Refractory metals have attracted increasing interest in recent years because of their use in many high-temperature applications. However, the characteristics of these metals calculated using loaded tests (such as tensile strength tests) differ considerably from those calculated using one of the most famous methods in NDT which is called time of flying of the wave (TOF).The present study presents two solutions based on calculating the pressure transmission coefficient (PTC) of the transmitted wave between the test sample and magnesium metal. The first is based on the development of a highly accurate algorithm that lowers the cost by determining the acoustic impedance of the test specimen to calculating mechanical properties. Up to 26 theoretical tests were done (10 of these tests for refractory materials) according to their known mechanical properties to verify the accuracy of the algorithm. The convergence in results ranged from 92% to 99%. The second solution was designed to solve the same problem for specimens with a thickness of less than 1mm. Eight experimental tests were done (five using refractory materials) to verify the accuracy of the second solution, with the convergence in the results ranging from 94% to 97%. The relationships of the Vrms measured from the oscilloscope with the PTC and with the Fourier transform spectrum were derived. The results of this research were closer to the standard mechanical properties for refractory metals compared with several recent acoustic tests.
This work proposes a long range ultrasonic transducers technique in conjunction with an active incremental Support Vector Machine (SVM) classification approach that is used for real-time pipeline defects prediction and condition monitoring. Oil and gas pipeline defects are detected using various techniques. One of the most prevalent techniques is the use of "smart pigs" to travel along the pipeline and detect defects using various types of sensors such as magnetic sensors and eddy-current sensors. A critical short coming of "smart pigs" is the inability to monitor continuously and predict the onset of defects. The emergence of permanently installed long range ultrasonics transducers systems enable continuous monitoring to be achieved. The needs for and the challenges of the proposed technique are presented. The experimental results show that the proposed technique achieves comparable classification accuracy as when batch training is used, while the computational time is decreased, using 56 feature data points acquired from a lab-scale pipeline defect generating experimental rig.
Theoretically, Ultrasound method is an economical and environmentally friendly or "green" technology, which has been of interest for more than six decades for the purpose of enhancement of oil/heavy-oil production. However, in spite of many studies, questions about the effective mechanisms causing increase in oil recovery still existed. In addition, the majority of the mechanisms mentioned in the previous studies are theoretical or speculative. One of the changes that could be recognized in the fluid properties is viscosity reduction due to radiation of ultrasound waves. In this study, a technique was developed to investigate directly the effect of ultrasonic waves (different frequencies of 25, 40, 68 kHz and powers of 100, 250, 500 W) on viscosity changes of three types of oil (Paraffin oil, Synthetic oil, and Kerosene) and a Brine sample. The viscosity calculations in the smooth capillary tube were based on the mathematical models developed from the Poiseuille's equation. The experiments were carried out for uncontrolled and controlled temperature conditions. It was observed that the viscosity of all the liquids was decreased under ultrasound in all the experiments. This reduction was more significant for uncontrolled temperature condition cases. However, the reduction in viscosity under ultrasound was higher for lighter liquids compare to heavier ones. Pressure difference was diminished by decreasing in the fluid viscosity in all the cases which increases fluid flow ability, which in turn aids to higher oil recovery in enhanced oil recovery (EOR) operations. Higher ultrasound power showed higher liquid viscosity reduction in all the cases. Higher ultrasound frequency revealed higher and lower viscosity reduction for uncontrolled and controlled temperature condition experiments, respectively. In other words, the reduction in viscosity was inversely proportional to increasing the frequency in temperature controlled experiments. It was concluded that cavitation, heat generation, and viscosity reduction are three of the promising mechanisms causing increase in oil recovery under ultrasound.
In typical Lamb wave simulation practices, effects of plate edge reflections are often not considered in order to simplify the wave signal interpretations. Methods that are based on infinite plates such as the semi-analytical finite element method is effective in simulating Lamb waves as it excludes the effect of plate edges. However, the inclusion of plate edges in a finite plate could render this method inapplicable, especially for transient response simulations. Here, by applying the ratio of Lamb mode reflections at plate edges, and representing the reflection at plate edges using infinite plate solutions, the semi-analytical finite element method can be applied for transient response simulation, even when the plate is no longer infinite.
