2pAO10 – What Can We Learn from Breaking Wave Noise?

Grant B. Deane – gdeane@ucsd.edu
M. Dale Stokes – dstokes@ucsd.edu
Scripps Institution of Oceanography, UCSD,
La Jolla, CA 92093-0206

David M. Farmer – farmer.david@gmail.com
School of Earth and Ocean Sciences,
Victoria BC, V8P 5C2, Canada

Eric D’Asaro – dasaro@apl.washington.edu
Zhongxiang Zhao – zzhao@apl.washington.edu
Applied Physics Laboratory, University of Washington,
Seattle, WA 98105

Popular version of paper 2pAO10
Presented Monday afternoon, June 26, 2017
173th ASA Meeting, Boston

Waves breaking on the ocean, often called “whitecaps,” limit the growth of ocean waves, transfer momentum between the atmosphere and ocean, generate marine aerosols, increase ocean albedo and enhance the air-sea transport of greenhouse gasses. Despite their importance for understanding weather and climate, they remain poorly understood.

The reason for this is clear: breaking waves are the product of storms at sea, they are a source of intense turbulence and they can destroy the sensitive instruments we might use to measure them. This makes them tricky to study in their natural ocean environment, and has encouraged the development of various remote sensing techniques using aircraft and satellites. While we have learned much about breaking waves from above, we still need to understand what is happening in the turbulent core. Here we probe the whitecaps’ inner structure from beneath using the natural sound they create.

 

The video (link broken) shows a breaking wave seen from above and below during a storm of Point Conception, California in 2000. Credit: Deane

The mass of bubbles that give the whitecap its bright appearance come from the air entrained as the wave breaks. The breaking process generates intense turbulence that fragments the trapped air cavity into a mass of small bubbles. These bubbles create underwater noise. The sounds of crashing surf, the tinkling fountain and the babbling brook are all made by bubbles, which emit a musical pulse of sound when they are first formed.

Each pulse of sound has its own tone that is determined by the size of the bubble making it. So, wave noise intensity and frequency contains information about the numbers and sizes of bubbles entrained by a wave. By measuring the sound safely beneath the fury of the ocean surface, we can learn what is going on within its turbulent interior.

Wave noise has been used over the years to learn many interesting things about breaking waves, including their intensity, how frequently they break and their movement across the sea surface. Wave noise has been used to probe the properties of recently formed bubbles left after a wave breaks and even to infer wind speed, which is closely related to the overall intensity of noise in the ocean.

We have been using wave noise to probe fluid turbulence in whitecaps. Our interest in whitecap turbulence is motivated by its relationship to bubble entrainment and breakup. Fluctuating pressure within the breaking wave driven by fluid turbulence can rupture bubbles by distorting them from their spherical form into irregular shapes.

Small bubbles are stabilized against rupture by surface tension, but large bubbles get ripped apart. These two forces are balanced at a spatial scale, the Hinze scale, which is related to the intensity of the turbulence. The Hinze scale plays a key role in setting the bubble size distribution in breaking waves. An important question is how does the Hinze scale, and therefore the bubble size distribution, change as the wind grows from a gentle breeze to a tropical cyclone?

We might reasonably expect the turbulence to increase with increasing wind speed. If this were true, the bubble distribution created by wave breaking would lead to smaller bubbles at higher wind speed. Surprisingly, this turns out not to be the case. Our experiments on breaking waves in a laboratory show that turbulence intensity in breaking waves, measured by both bubble sizes and a quite different method, reaches a maximum value, relatively independent of the size of the wave.

This leads us to suspect that the Hinze scale, and therefore the bubble size distribution, should be the same for a wide range of wind speeds. We call this phenomenon “turbulence saturation,” and it has important implications for transport processes linking the ocean and atmosphere. But, does this result translate from the laboratory to the open ocean?

Field measurements support this hypothesis. Wave noise was measured along 7 transects across 3 different tropical cyclones. Figure 1 shows measurements of wave noise as it depends on frequency for different wind speeds (colored lines) varying from 15 to 40 meters per second. Notice that all spectra change slope between 2000-4000 Hertz, annotated with the vertical, grey box. The frequency of this break point is thus nearly independent of wind speed.

wave noise

Figure 1. Measurements of wave noise for wind speeds ranging from 15 to 40 meters per second. The black curves show model calculations of the wave noise under conditions of changing turbulence.

