Dr. Goutham R. Kirikera Profile

Dr. Goutham R. Kirikera

Graduate Research Assistant at Northwestern Univ

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Publications (15)

Proceedings Article | 9 April 2009 Paper

Ultrasonic camera automatic image depth and stitching modifications for monitoring aerospace composites

Brad Regez, Goutham Kirikera, Martin Tan Hwai Yuen, Sridhar Krishnaswamy, Bob Lasser

Proceedings Volume 7295, 72952H (2009) https://doi.org/10.1117/12.815891

KEYWORDS: Ultrasonics, Cameras, Imaging systems, Inspection, Composites, Computer programming, Ultrasonography, Multichannel imaging systems, Image processing, Aerospace engineering

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Proceedings Article | 10 April 2008 Paper

Optimal demodulation of wavelength shifts in fiber Bragg grating sensors using an adaptive two wave mixing photorefractive interferometer

Oluwaseyi Balogun, Goutham Kirikera, Sridhar Krishnaswamy

Proceedings Volume 6932, 69322K (2008) https://doi.org/10.1117/12.778013

KEYWORDS: Sensors, Fiber Bragg gratings, Demodulation, Interferometers, Two wave mixing, Crystals, Photodetectors, Ferroelectric materials, Signal to noise ratio, Beam splitters

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Proceedings Article | 19 May 2007 Paper

Continuous sensors for mode selective actuation and reception of waves for structural health monitoring of woven composite laminates

Goutham Kirikera, Gabriela Petculescu, Sridhar Krishnaswamy, Jan Achenbach

Proceedings Volume 6532, 65321H (2007) https://doi.org/10.1117/12.715235

KEYWORDS: Sensors, Electrodes, Composites, Wave propagation, Transducers, Structural health monitoring, Actuators, Sensor networks, Aluminum, Analog electronics

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Proceedings Article | 10 April 2007 Paper

Multiple damage identification on a wind turbine blade using a structural neural system

Goutham Kirikera, Mark Schulz, Mannur Sundaresan

Proceedings Volume 6530, 65300T (2007) https://doi.org/10.1117/12.716071

KEYWORDS: Sensors, Structural health monitoring, Composites, Algorithm development, Data acquisition, Wave propagation, Actuators, Acoustic emission, Analog electronics, Wind turbine technology

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A large number of sensors are required to perform real-time structural health monitoring (SHM) to detect acoustic emissions (AE) produced by damage growth on large complicated structures. This requires a large number of high sampling rate data acquisition channels to analyze high frequency signals. To overcome the cost and complexity of having such a large data acquisition system, a structural neural system (SNS) was developed. The SNS reduces the required number of data acquisition channels and predicts the location of damage within a sensor grid. The sensor grid uses interconnected sensor nodes to form continuous sensors. The combination of continuous sensors and the biomimetic parallel processing of the SNS tremendously reduce the complexity of SHM. A wave simulation algorithm (WSA) was developed to understand the flexural wave propagation in composite structures and to utilize the code for developing the SNS. Simulation of AE responses in a plate and comparison with experimental results are shown in the paper. The SNS was recently tested by a team of researchers from University of Cincinnati and North Carolina A&T State University during a quasi-static proof test of a 9 meter long wind turbine blade at the National Renewable Energy Laboratory (NREL) test facility in Golden, Colorado. Twelve piezoelectric sensor nodes were used to form four continuous sensors to monitor the condition of the blade during the test. The four continuous sensors are used as inputs to the SNS. There are only two analog output channels of the SNS, and these signals are digitized and analyzed in a computer to detect damage. In the test of the wind turbine blade, multiple damages were identified and later verified by sectioning of the blade. The results of damage identification using the SNS during this proof test will be shown in this paper. Overall, the SNS is very sensitive and can detect damage on complex structures with ribs, joints, and different materials, and the system relatively inexpensive and simple to implement on large structures.

Proceedings Article | 11 April 2006 Paper

Health monitoring method for plate structures using continuous sensors and neural network analysis

Jong Won Lee, Goutham Kirikera, Inpil Kang, Mark Schulz, Vesselin Shanov

Proceedings Volume 6174, 61741R (2006) https://doi.org/10.1117/12.658099

KEYWORDS: Sensors, Wave propagation, Neural networks, Damage detection, Computer simulations, Structural health monitoring, Wave plates, Data acquisition, Analytical research, Numerical simulations

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Proceedings Article | 15 March 2006 Paper

Real-time health monitoring of a thin composite beam using a passive structural neural system

G. Kirikera, V. Shinde, M. Schulz, A. Ghoshal, M. Sundaresan, J. Lee

Proceedings Volume 6177, 61770H (2006) https://doi.org/10.1117/12.658674

KEYWORDS: Sensors, Composites, Neurons, Data acquisition, Analog electronics, Wave propagation, Diagnostics, Structural health monitoring, Signal processing, Prototyping

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Proceedings Article | 16 May 2005 Paper

Testing the analog processor of a structural neural system

G. Kirikera, I. Kang, J. Lee, V. Shinde, B. Westheider, Vesselin Shanov, M. Schulz, M. Sundaresan, A. Ghoshal

Proceedings Volume 5763, (2005) https://doi.org/10.1117/12.600243

KEYWORDS: Sensors, Neurons, Composites, Data acquisition, Analog electronics, Carbon nanotubes, Acoustic emission, Signal processing, MATLAB, Wave propagation

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Proceedings Article | 27 July 2004 Paper

