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Technical Consulting and Research Support

James S. Hall, Ph.D., P.E.

Research Overview

The following represents a very brief introduction to both past and on-going research.  All of the work referred to on this page has been published in some fashion.  Readers interested in additional details are referred to the published works cited when the mouse hovers over the section title (The publications are available for download from the Publications page).

Flow Estimation with Ultrasonic Sensor Array

Flow Estimation DiagramThe goal of this research effort was to measure flow (of a liquid or gas) across an interface, such as a doorway, without obstructing the interface.  This was achieved by sending an ultrasonic wave across the interface and looking at the reflected wavefront using an array of ultrasonic sensors.  From the perspective of the sensor array, the reflected wave will appear as though it originated from a location behind the reflector and shifted in the direction of flow.  The use of an array of ultrasonic sensors allows this shift to be quantified, which can then be converted to the flow rate that was present across the interface.

Relevant Publications
  • J. S. Hall, "Perpendicular fluid flow measurement with a spatial array of ultrasonic transducers", IEEE International Ultrasonics Symposium Proceedings, pp. 741-744, 2010.
    Accepted / Published

Structural Health Monitoring

The problem being addressed by the three efforts described below is largely that of damage detection and localization in plate-like structures using a set of permanently attached ultrasonic transducers.  The term "plate-like" is intentionally somewhat vague, and refers to flat, thin structures, such as aircraft skins, storage tank walls, ship hulls, bridge support gusset plates, etc., that are capable of supporting ultrasonic guided waves.  Ultrasonic guided waves are elastic waves that propagate along the plate, and are of interest for structural health monitoring applications because they are capable of propagating relatively long distances and are sensitive to both surface and subsurface features.

It is important to note that although the techniques and mathematical algorithms described here are applied to ultrasonic guided waves for structural health monitoring, they can be adapted to a wide range of applications and media, from seismic and sonar applications to radar and telecommunications.

Minimum Variance Imaging

Guided wave imaging is used to graphically display the data recorded from a set of ultrasonic transducers.  Ideally, the image can then be used to determine if the structure is damaged, and if so, where the damage is located.

Conventional ImageThe figure to the left was generated using the conventional (delay-and-sum) guided wave imaging algorithm.  Data was recorded from six piezoelectric transducers, which are arranged in an arbitrary pattern and depicted as small, white 'o's in the image on the left.  The structure itself is a 4'x4' plate of 1/8" thick aluminum 6061 with a 5 mm diameter through hole drilled in the plate at the location of the '+'.  The image is shown on a 20 dB color scale normalized so that the damage location has a value of 0 dB.

By reformulating the conventional guided wave imaging algorithm and incorporating a technique referred to as MVDR, significant improvements in imaging performance can be obtained.  The new imaging algorithm is referred to as minimum variance imaging.  The figure below illustrates minimum variance imaging with the same data as that used for the previous figure.  It should be pointed out that the improved imaging performance is obtained with only minor additional computational demands.MV Imaging

Although this image represents a dramatic improvement over conventional imaging, a significant amount of information is being ignored: that of phase information.  In order to use phase information, however, the dispersive nature of the guided waves and any variations in the transducers must be both characterized and compensated.

Relevant Publications
  • J. S. Hall and J. E. Michaels, "Minimum variance ultrasonic imaging applied to an in situ sparse guided wave array", IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 57 (10), pp. 2311-2323, 2010.
    Accepted / Published
  • J. S. Hall, P. McKeon, L. Satyanarayan, J. E. Michaels, N. F. Declercq, and Y. H. Berthelot, "Minimum variance guided wave imaging in a quasi-isotropic composite plate", Smart Materials and Structures, 20 (2), 025013, 2011.
    Accepted / Published
  • J. S. Hall and J. E. Michaels, "Computational efficiency of ultrasonic guided wave imaging algorithms", IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 58 (1), pp. 244-248, 2011.
    Accepted / Published
  • J. S. Hall, Adaptive Dispersion Compensation and Ultrasonic Imaging for Structural Health Monitoring, Georgia Institute of Technology, 2011.
    Download / Published

Model-Based Parameter Estimation

Model-based parameter estimation (MBPE) is an algorithm that was developed to provide data-driven estimates of dispersion, propagation loss, relative sensor distances, and transducer transfer functions, with minimal a priori information.  The algorithm is unique in that it is the only method currently available to estimate these parameters using a distributed array of sensors (such as considered here).  This is an important feature because these parameters change with the environment (temperature, pressure, load, etc.).  By solving for all of the parameters simultaneously and avoiding potentially erroneous a priori information, the resulting estimates are as accurate as possible.

Relevant Publications
  • J. S. Hall and J. E. Michaels, "Model-based parameter estimation for characterizing wave propagation in a homogeneous medium", Inverse Problems, 27 (3), 035002, 2011.
    Accepted / Published
  • J. S. Hall and J. E. Michaels, "A model-based approach to dispersion and parameter estimation for ultrasonic guided waves", Journal of the Acoustical Society of America, 127 (2), pp. 920-930, 2010.
    Accepted / Published
  • J. S. Hall, Adaptive Dispersion Compensation and Ultrasonic Imaging for Structural Health Monitoring, Georgia Institute of Technology, 2011.
    Download / Published

Adaptive Parameter Compensation

Adaptive MV ImagingWith estimates of both the dispersive nature of the material and the transducer transfer functions obtained from the MBPE algorithm, parameter compensation can be performed.  The figure to the left illustrates imaging performance achieved by (1) adaptively estimating and compensating for dispersion and transducer variations, and (2) using phase information in the imaging algorithm.  This figure represents a significant improvement over both of the previous images.  It should be pointed out that all three images were generated using the same experimental data.  The only difference between the images is how the data was interpreted and processed.  The marked improvement in imaging performance underscores both the importance of signal processing and the potential impact that it can have.

Relevant Publications
  • J. S. Hall, Adaptive Dispersion Compensation and Ultrasonic Imaging for Structural Health Monitoring, Georgia Institute of Technology, 2011.
    Download / Published

Other Applications

The research presented here is intended to provide a glimpse into the power of signal-processing and multi-channel estimation.  Please contact me to discuss your specific application and how I may be of assistance to your team.