Laura Mainini

Surrogate Modeling Approach to Support Real-Time Structural Assessment and Decision Making

This paper proposes a data-driven strategy to assist online rapid decision making for an unmanned aerial vehicle that uses sensed data to estimate its structural state, uses this estimate to update its corresponding flight capabilities, and then dynamically replans its mission accordingly. The approach comprises offline and online computational phases constructed to address the sense–plan–act information flow while avoiding a costly online inference step. During the offline phase, high-fidelity finite element simulations are used to construct reduced-order models and classification criteria: proper orthogonal decomposition approximations and self-organizing maps are combined to realize a fast mapping from measured quantities to system capabilities. During the online phase, the surrogate mapping is employed to directly estimate the vehicle’s evolving structural capability from sensor data. The approach is demonstrated for a test problem of a composite wing panel on an unmanned aerial vehicle that undergoes...

Multifidelity DDDAS Methods with Application to a Self-aware Aerospace Vehicle☆

Abstract A self-aware aerospace vehicle can dynamically adapt the way it performs missions by gathering information about itself and its surroundings and responding intelligently. We consider the specific challenge of an unmanned aerial vehicle that can dynamically and autonomously sense its structural state and re-plan its mission according to its estimated current structural health. The challenge is to achieve each of these tasks in real time–executing online models and exploiting dynamic data streams–while also accounting for uncertainty. Our approach combines information from physics-based models, simulated offline to build a scenario library, together with dynamic sensor data in order to estimate current flight capability. Our physics-based models analyze the system at both the local panel level and the global vehicle level.

An Offline/Online DDDAS Capability for Self-Aware Aerospace Vehicles

In this paper we develop initial offline and online capabilities for a self-aware aerospace vehicle. Such a vehicle can dynamically adapt the way it performs missions by gathering information about itself and its surroundings via sensors and responding intelligently. The key challenge to enabling such a self-aware aerospace vehicle is to achieve tasks of dynamically and autonomously sensing, planning, and acting in real time. Our first steps towards achieving this goal are presented here, where we consider the execution of online mapping strategies from sensed data to expected vehicle capability while accounting for uncertainty. Libraries of strain, capability, and maneuever loading are generated offline using vehicle and mission modeling capabilities we have developed in this work. These libraries are used dynamically online as part of a Bayesian classification process for estimating the capability state of the vehicle. Failure probabilities are then computed online for specific maneuvers. We demonstrate our models and methodology on decisions surrounding a standard rate turn maneuver.

Multidisciplinary Integrated Framework for the Optimal Design of a Jet Aircraft Wing

The preliminary design of a jet aircraft wing, through the use of an integrated multidisciplinary design environment, is presented in this paper. A framework for parametric studies of wing structures has been developed on the basis of a multilevel distributed analysis architecture with a “hybrid strategy” process that is able to perform deterministic optimizations and tradeoff studies simultaneously. The particular feature of the proposed multilevel optimization architecture is that it can use different set of variables, defined expressly for each level, in a multi-level scheme using “low fidelity” and “high fidelity” models, as well as surrogate models. The prototype of the design environment has been developed using both commercial codes and in-house tools and it can be implemented in a geographically distributed and heterogeneous IT context.

Sensitivity analysis of surrogate-based methodology for real time structural assessment

This paper considers a surrogate-based methodology for real-time structural assessment, and studies its accuracy and computational runtime tradeoffs as a function of algorithmic and modeling parameters. The methodology is based on a combination of reduced-order modeling, data-fit surrogate modeling, clustering, and classification strategies. The particular implementation considered uses the proper orthogonal decomposition (POD) to achieve information reduction, self-organizing maps to achieve model localization, and polynomial response surfaces to build localized models that map from measurement information to estimates of structural capabilities. Parametric investigations study the role of surrogate modeling choices, such as the number of POD modes retained and the polynomial degree of response surfaces employed, as well as the sensitivity of the approach to the amount of available sensor data. These investigations illustrate the effectiveness and adaptability of the strategy, and the resulting sensitivities provide a mechanism to tune the online process in order to meet problem-specific time and resource constraints.

A surrogate modeling approach to support real-time structural assessment and decision-making

This paper proposes a data-driven strategy to assist online rapid decision-making for an unmanned aerial vehicle that uses sensed data to estimate its structural state, uses this estimate to update its corresponding flight capabilities, and then dynamically re-plans its mission accordingly. Our approach comprises offline and online computational phases constructed to address the sense-plan-act information flow while avoiding a costly online inference step. During the offline phase, high-fidelity finite element simulations are used to construct reduced-order models and classification criteria: proper orthogonal decomposition approximations and self-organizing maps are combined to realize a fast mapping from measured quantities to system capabilities. During the online phase, the surrogate mapping is employed to directly estimate the vehicle’s evolving structural capability from sensor data. The approach is demonstrated for a test problem of a composite wing panel on an unmanned aerial vehicle that undergoes degradation in structural properties.

A multifidelity approach to aerodynamic analysis in an integrated design environment

Within the context of the multi-disciplinary aircraft design optimization, a multi-fidelity approach has been formulated and described which allows the aerodynamic analysis of a complete wing. Some preliminary results are presented to demonstrate the effectiveness of the methodology to achieve a good approximation of the results with an important reduction of the calculation time through the use of data-fit surrogate models, and the application of global and local corrections.