Due to partial understanding of mechanisms involved in application of ultrasonic waves as enhanced oil recovery method, series of straight (normal), and ultrasonic stimulated water-flooding experiments were conducted on a long unconsolidated sand pack using ultrasonic transducers. Kerosene, vaseline, and SAE-10 (engine oil) were used as non-wet phase in the system. In addition, a series of fluid flow and temperature rise experiments were conducted using ultrasonic bath in order to enhance the understanding about contributing mechanisms. 3-16% increase in the recovery of water-flooding was observed. Emulsification, viscosity reduction, and cavitation were identified as contributing mechanisms. The findings of this study are expected to increase the insight to involving mechanisms which lead to improving the recovery of oil as a result of application of ultrasound waves.
Rare-earth cobaltates Dy0.5-xErxBa0.5CoO3 (x=0, 0.03 and 0.05) have been systematically investigated to elucidate the effect of Er substitution on elastic as well as magnetic and transport properties. DC electrical resistance and AC susceptibility measurements showed that the x=0 sample exhibited an insulating behavior and an anti-ferromagnetic (AFM) transition, TN at 198 K as well as ferromagnetic (FM) transition, TC at 260 K. Increasing of Er content suppressed the FM and AFM state suggestively due to the increase in size disorder arising from the size mismatch between A-site cations as shown from our calculation of variance σ(2). On the other hand, both absolute longitudinal and shear velocities and related elastic moduli measured at 210 K decreased with Er content in conjunction with the declining in the FM domain indicating a weakening in elastic properties. A longitudinal velocity anomaly characterized by a drop in velocity upon cooling before hardening with further cooling was observed for all samples. This abnormal elastic anomaly can be attributed due to the Jahn-Teller (JT) distortion of intermediate-spin Co(3+) ions. Analysis of the elastic anomaly using the mean-field theory suggested that it is related to the JT effect which transformed from dynamic to static type with decreasing temperature. The elastic anomaly shifted to lower temperature from 129 K (x=0) to 124 K (x=0.05) with Er substitution indicating a weakening of the static JT effect.
Current traveling wave ultrasonic motor (TWUSM) utilizes comb-teeth structure as deflection amplifier. The position of the stator neutral axis to the stator contact surface is one of the factors that influences the deflection amplifier. Stator deflection directly effects on motor performance. In this study, the modification of the comb-teeth stator design is proposed to see its effect on motor efficiency. The modification is done so that the neutral axis position is further distance from the stator top contact surface. The proposed solution is to remove a selected mass element from the comb-teeth structure. Modeling, simulation and experimental work of the proposed concept is carried out utilizing Shinsei USR60 as the chosen TWUSM. The modeling and analyses are conducted through multi-physic finite element simulation MSC Marc Mentat. The results of the analyses and experimental work reveal that the modified comb-teeth stator increases the position of the neutral axis from the stator top surface. Due to the neutral axis shifting, the results also confirm that the proposed modified motor has higher efficiency compared to the non-modified motor.
Ultrasonic pulse velocity is affected by defects in material structure. This study applied soft computing techniques to predict the ultrasonic pulse velocity for various peats and cement content mixtures for several curing periods. First, this investigation constructed a process to simulate the ultrasonic pulse velocity with adaptive neuro-fuzzy inference system. Then, an ANFIS network with neurons was developed. The input and output layers consisted of four and one neurons, respectively. The four inputs were cement, peat, sand content (%) and curing period (days). The simulation results showed efficient performance of the proposed system. The ANFIS and experimental results were compared through the coefficient of determination and root-mean-square error. In conclusion, use of ANFIS network enhances prediction and generation of strength. The simulation results confirmed the effectiveness of the suggested strategies.
Ultrasound technique is an inexpensive and ecofriendly technology commonly used in oil and gas industry to improve oil recovery and its applications have been successfully tested in both laboratory and field scales. In this technique, high-power ultrasonic waves are utilized downhole to improve oil recovery and reduce formation damage in near wellbore region that causes a reduction in hydrocarbon production rate due to the penetration of mud, scale deposition, etc. In most of the cases, barriers for the oil flow to the wellbore are effectively removed by using the ultrasound technique and the effect of improved oil recovery may last up to several months. The aim of this paper is to provide an overview of recent laboratory, field and mathematical studies to serve as reference for future extensive examination of ultrasound assisted improved oil recovery. As an added value to this field of study, research gaps and opportunities based on the review of recent works were identified and factors that needs to be considered to improve the outcome of future studies were recommended.
