The Systems Modeling Language (OMG SysML) is a general purpose modeling language that allows users to express a broad range of information and knowledge about systems and systems engineering projects: from requirements to functional architectures, and from behavior to structural specifications. An important set of modeling constructs in SysML are parametric constraints as used in parametric diagrams. SysML parametrics allow systems engineers not only to define parametric system specifications but also to express analysis models relating mmeeaasusurrees s ooff eeffffeectctiivevenneess ss ttoo sysyststeemm spspeeciciffiicacattiioonns.s. IInn tthhiis s ttuuttoorriiaall,, tthhee uusese ooff paramtrics for analysis purposes will be reviewed in detail. To provide the appropriate context, we will start with a short overview of SysML, followed by a more detailed review of the syntax and semantics of parametric diagrams. We will then discuss different uses of parametrics, and provide best-practice examples, specifically focusing on analysis modeling. Parametric diagrams will also be compared with a new OMG specification for combining SysML and Modelica: The OMG SysML-Modelica Transformation Specification. A live demonstration will be included with the following two tools: The MagicDraw SysML Tool with the ParaMagic plug-in, and an open-source plug-in for solving parametric constraint models in ModelCenter.

MBSE

Lightning

Round

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MBSE at NASA and the EDS MBSE Template

• Making systems engineering easier for the workforce

• NASA MBSE Infusion And Modernization Initiative

(MIAMI) 2016-2020 and the on-going MBSE

Community of Practice

• Engineering Design Studio, NASA Langley Research

Center (LaRC)

• Tech development plan for collaboration and system

modeling

• AR/VR, Office 365 environment, SysML

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• Model Based Systems Engineering

• MBSE benefits

• Cameo MBSE ecosystem overview

Abstract

Aeronautical industry faces the increase of system complexity and reliability-based constraints, and in the meantime tries to reduce costs, to maximize the system performance and to improve the safety. The recent development of MBSE facilitates the transfer of models and enables to simulate the system behavior early in the development phase. In this context, the thesis aims to shape a collaborative and adaptive software environment to carry out uncertainty management on multidisciplinary aeronautical systems. Case Studies examine the implementation of a systematic CPM throughout the design process of a new commercial aircraft. This work raises the point of uncertainty-based optimization complexity and investigates different solutions to face this issue.

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Most of the analytical models tackled in the thesis derive from a set of regressions suited to an Airbus commercial aircraft. While Cameo Systems Modeler supports a modular modeling, ModelCenter bridges the gap between descriptive and analytical models while ensuring a great traceability. The multi-levels simulation enabled by ModelCenter helps identifying the critical parameters from the early steps of the design process. This holistic and data-driven approach drives the product development process by eliminating non-value-added activities. The variety of sensitivity analysis tools suits any type of system complexity.

The software environment supports the implementation of an uncertainty-based multidisciplinary optimization. Non-dominated Sorting Genetic Algorithm NSGA-II highlights the tradeoff between performance optimization and cost reduction and its influence on the optimal design. Reliability-based constraints reduce the solution space and affects the final design of the aircraft by shifting the Pareto-front away from the best objective values. ModelCenter provides effective tools to face the high level of complexity of optimization under uncertainty. While the parallelization of simulations on virtual machines enhances the computational performance, DOE screening enables reducing the design space by eliminating irrelevant inputs. The conversion of multi-objective into single objective function focuses the search for optimal on a part of the global Pareto-front and significantly shortens the computing time. However, this solution requires setting up a hierarchy between the objectives and thus leaves behind non-dominated design solutions.

Although the results show the ability of this software environment to design complex systems under uncertainty, it is difficult to extrapolate a general uncertainty-based multidisciplinary design optimization workflow for various aeronautical systems at Airbus. Each design under uncertainty depends on the model complexity, the size of the design space as well as the available computational resources. Improvements of the Case Studies models are possible by refining both performance and cost functions. While models linking them to the design parameters are difficult to set up, a precise definition may capture the complete product life cycle in the design process under uncertainty.

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Small satellite missions are becoming increasingly complex as scientists and engineers propose to utilize them to accomplish more ambitious science and technology goals. Small satellites such as CubeSats are challenging to design because they have limited resources, coupled subsystems, and must operate in dynamic environments.

The complex, multi-part nature of waste glass melters used in nuclear waste vitrification poses significant

modeling challenges. The focus of this project has been to couple a 1D MATLAB model of the cold cap

region within a melter with a 3D STAR-CCM+ model of the melter itself. The Multiphysics Integrated

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Coupling Environment (MICE), shown below, has been developed to create a cohesive simulation of a waste

glass melter that accurately represents the cold cap. The one-dimensional mathematical model of the cold cap

uses material properties, axial heat, and mass fluxes to obtain a temperature profile for the cold cap, the region

where feed-to-glass conversion occurs. The results from Matlab are used to update simulation data in the

three-dimensional STAR-CCM+ model so that the cold cap is appropriately incorporated into the 3D

simulation. The two processes are linked through ModelCenter integration software using time steps that are

specified for each process. Data is to be exchanged circularly between the two models, as the inputs and

outputs of each model depend on the other.

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GoalUnder development at DLR in the following aeronautics projects:TIVA I/IITechnology integration for the virtual aircraftUCAV 2010Unmanned combat air vehicleEVITAEvaluation of innovative turbine enginesCATSClimate-compatibleairtransportsystem…and moreto come!A DLR-wide system to enable the multi-disciplinary design andanalysis of airplane configurations in the predesign phase.

Transforming Systems Engineering through Model Centric Engineering

Sponsors: NAVAIR and ARDEC

By

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Dr. Mark Blackburn

10th Annual SERC Sponsor Research Review

November 8, 2018

FHI 360 CONFERENCE CENTER

1825 Connecticut Avenue NW, 8th Floor

Washington, DC 20009

www.sercuarc.org

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