Acoustics - Science topic

The second part of your question was too complex. I submitted some supplemental info.

Mk Karthi ,

Here's another list for you Mk Karthi .

Mel,

Distributed Acoustic Sensing (DAS) can indeed be used for the Multichannel Analysis of Surface Waves (MASW) purpose, and it offers a unique and versatile approach to seismic data acquisition. Typically, DAS utilizes a fiber optic cable as a distributed sensor to capture ground motion along its entire length. However, using traditional geophones planted on the surface in a vertical arrangement may not be the most effective way to complement DAS for MASW. Here's how you can effectively use DAS for MASW:

While DAS is a powerful tool for MASW surveys, the use of traditional geophones in a vertical arrangement on the surface may not be necessary in this context. DAS alone can provide comprehensive and high-resolution data for MASW purposes, making the additional use of geophones less common in such applications. However, the choice between DAS installation on the surface or in boreholes should be guided by the specific requirements of your project.

I wish I could help. Here's a shot in the dark:

Problem Statement:

The task is to effectively differentiate between tuff and dacite formations in work areas where both formations exhibit low GR (Gamma Ray) values and low DT (Acoustic Time Difference) values using well log data, specifically the natural gamma curve and acoustic time difference curve.

Solution Steps:

1. Data Collection and Preparation:

- Gather calibrated well log data, including natural gamma curve and acoustic time difference curve measurements.

2. Data Visualization and Analysis:

- Plot the well log data to identify depth intervals with low GR and DT values.

3. Statistical Analysis:

- Calculate mean, median, and standard deviation for both GR and DT curves in these depth intervals.

4. Threshold Determination:

- Set threshold values for GR and DT based on statistical analysis.

5. Formation Classification:

- Develop a classification algorithm to categorize formations as tuff or dacite.

6. Validation and Refinement:

- Validate results using core samples or nearby wells, refine the classification algorithm if needed.

7. Mapping and Visualization:

- Generate lithology maps using classified data.

8. Integration with Geological Models:

- Integrate classifications with geological models.

9. Documentation and Reporting:

- Document methodology, data sources, thresholds, and results.

10. Continuous Monitoring:

- Implement monitoring for changes in lithology.

References:

1. "Well Logging and Formation Evaluation" by Toby Darling

2. "Pattern Recognition and Machine Learning" by Christopher M. Bishop

3. Geological Society of America (GSA) Bulletin

4. "Introduction to Well Logs and Subsurface Maps" by Jonathan C. Evenick

5. "Geological Interpretation of Well Logs" by Malcolm Rider

6. "Log Analysis Handbook" by E. R. Crain

7. "Geological Well Logs: Their Use in Reservoir Modeling" by Mark S. Fayers

8. "The Log Analysis Handbook: A Comprehensive Guide to the Science of Log Analysis" by Oberto Serra

9. "Geostatistics for Natural Resources Evaluation" by Pierre Goovaerts

10. "Geostatistics: Modeling Spatial Uncertainty" by Jean-Paul Chilès and Pierre Delfiner

11. "Machine Learning: A Probabilistic Perspective" by Kevin P. Murphy

12. "Petroleum Geoscience" by Jon Gluyas and Richard Swarbrick

13. "Introduction to Artificial Intelligence" by Wolfgang Ertel

14. "Introduction to Geological Data Analysis" by Gareth Shaw

15. "Geological Methods in Mineral Exploration and Mining" by Roger Marjoribanks

These additional references cover various aspects of well log analysis, geological interpretation, machine learning, and geostatistics, providing a comprehensive resource list for tackling the problem effectively.

Individual viewpoints may please be put forward as per the above request

Raja Kumar Can you share the finite element of the room in an appropriate ASCII format?

In case of two coupled FM layers magnetized in-plane: For positive (FM-like) coupling, optical mode has higher resonant frequency, for negative (AFM-like) coupling, it is acoustic one which is higher in frequency.

Not recent but ...

Hi Pourya,

In Abaqus, infinite elements cannot be directly combined with Eulerian (CEL) elements. However, there are alternative approaches you can use to simulate wave propagation and absorption at the boundaries of your soil domain in a CEL model.

