Combining performance and numerical stability is a key issue in co-simulation. The Transmission Line Modeling method uses physically motivated communication delays to ensure numerical stability for stiff connections. However, using a fixed communication delay may limit performance for some models. This paper proposes Steady-State Identifcation for enabling variable communication delays. Three algorithms for online Steady-State Identification are evaluated in three different co-simulation models. All algorithms are able to identify steady-state and can thereby determine when communication delays can be allowed to increase without compromising accuracy and stability. The results show a reduction in number of the solver derivative evaluations by roughly 40-60% depending on the model. The proposed method additionally supports connections with asymmetric communication delays, which allows each sub-model to independently control the delay of its input variables. Models supporting delay-size control can thereby be connected to those that do not so that the step length of each individual sub-model is maximized. Controlling the delay-size in sub-models also makes the method independent of the master co-simulation algorithm. 

System simulations are particularly useful when analyzing complex systems. Simulations are often cheaper and safer than physical tests of the actual system(s) of interest. Models can be additionally created for systems that do not exist to find solutions that are impossible to analyze experimentally in early life-cycle stages. Models used in system simulations require appropriate input data to give results with the required fidelity and, in the end, credibility. Integration is often challenging as each system commonly constitutes contributions from several engineering domains. Relying on relevant open standards for information exchange is seen as a means of mitigation. The results of the presented work encompass a developed methodology that allows Computational Fluid Dynamics (CFD) results to be integrated into a simulator using system identification and open standards. Reduced Order Models (ROMs) are generated based on results from a CFD analysis. These ROMs are coupled to lumped parameter system simulation models through the mechanisms of the System Structure and Parameterization (SSP) and Functional Mock-up Interface (FMI) standards. In addition, several important factors to consider before using the proposed methodology are presented. These include the intended use of the ROM, knowing the flow inside the system, what resources are available, and any potential licensing issues.

Designing modern aircraft is not an easy task. Today, it is not enough to optimize aircraft subsystems at a subsystem level. Instead, a holistic approach is necessary whereby the constituent subsystems need to be designed for the best joint performance. The State-of-the-Art (SotA) in simulating and ex-changing simulation models is moving forward at a fast pace. As such, the feasible use of simulation models has increased and additional benefits can be exploited, such as analyzing coupled subsystems in simulators. Furthermore, if aircraft subsystem simulation models are to be utilized to their fullest extent, open-source tooling and the use of open standards, interoperability between domain specific modeling tools, alongside efficient and automated processes for model Verification and Validation (V&V) and credibility assessment are required.

The financial and safety related risks associated with aircraft development and operation require well founded design and operational decisions. If those decisions are to be founded upon information provided by models and simulators, then the credibility of that information needs to be assessed and communicated. Today, the large number of sensors available in modern aircraft enable model validation and credibility assessment on a different scale than what has been possible up to this point. This thesis aims to identify and address challenges to allow for automated, independent, and objective methods of integrating subsystem models into simulators while assessing and conveying the constituent models aggregated credibility.

The results of the work include a proposed method for presenting the individual models’ aggregated credibility in a simulator. As the communicated credibility of simulators here relies on the credibility of each included model, the assembly procedure itself cannot introduce unknown discrepancies with respect to the System of Interest (SoI). Available methods for the management, distribution, and accurate simulation of coupled models are therefore exploited and tailored to the applications of aircraft development under consideration. Finally, a framework for automated model validation is outlined and established that supports both on-line and on-line simulator credibility assessment.   

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.

This study presents a solution for connecting system simulation and aircraft concept development using solely open standards. An easy-to-use optimisation framework for aircraft concept development is created with the help of the Modelica, Functional Mock-up Interface (FMI), and System Structure and Parameterization (SSP) standards, and the open source tools OpenModelica and OMSimulator. The framework allows for conceptual aircraft design accounting for transient phenomena by means of standardised integration of dynamic simulation models of aircraft subsystems. The framework is applied to an industry-relevant use case concerning the concept development of a generic fighter aircraft. The generality and modularity of the framework and its straightforward implementation enables tailoring of the optimisation goals to the user needs and requirements. The adoption of industry-wide standards allows for the inclusion of system simulation models developed in the modelling tool best suited for each discipline, thus integrating dynamic system simulation already at the aircraft conceptual design stage.

Establishing interoperability is an essential aspect in the often pursued shift towards Model Based System Engineering (MBSE) of, for example, aircraft. 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. One possible solution is to rely on available open-source tools and standards. 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 are utilized along with the SSP support of OMSimulator. The developed technology is put into context by means of integration with M&S methodology for aircraft vehicle system development deployed at Saab Aeronautics. Finally, the established interoperability is demonstrated in an industrially relevant use-case. 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.

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.

Designing modern aircraft is not an easy task. Today, it is not enough to optimize aircraft sub-systems at a sub-system level. Instead, a holistic approach is taken whereby the constituent sub-systems need to be designed for the best joint performance. The State-of-the-Art (SotA) in simulating and exchanging simulation models is moving forward at a fast pace. As such, the feasible use of simulation models has increased and additional benefits can be exploited, such as analysing coupled sub-systems in simulators. Furthermore, if aircraft sub-system simulation models are to be utilized to their fullest extent, opensource tooling and the use of open standards, interoperability between domain specific modeling tools, alongside robust and automated processes for model Verification and Validation (V&V) are required.

The financial and safety related risks associated with aircraft development and operation require well founded design and operational decisions. If those decisions are to be founded upon information provided by models and simulators, then the credibility of that information needs to be assessed and communicated. Today, the large number of sensors available in modern aircraft enable model validation and credibility assessment on a different scale than what has been possible up to this point. This thesis aims to identify and address challenges to allow for automated, independent, and objective methods of integrating sub-system models into simulators while assessing and conveying the constituent models aggregated credibility.

The results of the work include a proposed method for presenting the individual models’ aggregated credibility in a simulator. As the communicated credibility of simulators here relies on the credibility of each included model, the assembly procedure itself cannot introduce unknown discrepancies with respect to the System of Interest (SoI). Available methods for the accurate simulation of coupled models are therefore exploited and tailored to the applications of aircraft development under consideration. Finally, a framework for automated model validation is outlined, supporting on-line simulator credibility assessment according to the presented proposed method.

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.