1. A method of predicting an active sonar array’s performance, comprising the steps of:
specifying an acoustic transmitter and an acoustic receiver of said active sonar array;
selecting a geographical location;
obtaining a bathymetry, a bottom composition and a sound speed profile database for said selected geographical location;
calculating a bottom-loss-and-scattering corrected-for-slope for said geographical location using said bottom composition and said bathometry; and
calculating a deterministic component of surface reverberation using said bathometry, said bottom-loss-and-scattering corrected-for-slope and said sound speed profile database.
2. The method of claim 1 wherein said step of calculating a deterministic component of surface reverberation further comprises modeling an acoustic signal propagating from said acoustic transmitter to said acoustic receiver using a normal mode model.
3. The method of claim 2 further comprising the steps of enhancing the spatial resolution of said bathymetry using fractal interpolation to provide an enhanced bathometry; and wherein said step of calculating a bottom-loss-and-scattering corrected-for-slope further comprises using said enhanced bathometry.
4. The method of claim 3 further including the step of characterizing said enhanced bathymetry to produce surface-roughness wave-numbers, and wherein said step of calculating a deterministic component of surface reverberation further comprises using said surface roughness wave-numbers.
5. The method of claim 4 further comprising the steps of obtaining a wind-speed data base; obtaining a semi-empirical scattering strength from said wind-speed data base; and calculating a deterministic component of surface reverberation using said semi-empirical scattering strength.
6. The method of claim 5 further comprising the steps of obtaining a biologic population database; using said biologic population database to provide a stochastic biologic realization; and calculating a deterministic component of volume reverberation using said stochastic biologic realization.
7. The method of claim 1 further comprising the steps of obtaining a Generalized Gamma distribution database, and calculating a stochastic realization of clutter using said Generalized Gamma distribution database.
8. The method of claim 7 wherein said Generalized Gamma distribution database comprises parameters alpha, beta and c that provide a best fit of equation:
f
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to experimental data from said selected bathymetry or to a bathymetry analogous to said selected bathometry, where f(x) represents a probability of a false alarm, x represents a strength of a return signal relative to a detection threshold of said active sonar, alpha, beta and c are numeric constants and \u0393(\u03b1) is the Gamma function which is related to the factorial function by \u0393(\u03b1)=(\u03b1\u22121)!.
9. The method of claim 7 wherein said step of calculating a stochastic realization of clutter further comprises modeling an acoustic signal propagating from said acoustic transmitter to said acoustic receiver using a normal mode model.
10. The method of claim 1 in which said active sonar array is responsive to frequencies in the range of 1 to 40 kHz.
11. An apparatus for predicting an active sonar array’s performance, comprising:
a computer configured to be capable of:
specifying an acoustic transmitter and an acoustic receiver of said active sonar array;
selecting a geographical location;
obtaining a bathymetry, a bottom composition and a sound speed profile database for said selected geographical location;
calculating a bottom-loss-and-scattering corrected-for-slope for said geographical location using said bottom composition and said bathometry; and
calculating a deterministic component of surface reverberation using said bathometry, said bottom-loss and scattering corrected for slope and said sound speed profile database by modeling an acoustic signal propagating from said acoustic transmitter to said acoustic receiver using a normal mode model.
12. The apparatus of claim 11 wherein said computer is further configured to be capable of enhancing the spatial resolution of said bathymetry using fractal interpolation to provide an enhanced bathometry; characterizing said enhanced bathymetry to produce surface-roughness wave-numbers, and wherein said calculating a deterministic component of surface reverberation further comprises using said surface roughness wave-numbers.
13. The apparatus of claim 11 wherein said computer is further configured to be capable of obtaining a Generalized Gamma distribution database; and calculating a stochastic realization of clutter using said Generalized Gamma distribution database by modeling an acoustic signal propagating from said acoustic transmitter to said acoustic receiver using a normal mode model.
14. The apparatus of claim 13 wherein said Generalized Gamma distribution database comprises parameters alpha, beta and c that provide a best fit of equation
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to experimental data from said selected bathymetry or to a bathymetry analogous to said selected bathometry, where f(x) represents a probability of a false alarm, x represents a strength of a return signal relative to a detection threshold of said active sonar, alpha, beta and c are numeric constants and \u0393(\u03b1) is the Gamma function which is related to the factorial function by \u0393(\u03b1)=(\u03b1\u22121)!.
15. A device for predicting an active sonar array’s performance, comprising:
means for specifying an acoustic transmitter and an acoustic receiver of said active sonar array;
means for selecting a geographical location;
means for obtaining a bathymetry, a bottom composition and a sound speed profile database for said selected geographical location;
means for calculating a bottom-loss-and-scattering corrected-for-slope for said geographical location using said bottom composition and said bathometry; and
means for calculating a deterministic component of surface reverberation using said bathometry, said bottom-loss and scattering corrected for slope and said sound speed profile database by modeling an acoustic signal propagating from said acoustic transmitter to said acoustic receiver using a normal mode model.
16. The device of claim 15 further comprising:
means for enhancing the spatial resolution of said bathymetry using fractal interpolation to provide an enhanced bathometry; means for characterizing said enhanced bathymetry to produce surface-roughness wave-numbers, and wherein said means for calculating a deterministic component of surface reverberation further comprises using said surface roughness wave-numbers.
