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  • 1.
    Andersson, Henric
    et al.
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Carlsson, Magnus
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Saab Aeronautics Handbook for Development of Simulation Models: Public Variant2012Report (Other academic)
    Abstract [en]

    This handbook describes a framework for development, validation, and integration of multipurpose simulation models. The presented methodology enables reuse of models in different applications with different purposes. The scope is simulation models representing physical environment, physical aircraft systems or subsystems, avionics equipment, and electronic hardware.

    The methodology has been developed by a small interdisciplinary team, with experience from Modeling and Simulation (M&S) of vehicle systems as well as development of simulators for verification and training. Special care has been taken to ensure usability of the workflow and method descriptions, mainly by means of 1) a user friendly format, easy to overview and update, 2) keeping the amount of text on an appropriate level, and 3) providing relevant examples, templates, and checklists. A simulation model of an aircraft Environmental Control System (ECS) is used as an example to guide the reader through the workflow of developing and validating multipurpose simulation models.

  • 2.
    Andersson, Henric
    et al.
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Carlsson, Magnus
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Ölvander, Johan
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Towards Configuration Support for Collaborative Simulator Development: A Product Line Approach in Model Based Systems Engineering2011In: Proceedings of the 2011 20th IEEE International Workshops on Enabling Technologies, WETICE 2011: Infrastructure for Collaborative Enterprises, IEEE conference proceedings, 2011, p. 185-192Conference paper (Other academic)
    Abstract [en]

    In development and support of complex products such as power plants, automotive vehicles, or aircrafts, modeling and simulation has become an important activity as a basis for knowledge capture. Simulation is used in several steps of the product lifecycle; for evaluation of early design, for system verification, and for user training. With emerging techniques such as tools for high-level modeling, multi-core computing, and visualization, the number of useful models is growing. This paper focuses on reuse of multipurpose models and configuration support in a product line context. A configurator prototype system is presented. The simulator set created from validated models is considered to be a secondary product line. The product set which the simulation models represent is considered to be the primary product line. The Saab Gripen fighter aircraft, together with simulators in which the aircraft behavior, performance, and handling qualities are represented, is used to exemplify application. Integration principles of the systems for simulator configuration, Software Configuration Management, and Product Data Management (PDM) are studied. Preliminary results show that a configurator tool can be used, but there is need to map structures between the simulation and PDM domains.

  • 3.
    Carlsson, Magnus
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Methods for Early Model Validation: Applied on Simulation Models of Aircraft Vehicle Systems2013Licentiate 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.

    List of papers
    1. Methodology for Development and Validation of Multipurpose Simulation Models
    Open this publication in new window or tab >>Methodology for Development and Validation of Multipurpose Simulation Models
    2012 (English)In: 50th AIAA Aerospace Sciences Meeting Online Proceedings including the New Horizons Forum and Aerospace Exposition (2012), AIAA , 2012Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper describes a framework for development and validation of multipurpose simulation models. The presented methodology enables reuse of models in different applications with different purposes. The scope is simulation models representing physical environment, physical aircraft systems or subsystems, avionics equipment, and electronic hardware. The methodology has been developed by a small interdisciplinary team, with experience from Modeling and Simulation (M&S) of vehicle systems as well as development of simulators for verification and training. Special care has been taken to ensure usability of the workflow and method descriptions, mainly by means of 1) a user friendly format, easy to overview and update, 2) keeping the amount of text down, and 3) providing relevant examples, templates, and checklists. A simulation model of the Environmental Control System (ECS) of a military fighter aircraft, the Saab Gripen, is used as an example to guide the reader through the workflow of developing and validating multipurpose simulation models. The methods described in the paper can be used in both military and civil applications, and are not limited to the aircraft industry.

