3aSA7 – Characterizing defects with nonlinear acoustics – Pierre-Yves Le Bas,  Brian E. Anderson, Marcel Remillieux, Lukasz Pieczonka, TJ Ulrich

3aSA7 – Characterizing defects with nonlinear acoustics – Pierre-Yves Le Bas, Brian E. Anderson, Marcel Remillieux, Lukasz Pieczonka, TJ Ulrich

Characterizing defects with nonlinear acoustics

 

Pierre-Yves Le Bas, pylb@lanl.gov1,  Brian E. Anderson1,2, Marcel Remillieux1, Lukasz Pieczonka3, TJ Ulrich1

1Geophysics group EES-17, Los Alamos National Laboratory, Los Alamos, NM 87545, USA

2Department of Physics and Astronomy, Brigham Young University, N377 Eyring Science Center, Provo, UT 84601, USA

3AGH University of Science and Technology, Krakow, Poland

 

Popular version of paper 3aSA7, “Elasticity Nonlinear Diagnostic method for crack detection and depth estimation”
Presented Wednesday morning, November 4, 2015, 10:20 AM, Daytona room
170th ASA Meeting, Jacksonville

 

One common problem in industry is to detect and characterize defects, especially at an early stage. Indeed, small cracks are difficult to detect with current techniques and, as a result, it is customary to replace parts after an estimated lifetime instead of keeping them in service until they are effectively approaching failure. Being able to detect early stage damage before it becomes structurally dangerous is a challenging problem of great economic importance. This is where nonlinear acoustics can help. Nonlinear acoustics is extremely sensitive to tiny cracks and thus early damage. The principle of nonlinear acoustics is easily understood if you consider a bell. If the bell is intact, it will ring with an agreeable tone determine by the geometry of the bell. If the bell is cracked, one will hear a dissonant sound, which is due to nonlinear phenomena. Thus, if an object is struck it is possible to determine, by listening to the tone(s) produced, whether or not it is damaged. Here the same principle is used but in a more quantitative way and, usually, at ultrasonic frequencies. Ideally, one would also like to know where the damage is and what its orientation is. Indeed, a crack growing thru an object could be more important to detect as it could lead to the object splitting in half, but in other circumstances, chipping might be more important, so knowing the orientation of a crack is critical in the health assessment of a part.

To localize and characterize a defect, time reversal is a useful technique. Time reversal is a technique that can be used to localize vibration in a known direction, i.e., a sample can be made to vibrate perpendicularly to the surface of the object or parallel to it, which are referred to as out-of-plane and in-plane motions, respectively. The movie below shows how time reversal is used to focus energy: a source broadcasts a wave from the back of a plate and signals are recorded on the edges using other transducers. The signals from this initial phase are then flipped in time and broadcast from all the edge receivers. Time reversal then dictates that these waves focus at the initial source location.

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Time reversal can also be more that the simple example in the video. Making use of the reciprocity principle, i.e., that a signal traveling from A to B is identical to the same signal traveling from B to A, the source in the back of the plate can be replaced by a receiver and the initial broadcast can be done from the side, meaning TR can focus energy anywhere a signal can be recorded; and with a laser as receiver, this means anywhere on the surface of an object.

In addition, the dominant vibration direction, e.g., in-plane or out-of plane, of the focus can be specified by recording specific directions of motion of the initial signals. If during the first step of the time reversal process, the receiver is set to record in-plane vibration, the focus will be primarily in that in-plane direction; similarly if the receiver records the out-of-plane vibration in the first step of the process, the focus will be essentially in the out-of-plane direction. This is important as the nonlinear response of a crack depends on the orientation of the vibration that makes it vibrate. To fully characterize a sample in terms of crack presence and orientation TR is used to focus energy at defined locations and at each point the nonlinear response is quantified.  This can be done for any orientation of the focused wave. To cover all possibilities, three scans are usually done in three orthogonal directions.

Figure 2 shows three scans on x, y and z directions of the same sample composed of a glass plate glued on an aluminum plate. The sample has 2 defects, one delamination due to a lack of glue between the 2 plates (in the (x,y) plane) at the top of the scan area and one crack perpendicular to the surface in the glass plate in the (x,z) plane in the middle of the scan area.

