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Beam profile measurements using light refractive tomography.

Ludwig Bahr¹, Reinhard Lerch

¹Department of Sensor Technology, Friedrich-Alexander-University, Erlandgen-Nuremberg, Germany. Ludwig.Bahr@lse.eei.uni-erlangen.de

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|March 13, 2008

Summary

This summary is machine-generated.

This study introduces light refractive tomography to reconstruct sound pressure profiles from interferometer data. It compares three methods, offering insights into acoustic measurement accuracy.

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Area of Science:

Acoustics and Optics
Tomography and Signal Processing

Background:

Interferometer measurements are crucial for acoustic analysis.
Reconstructing sound pressure profiles requires advanced computational methods.
Existing techniques may have limitations in accuracy and applicability.

Purpose of the Study:

To present a novel method for sound pressure profile reconstruction using computed tomography.
To introduce and evaluate 'light refractive tomography' based on sound pressure-induced refractive index changes.
To compare the performance of three distinct tomographic reconstruction algorithms for axisymmetric beam profiles.

Main Methods:

Development of light refractive tomography (LRT) based on computed tomography principles.
Application and comparison of three LRT reconstruction algorithms: filtered back-projection, Hankel Fourier method, and onion peeling (Nestor and Olsen algorithm).
Validation of results through comparison with traditional hydrophone measurements.

Main Results:

The study demonstrates the feasibility of reconstructing sound pressure profiles using LRT.
Comparative analysis highlights the performance characteristics of the three evaluated tomographic algorithms.
Hydrophone measurements serve as a benchmark for validating the accuracy of the LRT methods.

Conclusions:

Light refractive tomography offers a viable approach for non-invasive sound pressure profile reconstruction.
The choice of tomographic algorithm impacts the accuracy and fidelity of the reconstructed acoustic field.
Further research should consider the influence of noise on the performance of these reconstruction algorithms.