Advanced Acoustical Modeling Tools for ESME Martin Siderius and Michael Porter Science Applications Int. Corp. 10260 Campus Point Dr., San Diego, CA sideriust@saic.com michael.b.porter@saic.com

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

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 0-5000 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

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

Motivation

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

Mid Atlantic Bight: Example

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

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

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

Example: BTR from SWELLEX96

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

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:

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:

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.

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

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 7-100 m Receiver is moving at 5 knots

TL

Received Time-Series

Received Time-Series

Received Time-Series (with Source Functions)

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

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

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 1525-1625 m/s Sediment attenuation 0.2-0.7 dB/l Water depth 99-101 m

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

TL Variance

TL Variance