An efficient methodology for verification, validation, and credibility assessment of simulation models and simulator applications is an enabler for the aeronautical industrys increasing reliance on modeling and simulation in system design and verification and on training. As a complement to traditional document-centric approaches, this paper presents a method for credibility assessment of simulator applications, in which credibility information is presented to end users directly during simulation. The central idea is that each model in a simulator is extended with a metamodel describing different aspects of credibility. The metamodel includes a number of static credibility measures and a dynamic measure that may vary during simulation. The concept is implemented and tested in two system simulators for the Saab Gripen fighter aircraft. According to the evaluation, the concept facilitates an intuitive overview of model dependencies, as well as credibility information for individual models and for a simulator as a whole. 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. Furthermore, model developers and end users are encouraged to reflect upon central credibility aspects like intended use, model fidelity, and test worthiness in their daily work.

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.

Numerical stability is a key aspect in co-simulation of physical systems. Decoupling a system into independent sub-models will introduce time delays on interface variables. By utilizing physical time delays for decoupling, affecting the numerical stability can be avoided. This requires interpolation, to allow solvers to request input variables for the time slot where they are needed. The FMI for co-simulation standard does not support fine-grained interpolation using interpolation tables. Here, various modifications to the FMI standard are suggested for improved handling of interpolation. Mechanical and thermodynamic models are used to demonstrate the need for interpolation, as well as to provide an industrial context. It is shown that the suggested improvements are able to stabilize the otherwise unstable connections.

Modelling and Simulation is key in aircraft system development. This paper presents a novel, multi-purpose, desktop simulator that can be used for detailed studies of the overall performance of coupled sub-systems, preliminary control design, and multidisciplinary optimization. Here, interoperability between industrially relevant tools for model development and simulation is established via the Functional Mockup Interface (FMI) and System Structure and Parametrization (SSP) standards. Robust and distributed simulation is enabled via the Transmission Line element Method (TLM). The advantages of the presented simulator are demonstrated via an industrially relevant use-case where simulations of pilot thermal comfort are coupled to Environmental Control System (ECS) steadystate and transient performance.

Modeling and Simulation (M&S) is seen as a means to mitigate the difficulties associated with increased system complexity, integration, and cross-couplings effects encountered during development of aircraft subsystems. As a consequence, knowledge of model validity is necessary for taking robust and justified design decisions. This paper presents a method for using coverage metrics to formulate an optimal model validation strategy. Three fundamentally different and industrially relevant use-cases are presented. The first use-case entails the successive identification of validation settings, and the second considers the simultaneous identification of n validation settings. The latter of these two use-cases is finally expanded to incorporate a secondary model-based objective to the optimization problem in a third use-case. The approach presented is designed to be scalable and generic to models of industrially relevant complexity. As a result, selecting experiments for validation is done objectively with little required manual effort.

Establishing interoperability is an essential aspect of the often-pursued shift towards Model-Based Systems Engineering (MBSE) in, for example, aircraft development. If models are to be the primary information carriers during development, the applied methods to enable interaction between engineering domains need to be modular, reusable, and scalable. Given the long life cycles and often large and heterogeneous development organizations in the aircraft industry, a piece to the overall solution could be to rely on open standards and tools. In this paper, the standards Functional Mock-up Interface (FMI) and System Structure and Parameterization (SSP) are exploited to exchange data between the disciplines of systems simulation and geometry modeling. A method to export data from the 3D Computer Aided Design (CAD) Software (SW) CATIA in the SSP format is developed and presented. Analogously, FMI support of the Modeling & Simulation (M&S) tools OMSimulator, OpenModelica, and Dymola is utilized along with the SSP support of OMSimulator. The developed technology is put into context by means of integration with the M&S methodology for aircraft vehicle system development deployed at Saab Aeronautics. Finally, the established interoperability is demonstrated on two different industrially relevant application examples addressing varying aspects of complexity. A primary goal of the research is to prototype and demonstrate functionality, enabled by the SSP and FMI standards, that could improve on MBSE methodology implemented in industry and academia.

Two different metrics quantifying model and simulator predictive capability are formulated and evaluated; both metrics exploit results from conducted validation experiments where simulation results are compared to the corresponding measured quantities. The first metric is inspired by the modified nearest neighbor coverage metric and the second by the Kullback?Liebler divergence. The two different metrics are implemented in Python and in a here-developed general metamodel designed to be applicable for most physics-based simulation models. These two implementations together facilitate both offline and online metric evaluation. Additionally, a connection between the two, here separated, concepts of predictive capability and credibility is established and realized in the metamodel. The two implementations are, finally, evaluated in an aeronautical domain context.