One such approach is to implement a non-reflecting boundary condition using a user subroutine. The following steps outline a possible procedure:

It's important to note that writing and implementing user subroutines in Abaqus may require some experience with FORTRAN programming and a solid understanding of the problem at hand. If you're not familiar with this, you may want to seek the help of someone with experience in writing Abaqus subroutines.

Additionally, keep in mind that this approach may not be as efficient as using infinite elements, but it should provide a reasonable approximation of wave absorption at the boundaries of your Eulerian soil domain.

I hope this helps!

Best regards,

Alessandro

Sorry for the delay in response. When monitoring sand in deep water subsea wells using an Acoustic Sand Detector (ASD), there are a few threshold values that can be used to indicate the presence of sand versus fluid flow. However, it's important to note that these thresholds may vary depending on the specific well conditions and the type of ASD being used.

Generally, an ASD will produce an output signal that represents the amplitude of the acoustic signal reflected from the wellbore. When sand is present in the fluid flow, it can cause a higher amplitude signal due to the reflection of acoustic waves from the sand particles. This signal can be analyzed to determine the amount and size distribution of sand particles present.

One threshold that can be used is the Sand Rate Threshold, which is the amplitude level at which the ASD output signal is considered to represent sand flow rather than fluid flow. This threshold value can vary depending on the specific ASD used, the well conditions, and the desired sensitivity of the sand monitoring system.

Another threshold that can be used is the Sand Alarm Threshold, which is a higher amplitude level at which an alarm signal is triggered to alert operators of high sand production rates. This threshold value is typically set based on the maximum acceptable sand production rate for the well, and can also vary depending on the specific well conditions and ASD being used.

It's important to note that these threshold values are not fixed and may need to be adjusted over time as the well conditions change. Additionally, it's important to have a good understanding of the specific ASD being used and its limitations to accurately interpret the output signals and make informed decisions about sand production rates in deep water subsea wells.

The formula is as follows

Howdy Borys Kapochkin,

My wife told me about the joy/sorrow event of an earthquake close to home there. Nature is unaware of kindness ~Tao Te Ching. You may enjoy learning while you experience sorrow: survivors in centuries to come will benefit from what you learn by study of the current events that are tragic for today's casualties .

I had been concerned about the emphasis that developed in this thread on pressure that does enhance evaporation, since it is only a small effect, while "that lucky old sun" is far more important in supplying energy for the molecular unrest. When you again have time for evaporation it will be good to feel the energy in the molecular unrest without undue emphasis on pressure. Just a thought for future discussion.

Happy Trails, with sympathy for unhappy experiences, Len

Harold Berjamin "The effect of applied stress on the phase and group velocity of guided waves in anisotropic plates". It was in this paper that I discovered the third-order elastic constant of T300/5208. In fact, I need the third order elastic constants in order to build the stress measurement model based on the Lamb wave acoustoelastic effect. Because the acoustoelastic effect of Lamb wave has the dispersive and multi-modal characteristics, it is necessary to select the optimized mode-frequency combination to achieve a better measurement effect. Moreover, the consistency between experimental and theoretical analysis results can better illustrate the accuracy of the measurement method.

Unfortunately not in my field of expertise. My main expertise is in Acoustics relating to assessment and prediction of environmental and building acoustics and the same for ground borne vibration. You have presumably done an online search on Google for this topic?

Good luck with your research.

it depends on purpose of your acoustic analysis or method used i guess.

If you are trying analyze voice of the speaker, then ceptral measures may remain constant( CPP) or HNR as well. F0 may remain similar, but other parameter such as jitter and shimmer may be influenced by noise and revrbration of the room.

If it is speech of speaker is the focus of analysis, then intonation pattern , no of pauses, may remain unaffected by room changes.

you can read articles published on acoustic analysis of children with cleft palate by different authors. formant freq is just one of features important for characterizing nasal vowels. bandwidth, amplitude and antiresonance features etc..are also need to be considered.

1.

2. Chen MY. Acoustic correlates of English and French nasalized vowels. J Acoust Soc Am. 1997 Oct;102(4):2360-70. doi: 10.1121/1.419620. PMID: 9348695.

