclude with a description of our vision for applying EUSR signal analysis to structural health monitoring and embedded nondestructive evaluation. To this end, we envisage an automatic damage detection application utilizing embedded PWAS, EUSR, and advanced signal processing.
Active sensor wave propagation technique is a relatively new method for in-situ nondestructive evaluation (NDE). Elastic waves propagating in material carry the information of defects. These information can be extracted by analyzing the signals picked up by active sensors. Due to the physical property of wave propagation, large area can be interrogated by a few transducers. This simplifies the process of detecting and characterizing defects. To apply this method, efficient numerical modeling is required to predict signal amplitude and time history of elastic wave scattering and diffraction. In order to construct the model, good understanding of these physical phenomena must be achieved. This paper presents results of an investigation of the applicability of active sensors for in-situ health monitoring of aging aircraft structures. The project set forth to develop non-intrusive active sensors that can be applied on existing aging aerospace structures for monitoring the onset and progress of structural damage such as fatigue cracks and corrosion. Wave propagation approach was used for large area detection. In order to get the theoretical solution of elastic wave propagating in the material, wave functions of axial wave, share wave, flexure wave, Raleigh wave, and Lamb waves were thoroughly investigated. The wave velocities and the motion of these different types of waves were calculated and simulated using mathematical analysis programs. Finite Element Method was used to simulate and predict the wave propagating through the structure for different excitation and boundary conditions. Aluminum beams and plates were used to get experiment results. Structures both pristine and with known defects are used in our investigation. The experimental results were then compared with the theoretical results.
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