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  • 1.
    Hällqvist, Robert
    et al.
    Systems Simulation and Concept Design, Saab Aeronautics, Linköping, Sweden.
    Braun, Robert
    Linköping University, Department of Management and Engineering, Fluid and Mechatronic Systems. Linköping University, Faculty of Science & Engineering.
    Krus, Petter
    Linköping University, Department of Management and Engineering, Fluid and Mechatronic Systems. Linköping University, Faculty of Science & Engineering.
    Early Insights on FMI-based Co-Simulation of Aircraft Vehicle Systems2017In: Proceedings of 15:th Scandinavian International Conference on Fluid Power, June 7-9, 2017, Linköping, Sweden / [ed] Petter Krus, Liselott Eriksson and Magnus Sethson, Linköping: Linköping University Electronic Press, 2017, Vol. 144, p. 262-270Conference paper (Other academic)
    Abstract [en]

    Modelling and Simulation is extensively used for aircraft vehicle system development at Saab Aeronautics in Linköping, Sweden. There is an increased desire to simulate interacting sub-systems together in order to reveal, and get an understanding of, the present cross-coupling effects early on in the development cycle of aircraft vehicle systems. The co-simulation methods implemented at Saab require a significant amount of manual effort, resulting in scarcely updated simulation models, and challenges associated with simulation model scalability, etc. The Functional Mock-up Interface (FMI) standard is identified as a possible enabler for efficient and standardized export and co-simulation of simulation models developed in a wide variety of tools. However, the ability to export industrially relevant models in a standardized way is merely the first step in simulating the targeted coupled sub-systems. Selecting a platform for efficient simulation of the system under investigation is the next step. Here, a strategy for adapting coupled Modelica models of aircraft vehicle systems to TLM-based simulation is presented. An industry-grade application example is developed, implementing this strategy, to be used for preliminary investigation and evaluation of a cosimulation framework supporting the Transmission Line element Method (TLM). This application example comprises a prototype of a small-scale aircraft vehicle systems simulator. Examples of aircraft vehicle systems are environmental control systems, fuel systems, and hydraulic systems. The tightly coupled models included in the application example are developed in Dymola, OpenModelica, and Matlab/Simulink. The application example is implemented in the commercial modelling tool Dymola to provide a reference for a TLM-based master simulation tool, supporting both FMI and TLM. The TLM-based master simulation tool TLMSimulator is investigated in terms of model import according to the FMI standard with respect to a specified set of industrial needs and requirements.

  • 2.
    Hällqvist, Robert
    et al.
    Saab Aeronautics, Linköping, Sweden.
    Eek, Magnus
    Saab Aeronautics, Linköping, Sweden.
    Braun, Robert
    Linköping University, Department of Management and Engineering, Fluid and Mechatronic Systems. Linköping University, Faculty of Science & Engineering.
    Krus, Petter
    Linköping University, Department of Management and Engineering, Fluid and Mechatronic Systems. Linköping University, Faculty of Science & Engineering.
    METHODS FOR AUTOMATING MODEL VALIDATION: STEADY-STATE IDENTIFICATION APPLIED ON GRIPEN FIGHTER ENVIRONMENTAL CONTROL SYSTEM MEASUREMENTS2016In: Proceedings of the 30th congress of the International Council  of the Aeronautical Sciences, 2016Conference paper (Refereed)
    Abstract [en]

    Model Validation and Verification (V&V) has historically often been considered a final step in the model development process. However, to justify model-based design decisions throughout the entire system development process, a methodology for continuous model V&V is essential. That is, model V&V activities should be fast and easy to reiterate as new information becomes available. Using a high fidelity simulation model of the Environmental Control System (ECS) in the Saab Gripen fighter aircraft as a guiding example, this paper further extends to an existing semiautomatic framework for model steady-state validation developed during ECS model validation efforts. Generic methods for identification of steady-state operation are a prerequisite for steady-state validation of industry grade physics based models against insitu measurements. Four different established methods for steady-state identification are investigated and compared: steady-state conditions on the standard deviation estimated from in-situ measurements, conditions on the variation coefficient, t-test on the slope of a simple regression line, and comparison of differently estimated variances. The methods’ applicability, on ECS measurements in particular, is evaluated utilizing steady-state identification needs defined during Gripen ECS model validation activities.

    Model Validation and Verification (V&V) has historically often been considered a final step in the model development process. However, to justify model-based design decisions throughout the entire system development process, a methodology for continuous model V&V is essential. That is, model V&V activities should be fast and easy to reiterate as new information becomes available.

    Using a high fidelity simulation model of the Environmental Control System (ECS) in the Saab Gripen fighter aircraft as a guiding example, this paper further extends to an existing semi-automatic framework for model steady-state validation developed during ECS model validation efforts. Generic methods for identification of steady-state operation are a prerequisite for steady-state validation of industry grade physics based models against in-situ measurements. Four different established methods for steady-state identification are investigated and compared: steady-state conditions on the standard deviation estimated from in-situ measurements, conditions on the variation coefficient, t-test on the slope of a simple regression line, and comparison of differently estimated variances. The methods’ applicability, on ECS measurements in particular, is evaluated utilizing steady-state identification needs defined during Gripen ECS model validation activities.

  • 3.
    Hällqvist, Robert
    et al.
    Saab Aeronautics, Linköping, Sweden.
    Eek, Magnus
    Saab Aeronautics, Linköping, Sweden.
    Lind, Ingela
    Saab Aeronautics, Linköping, Sweden.
    Gavel, Hampus
    Saab Aeronautics, Linköping, Sweden.
    Validation Techniques Applied on the Saab Gripen FighterEnvironmental Control System Model2015In: Proceedings of the 56th SIMS / [ed] Lena Buffoni, Adrian Pop, and Bernhard Thiele, Linköping, 2015, p. 199-210, article id ecp15119199Conference paper (Refereed)
    Abstract [en]

    The Environmental Control System (ECS) of the Saab Gripen fighter provides a number of vital functions, such as provision of coolant air to the avionics, comfort air to the cockpit, and pressurization of the aircraft fuel system. To support system design, a detailed simulation model has been developed in the Modelica-based tool Dymola. The model needs to be a “good system representation”, during both steady-state operation and relevant dynamic events, if reliable predictions are to be made regarding cooling performance, static loads in terms of pressure and temperature, and various other types of system analyses. A framework for semi-automatic validation of the ECS model against measurements is developed and described in this paper. The framework extends a proposed formal methodology of semi-automaticmodel validation against in-situ measurements to the model development process implemented at Saab.Applied methods for validating the model in steady-state operation and during relevant dynamic events are presented in detail. The developed framework includes automatic filtering of measurement points defined as steady-state operation and visualization techniques applied on validation experiments conducted in the previously mentioned points. The proposed framework both simplify continuous validation throughout the system development process and enables a smooth transition towards a more independent verification and validation process.

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