Mimicking the biological neural system using electronic logic circuits

Goutham Kirikera, Vishal Shinde, Inpil Kang, Mark Schulz, Vesselin Shanov, Saurabh Datta, Doug Hurd, Bo Westheider, Mannur Sundaresan, Anindya Ghoshal

Proceedings Volume 5384, (2004) https://doi.org/10.1117/12.540174

KEYWORDS: Sensors, Aluminum, Acoustic emission, Ferroelectric materials, Analog electronics, Data acquisition, Neurons, Electronic circuits, Composites, Structural health monitoring

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Detecting and locating cracks in structural components and joints that have high feature densities is a challenging problem in the field of Structural Health Monitoring. There have been advances in piezoelectric sensors, actuators, wave propagation, MEMS, and optical fiber sensors. However, few sensor-signal processing techniques have been applied to the monitoring of joints and complex structural geometries. This is in part because maintaining and analyzing a large amount of data obtained from a large number of sensors that may be needed to monitor joints for cracks is difficult. Reliable low cost assessment of the health of structures is crucial to maintain operational availability and productivity, reduce maintenance cost, and prevent catastrophic failure of large structures such as wind turbines, aircraft, and civil infrastructure. Recently, there have also been advances in development of simple passive techniques for health monitoring including a technique based on mimicking the biological neural system using electronic logic circuits. This technique aids in reducing the required number of data acquisition channels by a factor of ten or more and is able to predict the location of a crack within a rectangular grid or within an arbitrarily arranged network of continuous sensors or neurons. The current paper shows results obtained by implementing this method on an aluminum plate and joint. The plates were tested using simulated acoustic emissions and also loading via an MTS machine. The testing indicates that the neural system can monitor complex joints and detect acoustic emissions due to propagating cracks. High sensitivity of the neural system is needed, and further sensor development and testing on different types of joints is required. Also indicated is that sensor geometry, sensor location, signal filtering, and logic parameters of the neural system will be specific to the particular type of joint (material, thickness, geometry) being monitored. Also, a novel piezoresistive carbon nanotube nerve crack sensor is presented that can become a neuron and respond to local crack growth.

Proceedings Article | 21 July 2004 Paper

Modeling delamination in composite structures by incorporating the Fermi-Dirac distribution function and hybrid damage indicators

Anindya Ghoshal, Heung Soo Kim, William Prosser, Hsiang Tai, Mark Schulz, Goutham Kirikera

Proceedings Volume 5394, (2004) https://doi.org/10.1117/12.538270

KEYWORDS: Composites, Sensors, Interfaces, Fiber optics sensors, Quantum mechanics, Fermions, Particles, Mathematical modeling, Carbon, Nondestructive evaluation

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Proceedings Article | 14 October 2003 Paper

Concepts for smart nanocomposite materials

SriLaxmi Pammi, Courtney Brown, Saurabh Datta, Goutham Kirikera, Mark Schulz

Proceedings Volume 5062, (2003) https://doi.org/10.1117/12.514662

KEYWORDS: Sensors, Carbon nanotubes, Nanocomposites, Carbon, Composites, Resistance, Capacitance, Actuators, Epoxies, Electrons

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Proceedings Article | 14 October 2003 Paper

Active fiber composite continuous sensors

Saurabh Datta, Goutham Kirikera, Mark Schulz, Mannur Sundaresan

Proceedings Volume 5062, (2003) https://doi.org/10.1117/12.514679

KEYWORDS: Sensors, Composites, Atrial fibrillation, Fiber optics sensors, Acoustic emission, Electrodes, Manufacturing, Structured optical fibers, Active sensors, Ferroelectric materials

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Proceedings Article | 1 August 2003 Paper

Continuous sensors for structural health monitoring

Saurabh Datta, Goutham Kirikera, Mark Schulz, Mannur Sundaresan

Proceedings Volume 5046, (2003) https://doi.org/10.1117/12.484120

KEYWORDS: Sensors, Composites, Atrial fibrillation, Fiber optics sensors, Acoustic emission, Electrodes, Data acquisition, Active sensors, Manufacturing, Ferroelectric materials

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Proceedings Article | 1 August 2003 Paper

Recent advances in an artificial neural system for structural health monitoring

Goutham Kirikera, Saurabh Datta, Mark Schulz, Anindya Ghoshal, Mannur Sundaresan, Jeff Feaster, Derke Hughes

Proceedings Volume 5046, (2003) https://doi.org/10.1117/12.484118

KEYWORDS: Sensors, Neurons, Wave propagation, Structural health monitoring, Acoustic emission, Ferroelectric materials, Systems modeling, Composites, Glasses, Nerve

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Proceedings Article | 14 November 2002 Paper

Embedded continuous sensor for monitoring damage evolution in composite materials

Mannur Sundaresan, Gangadhararao Grandhi, Mark Schulz, Goutham Kirikera

Proceedings Volume 4935, (2002) https://doi.org/10.1117/12.476119

KEYWORDS: Sensors, Neurons, Acoustic emission, Signal processing, Composites, Signal detection, Biomimetics, Structural health monitoring, Digital signal processing, Structural design

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Proceedings Article | 11 June 2002 Paper

Piesoceramic and nanotube materials for health monitoring

Mark Schulz, Goutham Kirikera, Saurabh Datta, Mannur Sundaresan

Proceedings Volume 4702, (2002) https://doi.org/10.1117/12.469883

KEYWORDS: Sensors, Neurons, Composites, Carbon nanotubes, Carbon, Boron, Actuators, Signal processing, Structural health monitoring, Atrial fibrillation

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Showing 5 of 15 publications

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