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

Allaire, Douglas; Kordonowy, D.; Lecerf, M.; Mainini, Laura; Willcox, Karen

doi:10.1016/j.procs.2014.05.106

Cited by 43 publications

(12 citation statements)

References 12 publications

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“…The recent advances in parametric model reduction surveyed in this paper have made significant strides towards scalable methods that reduce the cost of the offline phase; however, once again, a definitive answer to this question remains problem and context dependent. There are some cases, such as onboard structural assessment of an aircraft wing to support real-time mission replanning [4,166], where one is willing to tolerate an expensive offline phase requiring high-performance computing in order to have the capability to react to aircraft data acquired in real time. There are other cases, such as the design optimization setting discussed above, where it may be just as efficient (or even more so) to solve the original problem using the full model than to derive the reduced model.…”

Section: Parametric Model Reduction In Actionmentioning

confidence: 99%

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A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems

2015

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Abstract. Numerical simulation of large-scale dynamical systems plays a fundamental role in studying a wide range of complex physical phenomena; however, the inherent large-scale nature of the models often leads to unmanageable demands on computational resources. Model reduction aims to reduce this computational burden by generating reduced models that are faster and cheaper to simulate, yet accurately represent the original large-scale system behavior. Model reduction of linear, nonparametric dynamical systems has reached a considerable level of maturity, as reflected by several survey papers and books. However, parametric model reduction has emerged only more recently as an important and vibrant research area, with several recent advances making a survey paper timely. Thus, this paper aims to provide a resource that draws together recent contributions in different communities to survey the state of the art in parametric model reduction methods. Parametric model reduction targets the broad class of problems for which the equations governing the system behavior depend on a set of parameters. Examples include parameterized partial differential equations and large-scale systems of parameterized ordinary differential equations. The goal of parametric model reduction is to generate low-cost but accurate models that characterize system response for different values of the parameters. This paper surveys state-of-the-art methods in projection-based parametric model reduction, describing the different approaches within each class of methods for handling parametric variation and providing a comparative discussion that lends insights to potential advantages and disadvantages in applying each of the methods. We highlight the important role played by parametric model reduction in design, control, optimization, and uncertainty quantification-settings that require repeated model evaluations over different parameter values.

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Section: Parametric Model Reduction In Actionmentioning

confidence: 99%

“…The (thin) singular value decomposition of X is written This yields an orthonormal basis. 4 The POD basis is "optimal" in the sense that, for an orthonormal basis of size r, it minimizes the least squares error of snapshot reconstruction, (3.18) min…”

Section: Proper Orthogonal Decompositionmentioning

confidence: 99%

A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems

2015

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“…Rich multi-fidelity and multimodal modeling and instrumentation have become keys for enabling the above referenced capabiltiues for physical world systems (natural, engineered) and human systems. Beyond present notions of CIoT [Wu et al, 2014;Zaidi et al, 2015], new capabilties derived through Dynamic Data Driven Applications Systems (DDDAS)-based methods, whereby modeling is dynamically and synergistically integrated with instrumentation in a feedback control loop, are emerging [Darema 2000[Darema , 2005Allaire et al 2014, Bazilevs et al 2012Celik 2011, Celik et al 2010. The trend towards higher fidelity and (semi-)autonomy with humans in the loop is accelerating [Jense et al, 1997].…”

Section: Introductionmentioning

confidence: 99%

“…Related methods and opportunities include fusing multiple world models can be used to extract insights and capturing dynamic intelligent interactions in order to augment and enrich these in a CIoT environment as the connectivity and bandwidth could be severely challenged at times. This framework is validated through use cases such as load redistribution and disaster recovery for smart grid and structural health monitoring for intelligent aerial platforms (as for example discussed in [Darema 2000[Darema , 2005Allaire et al 2014, Bazilevs et al 2012Celik 2011, Celik et al 2010). interdependent world models.…”

Section: Introductionmentioning

confidence: 99%

Distributed behavior model orchestration in cognitive internet of things solution

Darema

Chang

2017

Enterprise Information Systems

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The introduction of pervasive and ubiquitous instrumentation within Internet of Things (IoT) leads to unprecedented real-time visibility (instrumentation), optimization and fault-tolerance of the power grid, traffic, transportation, water, oil & gas, to give some examples. Interconnecting those distinct physical, people, and business worlds through ubiquitous instrumentation, even though still in its embryonic stage, has the potential to create intelligent IoT solutions that are much greener, more efficient, comfortable, and safer. An essential new direction to materialize this potential is to develop comprehensive models of such systems dynamically interacting with the instrumentation in a feedback control loop. We describe here opportunities in applying cognitive computing on interconnected and instrumented worlds (Cognitive Internet of Things-CIoT) and call out the system-of-systems trend among distinct but interdependent worlds, and Dynamic Data-Driven Application System (DDDAS)-based methods for advanced understanding, analysis, and real-time decision support capabilities with the accuracy of full-scale models.

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“…It is also important to investigate high-level abstractions (e.g., see [14]) that will enable data scientists and engineers to more easily develop concurrent software to analyze data, and that will facilitate distributed computing optimizations. Finally, uncertainty quantification [15,16] is an important future direction to associate confidence to data and error estimations in support of decision making.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Data-driven Avionics Systems: Inferring Failure Modes from Data Streams

Imai

Galli

Varela

2015

Procedia Computer Science

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Dynamic Data-Driven Avionics Systems (DDDAS) embody ideas from the Dynamic DataDriven Application Systems paradigm by creating a data-driven feedback loop that analyzes spatio-temporal data streams coming from aircraft sensors and instruments, looks for errors in the data signaling potential failure modes, and corrects for erroneous data when possible. In case of emergency, DDDAS need to provide enough information about the failure to pilots to support their decision making in real-time. We have developed the PILOTS system, which supports data-error tolerant spatio-temporal stream processing, as an initial step to realize the concept of DDDAS. In this paper, we apply the PILOTS system to actual data from the Tuninter 1153 (TU1153) flight accident in August 2005, where the installation of an incorrect fuel sensor led to a fatal accident. The underweight condition suggesting an incorrect fuel indication for TU1153 is successfully detected with 100% accuracy during cruise flight phases. Adding logical redundancy to avionics through a dynamic data-driven approach can significantly improve the safety of flight.

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

Cited by 43 publications

References 12 publications

A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems

A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems

Distributed behavior model orchestration in cognitive internet of things solution

Dynamic Data-driven Avionics Systems: Inferring Failure Modes from Data Streams

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