Since we expect this frequency to be related to the Hinze scale, these data suggests that the Hinze scale, and therefore the bubble size distribution, is the same across the entire range of wind speeds. We support this conclusion with a model of sound generation by bubbles (yellow/black lines). The model predicts a peak near the Hinze frequency. Sound generation at lower frequencies is due to other physics and not modeled here. Changing the turbulence dissipation rate by a factor of 5 moves the location of the peak by about a factor of 3, suggesting that if the turbulence intensity did change then we would see evidence of it in the wave noise.

This combination of laboratory and field measurements with theory provide us with evidence of “scale invariance” of turbulence within breaking waves in the open ocean up to 40 meters per second wind speeds, supporting the turbulence saturation hypothesis and demonstrating the unique contributions that ambient sound measurements can make under severe conditions.

[Work supported by ONR, Ocean Acoustics Division and NSF. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the Office of Naval Research].

4aSAb12 – Designing Tunable Acoustic Metamaterials Using 3-D Computer Graphics

Mark J. Cops – mcops@bu.edu
J. Gregory McDaniel – jgm@bu.edu
Boston University
110 Cummington Mall
Boston, MA 02215

Elizabeth A. Magliula – Elizabeth.magliula@navy.mil
Naval Undersea Warfare Center
1176 Howell Street, Building 1302
Newport, RI 02841

Popular version of paper 4aSAb12
Presented Wednesday morning, June, 28, 2017
173rd ASA Meeting, Boston

In this work, software originally designed for display rendering, artistic graphics, animation creating, and video game creation is being used to create new materials with tunable properties. This work has produced digital designs of materials that are essential to reducing sound and vibration.

Metamaterials are specially engineered materials which use a combination of structure and host materials to enable a wide range of material properties not ordinarily found in nature. Metallic foams are one such subset of metamaterials, which provide advantages for structural applications due to their high strength-to-weight ratio. Metallic foams can be manufactured through a variety of processes, such as casting or sintering, and can either be closed cell or open cell (Figure 1).

Figure 1. An open cell aluminum foam manufactured by ERG Aerospace Corp.

The ability to tune metallic foam properties for various noise and vibration mitigation applications is a valuable tool for industrial designers and engineers. The combination of 3-D computer graphics and finite-element software can be used to rapidly design, investigate, and classify material properties. OpenGL is a programming language used widely in computer graphics. Using OpenGL, the programmer can create complex cellular structures by effectively controlling the pixel display in a 3-D array of pixels by using signed distance functions to specify locations of solid material or void space. One remarkable thing about using OpenGL is its inherent simplicity and ability to create any surface described mathematically. Two such materials, created from the described approach, are shown in Figure 2.

Figure 2. (a) an Aluminum tetrahedron lattice with triangular struts. (b) A copper minimal surface geometry structure.

The relative density of these two foams was altered by keeping the pore spacing (the distance between void openings in the surface) constant and increasing the thickness of material. To determine effective materials properties, the designed foam structures were analyzed using the finite element method software, Abaqus. Six different strain loading scenarios were imposed on the structure: representing tensile and shear loading on all orientations, shown in Figure 3.

Metamaterials

Figure 3. Strain loading scenarios used in determining effective material properties.

We then determined numerically the effective static material properties, such as Young’s modulus and Poisson ratio. Figure 4 shows relative Young’s Modulus and Poisson ratio values vs. relative density for the foam in Figure 2b. Each blue point is one foam that was digitally designed and analyzed by the discussed approach. It is interesting to note that there is a very significant trend — properties are a quadratic function of relative density.

Figure 4. Material property curves for the foam in Figure 2b.

The useful feature about curves such as those in Figure 4, and others generated by the discussed approach, is the ability for designers to visualize the design space and availability of material properties and select a desired relative density foam to meet design criteria. Such foams can then be fabricated and implemented to serve a wide range of structural applications.

Effects of noise for workers in the transportation industry

Marion Burgess m.burgess@adfa.edu.au
Brett Molesworth b.molesworth@unsw.edu.au

University of New South Wales, Australia

Popular version of paper
Presented June 28, 2017, in session 4aNSa, Measuring, Modeling, and Managing Transportation Noise I. 8:00 AM – 12:20 PM
173rd ASA Meeting, Boston

There are well established limits for workplace noise based on the risk of hearing damage. For example, an 8-hour noise exposure level is limited to 85 decibels (when the sound is this loud you need to shout to talk to someone near you). There are also guidelines for acceptable noise levels in workplaces that aim to ensure the noise will not be intrusive or affect the ability of the worker to do the tasks. For example, a design level for a general office may be 40 to 45 decibels (dBA), while for a ticket sales area, 45 to 50 dBA. In this range, noise should not have an adverse affect on your ability to complete a task.

