Unmanned Aerial Vehicle Damage Prognosis and Reliability
UCSD Faculty and Graduate Students
- Professor John Kosmatka, Structural Engineering
- Jesse Oliver, Graduate Student
- Professor Joel Conte, Structural Engineering
- Maurizio Gobbato, Graduate Student
- Profesor Francesco Lanza di Scalea, Structural Engineering
- Howard Matt, Graduate Student
LANL Collaborators
- Dr. Michael Anderson (MST-6: MATERIALS TECHNOLOGY-METALLURGY)
- Dr. Francois Hemez (X-1-MV: METHODS AND VERIFICATION)
- Dr. Gyuhae Park (INST-OFF: INSTITUTES)
- Dr. Irene Beyerlein (T-03: FLUID DYNAMICS)
Unmanned Air Vehicles (UAV’s) are being used by the military for surveillance as well as by the science community for monitoring the environment. The vehicles are typically made of lightweight advanced composite materials to reduce their weight and improve their performance (longer flight times). Upon landing, the vehicles must be quickly inspected and maintained before being sent back on another mission. Without a pilot, there is very little that is known about its in-flight performance or problems except from what is concluded from its few autopilot sensors. What is needed is a system that will monitor the composite airframe (wings, fuselage, and empennage), assess its structural integrity, identify a maintenance schedule, and predict the remaining life of critical components (prognosis). Both LANL and UCSD are working together to develop a suitable system. This system, which could either be an in-flight on-line system or a preflight modal/acoustic test, would monitor stiffness changes (flutter prevention), or strength reductions (fatigue), or ballistic damage. The system sensors could either be attached (strain gauges, accelerometers, piezo-patches) or embedded (fiber optics) into the structure, and it could be passive (sensors only) or active (embedded sensors and actuators). This sensor data along with reduced-order analytical models will be used to identify regions in the structure that need further inspection. In order to predict the remaining component life (prognosis), one needs a robust analytical structural model that correctly accounts for geometric and material uncertainties, as well as the uncertainties associated with current and future loadings and sensors. Methods used in structural reliability (uncertainty) analysis that will be investigated include Monte Carlo simulation, importance sampling, Bayesian up-dating, First- and Second-Order Reliability Methods (FORM and SORM) and finite element reliability analysis.
During Phase 1 and Phase 2 of this project, the UCSD-LANL research team has made substantial progress on the development of all the building blocks needed to solve this ambitious problem. These include:
- Sensor selection and sensor data extraction algorithm development,
- Validation of sensor sensitivity using experimentally simulated damage (test specimens),
- Computational framework development for prognosis software,
- Selection of damage evolution models for adhesive and composite failures,
- Damage location in composite test specimens via modal testing technique
- Dynamic damage location using HHT algorithms,
- Fabrication of undamaged and damaged 1/3-scale composite UAV wings with and without embedded sensors (test beds), and
- Technology commercialization roadmap – identified commercial UAV customers and funding agencies for future UCSD-LANL sponsorship.
The proposed research plan involves using all these critical building blocks along with developing a few more key building blocks to perform damage detection, identification, and prognosis on a scaled UAV wing. During this phase, the UCSD-LANL team will also scale the system for a full-size UAV and locate funding partners for future research opportunities. The proposed research tasks for each of the three PI’s (Kosmatka, Conte, Lanza) follow.
