Dynamic Data Driven Methods for Self-aware Aerospace Vehicles

Abstract

This project aimed to develop novel inference approaches for dynamic vehicle state estimation and methods for online management of multifidelity models and sensor data, and to apply the new methods to quantify the benefits of a self-aware unmanned aerial vehicle (UAV) in terms of reliability, maneuverability and survivability. The project accomplished all objectives and resulted in the development of new DDDAS methodology and DDDAS algorithms, new models for a DDDAS-enabled self-aware UAV, and a demonstration of the value of DDDAS in the context of dynamic data-driven structural assessment to support decision-making for a damaged vehicle taking evasive action in a hostile environment.

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Document Details

Document Type
Technical Report
Publication Date
Apr 08, 2015
Accession Number
ADA619948

Entities

People

  • David Kordonowy
  • Douglas Allaire
  • George Biros
  • Jeffrey Chambers
  • Karen Willcox
  • Omar Ghattas

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • C4I
  • Space

DTIC Thesaurus Topics

  • Aerodynamic Configurations
  • Aerospace Craft
  • Aircrafts
  • Algorithms
  • Bayesian Networks
  • Computational Science
  • Computer Science
  • Data Mining
  • Flight Speeds
  • Kernel Functions
  • Load Cells
  • Machine Learning
  • Mechanics
  • Spars
  • Supervised Machine Learning
  • Unmanned Aerial Vehicles
  • Unmanned Vehicles

Readers

  • Computational Fluid Dynamics (CFD)
  • Operations Research
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy
  • Autonomy - Autonomous System Control
  • Space
  • Space - Spacecraft Maneuvers