3.

book : Speech Science Primer: Physiology, Acoustics, and Perception of Speech

yes , there are tests of hearing that are routinely done to detect changes in hearing thresholds when a subject is exposed to loud level of noise for a prolonged period of time.

1. Pure tone audiogram is a basic evaluation which can show some typical changes ( 4Khz notch)if some one is exposed hazardous level of noise

2. high frequency extended audiometry is important when exposure is recent. Effect on hearing maynot be that much. So High freq audiometry is an early warning sign

3. OAE- otoacoustic emissions are sensitive test of inner ears. Any changes , temporary or permanent can be pickedup early on , On this test.

Again like many opined here, duration of exposure is important factor along with frequency of sound and level of sound. For continuous noises like machinery noise / music, there is an occupational hearing health standards.

if noise level is 90dB and exposure is 8hrs/ day for 5 days a week then it can be damaging. levels of sound lower than 80dB are generally not considered hazardous.

That is right, there are as well analytical equations like u = a/omega^2 or u = v/omega. Unfortuntely this only holds true for a plane wave, as i assume your is with you reflection theorie.

I would like to find a way, to derive particle displacement in every FE solution, which does seem to be alot more difficult.

Hello Mahmoud Rastegaripour ,

In an unbounded solid, there are two types of elastic waves: 1) dilatational (longitudinal waves), and 2) distortional (transverse waves); the distortional waves arise because solids can support shear, which true liquids and gases cannot support. In a bounded solid, the surface is subjected to Rayleigh waves (surface waves). Rayleigh waves are similar - but not identical - to gravity waves found at the surface of a bounded liquid.

[1] H. Kolsky; Stress Waves in Solids; Dover Publications, Inc.; 1963; pp. 4 & 16.

[2] Francis Weston Sears, Mark W. Zemansky; University Physics, Part 1 - Mechanics, Heat, and Sound; Addison-Wesley Publishing Company, Inc.; 1963; pp. 488-491.

Regards,

Tom Cuff

Take a look at acoustic sensors based on piezo-polymer such as pvdf film. We produced such sensors\transducers with wideband spectral range 0.1-15 MHz and it works fine for high precision photo/optoacoustical mesurements. But unfortunately can't provide any information where to buy one

Depends on the characteristics in the subsea environment etc. generally, in a short distance channel (tens/hundreds of metres) you can get tens of kb/sec maximum depending on the system, in a long distance channel (several km) it diminishes down accordingly.

There are research papers studying acoustic MIMO systems that are looking to improve this.

M. Stojanovic has numerous papers in this field, I refer to her works regularly myself.

I and my undergrad student put together a list with several toolboxes at this address:

Matlab and Python based.

Hello everyone, I'm sorry I don't have an answer. But since this thread is related to flow visualization, I would like to let you know that we've started a Flow visualization Stack exchange forum. We are building a community currently. Once we have 60 people we will be allowed to proceed to a private beta version of the forum. Please join us if you are interested in flow vis and have questions to ask. Here's the link: https://area51.stackexchange.com/proposals/127312/flow-visualization?referrer=NTJlZjIyYzI3Zjk4N2I1NDZmMTJhZDUxMTViODcwMWUyNTM4OTI1YTU1OTYxN2ZkNDcwY2U2ZWI5NmU2OTY5OGhTtnNa4jEjFBFgB4o_K-u-LTdCqWX8yd8vul6HHcUb0

The elastic properties are described by the youngs moduluses, the density, the poissons ratios and any twisting moduluses (G-modules), as well as the damping. Harder materials have higher moduluses and tend to have lower damping, but not necessarily.

Next, the geometry of the material play an important role, as well as the boundary conditions. Eg a thicker plate will be more bending stiff. It vibrational mode shapes depend on the boundary conditions. Free free, simply supported edges, clamped edges, or combinations of these. The resulting damping will depend on the boundary conditions too, as in general damping is mode shape dependant.

If the material is isotropic the description becomes simpler. For ortotropic plates eg more elastic parameters and direction dependant damping may occur, as for wood or wood composite plates.