Wavefield imaging is a powerful visualization tool in nondestructive evaluation for studying ultrasonic wave propagation and its interactions with damage. To isolate and study damage scattering, damage-free baseline data is often subtracted from a wavefield. This is often necessary because the damage wavefield can be orders of magnitude weaker than the incident waves. Yet, baselines are not always accessible. When the baselines are accessible, the experimental conditions for the baseline and test data must be extremely similar. Researchers have created several baseline-free approaches for isolating damage wavefields, but these often rely on specific experimental setups. In this paper, we discuss a flexible approach based on ultrasonic guided wave digital surrogates (i.e., numerical simulations of incident waves) and transfer learning. We demonstrate this approach with two setups. We first isolate reflections from a circular, 2 mm diameter half-thickness hole on a 10 × 10 cm steel plate. We then isolate 8 circular, half-thickness holes of various diameters from 1 mm to 40 mm on a 60 × 60 cm steel plate. The second plate has a non-square geometry and the data has multi-path reflections. With both data sets, we isolate damage reflections without explicit experimental baselines. We also briefly illustrate the comparison of our dictionary learning method with wavenumber filtering technique which is often used to enhance the defect wavefields.
Ultrasonic insertion technique (IT) is an ultrasonic technique which involves sample immersion in a solution to determine its acoustic properties. IT is normally used to determine the acoustic properties of a medical phantom. We proposed the use of IT as an alternative technique to the common contact ultrasonic technique: through-transmission technique (TT) for determining the elastic constant of hardwoods in longitudinal, tangential and radial directions. The elastic constant of twelve rectangular-shaped Malaysian hardwoods from three different categories; heavy, medium and light with the density ranging from 602 to 992kgm-3 were determined using IT and TT. Both techniques were carried out at 24.0°C surrounding temperature and utilized 2.25MHz ultrasonic transducers. Data from both techniques were compared to validate the use of the proposed technique. Findings indicated that IT offers consistent and accurate results for, tangential and radial elastic constants (TEC and REC) within 8.89% and 5.86% differences, respectively compared to TT for all tested hardwoods. IT offers an alternative technique for TEC and REC determinations of precious wood samples.
Thyroid is a small gland situated at the anterior side of the neck and one of the largest glands of the endocrine system. The abrupt cell growth or malignancy in the thyroid gland may cause thyroid cancer. Ultrasound images distinctly represent benign and malignant lesions, but accuracy may be poor due to subjective interpretation. Computer Aided Diagnosis (CAD) can minimize the errors created due to subjective interpretation and assists to make fast accurate diagnosis. In this work, fusion of Spatial Gray Level Dependence Features (SGLDF) and fractal textures are used to decipher the intrinsic structure of benign and malignant thyroid lesions. These features are subjected to graph based Marginal Fisher Analysis (MFA) to reduce the number of features. The reduced features are subjected to various ranking methods and classifiers. We have achieved an average accuracy, sensitivity and specificity of 97.52%, 90.32% and 98.57% respectively using Support Vector Machine (SVM) classifier. The achieved maximum Area Under Curve (AUC) is 0.9445. Finally, Thyroid Clinical Risk Index (TCRI) a single number is developed using two MFA features to discriminate the two classes. This prototype system is ready to be tested with huge diverse database.
In this paper, we report the simulation and fabrication of thickness-shear mode langasite resonators with stepped elliptical electrode designs to investigate their effects on energy trapping and suppression of spurious modes at elevated temperatures. Finite element analysis was conducted to analyze the design of a stepped elliptical electrode on a contoured langasite crystal. Based on the simulation findings, langasite resonators with stepped electrodes were fabricated, and their displacement profiles and frequency-temperature properties were characterized using network analysis and laser Doppler vibrometry. Results demonstrate improved frequency separation between the resonant and spurious modes, and enhanced spurious mode suppression at both room and higher temperatures, suggesting that stepped elliptical electrode designs can effectively enhance the sensing performance of langasite resonators.