    Place, publisher, year, edition, pages
    AIAA, 2012
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-74716 (URN)10.2514/6.2012-877 (DOI)
    Conference
    50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, 9–12 January, 2012, Gaylord Opryland Resort & Convention Center, 9-12 January, Nashville, Tennessee
    Available from: 2012-02-06 Created: 2012-02-06 Last updated: 2016-04-25
    2. Utilizing Uncertainty Information in Early Model Validation
    Open this publication in new window or tab >>Utilizing Uncertainty Information in Early Model Validation
    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.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-80110 (URN)10.2514/6.2012-4852 (DOI)978-162410182-3 (ISBN)
    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
    3. Evaluating Model Uncertainty Based on Probabilistic Analysis and Component Output Uncertainty Descriptions
    Open this publication in new window or tab >>Evaluating Model Uncertainty Based on Probabilistic Analysis and Component Output Uncertainty Descriptions
    2012 (English)In: Proceedings of the ASME 2012 International Mechanical Engineering Congress & Exposition: IMECE2012-85236 / [ed] ASME, 2012Conference paper, Published paper (Other academic)
    Abstract [en]

    To support early model validation, this paper describes a method utilizing information obtained from the common practice component level validation to assess uncertainties on model top level. Initiated in previous research, a generic output uncertainty description component, intended for power-port based simulation models of physical systems, has been implemented in Modelica. A set of model components has been extended with the generic output uncertainty description, and the concept of using component level output uncertainty to assess model top level uncertainty has been applied on a simulation model of a radar liquid cooling system. The focus of this paper is on investigating the applicability of combining the output uncertainty method with probabilistic techniques, not only to provide upper and lower bounds on model uncertaintiesbut also to accompany the uncertainties with estimated probabilities.It is shown that the method may result in a significant improvement in the conditions for conducting an assessment of model uncertainties. The primary use of the method, in combination with either deterministic or probabilistic techniques, is in the early development phases when system level measurement data are scarce. The method may also be used to point out which model components contribute most to the uncertainty on model top level. Such information can be used to concentrate physical testing activities to areas where it is needed most. In this context, the method supports the concept of Virtual Testing.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-85455 (URN)
    Conference
    ASME 2012 International Mechanical Engineering Congress & Exposition, IMECE2012, 9-15 November, Houston, Texas, USA
    Available from: 2012-11-19 Created: 2012-11-19 Last updated: 2015-01-15Bibliographically approved
  • 4.
    Carlsson, Magnus
    et al.
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Andersson, Henric
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Gavel, Hampus
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Ölvander, Johan
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Methodology for Development and Validation of Multipurpose Simulation Models2012In: 50th AIAA Aerospace Sciences Meeting Online Proceedings including the New Horizons Forum and Aerospace Exposition (2012), AIAA , 2012Conference paper (Refereed)
    Abstract [en]

    This paper describes a framework for development and validation of multipurpose simulation models. The presented methodology enables reuse of models in different applications with different purposes. The scope is simulation models representing physical environment, physical aircraft systems or subsystems, avionics equipment, and electronic hardware. The methodology has been developed by a small interdisciplinary team, with experience from Modeling and Simulation (M&S) of vehicle systems as well as development of simulators for verification and training. Special care has been taken to ensure usability of the workflow and method descriptions, mainly by means of 1) a user friendly format, easy to overview and update, 2) keeping the amount of text down, and 3) providing relevant examples, templates, and checklists. A simulation model of the Environmental Control System (ECS) of a military fighter aircraft, the Saab Gripen, is used as an example to guide the reader through the workflow of developing and validating multipurpose simulation models. The methods described in the paper can be used in both military and civil applications, and are not limited to the aircraft industry.

  • 5.
    Carlsson, Magnus
    et al.
    Saab Aeronautics, Linköping, Sweden.
    Gavel, Hampus
    Saab Aeronautics, Linköping, Sweden.
    Ölvander, Johan
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Evaluating Model Uncertainty Based on Probabilistic Analysis and Component Output Uncertainty Descriptions2012In: Proceedings of the ASME 2012 International Mechanical Engineering Congress & Exposition: IMECE2012-85236 / [ed] ASME, 2012Conference paper (Other academic)
    Abstract [en]

    To support early model validation, this paper describes a method utilizing information obtained from the common practice component level validation to assess uncertainties on model top level. Initiated in previous research, a generic output uncertainty description component, intended for power-port based simulation models of physical systems, has been implemented in Modelica. A set of model components has been extended with the generic output uncertainty description, and the concept of using component level output uncertainty to assess model top level uncertainty has been applied on a simulation model of a radar liquid cooling system. The focus of this paper is on investigating the applicability of combining the output uncertainty method with probabilistic techniques, not only to provide upper and lower bounds on model uncertaintiesbut also to accompany the uncertainties with estimated probabilities.It is shown that the method may result in a significant improvement in the conditions for conducting an assessment of model uncertainties. The primary use of the method, in combination with either deterministic or probabilistic techniques, is in the early development phases when system level measurement data are scarce. The method may also be used to point out which model components contribute most to the uncertainty on model top level. Such information can be used to concentrate physical testing activities to areas where it is needed most. In this context, the method supports the concept of Virtual Testing.