Well, you might imagine designing the membrane to have the same resonant frequency as the Helmholtz resonator; but I think that I would prefer to call that (e.g.) two degenerate resonant frequencies (due to their different underlying mechanisms); rather than to call it "one resonant frequency".

Jaedeok Seo Can you share your Comsol model in Nastran format (.bdf)?

Very interesting project.

I stumbled upon

There also are some other interesting findings when doing a google search for "training data animal sounds"

If you use Pipe Acoustics Transient Interface apply End Impedance and set zero reflection condition.

See COMSOL Documentation:

Acoustics Module > User's Guide > Pipe Acoustics Interfaces > The Pipe Acoustics Frequency Domain and Transient Interfaces > End Impedance

Good luck!

Do you mean like "speaker recognition"? If so, wikipedia provides the following summary of methods:

"Speaker recognition is a pattern recognition problem. The various technologies used to process and store voice prints include frequency estimation, hidden Markov models, Gaussian mixture models, pattern matching algorithms, neural networks, matrix representation, vector quantization and decision trees. For comparing utterances against voice prints, more basic methods like cosine similarity are traditionally used for their simplicity and performance. Some systems also use "anti-speaker" techniques such as cohort models and world models. Spectral features are predominantly used in representing speaker characteristics. Linear predictive coding (LPC) is a speech coding method used in speaker recognition and speech verification".

The following research article is related to this thread:

Best Regards.

Dear Mohammed Helal,

There are some mechanisms of interaction of electromagnetic and acoustic waves. For example Acoustooptics and Optoacoustics effects. You can find the theory in Internet

Dear colleague Miroslav Rusko, thank you for the information

شكرا جزيلا زميلي العزيز

Hello again, Farah. I see from your profile that your interest is primarily in phonetics. I am not a phonetics expert, but I know that Praat (https://www.fon.hum.uva.nl/praat/) is a very widely used package in this field, specifically designed for phonetic analysis, with a large set of features. It is free and open source and runs on most operating systems. Although it can be challenging to learn, there is an enormous amount of documentation online provided by the many users around the world.

Hello Rolf,

I plan to use it on an aquarium / lab during days, a couple of weeks maximum.

We were using this (https://dnhloudspeakers.com/loudspeakers/underwater/aqua-30-2/) during a time but didn't work very good and easily get broken when we use more than 100 dB. I'm looking for something similar but more durable.

Thanks for your help!

The following RG link is also very useful:

I am not sure but Restart analysis could work in your case. Define input parameter (dot_k) for the time limit up to the loading phase. Termination time should be equal to the time duration of the loading phase. After, normal termination you should have a dump file. Further, modify your dot_k file based on the requirement of non-reflecting surface and then do a Full restart analysis using earthe lier created dump file and modified input file.

I hope that this will work for you.

Thanks,

Suman

Dear Abhishek,

If I understand well your question, you are looking for a simple analytical model for the prediction of MPPs impedance.

Such model is given by Maa (1998) [ ], Eq. (5).

Note that the impedance is a complex number.

I hope that helps

Regarding your question on the Nocke paper, Equation 6 is derived from the previous 5 equations. The numerical value of Equation 6 depends on the values chosen for the parameters b, d, t and D. If the system is linear, the impedance should not be dependent on sound pressure, so I am guessing that the p in the denominator should not be there.

However, If you look up matching layers in general, you will find that they are quarter wave (in the matching layer material) thick and of impedance (Zh*Zw)^.5, where Zh is the acoustic impedance of the horn material and Zw the acoustic impedance of the water (1.5 MRayls). Ideally, you should also have a matching layer from the transducer into the horn as well, same formula but using the impedances of the ceramic and horn material. The matching layer is designed for one frequency (where the matching layer is a quarter-wave thick) so will not help a lot for high bandwidth (very short) pulses.

I think you may find what you need on the website of the transducer manufacturer, who may have application notes about the use of transducers, or the website of the horn manufacturer. They may all be optimised for water, or IPA or MEBK.

I am currently looking for the concept of Sezawa wave. And I find one review paper on Sezawa SAW devices ( ).

you can see the following links:

1.

