Multifidelity DDDAS Methods with Application to a Self-Aware Aerospace

Vehicle

Douglas Allaire, David Kordonowy, Marc Lecerf,Laura Mainini, Karen Willcox

ICCS 2014Cairn, Australia

June 10-12, 2014

This work was supported by AFOSR grant FA9550-11-1-0339 under the Dynamic Data-Driven Application Systems (DDDAS) Program, Program Manager Dr. Frederica Darema.

Motivation and Goals

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A self-aware aerospace vehicle can dynamically adapt the way it performs missions by gathering information about itself and its surroundings and responding intelligently.

Research Goal: Create a multifidelity framework for the DDDAS paradigm.

• DDDAS process draws on multiple modeling options and data sources to evolve models, sensing strategies, and predictions as the flight proceeds

• Dynamic data inform online adaptation of structural damage models and reduced-order models

• Dynamic guidance of sensing strategies

• Dynamic, online management of multifidelity structural response modelsand sensor data, ensuring that predictions have sufficient confidence

Leading to dynamic health-aware mission re-planning withquantifiable benefits in reliability, maneuverability and survivability.

An offline/online DDDAS approach

Offline

Generate libraries of damage states, kinematic states, and capability states using high-fidelity information.

Generate probabilistic classifiers.

Generate reduced-order models.

Online

Dynamically collect data from sensors; classify vehicle damage and capability states, using a combination of machine learning techniques and reduced-order models.

Update vehicle flight envelope; conduct state-aware mission replanning.

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Baseline UAV and vehicle model

• Wing span: 55 ft

• Cruise velocity: 140 knots

• Cruise altitude: 25,000 ft

• Payload: 500 lb

• Range 2500 nmi

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Source: www.aurora.aero

ASWING model

Vehicle design model

Orion

Composite panel model

• Panel located 260” outboard

• Sized for strength

• 4 plies of MTM45-1/AS4 carbon composite– [45,0,0,45] quasi-isotropic layup

– Meets FAR 23 loading requirements

• Analyze offline with NASTRAN to generate damage library

• Also working on generating experimental results (composite test specimens)

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18” x 18” Panel

NASTRAN model

Offline models: Aircraft model (ASWING)

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• To simulate global aircraft behavior: ASWING– Aerodynamic, structural, and control-response analysis of aircraft with

flexible wings and fuselages of high to moderate aspect ratio

– Aircraft modeled as interconnected 1D beams representing lifting surfaces or slender bodies

– Control surfaces, engine model

Ref.: M. Drela, AIAA 99-1394, Integrated Simulation Model for Preliminary Aerodynamic, Structural, and Control-Law Design of Aircraft.

http://web.mit.edu/drela/Public/web/aswing/

DDDAS Aircraft Model

Offline models: Wing beam model (VABS)

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1D Beam Solver

Specify 2D FEMs of cross sections

Specify reference line geometry

U Mich. (Cesnik) Variational Asymptotic Beam Cross-Section Analysis (VABS) calculates:

• Stiffness properties along reference line• Influence coefficients relating reference line solution to cross-sectional

warping

Any 1D beam solver can find the force + moment solution along reference line based on boundary conditions

Internal stress and strain profiles can be recovered

Stiffness properties

Influence coefficients

Reference line forces and moments

Ref.: Palacios, R. and Cesnik, C., AIAA Journal, 2005

Offline analysis: ASWING + VABS

Offline analysis: Building the damage library

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Offline analysis: Vehicle capability estimation

Characterize the flight envelope by classifying samples based on failure indices

• Build probabilistic support vector machine (characterize boundary and represent our uncertainty)

• Adaptive sampling (Dribusch and Missoum, 2012)

Online classification process

1. From sensor data, classify the current vehicle behavior into cases represented in the library (Bayesian formulation)

2. Using the probabilistic classifiers that were pre-computed and stored for each record in the library, retrieve the probability that a query vehicle state lies within the current capability set

Two approaches:

• Inference using the maximum likelihood (qML)

• Inference using a mixture distribution (qMD)

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𝑞𝑀𝐿 𝑥, 𝑠 = p(𝑥 ∈ 𝒞|𝐷𝑗𝑚𝑎𝑥)

𝑞𝑀𝐷 𝑥, 𝑠 =

𝑗=1

𝑅p( 𝑠|𝐷𝑗)p(𝐷𝑗)

𝑗′=1𝑅 p( 𝑠|𝐷𝑗′)p(𝐷𝑗′)

p(𝑥 ∈ 𝒞|𝐷𝑗)

Example mission scenario

• Pull-up maneuver to avoid a threat

• Vehicle initiates the evasive action at an airspeed ofV=210 ft/s and an initial load factor of n=1.3

• Vehicle requires knowledge of its maximum maneuvering capability (nmax) 12

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Visualizing sample damage cases

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Trade-off curves for flight scenario decision strategies

Average fraction of vehicle capability utilized

Pro

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ility

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mis

sio

n s

ucc

ess

Dynamic capability from 𝒒𝑴𝑳

Dynamic capability from 𝒒𝑴𝑫

Static capability

Conclusions

• Our offline/online DDDAS approach:

– uses physics-based models and experimental data to build offline libraries, probabilistic classifier models, and reduced-order models that map from data to capability state

– acquires dynamic data to perform rapid online estimation of vehicle capability and dynamic flight envelope updating

• Example mission scenarios (pull-up maneuver, constrained turn maneuver) show the benefits of the DDDAS approach

• Current and next steps:

– improving damage models and panel/vehicle integration

– online dynamic information gathering strategies

– generating experimental data

– design methods for DDDAS-enabled aircraft

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