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Figure 2. Nonlinear component of the time reversal focus at each point of a scan grid with wave focused in the x, y and z direction (from left to right)

As can be seen on those scans, the delamination in the (x,y) plane is visible only when the wave is focused in the Z direction while the crack in the (x,z) plane is visible only in the Y scan. This means that cracks have a strong nonlinear behavior when excited in a direction perpendicular to their main orientation. So by scanning with three different orientations of the focused vibration one should be able to recreate the orientation of a crack.

Another feature of the time reversal focus is that its spatial extent is about a wavelength of the focus wave. Which means the higher the frequency, the smaller the spot size, i.e., the area of the focused energy. One can then think that the higher the frequency the better the resolution and thus higher frequency is always best. However, the extent of the focus is also the depth that this technique can probe; so lower frequency means a deeper investigation and thus a more complete characterization of the sample. Therefore there is a tradeoff between depth of investigation and resolution. However, by doing several scans at different frequencies, one can extract additional information about a crack. For example, Figure 3 shows 2 scans done on a metallic sample with the only difference being the frequency of the focused wave.

 

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Figure 3. From left to right: Nonlinear component of the time reversal focus at each point of a scan grid at 200kHz and 100kHz and photography of the sample from its side.

 

At 200kHz, it looks like there is only a thin crack while at 100kHz the extent of this crack is larger toward the bottom of the scan and more than double so there is more than just a resolution issue. At 200kHz the depth of investigation is about 5mm; at 100kHz it is about 10mm. Looking on the side of the sample in the right panel of figure 3, the crack is seen to be perpendicular to the surface for about 6mm and then dip severely. At 200kHz, the scan is only sensitive to the part perpendicular to the surface while at 100kHz, the scan will also show the dipping part. So doing several scans at different frequencies can give some information on the depth profile of the crack.

In conclusion, using time reversal to focus energy in several directions and at different frequencies and studying the nonlinear component of this focus can lead to a characterization of a crack, its orientation and depth profile, something that is currently only available using techniques, like X-ray CT, which are not as easily deployable as ultrasonic ones.

 

1pABb1 – Mice ultrasonic detection and localization in laboratory environment – Yegor Sinelnikov

Mice ultrasonic detection and localization in laboratory environment

 

Yegor Sinelnikov – yegor.sinelnikov@gmail.com
Alexander Sutin, Hady Salloum, Nikolay Sedunov, Alexander Sedunov
Stevens Institute of Technology
Hoboken, NJ 07030

Tom Zimmerman, Laurie Levine
DLAR Stony Brook University
Stony Brook, NY 11790

David Masters
Department of Homeland Security
Science and Technology Directorate
Washington, DC

 

Popular version of poster 1pABb1, “Mice ultrasonic detection and localization in laboratory environment”
Presented Tuesday afternoon, November 3, 2015, 3:30 PM, Grand Ballroom 3
170th ASA Meeting, Jacksonville

 

A house mouse, mus musculus, historically shares the human environment without much permission. It lives in our homes, enjoys our husbandry, and passes through walls and administrative borders unnoticed and unaware of our wary attention. Over the thousands of years of coexistence, mice excelled in a carrot and stick approach. Likewise, an ordinary wild mouse brings both danger and cure to humans todays. A danger is in the form of rodent-borne diseases, amongst them plague epidemics, well remembered in European medieval history, continue to pose a threat to human health. A cure is in the form of lending themselves as research subjects for new therapeutic agents, an airily misapprehension of genomic similarities, small size, and short life span. Moreover, physiological similarity in inner ear construction, brain auditory responses and unexpected richness in vocal signaling attested to the tremendous interest to mice bioacoustics and emotion perception.

The goal of this work is to start addressing possible threats reportedly carried by invasive species crossing US borders unnoticed in multiple cargo containers. This study focuses on demonstrating the feasibility of acoustic detection of potential rodent intrusions.

Animals communicate with smell, touch, movement, visual signaling and sound. Mice came well versed in sensorial abilities to face the challenge of sharing habitat with humans. Mice gave up color vision, developed exceptional stereoscopic smell, and learned to be deceptively quiet in human auditory range, discretely shifting their social acoustic interaction to higher frequencies. They predominantly use ultrasonic frequencies above the human hearing range as a part of their day-to-day non aggressive social interaction. Intricate ultrasonic mice songs composed of multiple syllable sounds often constituting complex phrases separated by periods of silence are well known to researchers.