Sonothrombolysis is a technique that utilises ultrasound waves to excite microbubbles surrounding a clot. Clot lysis is achieved through mechanical damage induced by acoustic cavitation and through local clot displacement induced by acoustic radiation force (ARF). Despite the potential of microbubble-mediated sonothrombolysis, the selection of the optimal ultrasound and microbubble parameters remains a challenge. Existing experimental studies are not able to provide a complete picture of how ultrasound and microbubble characteristics influence the outcome of sonothrombolysis. Likewise, computational studies have not been applied in detail in the context of sonothrombolysis. Hence, the effect of interaction between the bubble dynamics and acoustic propagation on the acoustic streaming and clot deformation remains unclear. In the present study, we report for the first time the computational framework that couples the bubble dynamic phenomena with the acoustic propagation in a bubbly medium to simulate microbubble-mediated sonothrombolysis using a forward-viewing transducer. The computational framework was used to investigate the effects of ultrasound properties (pressure and frequency) and microbubble characteristics (radius and concentration) on the outcome of sonothrombolysis. Four major findings were obtained from the simulation results: (i) ultrasound pressure plays the most dominant role over all the other parameters in affecting the bubble dynamics, acoustic attenuation, ARF, acoustic streaming, and clot displacement, (ii) smaller microbubbles could contribute to a more violent oscillation and improve the ARF simultaneously when they are stimulated at higher ultrasound pressure, (iii) higher microbubbles concentration increases the ARF, and (iv) the effect of ultrasound frequency on acoustic attenuation is dependent on the ultrasound pressure. These results may provide fundamental insight that is crucial in bringing sonothrombolysis closer to clinical implementation.
Fatigue strength is one of the most important properties of composite materials because it directly relates to their lifespan. Acoustic emission (AE) is a passive structural health monitoring (SHM) technique that provides real-time damage detection based on stress waves generated by cracks in the structure. This study evaluates the damage progression on glass fiber reinforced polyester composite specimens using different approaches of machine learning. Different methodologies for damage detection and characterization of AE parameters are presented. Three different ensemble learning methods namely, XGboost, LightGBM, and CatBoost were chosen to predict damages and AE parameters. SHAP values were used to select AE key features and K-means algorithms were employed to classify damage severity. The accuracy of these approaches demonstrates the reliability of various machine learning techniques in predicting the fatigue life of composite materials using acoustic emission.
The application of ultrasound shear wave elastography for detecting chronic kidney disease, namely renal fibrosis, has been widely studied. A good correlation between tissue Young's modulus and the degree of renal impairment has been established. However, the current limitation of this imaging modality pertains to the linear elastic assumption used in quantifying the stiffness of renal tissue in commercial shear wave elastography systems. As such, when underlying medical conditions such as acquired cystic kidney disease, which may potentially influence the viscous component of renal tissue, is present concurrently with renal fibrosis, the accuracy of the imaging modality in detecting chronic kidney disease may be affected. The findings in this study demonstrate that quantifying the stiffness of linear viscoelastic tissue using an approach similar to those implemented in commercial shear wave elastography systems led to percentage errors as high as 87%. The findings presented indicate that use of shear viscosity to detect changes in renal impairment led to a reduction in percentage error to values as low as 0.3%. For cases in which renal tissue was affected by multiple medical conditions, shear viscosity was found to be a good indicator in gauging the reliability of the Young's modulus (quantified through a shear wave dispersion analysis) in detecting chronic kidney disease. The findings show that percentage error in stiffness quantification can be reduced to as low as 0.6%. The present study demonstrates the potential use of renal shear viscosity as a biomarker to improve the detection of chronic kidney disease.
The development of alternative techniques to efficiently inactivate bacterial suspensions is crucial to prevent transmission of waterborne illness, particularly when commonly used techniques such as heating, filtration, chlorination, or ultraviolet treatment are not practical or feasible. We examine the effect of MHz-order acoustic wave irradiation in the form of surface acoustic waves (SAWs) on Gram-positive (Escherichia coli) and Gram-negative (Brevibacillus borstelensis and Staphylococcus aureus) bacteria suspended in water droplets. A significant increase in the relative bacterial load reduction of colony-forming units (up to 74%) can be achieved by either increasing (1) the excitation power, or, (2) the acoustic treatment duration, which we attributed to the effect of the acoustic radiation force exerted on the bacteria. Consequently, by increasing the maximum pressure amplitude via a hybrid modulation scheme involving a combination of amplitude and pulse-width modulation, we observe that the bacterial inactivation efficiency can be further increased by approximately 14%. By combining this scalable acoustic-based bacterial inactivation platform with plasma-activated water, a 100% reduction in E. coli is observed in less than 10 mins, therefore demonstrating the potential of the synergistic effects of MHz-order acoustic irradiation and plasma-activated water as an efficient strategy for water decontamination.