2. https://www.lesker.com/newweb/technical_info/questions/thinfilms.cfm

3. https://www.linkedin.com/pulse/determination-acoustic-impedance-ratio-thermal-april-19-kyle-fenton

Yes.

If you want to learn more about acoustic of porous media, you can refer to the book of Allard and Atalla, "Propagation of sound in porous media" or to the Matelys lab website https://apmr.matelys.com/

The propagation models are listed and explained in both references.

Thomas Cuff , of course I am at liberty. But this is more general, than you think. For example, ballistic calculator in rifles upon shooting.

The article says that high intensity waves are non-linear. If waves are non-linear they interact with each other. If they are linear, then that do not interact with each other. This means that they each behave as if the other is not there.

Calculations using ASR help us to get rid from the first three mahipal negative frequencies which sometimes is because of the computational errors .

So there are many methods of ASR as implemented in quantum espresso like

asr ='simple'

asr= 'crystal'

In q2r.in and matdyn.in you can add these blocks and can compare the phonons for better understanding.

At any way statistically standard deviation can be used to refer for the root mean square value, of signal, then separate information signal from noisy signal by the means of signal processing to calculate SNR.

Dr. Utkarsh Chhibber, in addition to all the instructive & pedagogical answers to your thread, I would like to mention that in acoustics, the Green function method can be used also to study the time-reversal invariance problem.

Please look at this very instructive review article:

Open access link:

.../royalsocietypublishing.org/doi/10.1098/rsta.2015.0156

Best Regards.

Try to calculate the lighting and shadow zones from light sources placed at different points on the relief. Naturally, the light sources should be higher than the treetops. The situation is paradoxical in that the points that create the maximum illuminated area are located near the depressions of the relief, and not near the peaks.

HI Nazeer Ahmad

I have a suggestion for you the Comsol Multiphysics to model the acoustic systems, it's a software wich based on finit element metod to solve the model. you can also found a librery on acoustics field integreted in this software .please check the link:

Let's say I play a voice signal that will from a phone speaker and then capture it with the same phone's mic. If AEC is there, a played signal will be set as a reference signal and it will remove the captured signal. That's what I think.

Do you have measurements or a numerical model to compare with (validation/verification)? If not, then it is difficult to judge which is 'best', unless there are some clear errors or unrealistic assumptions in the different models.

Hello,

You're talking about the Broadband Noise Source Model, correct?

Keeping in mind that it is a near-field approach, the model does not predict the sound at receivers far away from the flow field. Otherwise, it is a fairly good prediction.

The model can also be employed to extract diagnostic information on the noise source to check portions of the flow primarily responsible for noise generation.

In what way and why would you like to see acoustic infinite elements? They are just there to approximate a BC free of reflection (or a Sommerfeld condition). Thus, by them self there is noting to see, but an incoming wave will not result in a reflected sound field.

Just to complement the accurate comment of Malcolm, it shows this test bed allows a well mastered rotational speed increase, because the sloping bands are so perfectly linear! When doing such measurement on an assembled machine in standard operation, the powertrain RPM increase with time would not generally be linear: anyway all sloping bands would appear homothetic as they are perfectly harmonic (the number of teeth of each gear is invariant). The graph would not be easy to read if the measurement would be taken at constant speed as the gears signatures would show-up as vertical lines, possibly confused with the natural frequencies. So to follow the gearbox vibrational signature you better monitor the start or stop periods, unless you look for possible unsteadiness issues (e.g. instabilities) where constant speed tests are more relevant...

As a whole this graph is usually described as a "waterfall plot". It belongs to the global family of time-frequency representations. In this example, the right setting of the colour scale is essential for maximizing the legibility of the gears signatures!

Eivind Nag Mosland I think you can just add a domain which has the surface that you want to be averaged. Mesh it. And make an average operation over pext on that surface. Works fine.

visit the following link below:

For the part relating to the listeners, yes. For the recognition and measurement an earlier echo detection may be useful for potential risky ones. I have never processed this myself, but have a lot of room acoustics measurement and prediction experience. To figure out if there are echoes in rooms or measured impulse responses listening to the impulse responses is the best method in my experience. You need to take that further. Althoiugh video conference systems and possibly mobile phones have such algoritmes already. Theree should be vast amounts of literature on the phenomenon.