In this study, mice sounds were recorded in a laboratory environment at an animal facility at Stony Brook University Hospital. The mice were allowed to move freely, a major condition for their vocalization in ultrasonic range. Confined to cages, mice did not produce ultrasonic signals. Four different microphones with flat ultrasonic frequency response were positioned in various arrangements and distances from the subjects. The distances varied from a few centimeters to several meters. An exemplary setup is shown in Figure 1. Three microphones, sensitive in the frequency range between 20 kHz and 100 kHz, were connected to preamplifiers via digital converters to a computer equipped with dedicated sound recording software. The fourth calibrated microphone was used for measurements of absolute sound level produced by a mouse. The spectrograms were monitored by an operator in real time to detect the onset of mice communications and simplify line data processing.

 

Sinenikov fig 1

Figure 1. Setup of experiment showing the three microphones (a) on a table with unrestrained mouse (b),  recording equipment preamplifiers and digitizers (c) and computer (d).
Listen to a single motif of mice ultrasonic vocalization and observe mouse movement here:

This sound fragment was down converted (slowed down) fifteen times to be audible. In reality, mice social songs are well above the human audible range and are very fast. The spectrograms of mice vocalization at distances of 1 m and 5 m are shown in Figure 2. Mice vocalization was detectable at 5 m and retained recognizable vocalization pattern. Farther distances were not tested due to the limitation of the room size.

The real time detection of mice vocalization required detection of the fast, noise insensitive and automated algorithm. An innovative approach was required. Recognizing that no animal communication comes close to become a language, the richness and diversity of mice ultrasonic vocalization prompted us to apply speech processing measures for their real time detection. A number of generic speech processing measures such temporal signal to noise ratio, cepstral distance, and likelihood ratio were tested for the detection of mice vocalization events in the presence of background noise.  These measures were calculated from acoustical measurements and compared with conventional techniques, such as bandpass filtering, spectral power, or continuous monitoring of signal frames for the presence of expected tones.

 

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Figure 2. Sonograms of short ultrasonic vocalization syllables produced by mice at 1 m (left) and 5 m (right) distances from microphones.  The color scale is in the decibels.

Although speech processing measures were invented to assess human speech intelligibility, we found them applicable for the acoustic mice detection within few meters. Leaving aside the question about mice vocalization intelligibly, we concluded that selected speech processing measures enabled us to detect events of mice vocalization better than other generic signal processing techniques.

As a secondary goal of this study, upon successful acoustic detection, the mice vocalization needed to be processed to determine animal location. It was of main interest for border patrol applications, where both acoustic detection and spatial localization are critical, and because mice movement has a behavioral specificity. To prove the localization feasibility, detected vocalization events from each microphone pair were processed to determine the time difference of arrival (TDOA). The analysis was limited to nearby locations by relatively short cabling system. Because the animals were moving freely on the surface of a laboratory table, roughly coplanar with microphones, the TDOA values were converted to the animal location using simple triangulation scheme. The process is illustrated schematically in Figure 3 for two selected microphones. Note that despite low signal to noise ratio for the microphone 2, the vocalization events were successfully detected. The cross correlograms, calculated in spectral domain with empirical normalization to suppress the effect of uncorrelated noise, yielded reliable TDOA. A simple check for the zero sum of TDOA was used as a consistency control. Calculated TDOA were converted into spatial locations, which were assessed for correctness, experimental and computational uncertainties and compared with available video recordings. Despite relatively high level of technogenic noise, the TDOA calculated locations agreed well with video recordings. The TDOA localization uncertainty was estimated on the order of the mouse size, roughly corresponding to several wavelengths at 50 kHz. A larger number of microphones is expected to improve detectability and enable more precise three dimensional localization.

Hence, mice ultrasonic socialization sounds are detectable by the application of speech processing techniques, their TDOA are identifiable by cross correlation and provide decent spatial localization of animals in agreement with video observations.

