An approximate epistemic uncertainty analysis approach in the presence of epistemic and aleatory uncertainties

Hofer, Ėduard; Kloos, Martina; Krzykacz-Hausmann, B.; Peschke, J.; Woltereck, Martin

doi:10.1016/s0951-8320(02)00056-x

Cited by 145 publications

(47 citation statements)

References 8 publications

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“…It is also noted by Roy and Oberkampf that "for more than a handful of epistemic uncertainties, the total number of samples required for convergence becomes extraordinarily large, and other approaches should be considered" (Roy and Oberkampf, 2011). Alternative approximate single-loop sampling methods which reduce the number of samples significantly are proposed by Hofer et al (2002) and Krzykacz-Hausmann (2006).…”

Section: Propagation Of Uncertaintymentioning

confidence: 99%

On Credibility Assessment in Aircraft System Simulation

Eek¹

2016

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HE aeronautical industry is becoming increasingly reliant on Modeling and Simulation (M&S) for use throughout all system development phases, for system verification, and end-user training. To justify and to utilize the full potential of today's model-based approach and to enable the industrially requested transition towards virtual testing and virtual certification, the development of efficient and industrially applicable methods for M&S credibility assessment is a key challenge. Credibility assessment of an M&S effort using one individual simulation model is undoubtedly a challenging task involving several vital aspects like verification, validation, uncertainty quantification, and model use history. Building confidence in simulator applications in which several individual models are connected and simulated together is even more problematic.This work addresses methods facilitating credibility assessment of simulation models and simulator applications used in aircraft system development. For models of individual aircraft subsystems, like liquid cooling or environmental control systems, an uncertainty aggregation method is proposed that facilitates early model validation through approximate uncertainty quantification. The central idea is to integrate information obtained during component level validation directly into the component equations, and to utilize this information in model level uncertainty quantification. The method is applicable in early development phases when the availability of system level measurement data for traditional model validation purposes is typically very limited. To clarify, the model may describe a system not yet realized, neither in a test rig nor in a test aircraft.In addition to methods intended for models of individual subsystems, this work also proposes a method and an associated tool for credibility assessment of large-scale simulator applications. As a complement to traditional document-centric approaches, static and dynamic credibility information is here presented to end-users directly during simulation. This implies a support for detecting test plan deficiencies, or that a simulator configuration is not a suitable platform for the execution of a particular test. The credibility assessment tool has been implemented and evaluated in two large-scale system simulators for the Saab Gripen fighter aircraft. The work presented herein also includes an industrially applicable workflow for development, validation, and export of simulation models.Thanks to the close connection to industry, some of the research results have already been successfully implemented in operations and are currently in industrial use. T vii Populärvetenskaplig SammanfattningLYGINDUSTRIN förlitar sig i allt högre utsträckning på modellering och simulering inom såväl systemutveckling som verifiering, samt för träning av piloter och flygtekniker. För att kunna styrka och dra maximal nytta av dagens modellbaserade arbetssätt krävs utveckling av effektiv och industriellt relevant metodik för bedömning av tro...

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Section: Propagation Of Uncertaintymentioning

confidence: 99%

On Credibility Assessment in Aircraft System Simulation

Eek¹

2016

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“…The software that are available for discrete-time dynamic probabilistic safety analysis are ADS-IDAC (Chang and Mosleh, 1998), DENDROS (Munoz et al, 1999), MCDET (Hofer et al, 2002), ADAPT (Catalyurek et al, 2010), ADAPT (Catalyurek et al, 2010), and GA-DPRA (Voroyev and Kudinov, 2011). However, these tools suffer the problem of handling and processing the huge amount of data generated during the analysis and they are computationally intensive.…”

Section: Frontiers In Energy Research | Nuclear Energymentioning

confidence: 99%

A review: passive system reliability analysis – accomplishments and unresolved issues

2014

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“…The computational dynamics models may require different uncertainty quantification techniques than the simpler Aspen MEA simulator model. Parameter-related uncertainties are divided into two categories called aleatoric and epistemic uncertainties (Helton et al 2000a;Helton et al 2000b;Hofer et al 2002;Helton and Oberkampf 2004). Aleatory uncertainty arises because of natural, unpredictable variation in the performance of the system under study.…”

Section: Model Characteristicsmentioning

confidence: 99%

“…The computational effort required for the model runs of complex models may be prohibitive when addressing this situation. Although some progress has been made in developing alternative or approximate methods (Hofer et al 2002;Helton et al 2006;McFarland and Riha 2011) no efficient tool currently exists to conduct this type of analysis.…”

Section: Mixed Aleatoric and Epistemic Uncertaintiesmentioning

confidence: 99%