liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
Uncertainty Quantification of a Large 1-D Dynamic Aircraft System Simulation Model
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

A 1-D dynamic simulation model of a new cooling system for the upcoming Gripen E aircraft has been developed in the Modelica-based tool Dymola in order to examine the cooling performance. These types of low-dimensioned simulation models, which generally are described by ordinary differential equations or differential-algebraic equations, are often used to describe entire fluid systems. These equations are easier to solve than partial differential equations, which are used in 2-D and 3-D simulation models. Some approximations and assumptions of the physical system have to be made when developing this type of 1-D dynamic simulation model. The impact from these approximations and assumptions can be examined with an uncertainty analysis in order to increase the understanding of the simulation results. Most uncertainty analysis methods are not practically feasible when analyzing large 1-D dynamic simulation models with many uncertainties, implying the importance to simplify these methods in order to make them practically feasible. This study was aimed at finding a method that is easy to realize with low computational expense and engineering workload.

The evaluated simulation model consists of several sub-models that are linked together. These sub-models run much faster when simulated as standalone models, compared to running the total simulation model as a whole. It has been found that this feature of the sub-models can be utilized in an interval-based uncertainty analysis where the uncertainty parameter settings that give the minimum and maximum simulation model response can be derived. The number of simulations needed of the total simulation model, in order to perform an uncertainty analysis, is thereby significantly reduced.

The interval-based method has been found to be enough for most simulations since the control software in the simulation model controls the liquid cooling temperature to a specific reference value. The control system might be able to keep this reference value, even for the worst case uncertainty combinations, implying no need to further analyze these simulations with a more refined uncertainty propagation, such as a probabilistic propagation approach, where different uncertainty combinations are examined.

While the interval-based uncertainty analysis method lacks probability information it can still increase the understanding of the simulation results. It is also computationally inexpensive and does not rely on an accurate and time-consuming characterization of the probability distribution of the uncertainties.

Uncertainties from all sub-models in the evaluated simulation model have not been included in the uncertainty analysis made in this thesis. These neglected sub-model uncertainties can be included using the interval-based method, as a future work. Also, a method for combining the interval-based method with aleatory uncertainties is proposed in the end of this thesis and can be examined.

Place, publisher, year, edition, pages
2015. , 58 p.
Keyword [en]
Uncertainty, Quantification, Dynamic, Modelica, Dymola
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-120189ISRN: LiTH-ISY-EX--15/4862--SEOAI: diva2:841663
External cooperation
Subject / course
Automatic Control
Available from: 2015-08-10 Created: 2015-07-14 Last updated: 2015-08-17Bibliographically approved

Open Access in DiVA

fulltext(1530 kB)126 downloads
File information
File name FULLTEXT01.pdfFile size 1530 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Karlén, Johan
By organisation
Automatic ControlFaculty of Science & Engineering
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 126 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 371 hits
ReferencesLink to record
Permanent link

Direct link