17. The device of claim 15 further comprising:
means for obtaining a Generalized Gamma distribution database; and means for calculating a stochastic realization of clutter using said Generalized Gamma distribution database by modeling an acoustic signal propagating from said acoustic transmitter to said acoustic receiver using a normal mode model.
18. The device of claim 17 wherein said Generalized Gamma distribution database comprises parameters alpha, beta and c that provide a best fit of equation
f
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to experimental data from said selected bathymetry or to a bathymetry analogous to said selected bathometry, where f(x) represents a probability of a false alarm, x represents a strength of a return signal relative to a detection threshold of said active sonar, alpha, beta and c are numeric constants and \u0393(\u03b1) is the Gamma function which is related to the factorial function by \u0393(\u03b1)=(\u03b1\u22121)!.
19. A computer-readable medium, comprising instructions for: specifying an acoustic transmitter and an acoustic receiver of said active sonar array;
selecting a geographical location;
obtaining a bathymetry, a bottom composition and a sound speed profile database for said selected geographical location;
calculating a bottom-loss-and-scattering corrected-for-slope for said geographical location using said bottom composition and said bathometry; and
calculating a deterministic component of surface reverberation using said bathometry, said bottom-loss and scattering corrected for slope and said sound speed profile database by modeling an acoustic signal propagating from said acoustic transmitter to said acoustic receiver using a normal mode model.
20. A computing device comprising: a computer-readable medium comprising instructions for:
specifying an acoustic transmitter and an acoustic receiver of said active sonar array;
selecting a geographical location;
obtaining a bathymetry, a bottom composition and a sound speed profile database for said selected geographical location;
calculating a bottom-loss-and-scattering corrected-for-slope for said geographical location using said bottom composition and said bathometry; and
calculating a deterministic component of surface reverberation using said bathometry, said bottom-loss and scattering corrected for slope and said sound speed profile database by modeling an acoustic signal propagating from said acoustic transmitter to said acoustic receiver using a normal mode model.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A device for use in energy storage comprising:
(a) a composite electrode structure comprising a nanostructured electrically conductive substrate interpenetrated by a continuous mesoporous network and coated with a metal oxide; and
(b) an ultrathin conformal polymer coating on said metal oxide;
wherein said composite electrode structure has a mesoporous structure and said metal oxide retains said mesoporous network.
2. The device of claim 1 wherein said metal oxide is selected from the group consisting of manganese oxides, vanadium oxides, nickel oxides, iron oxides, and physical or compositional mixtures thereof.
3. The device of claim 1 wherein said polymer coating is based on an aryl monomer that can be electrochemically oxidized under self-limiting conditions.
4. The device of claim 1 wherein said polymer coating is selected from the group consisting of o-phenylenediamine, aniline, and mixtures thereof.
5. The device of claim 1 wherein said composite electrode structure has a porous architecture and wherein said polymer coating is deposited electrochemically in a self-limiting fashion on said metal oxide such that said metal oxide architecture retains said porosity.
6. The device of claim 1 wherein said polymer coating is less than 10-nm thick.
7. The device of claim 1 wherein said nanostructured electrically conductive substrate is porous carbon.
8. A method for making a device for use in energy storage, comprising the steps of:
(a) preparing a composite electrode structure comprising a nanostructured electrically conductive substrate interpenetrated by a continuous mesoporous network and coated with a metal oxide; and
(b) depositing a polymer coating on said metal oxide;
wherein said composite electrode structure has a mesoporous structure and said metal oxide retains said mesoporous network.
9. The method of claim 8 wherein said metal oxide is selected from the group consisting of manganese oxides, vanadium oxides, nickel oxides, iron oxides, and physical or compositional mixtures thereof.
10. The method of claim 8 wherein said polymer coating is based on an arylamine monomer.
11. The method of claim 8 wherein said polymer coating is selected from the group consisting of o-phenylenediamine, aniline, and mixtures thereof.
12. The method of claim 8 wherein said composite electrode structure has a porous architecture and wherein said polymer coating is deposited electrochemically in a self-limiting fashion on said metal oxide such that said metal oxide architecture retains said porosity.
13. The method of claim 8 wherein said polymer coating is less than 10-nm thick.
14. The method of claim 8 wherein said nanostructured electrically conductive substrate is porous carbon.
15. An electrode for use in energy storage comprising:
(a) a nanostructured electrically conductive substrate interpenetrated by a continuous mesoporous network and a metal oxide coating; and
(b) an ultrathin conformal polymer coating on said metal oxide.
16. The electrode of claim 15 wherein said nanostructured electrically conductive substrate is porous carbon.
17. The electrode of claim 15 wherein said metal oxide coating retains said mesoporous network.
18. The electrode of claim 15 wherein said metal oxide coating is selected from the group consisting of manganese oxides, vanadium oxides, nickel oxides, iron oxides, and physical or compositional mixtures thereof.
19. The electrode of claim 15 wherein said polymer coating is selected from the group consisting of o-phenylenediamine, aryl monomer, aniline, and mixtures thereof and wherein said polymer coating can be electrochemically oxidized under self-limiting conditions.
20. The electrode of claim 15 wherein said polymer coating is less than 10-nm thick.