  • 6.
    Carlsson, Magnus
    et al.
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Gavel, Hampus
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Ölvander, Johan
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Utilizing Uncertainty Information in Early Model Validation2012In: AIAA Modeling and Simulation Technologies Conference / [ed] AIAA, 2012Conference 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.

  • 7.
    Carlsson, Magnus
    et al.
    Saab Aeronautics, Linköping, Sweden.
    Steinkellner, Sören
    Saab Aeronautics, Linköping, Sweden.
    Gavel, Hampus
    Saab Aeronautics, Linköping, Sweden.
    Ölvander, Johan
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Enabling Uncertainty Quantification of Large Aircraft System Simulation Models2013In: 4:th CEAS conference, 2013 / [ed] Tomas Melin, Petter Krus, Emil Vinterhav, Knut Övrebo, Linköping University Electronic Press , 2013Conference paper (Refereed)
    Abstract [en]

    A common viewpoint in both academia and industry is that that Verification, Validation and Uncertainty Quantification (VV&UQ) of simulation models are vital activities for a successful deployment of model-based system engineering. In the literature, there is no lack of advice regarding methods for VV&UQ. However, for industrial applications available methods for Uncertainty Quantification (UQ) often seem too detailed or tedious to even try. The consequence is that no UQ is performed, resulting in simulation models not being used to their full potential.

    In this paper, the effort required for UQ of a detailed aircraft vehicle system model is estimated. A number of methodological steps that aim to achieve a more feasible UQ are proposed. The paper is focused on 1‑D dynamic simulation models of physical systems with or without control software, typically described by Ordinary Differential Equations (ODEs) or Differential Algebraic Equations (DAEs). An application example of an aircraft vehicle system model is used for method evaluation.

  • 8.
    Eek, Magnus
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, Faculty of Science & Engineering.
    On Credibility Assessment in Aircraft System Simulation2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The 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, the development of efficient and industrially applicable methods for credibility assessment of M&S efforts is a key challenge.

    This work addresses methods facilitating credibility assessment of simulation models and simulator applications used in aircraft system development. For models of individual aircraft subsystems, 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.

    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.

    List of papers
    1. Methodology for Development and Validation of Multipurpose Simulation Models
    Open this publication in new window or tab >>Methodology for Development and Validation of Multipurpose Simulation Models
    2012 (English)In: 50th AIAA Aerospace Sciences Meeting Online Proceedings including the New Horizons Forum and Aerospace Exposition (2012), AIAA , 2012Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper describes a framework for development and validation of multipurpose simulation models. The presented methodology enables reuse of models in different applications with different purposes. The scope is simulation models representing physical environment, physical aircraft systems or subsystems, avionics equipment, and electronic hardware. The methodology has been developed by a small interdisciplinary team, with experience from Modeling and Simulation (M&S) of vehicle systems as well as development of simulators for verification and training. Special care has been taken to ensure usability of the workflow and method descriptions, mainly by means of 1) a user friendly format, easy to overview and update, 2) keeping the amount of text down, and 3) providing relevant examples, templates, and checklists. A simulation model of the Environmental Control System (ECS) of a military fighter aircraft, the Saab Gripen, is used as an example to guide the reader through the workflow of developing and validating multipurpose simulation models. The methods described in the paper can be used in both military and civil applications, and are not limited to the aircraft industry.