I also have a conference article on the acoustics of coupled rooms. The environments needs to be drier than usual to work well. It is impossible to get rid of reverb that are already on the signal..

I just want to get output of it and also 2-3 pages of of its diagram .

Thank you very much sir. René Christensen

I think the following research work related to the acoustical studies on wood can be of some use to you to figure out the answer to your question.

Acoustic Studies on Wood - University of Canterbury

ir.canterbury.ac.nz › handle › thesis_fulltext

PDF

by HJ Hansen · 2006 · Cited by 13 — structural timber in housing, wood-based panels and pulp and paper. Wood shows ... thought that the acoustic waves find their fastest path within a distance of.

I can now answer my own question - BRT models circumvent temporal autocorrelation by randomly selecting (without replacement) a proportion of the data at each iteration, which is controlled by the bag fraction argument. This data is also further split during the cross-validation (CV) process. Thus, the probability of a contiguous time-series being used is extremely small.

The threshold level depends strongly on what material system you are looking at in general. Composites will produce a high level of incoherent (but structured) noise. I would suggest testing at small distances and going farther and father but I guess you can't go too far because of the large attenuation. After all, this will be very much like empirical where some preliminary efforts are required. Good luck!

Hi

A hand sketch or similar of the setup would help.

Impedance BC only apply for normal angle of incidence. They work on boundaries at a distance from the source, say one wavelength out or so and then, still are reflecting.

As per usual - the advice when tweaking models - start with simulating a Kundt's tube experiment first, as you there can use impedance BCs. Move to 3d from there.

/C

Hello,

Here are 2 papers that are related to your problem. They can be uploaded following this link : http://www.buchner-net.com/mcaec.html

I hope this will help,

Pascal

Mariano J. Feldman, when considering the effects of background noise on detection capabilities of specific sounds, you would want to measure sound levels over the frequency range of the signal (sound) of interest. For example if frogs produce sounds from 50 Hz to 1 kHz, you may find it useful to look at background noise over that bandwidth. Filters can be used to measure the RMS levels over specific bands of frequencies, but PSD values are probably more useful as you can integrate those values over the desired frequencies. Also 1/3 Octave and Octave levels are useful for monitoring patterns in sound levels over standardized frequency ranges. I am not sure if you are familiar with R or Matlab, but I would search for a program called PamGuide by Merchant et al. if you do not want to write your own code. There is also a paper associated with that software that is useful for understanding those measurements, also by Merchant et al. Lastly, when considering background noise, you will need to be aware that the signals of interest will likely influence those background noise levels. Picking a time when those signals do not occur will give you an understanding of baseline sound levels in the absence of those sounds.

I have used it for feature selection purposes in my study but did not work better than other methods (PCA and LDA).

rica(X,q,Name,Value is the implemented function in MATLAB2019.

René Christensen ,

Far-feild acoustic pressure is measured from the field at a point which is atleast two wavelenghs away from the source.

By subtracting the total pressure from the mean pressure we get the acoustic pressure.

hello,

the room impulse response h = [h(1) h(2) ....h(N)] is modelled as a digital filter with a L taps-finite impulse response (FIR). In room acoustics, to measure this impulse response, a white noise (or pseudo-white noise as MLS sequence) x[n] n=0,1,... is presented at the input of a lousdspeaker, and the corresponding received signal y[n], n=0,1,.... is recorded at the output of a microphone that is placed in the same acoustical environment.

This room impulse response is then estimated by computing the intercorrelation function betwenen the received and transmited signal :

Ryx[k] = E( y[n] x[n+k] )

= h[n] * Rxx[k]

= h [n]

since the autocorrelation function of the input sequence is Rxx[k]= Dirac_Impulse[k] (as we have used a (pseudo)-white noise).

So by computing the intercorrelation function between the known transmitted (i.e; x[n]) and received (i.e. y[n]) sequences, we have directly access to an estimate of the L-taps of the room impulse response h .

I hope this will help.

Pascal.