 

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Figure 3. The localization process. First, the detected vocalization events from two microphones (left) are paired and their cross correlogram is calculated (middle). The maxima, marked by asterisks, define a set of identified TDOA.  The process is repeated for every pair of microphones. Second, the triangulation is performed (right). The colored hyperbolas illustrate possible locations of animal on a laboratory table based on calculated TDOA. Hyperbolas intersection provides the location of animal. The numbered squares mark the location of microphones.

 

1The constructed recording system is particularly important for the detection of mice in containers  at US ports of entry, where low frequency noises are high. This pilot study confirms the feasibility of using Stevens Institute’s ultrasonic recording system for simultaneous detection of mice vocalization and movement.

This work was funded by the U.S. Department of Homeland Security’s Science and Technology Directorate. The views and conclusions contained in this paper are those of the authors and should not necessarily be interpreted as representing the official policies, either expressed or implied of the U.S. Department of Homeland Security.

 

 

 

4aEA2 – How soon can you use your new concrete driveway? –  Jinying Zhu

4aEA2 – How soon can you use your new concrete driveway? – Jinying Zhu

How soon can you use your new concrete driveway?

Jinying Zhu: jyzhu@unl.edu

 

Department of Civil Engineering

University of Nebraska-Lincoln

1110 S 67th St., Omaha, NE 68182, USA

 

Popular version of paper 4aEA2, “Monitoring hardening of concrete using ultrasonic guided waves” Presented Thursday morning, Nov. 5, 2015, 8:50 AM, ORLANDO room,
170th ASA Meeting, Jacksonville, FL

 

Concrete is the most commonly used construction material in the world. The performance of concrete structures is largely determined by properties of fresh concrete at early ages. Concrete gains strength through a chemical reaction between water and cement (hydration), which gradually change a fluid fresh concrete mix to a rigid and hard solid. The process is called setting and hardening.  It is important to measure the setting times, because you may not have enough time to mix and place concrete if the setting time is too early, while too late setting will cause delay in strength gain.  The setting and hardening process is affected by many parameters, including water and cement ratio, temperature, and chemical admixtures.  The standard method to test setting time is to measure penetration resistance of fresh concrete samples in laboratory, which may not represent the real condition in field.

Ultrasonic waves have been proposed to monitor the setting and hardening process of concrete by measuring wave velocity change. When concrete becomes hard, the stiffness increases, and the ultrasonic velocity also increases. The authors found there is a clear relationship between the shear wave velocity and the traditional penetration resistance. However, most ultrasonic tests measure a small volume of concrete sample in laboratory, and they are not suitable for field application. In this paper, the authors proposed an ultrasonic guided wave test method. Steel reinforcements (rebars) are used in most concrete structures. When ultrasonic guided waves propagate within rebar, they leak energy to surrounding concrete, and the energy leakage rate is proportion to the stiffness of concrete.  Ultrasonic waves can be introduced into rebars from one end and the echo signal will be received at the same end using the same ultrasonic sensor.  This test method has a simple test setup, and is able to monitor the concrete hardening process continuously.

Figure 2 shows guided wave echo signals measured on a 19mm diameter rebar embedded in concrete. It is clear that the signal amplitude decreases with the age of concrete (2 ~ 6 hours). The attenuation can be plotted vs. age for different cement/concrete mixes. Figure 3 shows the attenuation curves for 3 cement paste mixes. It is known that a cement mix with larger water cement ratio (w/c) will have slower strength gain, which agrees with the ultrasonic guided wave test, where the w/c=0.5 mix has lower attenuation rate.  When there is a void around the rebar, energy leakage will be less than the case without a void, which is also confirmed by the test result in Figure 3.

Summary: This study presents experimental results using ultrasonic guided waves to monitor concrete setting and hardening process. It shows the guided wave leakage attenuation is proportional to the stiffness change of fresh concrete. Therefore the leakage rate can be used to monitor the concrete strength gain at early ages. This study may have broader applications in other disciplines to measure mechanical property of material using guided wave.

Zhu1

Figure. 1 Principle of ultrasonic guided wave test.

zhu2

Figure. 2 Ultrasonic echo signals measured in an embedded rebar for concrete age of 2~6 hours.

Zhu3

Figure. 3 Guided wave attenuation rate in a rebar embedded in different cement pastes.