    Place, publisher, year, edition, pages
    AIAA, 2012
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-74716 (URN)10.2514/6.2012-877 (DOI)
    Conference
    50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, 9–12 January, 2012, Gaylord Opryland Resort & Convention Center, 9-12 January, Nashville, Tennessee
    Available from: 2012-02-06 Created: 2012-02-06 Last updated: 2016-04-25
    2. Study of Industrially Applied Methods for Verification, Validation & Uncertainty Quantification of Simulator Models
    Open this publication in new window or tab >>Study of Industrially Applied Methods for Verification, Validation & Uncertainty Quantification of Simulator Models
    2015 (English)In: International Journal of Modeling, Simulation, and Scientific Computing, ISSN 1793-9623, E-ISSN 1793-9615, Vol. 6, no 2, article id 1550014Article in journal (Refereed) Published
    Abstract [en]

    To better utilize the potential of system simulation models and simulators, industrially applicable methods for Verification, Validation and Uncertainty Quantification(VV&UQ) are crucial. This paper presents an exploratory case study of VV&UQ techniquesapplied on models integrated in aircraft system simulators at Saab Aeronauticsand in driving simulators at the Swedish National Road and Transport Research Institute(VTI). Results show that a large number of Verification and Validation (V&V)techniques are applied, some of which are promising for further development and use insimulator credibility assessment. Regarding the application of UQ, a large gap betweenacademia and this part of industry has been identified, and simplified methods areneeded. The applicability of the NASA Credibility Assessment Scale (CAS) at the studied organizations is also evaluated and it can be concluded that the CAS is consideredto be a usable tool for achieving a uniform level of V&V for all models included in asimulator, although its implementation at the studied organizations requires tailoringand coordination.

    Place, publisher, year, edition, pages
    World Scientific, 2015
    Keywords
    Simulator credibility; simulation model; verification; validation; uncertainty quantification; V&V; VV&UQ; NASA Credibility Assessment Scale
    National Category
    Production Engineering, Human Work Science and Ergonomics
    Identifiers
    urn:nbn:se:liu:diva-115105 (URN)10.1142/S1793962315500142 (DOI)000365772300005 ()
    Projects
    NFFP6 2013-01211
    Funder
    VINNOVA, NFFP6 2013-01211
    Available from: 2015-03-09 Created: 2015-03-09 Last updated: 2017-12-04
    3. Enabling Uncertainty Quantification of Large Aircraft System Simulation Models
    Open this publication in new window or tab >>Enabling Uncertainty Quantification of Large Aircraft System Simulation Models
    2013 (English)In: 4:th CEAS conference, 2013 / [ed] Tomas Melin, Petter Krus, Emil Vinterhav, Knut Övrebo, Linköping University Electronic Press , 2013Conference paper, Published paper (Refereed)
    Abstract [en]

    A common viewpoint in both academia and industry is that that Verification, Validation and Uncertainty Quantification (VV&UQ) of simulation models are vital activities for a successful deployment of model-based system engineering. In the literature, there is no lack of advice regarding methods for VV&UQ. However, for industrial applications available methods for Uncertainty Quantification (UQ) often seem too detailed or tedious to even try. The consequence is that no UQ is performed, resulting in simulation models not being used to their full potential.

    In this paper, the effort required for UQ of a detailed aircraft vehicle system model is estimated. A number of methodological steps that aim to achieve a more feasible UQ are proposed. The paper is focused on 1‑D dynamic simulation models of physical systems with or without control software, typically described by Ordinary Differential Equations (ODEs) or Differential Algebraic Equations (DAEs). An application example of an aircraft vehicle system model is used for method evaluation.

    Place, publisher, year, edition, pages
    Linköping University Electronic Press, 2013
    Keywords
    Model validation, uncertainty analysis, uncertainty quantification
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-99838 (URN)978-91-7519-519-3 (ISBN)
    Conference
    CEAS 2013 - International Conference of the European Aerospace Societies, 16-19 September 2013, Linköping, Sweden
    Available from: 2013-10-21 Created: 2013-10-21 Last updated: 2016-04-25Bibliographically approved
    4. A Framework for Early and Approximate Uncertainty Quantification of Large System Simulation Models
    Open this publication in new window or tab >>A Framework for Early and Approximate Uncertainty Quantification of Large System Simulation Models
    2015 (English)In: Proceedings of the 56th Conference on Simulation and Modelling (SIMS 56), October, 7-9, 2015, Linköping University, Sweden, Linköping: Linköping University Electronic Press, 2015, p. 91-104Conference paper, Published paper (Refereed)
    Abstract [en]

