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Dynamic Data-Driven Approach for Unmanned Aircraft Systems Aero-elastic Response Analysis

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In this chapter, we will discuss how DDDAS ideas can be used to enhance the autonomy of an unmanned system, while accounting for nonlinear behavior of the system. Our approach is illustrated in the context of the unmanned aerial vehicle, such as the SensorCraft. It will be shown as to how DDDAS can be used to enhance the performance envelope as well as avoid aero-elastic instabilities, while reducing the need for user input. The DDDAS methodology and its application to this field are described in a framework that consists of an offline component and an online component.

During the offline phase, user supplied mission objectives such as required payload along with initial data such as weather forecasts and operation history of the aircraft are used to simulate and optimize for creating a robust optimal mission configuration, all prior to take-off of the SensorCraft. In this phase, with the aero-elastic simulator, preliminary stability envelopes are constructed to determine the flutter boundary of the aircraft with damage and without damage to the aircraft. By using available simulation results, an initial meta-model is trained offline. During the online phase, sensor data is to be acquired for the decision support process. This data is to be filtered and then fused with the meta-model to achieve a fast and reasonable estimate of the system response compared to those obtained from the computationally expensive aero-elastic simulator. As the responses are estimated and updated, they are evaluated based on the objectives so that optimal maneuvers can be determined with assistance of a decision support system. The DDDAS framework is composed of these three components, namely, the aero-elastic simulator, data-driven prediction scheme, and decision support system. The aero-elastic simulation is used to obtain information on the unmanned vehicle’s dynamic response and this information is combined with sensor data for use in the online application of a decision support system.


  • Aero-elasticity
  • Active robust optimization
  • Co-simulation
  • Decision support system
  • Sensor data
  • Data fusion

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  • DOI: 10.1007/978-3-030-74568-4_10
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The authors gratefully acknowledge partial support received for this work through AFOSR DDDAS Program Grant FA9550-15-1-0134. They also express their appreciation to Dr. Erik P. Blasch and Dr. Frederica Darema of AFOSR, for the constructive suggestions provided during the course of this work.

For SensorCraft related data, the authors have had extended interactions with researchers in the AFRL supported Collaborative Center for Multidisciplinary Sciences at Virginia Tech, Blacksburg, VA (in particular, Dr. Robert Canfield) and Dr. Robert Scott of the NASA Langley Research Center, Hampton, VA. The team has also benefited from ongoing collaborations with Professor Sergio Preidikman of the National University of Córdoba, Argentina on UVLM studies and fluid-structure simulations. Thanks are also due to Dr. Nail Gumerov of the University Of Maryland Institute Of Advanced Computer Studies for his help with the use of the fast multipole method.

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Correspondence to B. Balachandran .

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Kania, R., Kebbie-Anthony, A., Zhao, X., Azarm, S., Balachandran, B. (2022). Dynamic Data-Driven Approach for Unmanned Aircraft Systems Aero-elastic Response Analysis. In: Blasch, E.P., Darema, F., Ravela, S., Aved, A.J. (eds) Handbook of Dynamic Data Driven Applications Systems. Springer, Cham.

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