However, there are many work environments, particularly in the transportation industry, in which the noise levels are above 50 dBA but the employees are required to perform tasks that require a high level of concentration and attention. For pilots and bus, truck and train drivers, the noise levels in the area they are working can be 65 to more than 75 dBA at times.

These workers all need to make safety-critical decisions and operate technical equipment in the presence of continuous noise generated from their vehicle’s engine. Transport check-in staff need to communicate and process passengers in noisy check-in halls where there is both vehicle and equipment noise as well as the noise from personnel around, such as “babble.”

In this paper, we discuss findings from a number of studies investigating the effect of constant noise at 65 dBA on various cognitive and memory skills. Two noise sources were used: One, a wideband noise like constant mechanical noise from an engine, and the other a babble noise of multiple persons’ incomprehensible speech. Language background is another factor that can increase cognitive load for those workers who are communicating in a language that is not native.

The cognitive tasks aimed to test working memory with an alphabet span test and recognition memory using a cued recall task. The signal to noise ratio used was 0, -5 and -10 dBA. Wideband noise was found to have a greater effect on working memory and recognition memory than babble noise.
Those who were not native English speakers were also more affected by the wideband noise than the babble noise. The subjective assessment, when the subjects were asked their opinion of the effect of the noise and the annoyance, was also greater for broadband noise.

These findings reinforce the limitations of basing acceptability on a simple overall dBA value alone. The reduction in performance demonstrates the importance of reducing the noise levels within transportation workplaces.

3pAB1 – A Welcoming Whinny

David G. Browning decibeldb@aol.com
Peter D. Herstein – netsailor.ph@cox.net
BROWNING BIOTECH
139 Old North Road
Kingston, RI 02881
Popular Version of paper 3pAB1
Presented Tuesday afternoon, June 27, 2017
173rd ASA Meeting, Boston

Are you greeted with a welcoming whinny when you enter the barn? When doing research on horse whinnys (as part of the Equinne Vocalization Project) we realized we were hearing more whinnys when horses were inside the barn than out. This led us to investigate further and we came to realize it was vocalization adaptation. Horses have remarkable eyesight, with almost a 360° field of view, which they primarily rely on to observe and communicate when out in the open. In a barn, confined to a stall, their line of sight is often blocked. Quite remarkably, they learn to compensate by recognizing the sounds that are of interest — like that of the feed-cart or even their owner’s footsteps — which they often salute with a whinny.

We were curious as to how universal vocalization adaptation occurred in the animal world and in searching the literature we found numerous interesting examples. Asian Wild Dogs (Dholes), for example, hunt prey in packs, usually out in the open where they can visually keep track of the prey and their pack mates. When they encounter some sight-limiting vegetation, however, they have developed a short, flat whistle to keep track of each other but not interfere with their listening for the prey.

Jungles, presenting further examples, are uniquely challenging to animals for three reasons: visibility is limited, moving is difficult, and the vocalization has to be heard despite many others’ sounds. African rhinos out on the plain can make do with a simple bellow, as it would be easy to trot over and check them out. In contrast, a Sumartran rhino, always in the jungle, has a complex vocalization. Often compared to that of a whale, the vocalization is complex in order to be heard among the competing calls while providing enough information so to entice another to slog over to check it out (or not).

The military use a term “situational awareness,” that also refers the awareness that is crucial to animals, and this work provides some examples of their acoustic compensations when visibility is limited for some reason.

2pSAa – Three-in-one Sound Effects: A redirecting antenna, beam splitter and a sonar

Andrii Bozhko – AndriiBozhko@my.unt.edu
Arkadii Krokhin – Arkadii.Krokhin@unt.edu
Department of Physics
University of North Texas
1155 Union Circle #311427
Denton, TX 76201, USA

José Sánchez-Dehesa – jsdehesa@upvnet.upv.es
Francisco Cervera – fcervera@upvnet.upv.es
Wave Phenomena Group
Universitat Politècnica de València
Camino de Vera s/n
Valencia, ES-46022, Spain

Popular version of paper 2pSAa, “Redirection and splitting of sound waves by a periodic chain of thin perforated cylindrical shell.”
Presented Monday afternoon, June 26, 2017, 2:20, Room 201
173rd ASA Meeting, Boston

Any sound, whether the warble of an exotic bird or the noise of clucky machinery, what scientists percieve is a complex mixture of many primitive sound waves — the so-called pure tones, which are simply vibrations of certain distinct frequencies. So, is it possible, we wondered, to break down such an acoustic compound into its constituents and separate one of those pure tones from the rest?

