Ratneshwar (Ratan) Jha
Associate Professor
364 CAMP
Clarkson University
PO Box 5725
Potsdam, NY 13699-5725
Phone: 315-268-7686
Fax: 315-268-6695
E-mail: rjha@clarkson.edu
Curriculum Vitae
Educational Background
B.Tech., Aeronautical Engineering (Honors), Indian Institute of
Technology (1981)
M.S., Aerospace Engineering, Georgia Institute of Technology, (1982)
Ph.D., Mechanical Engineering, Arizona State University, (1999)
Teaching
Dr. Jha has graduated one Ph.D. and four M.S. students and currently advises five graduate students. He has also supervised two undergraduate Honors theses.
Courses taught include:
AE200- Aeronautical Engineering Seminar
AE350 - Aircraft Structural Analysis
AE429 - Aircraft Performance and Flight Mechanics
AE430 - Stability and Control of Aerospace Vehicles
AE458 - Design of Aircraft Structures
Research Interests
Dr. Jha has conducted research in adaptive control of structural vibrations, structural health monitoring, dynamics and control of space structures, modeling of composite and smart structures, multidisciplinary design optimization (MDO), and intelligent flight controls. Dr. Jha’s contributions include both theoretical and experimental research which have resulted in over 40 papers in international archival journals and refereed conferences. He is an Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA), a member of the AIAA Adaptive Structures Technical Committee and has served as Session Chair for Adaptive Structures Conferences. Dr. Jha is the PI or Co-PI for research grants from National Science Foundation, NASA, US Army, and New York State Energy Research and Development Authority. Before beginning his academic career, Dr. Jha worked with the aircraft industry for 12 years where his responsibilities included aircraft conceptual design, wind tunnel data analysis, aerodynamic data set definition, and aeroelastic analysis and optimization. More information can be found on his research Web site.
Current Research Projects
Adaptive Control of Smart Structures (.pdf)
Smart structures represent a fundamental shift in design thinking by requiring structures to perform new functions in addition to the primary function of carrying load. Envisioned added functions include vibration control, shape change on demand (morphing), health monitoring, and self-healing, leading to several high payoff applications. The real-time sensing and actuation capabilities of smart structures provide powerful means for active vibration, shape, and position control of flexible structures. Development of active control algorithms and experimental validation of their performance is a key requirement for practical applications of smart structures. Complex smart structures, employing a large number of distributed sensors and actuators, are likely to exhibit nonlinearity and variations with time. We have developed the neural adaptive predictive controller by combining the adaptive neural network autoregressive external input model with the generalized predictive control technique. We have validated its performance experimentally and compared with other neurocontrollers and standard LQR. This controller is capable of coping with uncertainty, nonlinearity, and time variation, which are characteristics of complex structures.
Structural Health Monitoring (.pdf)
Structural health monitoring (SHM) aims at developing a damage identification method that provides complete damage information (location, type, and severity), without false indication, for changing environmental and operational conditions and noisy data (i.e., ‘real world’ situations). The empirical mode decomposition (EMD) of data is a novel signal processing method based that produces instantaneous magnitude, frequency, and phase information. The EMD may be used for analyzing non-stationary and nonlinear processes (in addition to linear and stationary signals), which makes this approach particularly suitable for identifying structural damages under ambient loading. Our research focuses on using the EMD for experimental damage identification in a continuously monitored multi-level structure. Both the presence and location of damages were determined successfully when the damages occurred suddenly (such that the damaging event had a high frequency response) and the sensor noise was low.
Deployment Dynamics of Solar Sails (.pdf)
The attitude dynamics and stability of a solar sail spacecraft during deployment are of great interest since the deployment process has a large effect on the overall stability and control of the vehicle. Solar sailcraft dynamics are very complex and nonlinear due to the highly flexible sail membrane, modal interactions among sail components, and contributions from multibody dynamics. We have developed the equations of motion which describe the rigid body dynamics of the solar sail during deployment. These equations account for the time-dependency of the sail area, spacecraft moments of inertia, forces generated by solar radiation pressure, and other significant external disturbances. The stability of the resulting nonlinear attitude dynamics is then explored. The effects of parametric variations, such as sailcraft pointing error or an unknown center of mass versus center of pressure offset, and uncertainties in the deployment process are also considered to ensure robust vehicle stability.
Current Graduate Students
Shaoqing Xu (M.S.)
Brian LeFevre (Ph.D.)
Kevin Cross (M.S.)
Ali Alavinasab (Ph.D.)
Selected Publications
LeFevre, B., Jha, R., and Whorton, M., “Attitude Dynamics and Stability of Solar Sails During Deployment,” AIAA-2006-1704, 7th AIAA Gossamer Spacecraft Forum, 1-4 May 2006, Newport, Rhode Island.
Jha, R., Xu, S., and Ahmadi, G, “Health Monitoring of a Multi-Level Structure Based on Empirical Mode Decomposition and Hilbert Spectral Analysis,” Fifth International Workshop on Structural Health Monitoring (Editor: Fu-Kuo Chang), September 12–14, 2005, Stanford University, CA
Thomson, J., Jha, R., and Doorly, D., “Evolutionary Neuro-Controller Design for Autonomous Unmanned Aerial Vehicles,” AIAA-2005-0913, 43rd AIAA Aerospace Sciences Meeting, 10-13 January 2005, Reno, NV.
Jha, R., and He, C., “Adaptive Neurocontrollers for Vibration Suppression of Nonlinear and Time Varying Structures,” Journal of Intelligent Material Systems and Structures, Vol. 15, No. 9-10, Sept.- Oct. 2004, pp. 771-781.
Jha, R., and He, C., “A Comparative Study of Neural and Conventional Adaptive Predictive Controllers for Vibration Suppression,” Smart Materials and Structures, Vol. 13, No. 4, 2004, pp. 811-818.
Jha, R., Pausley, M.*, and Ahmadi, G., “Optimal Active Control of Launch Vibrations of Space Structures,” Journal of Spacecraft and Rockets, Vol. 40, No. 6, November 2003, pp. 868-874.
Chen, P. C., Sarhaddi, D., Jha, R., Liu, D.D., Griffin, K. and Yurkovich R., "Variable Stiffness Spar Approach for Aircraft Maneuver Enhancement Using ASTROS," Journal of Aircraft, Vol. 37, No. 5, Sept. - Oct. 2000, pp. 865-871.
Jha, R., and Chattopadhyay, A., "Multidisciplinary Optimization of Composite Wings Using Refined Structural and Aeroelastic Analysis Methodologies," Engineering Optimization, 1999, Vol. 32, pp. 59-78.
Jha, R. and Chattopadhyay, A., “Smart Composite Wing Design for Optimal Aeroelastic Control,” AIAA 99-1514, 40th SDM Conference, April 12-15, 1999, St. Louis, Missouri.
Jha, R., Chattopadhyay, A. and Rajadas, J. N., “Optimization of Turbomachinery Airfoil Shape for Improved Performance,” AIAA 98-1917, 39th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conf., Long Beach, CA, April 20-23, 1998.