    Uncertainty Quantification (UQ) is vital to ensure credibility in simulation results and to justify model-based design decisions – especially in early development phases when system level measurement data for traditional model validation purposes are scarce. Central UQ challenges in industrial applications are computational cost and availability of information and resources for uncertainty characterization. In an attempt to meet these challenges, this paper proposes a framework for early and approximate UQ intended for large simulation models of dynamical systems. A Modelica simulation model of an aircraft environmental control system including a liquid cooling circuit is used to evaluate the industrial applicability of the proposed framework.

    Place, publisher, year, edition, pages
    Linköping: Linköping University Electronic Press, 2015
    Series
    Linköping Electronic Conference Proceedings, ISSN 1650-3686, E-ISSN 1650-3740 ; 119
    Keywords
    Uncertainty quantification; aleatory uncertainty; epistemic uncertainty; model validation; aircraft system simulation models; Modelica
    National Category
    Aerospace Engineering
    Identifiers
    urn:nbn:se:liu:diva-122480 (URN)10.3384/ecp1511991 (DOI)9789176859001 (ISBN)
    Conference
    The 56th Conference on Simulation and Modelling (SIMS 56) 7-9 October 2015
    Funder
    VINNOVA, NFFP6 2013-01211
    Available from: 2015-11-04 Created: 2015-11-04 Last updated: 2018-01-25Bibliographically approved
  • 9.
    Eek, Magnus
    et al.
    Saab Aeronautics, Linköping, Sweden.
    Karlén, Johan
    Saab Aeronautics, Linköping, Sweden.
    Ölvander, Johan
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, Faculty of Science & Engineering.
    A Framework for Early and Approximate Uncertainty Quantification of Large System Simulation Models2015In: Proceedings of the 56th Conference on Simulation and Modelling (SIMS 56), October, 7-9, 2015, Linköping University, Sweden, Linköping: Linköping University Electronic Press, 2015, p. 91-104Conference paper (Refereed)
    Abstract [en]

    Uncertainty Quantification (UQ) is vital to ensure credibility in simulation results and to justify model-based design decisions – especially in early development phases when system level measurement data for traditional model validation purposes are scarce. Central UQ challenges in industrial applications are computational cost and availability of information and resources for uncertainty characterization. In an attempt to meet these challenges, this paper proposes a framework for early and approximate UQ intended for large simulation models of dynamical systems. A Modelica simulation model of an aircraft environmental control system including a liquid cooling circuit is used to evaluate the industrial applicability of the proposed framework.

  • 10.
    Eek, Magnus
    et al.
    Saab Aeronautics, Linköping, Sweden.
    Kharrazi, Sogol
    Swedish National Road and Transport Research Institute, Linköping, Sweden.
    Gavel, Hampus
    Saab Aeronautics, Linköping, Sweden.
    Ölvander, Johan
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Study of Industrially Applied Methods for Verification, Validation & Uncertainty Quantification of Simulator Models2015In: International Journal of Modeling, Simulation, and Scientific Computing, ISSN 1793-9623, E-ISSN 1793-9615, Vol. 6, no 2, article id 1550014Article in journal (Refereed)
    Abstract [en]

    To better utilize the potential of system simulation models and simulators, industrially applicable methods for Verification, Validation and Uncertainty Quantification(VV&UQ) are crucial. This paper presents an exploratory case study of VV&UQ techniquesapplied on models integrated in aircraft system simulators at Saab Aeronauticsand in driving simulators at the Swedish National Road and Transport Research Institute(VTI). Results show that a large number of Verification and Validation (V&V)techniques are applied, some of which are promising for further development and use insimulator credibility assessment. Regarding the application of UQ, a large gap betweenacademia and this part of industry has been identified, and simplified methods areneeded. The applicability of the NASA Credibility Assessment Scale (CAS) at the studied organizations is also evaluated and it can be concluded that the CAS is consideredto be a usable tool for achieving a uniform level of V&V for all models included in asimulator, although its implementation at the studied organizations requires tailoringand coordination.

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