Dr. Gregory W. Reich Profile

Dr. Gregory W. Reich

Research Aerospace Engineer at Air Force Research Lab

SPIE Involvement:

Symposium Chair | Author

Publications (12)

PROCEEDINGS ARTICLE | March 22, 2018

The effects of surface topography control using liquid crystal elastomers on bodies in flow

Proc. SPIE. 10596, Behavior and Mechanics of Multifunctional Materials and Composites XII

KEYWORDS: Cameras, Control systems, 3D modeling, Liquid crystals, Digital image correlation, Aluminum, Analytical research, Statistical modeling, Temperature metrology, Protactinium

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PROCEEDINGS ARTICLE | April 10, 2017

Gust prediction via artificial hair sensor array and neural network

Proc. SPIE. 10172, A Tribute Conference Honoring Daniel Inman

KEYWORDS: Matrices, Analytical research, Velocity measurements, Neurons

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Gust Load Alleviation (GLA) is an important aspect of flight dynamics and control that reduces structural loadings and enhances ride quality. In conventional GLA systems, the structural response to aerodynamic excitation informs the control scheme. A phase lag, imposed by inertia, between the excitation and the measurement inherently limits the effectiveness of these systems. Hence, direct measurement of the aerodynamic loading can eliminate this lag, providing valuable information for effective GLA system design. Distributed arrays of Artificial Hair Sensors (AHS) are ideal for surface flow measurements that can be used to predict other necessary parameters such as aerodynamic forces, moments, and turbulence. In previous work, the spatially distributed surface flow velocities obtained from an array of artificial hair sensors using a Single-State (or feedforward) Neural Network were found to be effective in estimating the steady aerodynamic parameters such as air speed, angle of attack, lift and moment coefficient. This paper extends the investigation of the same configuration to unsteady force and moment estimation, which is important for active GLA control design. Implementing a Recurrent Neural Network that includes previous-timestep sensor information, the hair sensor array is shown to be capable of capturing gust disturbances with a wide range of periods, reducing predictive error in lift and moment by 68% and 52% respectively. The L2 norms of the first layer of the weight matrices were compared showing a 23% emphasis on prior versus current information. The Recurrent architecture also improves robustness, exhibiting only a 30% increase in predictive error when undertrained as compared to a 170% increase by the Single-State NN. This diverse, localized information can thus be directly implemented into a control scheme that alleviates the gusts without waiting for a structural response or requiring user-intensive sensor calibration.

PROCEEDINGS ARTICLE | May 12, 2016

Spatial tuning of a RF frequency selective surface through origami

Proc. SPIE. 9844, Automatic Target Recognition XXVI

KEYWORDS: Polarization, Sensors, Copper, Dielectrics, Additive manufacturing, Finite element methods, Antennas, Neodymium, S band, FSS based metamaterials

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PROCEEDINGS ARTICLE | April 20, 2016

Artificial hair sensor designs for flow measurement of UAVs with different scales

Proc. SPIE. 9803, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2016

KEYWORDS: Unmanned aerial vehicles, Aerospace engineering, Sensors, Electrodes, Capillaries, Glasses, Control systems, Analytical research, Aerodynamics, Carbon nanotubes

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PROCEEDINGS ARTICLE | April 20, 2016

Estimation of morphing airfoil shape and aerodynamic load using artificial hair sensors

Proc. SPIE. 9803, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2016

KEYWORDS: Biosensing, Sensors, Error analysis, Chromium, Control systems, Artificial neural networks, Sensor performance, Biomimetics, Data processing, Neural networks, Velocity measurements, Aerodynamics, Performance modeling, Smart materials

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PROCEEDINGS ARTICLE | June 10, 2015

Design tools for adaptive origami devices

Proc. SPIE. 9467, Micro- and Nanotechnology Sensors, Systems, and Applications VII

KEYWORDS: Packaging, Actuators, Fourier transforms, Computer programming, Antennas, Analytical research, Chemical elements, Optimization (mathematics), Spherical lenses, Network architectures

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PROCEEDINGS ARTICLE | April 3, 2015

Gust alleviation of highly flexible UAVs with artificial hair sensors

Proc. SPIE. 9435, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015

KEYWORDS: Unmanned aerial vehicles, Sensors, Control systems, Analytical research, Chemical elements, Algorithm development, Aerodynamics, Feedback control, Stochastic processes, Device simulation

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PROCEEDINGS ARTICLE | March 26, 2015

Variable deflection response of sensitive CNT-on-fiber artificial hair sensors from CNT synthesis in high aspect ratio microcavities

Proc. SPIE. 9429, Bioinspiration, Biomimetics, and Bioreplication 2015

KEYWORDS: Sensors, Electrodes, Capillaries, Glasses, Diffusion, Resistance, Image sensors, Signal detection, Optical microcavities, Carbon nanotubes

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PROCEEDINGS ARTICLE | August 5, 2003

Structural shape sensing for morphing aircraft

Proc. SPIE. 5056, Smart Structures and Materials 2003: Smart Structures and Integrated Systems

KEYWORDS: Genetic algorithms, Fiber optics, Sensors, Shape analysis, Antennas, Analytical research, Electronic filtering, Structural dynamics, Systems modeling, Filtering (signal processing)

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PROCEEDINGS ARTICLE | July 15, 2002

Simulation-based research and development: technology assessment process

Proc. SPIE. 4716, Enabling Technologies for Simulation Science VI

KEYWORDS: Unmanned aerial vehicles, Data modeling, Sensors, Databases, X band, Antennas, Analytical research, Aerodynamics, Sensor technology, Wind measurement

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PROCEEDINGS ARTICLE | June 19, 2000

Theory of localized vibration control via partitioned LQR synthesis

Proc. SPIE. 3984, Smart Structures and Materials 2000: Mathematics and Control in Smart Structures

KEYWORDS: Actuators, Signal attenuation, Matrices, Interfaces, Control systems, Computer simulations, Vibration control, Chemical elements, Device simulation, Mendelevium

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PROCEEDINGS ARTICLE | April 20, 2000

Experimental application of a structural health monitoring methodology

Proc. SPIE. 3988, Smart Structures and Materials 2000: Smart Systems for Bridges, Structures, and Highways

KEYWORDS: Sensors, Matrices, 3D modeling, Signal processing, Structural health monitoring, Modal analysis, Damage detection, System identification, Bridges, Dynamical systems

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Showing 5 of 12 publications

Conference Committee Involvement (4)

SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring

3 March 2019 | Denver, United States

SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring

4 March 2018 | Denver, United States

SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring

25 March 2017 | Portland, United States

SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring

20 March 2016 | Las Vegas, United States

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