 

4pEA4 – “See”  subsurface soils using surface waves – Zhiqu Lu

4pEA4 – “See” subsurface soils using surface waves – Zhiqu Lu

 “See”  subsurface soils using surface waves

Zhiqu Lu — zhiqulu@olemiss.edu

National Center for Physical Acoustics, The University of Mississippi,

1 Chucky Mullins,

University, MS, 38677

 

Lay language paper 4pEA4

Presented Thursday afternoon, November 5, 2015

170th ASA Meeting, Jacksonville

 

Within a few meters beneath the earth surface, three distinctive soil layers are formed: a top dry and hard layer, a middle moist and soft region, and a deeper zone where the mechanical strength of the soil increases with depth.  The information of this subsurface soil is required for agricultural, environmental, civil engineering, and military applications. A seismic surface wave method has been recently developed to non-invasively obtain such information (Lu, 2014; Lu, 2015).  The method, known as the multichannel analysis of surface wave method (MASW) (Park, et al., 1999; Xia, et al., 1999), consists of three essential parts: surface wave generation and collection (Figure 1), spectrum analysis, and inversion process. The implement of the technique employs sophisticated sensor technology, wave propagation modeling, and inversion algorithm.

Lu1

“Figure 1. The experimental setup for the MASW method”

The technique makes use of the characteristic of one type of surface waves, the so-called Rayleigh waves that travel along the earth’s surface within a depth of one and a half wavelengths. Therefore the components of surface waves with short wavelength contain information of shallow soil, whereas the longer wavelength surface waves provide the properties of deep soil (Figure 2).

Lu2

“Figure 2. Rayleigh wave propagation”

The outcome of the MASW method is a soil vertical profile, i.e., the acoustic shear (S) wave velocity as a function of depth (Figure 3).

Lu3

“Figure 3. A typical soil profile”

By repeating the MASW measurements either spatially or temporarily, one can measure and “see” the spatial and temporal variations of the subsurface soils. Figure 4 shows a typical vertical cross-section image in which the intensity of the image represents the value of the shear wave velocity. From this image, three different layers mentioned above are identified.

 

Lu4

“Figure 4. A typical example of soil vertical cross-section image “

 

Lu5

Figure 5 displays another two-dimensional image in which a middle high velocity zone (red area) appears. This high velocity zone represents a geological anomaly, known as a fragipan, a naturally occurring dense and hard soil layer (Lu, et al., 2014). The detection of fragipan is important in agricultural land managements.

“Figure 5. A vertical cross-section image showing the presence of a fragipan layer”

The MASW method can also be applied to monitor weather influence on soil properties (Lu 2014). Figure 6 shows the temporal variations of the underground soil.  This is a result of a long term survey conducted in 2012.  By drawing a vertical line and moving it from left side to right side, i.e., along the time index number axis, the evolution of the soil profile due to weather effects can be evaluated. In particular, the high velocity zones occurred in the summer of 2012, reflecting very dry soil conditions.

“Figure 6. The  temporal variations of soil profile due to weather effects”

Lu6

 

 

Lu,  Z., 2014.  Feasibility of using a seismic surface wave method to study seasonal and weather effects on shallow surface soils. Journal of Environmental & Engineering Geophysics, DOI: 10.2113/JEEG19.2.71, Vol.19, 71–85.

Lu, Z. 2015. Self-adaptive method for high frequency multi-channel analysis of surface wave method, Journal of Applied Geophysics, Vol. 121, 128-139. http://dx.doi.org/10.1016/j.jappgeo.2015.08.003

Lu, Z., Wilson, G.V., Hickey, C.J., 2014. Imaging a soil fragipan using a high-frequency MASW method. In Proceedings of the Symposium on the Application of Geophysics to Engineering and Environmental Problems (SAGEEP 2014), Boston, MA., Mar. 16-20.

Park, C.B., Miller, R.D., Xia, J., 1999. Multichannel analysis of surface waves. Geophysics, Vol. 64, 800-808.

Xia, J., Miller, R.D., Park, C.B., 1999. Estimation of near-surface shear-wave velocity by inversion of Rayleigh waves. Geophysics, Vol. 64, 691-700.