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Utilizing Uncertainty Information in Early Model Validation
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-3120-1361
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
2012 (English)In: AIAA Modeling and Simulation Technologies Conference / [ed] AIAA, 2012Conference paper, Published paper (Other academic)
Abstract [en]

This paper proposes a pragmatic approach enabling early model validation activities with a limited availability of system level measurement data. The method utilizes information obtained from the common practice of component validation to assess uncertainties on model top level. Focusing on industrial applicability, the method makes use of information normally available to engineers developing simulation models of existing or not yet existing systems. This is in contrast to the traditional sensitivity analysis requiring the user to quantify component parameter uncertainties – a task which, according to the authors’ experience, may be far from intuitive. As the proposed method enables uncertainties to be defined for a component’s outputs (characteristics) rather than its inputs (parameters), it is hereby termed output uncertainty. The method is primarily intended for use in large-scale mathematical 1-D dynamic simulation models of physical systems with or without control software, typically described by Ordinary Differential Equations (ODE) or Differential Algebraic Equations (DAE).It is shown that the method may result in a significant reduction in the number of uncertain parameters that require consideration in a simulation model. The uncertainty quantification of these parameters also becomes more intuitive. Since this implies a substantial improvement in the conditions of conducting sensitivity analysis or optimization on large-scale simulation models, the method facilitates early model validation. In contrast to sensitivity analysis with respect to a model’s original component parameters, which only covers one aspect of model uncertainty, the output uncertainty method enables assessment also of other kinds of uncertainties, such as uncertainties in underlying equations or uncertainties due to model simplifications. To increase the relevance of the method, a simulation model of a radar liquid cooling system is used as an industrial application example.

Place, publisher, year, edition, pages
2012.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-80110DOI: 10.2514/6.2012-4852ISBN: 978-162410182-3 (print)OAI: oai:DiVA.org:liu-80110DiVA: diva2:545648
Conference
AIAA Modeling and Simulation Technologies Conference 2012, 13-16 August, Minneapolis, Minnesota, USA
Available from: 2012-08-21 Created: 2012-08-21 Last updated: 2015-01-15
In thesis
1. Methods for Early Model Validation: Applied on Simulation Models of Aircraft Vehicle Systems
Open this publication in new window or tab >>Methods for Early Model Validation: Applied on Simulation Models of Aircraft Vehicle Systems
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Simulation  models of physical systems, with or without control software, are widely used in the aeronautic industry in applications ranging from system development to verification and end-user training. With the main drivers of reducing the cost of physical testing and in general enhancing the ability to take early model-based design decisions, there is an ongoing trend of further increasing the portion of modeling and simulation.

The work presented in this thesis is focused on development of methodology for model validation, which is a key enabler for successfully reducing the amount of physical testing without compromising safety. Reducing the amount of physical testing is especially interesting in the aeronautic industry, where each physical test commonly represents a significant cost. Besides the cost aspect, it may also be difficult or hazardous to carry out physical testing. Specific to the aeronautic industry are also the relatively long development cycles, implying long periods of uncertainty during product development. In both industry and academia a common viewpoint is that verification, validation, and uncertainty quantification of simulation models are critical activities for a successful deployment of model-based systems engineering. However, quantification of simulation results uncertainty commonly requires a large amount of certain information, and for industrial applications available methods often seem too detailed or tedious to even try. This in total constitutes more than sufficient reason to invest in research on methodology for model validation, with special focus on simplified methods for use in early development phases when system measurement data are scarce.

Results from the work include a method supporting early model validation. When sufficient system level measurement data for validation purposes is unavailable, this method provides a means to use knowledge of component level uncertainty for assessment of model top level uncertainty. Also, the common situation of lacking data for characterization of parameter uncertainties is to some degree mitigated. A novel concept has been developed for integrating uncertainty information obtained from component level validation directly into components, enabling assessment of model level uncertainty. In this way, the level of abstraction is raised from uncertainty of component input parameters to uncertainty of component output  characteristics. The method is integrated in a Modelica component library for modeling and simulation of aircraft vehicle systems, and is evaluated in both deterministic and probabilistic frameworks using an industrial application example. Results also include an industrial applicable process for model development, validation, and export, and the concept of virtual testing and virtual certification is discussed.

Abstract [sv]

Simmuleringsmodeller av fysikaliska system, med eller utan reglerande mjukvara, har sedan lång tid tillbaka ett brett användningsområde inom flygindustrin. Tillämpningar finns inom allt från systemutveckling till produktverifiering och träning. Med de huvudsakliga drivkrafterna att reducera mängden fysisk provning samt att öka förutsättningarna till att fatta välgrundade modellbaserade designbeslut pågår en trend att ytterligare öka andelen modellering och simulering.

Arbetet som presenteras i denna avhandling är fokuserat på utveckling av metodik för validering av simuleringsmodeller, vilket anses vara ett kritiskt område för att framgångsrikt minska mängden fysisk provning utan att äventyra säkerheten. Utveckling av metoder för att på ett säkert sätt minska mängden fysisk provning är speciellt intressant inom flygindustrin där varje fysiskt prov vanligen utgör en betydande kostnad. Utöver de stora kostnaderna kan det även vara svårt eller riskfyllt att genomföra fysisk provning. Specifikt är även de långa utvecklingscyklerna som innebär att man har långa perioder av osäkerhet under produktutvecklingen. Inom såväl industri som akademi ses verifiering, validering och osäkerhetsanalys av simuleringsmodeller som kritiska aktiviteter för en framgångsrik tillämpning av modellbaserad systemutveckling. Kvantifiering av osäkerheterna i ett simuleringsresultat kräver dock vanligen en betydande mängd säker information, och för industriella tillämpningar framstår tillgängliga metoder ofta som alltför detaljerade eller arbetskrävande. Totalt sett ger detta särskild anledning till forskning inom metodik för modellvalidering, med speciellt fokus på förenklade metoder för användning i tidiga utvecklingsfaser då tillgången på mätdata är knapp.

Resultatet från arbetet inkluderar en metod som stöttar tidig modellvalidering. Metoden är avsedd att tillämpas vid brist på mätdata från aktuellt system, och möjliggör utnyttjande av osäkerhetsinformation från komponentnivå för bedömning av osäkerhet på modellnivå. Avsaknad av data för karaktärisering av parameterosäkerheter är även ett vanligt förekommande problem som till viss mån mildras genom användning av metoden. Ett koncept har utvecklats för att integrera osäkerhetsinformation hämtad från komponentvalidering direkt i en modells komponenter, vilket möjliggör en förenklad osäkerhetsanalys på modellnivå. Abstraktionsnivån vid osäkerhetsanalysen höjs på så sätt från parameternivå till komponentnivå. Metoden är implementerad i ett Modelica-baserat komponentbibliotek för modellering och simulering av grundflygplansystem, och har utvärderats i en industriell tillämpning i kombination med både deterministiska och probabilistiska tekniker. Resultatet från arbetet inkluderar även en industriellt tillämplig process för utveckling, validering och export av simuleringsmodeller, och begreppen virtuell provning och virtuell certifiering diskuteras.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. 61 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1591
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-91277 (URN)978-91-7519-627-5 (ISBN)
Presentation
2013-05-03, Sal A35, A-huset, Campus Valla, Linköpings universitet, Linköping, 11:00 (Swedish)
Opponent
Supervisors
Available from: 2013-04-18 Created: 2013-04-18 Last updated: 2015-01-15Bibliographically approved

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Carlsson, MagnusGavel, HampusÖlvander, Johan

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