1. Advanced Acoustical Modeling Tools for ESME
Martin Siderius and Michael Porter
Science Applications Int. Corp.
10260 Campus Point Dr., San Diego, CA

2. Acoustic Modeling Goals
Through modeling, try to duplicate sounds heard by marine mammals (e.g. SONAR, shipping)
Develop both high fidelity and very efficient simulation tools

3. Acoustic Modeling Goals
Accurate field predictions in 3 dimensions
Computational efficiency (i.e. fast run times)
Propagation ranges up to 200 km
R-D bathymetry/SSP/seabed with depths m
Frequency band 0-10 kHz (or higher)
Moving receiver platform
Arbitrary waveforms (broadband time-series)
Directional sources
Difficult task for any single propagation code
Approach is to use PE, Rays and Normal Modes

4. Model Comparisons Accuracy Rays NM and PE Computation Time Rays
Frequency
Frequency

5. Motivation

6. Fast Coupled NM Method
Range dependent environment is treated as series of range independent sectors
Each sector has a set of normal modes
Modes are projected between sectors allowing for transfer of energy between modes (matrix multiply)
Algorithm marches through sectors
Speeds up in flat bathymetry areas
Pre-calculation of modes allows for gains in run-time (important for 3D calculation)
Very fast at lower frequencies and shallow water

7. Mid Atlantic Bight: Example

9. Mammal Risk Mitigation Map
5 dB more loss
5 dB less loss
SD = 50 m
SL = 230 dB
Freq = 400 Hz
Lat = 49.0o N
Long = 61.0o W

10. Shipping Simulator
Using the fast coupled normal-mode routine shipping noise can be simulated
This approach can rapidly produce snapshots of acoustic data (quasi-static approximation)
Self noise can also be simulated (i.e. on a towed array)
Together with a wind noise model this can predict the background ambient noise level

11. Example: Simulated BTR
Input environment, array geometry (e.g. towed array hydrophone positions) and specify ship tracks (SL, ranges, bearings, time)

12. Example: BTR from SWELLEX96

13. Computing Time-Series Data for Moving Receiver
How is the impulse response interpolated between grid points?
How are these responses “stitched” together?

14. 1. Interpolating the Impulse Response
In most cases the broad band impulse response cannot be simply interpolated
For example, take responses from 2 points at slightly different ranges:

15. 2. “Stitching” the Responses Together
Even if the impulse response is calculated on a fine grid, there can be glitches in the time-series data (due to discrete grid points)
For example, take the received time-series data at points 1 m apart:

16. Solution: Interpolate in Arrival Space
The arrival amplitudes and delays can be computed on a very course grid and since these are well behaved, they can be interpolated for positions in between.
Using the “exact” arrival amplitudes and delays at each point, the convolution with the source function is always smooth.

17. Ray/Beam Arrival Interpolation
Interpolated
Endpoint #2
Endpoint #1
Advantage: very fast and broadband

18. Test Case: Determine Long Time Series Over RD Track
Source frequency is 3500 Hz
Source depth is 7 m
Environment taken from ESME test case
Receiver depth is m
Receiver is moving at 5 knots

19. TL

20. Received Time-Series

21. Received Time-Series

22. Received Time-Series (with Source Functions)

23. Computing TL Variance Fast Coupled Mode approach allows for:
TL computations in 3D (rapid enough to compute for several environments)
Changing source/receiver geometry
Ray arrivals interpolation allows for Monte-Carlo simulations of TL over thousands of bottom types to arrive at TL variance

24. Ray/Beam Arrival Interpolation
Interpolated
Endpoint #2
Endpoint #1
Advantage: very fast and broadband

25. Does it work? TL example 100-m shallow water test case:
Source depth 40-m
Receiver depth 40-m
Downward refracting sound speed profile
350 Hz
3 parameters with uncertainty:
Sediment sound speed m/s
Sediment attenuation dB/l
Water depth m

26. Does it work? TL example
Interpolated (red) is about 100X faster than calculated (black)

27. TL Variance

28. TL Variance