It can be achieved using any of the signal processing techniques, however, a simple mechanistic solution also exists in the form of a passive system. That is to say, one that doesn’t have to be turned on to operate.

Here we demonstrate such a system: A linear, periodic arrangement of metallic perforated cylindrical shells in air (see Fig. 1), which serves as a redirecting antenna and a splitter for sound within an audible range.

Figure 1 – A periodic array of perforated cylindrical shells mounted outside the Department of Electronic Engineering, Polytechnic University of Valencia. Credit: Sánchez-Dehesa

Each shell in the chain (see Fig. 2) is a weak scatterer, meaning the sound wave would pass through it virtually undistorted, and strong redirection of an incoming signal might occur only if the chain is sufficiently long. When the number of shells in the chain is large enough, e.g. several dozens, each shell participates in a collective oscillatory motion, with each one of them transferring its vibration to its neighbor via the environment. Such a self-consistent wave is referred to as an eigenmode of our system, and it is best thought of as collective oscillations of air localized in the vicinity of the shells’ surfaces.

Figure 2 – A close-up of an aluminum perforated cylindrical shell. Credit: Sánchez-Dehesa

Now, there are two substantial concepts regarding the wave motion that deserve careful clarification. When describing an acoustic wave, we can look at how and where the regions of maximum (or minimum) pressure move through the medium (air in this case), and combine the information with that of the pace and direction of their motion into a single characteristic — called the phase velocity of the wave.

Another important property of the wave is its group velocity, which indicates how fast and in which direction the actual sound propagates. In many cases, the phase velocity and the group velocity of the wave have the same direction (the case of normal dispersion), but it is also not uncommon for the group velocity of a wave to be opposite to the phase velocity (the case of anomalous dispersion).

The idea of exploiting the fundamental eigenmodes of our system with either normal or anomalous dispersion is what enables the chain of perforated shells to redirect and focus sound. Namely, an acoustic signal that impinges on the chain can trigger the collective vibration of the shells – the eigenmode – and, thus, launch a wave running along the chain.

Of course, most of the sound would pass through the chain, but nevertheless the amount of energy that is redirected along the chain in the form of an eigenmode is quite noticeable. The eigenmode excitation only occurs if the phase velocity of the eigenmode matches that of the incoming signal, and for a specific incident angle, the matching condition supports several frequencies within the audible range.

What is crucial here is that the dispersion of the chain’s eigenmodes on those frequencies is alternating between normal and anomalous, which means that varying only the frequency of the incident acoustic wave (with everything else remaining unchanged) one can virtually switch the direction of the eigenmode propagation along the chain.

Animation 1 – An acoustic wave of frequency 2625 Hz is incident on the chain of perforated shells at the angle of 10o. The excited eigenmode having anomalous dispersion propagates down the chain. Credit: Bozhko

Animation 2 – Same as in animation 1, but the frequency is 3715 Hz, with the excited eigenmode having normal dispersion now. The redirected sound then propagates upwards along the chain. Credit: Bozhko

Animations 1 and 2 illustrate such intriguing behavior of the chain of perforated shells. In one case, the eigenmode that is excited has normal dispersion and carries energy upwards along the chain. In the other case, the dispersion is anomalous and the eigenmode travels downwards. The 10° incidence angle of the sound in both cases is the same, but the frequencies are different.

One possible application of such a redirecting antenna would be an acoustic beam splitter. Indeed, if an incoming signal has a wide spectrum of frequencies, then two pure tones with frequencies depending on the parameters of the chain and the angle of incidence can be extracted and redirected along the chain.

Due to different dispersion behavior of the eigenmodes corresponding to these two tones, the eigenmodes propagate in opposite directions. Thus, splitting of two pure tones becomes possible if we use a chain of perforated shells. Since the frequencies of the eigenmodes change smoothly with changing incidence angle, this angle can be recovered. This means that the chain may also serve as a passive acoustic detector which determines the direction to the source of incoming signal.