^{4th International Conference and Exhibition on} Mechanical & Aerospace Engineering October 03-04, 2016 Orlando, Florida, USA

Biography:

James D. Turner completed his PhD in 1980 from Virginia Tech.  He has held several positions of increasing responsibility in Industry, before returning to Academia in 2006.  He is a research professor in the Aerospace Engineering Department of Texas A&M University.  His research spans dynamics and control, estimation theory, optimization, computational vision, and bioinformatics.  He co-founded the nationally unique Land, Air, and Space Robotic Laboratory at Texas A&M, for spacecraft and robotic proximity operations in an operationally relevant environment.  He has published one book, more than 92 papers in reputed journals, and 155 Conference Proceedings.

Abstract:

Taylor series methods for generating solutions to differential equations have existed since the earliest developments Calculus.  For many years these methods have fallen out of favor because of the complexity and sheer volume of work required to derive and code vector differential equation time derivative models.   Experience for these solution strategies has indicated that >10 derivative terms are often required.  The advantage of Taylor series models is that larger step sizes can be used for propagating the solutions.  Many tools exist for linking computer-aided algebra tools for generating symbolic Taylor series models.  This work develops closed-form arbitrary order analytic time derivative models for celestial mechanics applications that allow nonlinear Taylor series models to outperform the state-of-the-art numerical integration methods.  Three computational advantages are realized: (1) self-adapting step size algorithm (no tuning or analyst intervention required), (2) double precision accuracy achieved over the entire LEO to GEO range of applications, and (3) very high-speed computation achieved.  Though initially derived for particle models these same computational benefits are expected for Taylor series models that extend to rigid-body attitude/trajectory coupling behaviors.  The improved integration performance is attributed to these models retaining 10+ derivative terms, whereas, existing numerical methods sample an equation multiple time to generate an average estimate of the behavior, where 4-8 derivative orders are approximated.

Biography:

Mark Balas is a distinguished faculty member in Aerospace Engineering at Embry-Riddle Aeronautical University. He was formerly the Guthrie Nicholson Professor of Electrical Engineering and former Head of the Electrical and Computer Engineering Department at the University of Wyoming. He has the following technical degrees: PhD in Mathematics, MS Electrical Engineering, MA Mathematics, and BS Electrical Engineering. He has held various positions in industry, academia, and government. Among his careers, he has been a university professor for over 30 years with RPI, MIT, University of Colorado-Boulder, and University of Wyoming, and has mentored 42 doctoral students. He has over 350 publications in archive journals, refereed conference proceedings and technical book chapters. He has been visiting faculty with the Institute for Quantum Information and the Control and Dynamics Division at the California Institute of Technology, the US Air Force Research Laboratory-Kirtland AFB, the NASA-Jet Propulsion Laboratory, the NASA Ames Research Center, and was the Associate Director of the University of Wyoming Wind Energy Research Center and adjunct faculty with the School of Energy Resources. He is a life fellow of the AIAA, a life fellow of the IEEE, and a fellow of ASME.  But he is best known as the father of the Denver drum and bass DJ known as Despise, who is his daughter Maggie.

Abstract:

Many control systems are inherently infinite dimensional when they are described by partial differential equations. Currently there is renewed interest in the control of these kinds of systems especially in flexible aerospace structures and the quantum information field. Since the dynamics of these systems will not be perfectly known, it is especially of interest to control these systems adaptively via low-order finite-dimensional controllers. When systems are subjected to unknown internal delays, they are also fundamentally infinite-dimensional in nature.  In our work, we have developed direct model reference adaptive control and disturbance rejection with very low-order adaptive gain laws for as infinite –dimensional systems on Hilbert spaces.

Quantum Information Systems are fundamentally infinite dimensional. And the basic operations that can be performed on quantum systems to manipulate information are unitary quantum gates. Because of the nature of entanglement at the quantum level these gates suffer from decoherence and cannot operate in a fully unitary way. It is also quite difficult to perform experiments that would identify all the parametric data needed to create precise models of a particular quantum system. Instead direct adaptive control that is suited to infinite dimensional systems could provide a reduction in the decoherence and allow the quantum gates to function in a more idealized unitary way.

This talk will focus on the effect of infinite dimensionality on the adaptive control approach and the conditions required for asymptotic stability with adaptive control. Then I would like to go on and consider some of the issues in the control of quantum information systems. The topics here may sound highly technical, maybe even forbidding, and to some extent they are. But I hope to give you a version of them that will be reasonably accessible and will still remain as exciting and attractive to you as they are to me.

Biography:

Ramesh Agarwal received PhD from Stanford University in 1975 and post-doctoral training at NASA Ames Research Center in 1976. From 1976 to 1994, he was the Program Director and McDonnell Douglas Fellow at McDonnell Douglas Research Laboratories in St. Louis. From 1994 to 2001, he was the Sam Bloomfield Distinguished Professor and Executive Director of National Institute for Aviation Research at Wichita State University in Wichita, KS. He is currently the William Palm Professor of Engineering at Washington University in St. Louis. He is the author/co-author of nearly 250 archival papers and over 500 conference papers. He is on the editorial board of 20+ journals.  He is a Fellow of eighteen societies including AIAA, ASME, ASEE, SAE, IEEE, APS, and AAAS among others. He is the recipient of many honors and awards.

 

Abstract:

The focus of this paper is on the simulation and shape optimization of the Lockheed SEEB-ALR and 69 Degree Delta Wing-Body in supersonic flow. For flow field calculation, the commercial CFD flow solver ANSYS Fluent is employed. The near field pressure disturbance is used to determine the strength of the sonic boom signature. The computational results for the two experimental test cases are first compared with the experimental data. The body shapes are then optimized using a single-objective genetic algorithm. The results show a significant decrease in strength of the sonic boom. The sonic boom propagation code s-Boom is employed to compute the signatures on the ground. Appropriate scaling law relating the boom from a full size vehicle to the boom from a small scale model is employed.