A Product Line Systems Engineering Process for Variability Identification and Reduction

Software Product Line Engineering has attracted attention in the last two decades due to its promising capabilities to reduce costs and time to market through reuse of requirements and components. In practice, developing system level product lines in a large-scale company is not an easy task as there may be thousands of variants and multiple disciplines involved. The manual reuse of legacy system models at domain engineering to build reusable system libraries and configurations of variants to derive target products can be infeasible. To tackle this challenge, a Product Line Systems Engineering process is proposed. Specifically, the process extends research in the System Orthogonal Variability Model to support hierarchical variability modeling with formal definitions; utilizes Systems Engineering concepts and legacy system models to build the hierarchy for the variability model and to identify essential relations between variants; and finally, analyzes the identified relations to reduce the number of variation points. The process, which is automated by computational algorithms, is demonstrated through an illustrative example on generalized Rolls-Royce aircraft engine control systems. To evaluate the effectiveness of the process in the reduction of variation points, it is further applied to case studies in different engineering domains at different levels of complexity. Subject to system model availability, reduction of 14% to 40% in the number of variation points are demonstrated in the case studies.Comment: 12 pages, 6 figures, 2 tables; submitted to the IEEE Systems Journal on 3rd June 201

Clafer: Lightweight Modeling of Structure, Behaviour, and Variability

Embedded software is growing fast in size and complexity, leading to intimate mixture of complex architectures and complex control. Consequently, software specification requires modeling both structures and behaviour of systems. Unfortunately, existing languages do not integrate these aspects well, usually prioritizing one of them. It is common to develop a separate language for each of these facets. In this paper, we contribute Clafer: a small language that attempts to tackle this challenge. It combines rich structural modeling with state of the art behavioural formalisms. We are not aware of any other modeling language that seamlessly combines these facets common to system and software modeling. We show how Clafer, in a single unified syntax and semantics, allows capturing feature models (variability), component models, discrete control models (automata) and variability encompassing all these aspects. The language is built on top of first order logic with quantifiers over basic entities (for modeling structures) combined with linear temporal logic (for modeling behaviour). On top of this semantic foundation we build a simple but expressive syntax, enriched with carefully selected syntactic expansions that cover hierarchical modeling, associations, automata, scenarios, and Dwyer's property patterns. We evaluate Clafer using a power window case study, and comparing it against other notations that substantially overlap with its scope (SysML, AADL, Temporal OCL and Live Sequence Charts), discussing benefits and perils of using a single notation for the purpose

Towards an Integrated Conceptual Design Evaluation of Mechatronic Systems: The SysDICE Approach

National audienceMechatronic systems play a significant role in different types of industry, especially in trans- portation, aerospace, automotive and manufacturing. Although their multidisciplinary nature provides enormous functionalities, it is still one of the substantial challenges which frequently impede their design process. Notably, the conceptual design phase aggregates various engi- neering disciplines, project and business management fields, where different methods, modeling languages and software tools are applied. Therefore, an integrated environment is required to intimately engage the different domains together. This paper outlines a model-based research approach for an integrated conceptual design evaluation of mechatronic systems using SysML. Particularly, the state of the art is highlighted, most important challenges, remaining problems in this field and a novel solution is proposed, named SysDICE, combining model based system engineering and artificial intelligence techniques to support for achieving efficient design

SysML Modeling For Embedded Systems Design Optimization: A Case Study

Model-Based Systems Engineering (MBSE) with the SysML language allows the designer to include requirement capture and design representation in a single model. This paper proposes a methodology to obtain the best design alternative, from a SysML design, by using multi-objective optimization techniques. A SysML model is extended with stereotypes, objective functions, variability and constraints. Then an integer representation of the problem can be generated and solved as a constraint satisfaction problem (CSP). The paper illustrates our methodology using an Embedded Cognitive Safety System (ECSS) design. From a component repository and redundancy alternatives, the best design alternatives are generated, to minimize the total cost and maximize the estimated system reliability

Architecture Optimization with SysML Modeling: A Case Study Using Variability

Obtaining the set of trade-off architectures from a SysML model is an important objective for the system designer. To achieve this goal, we propose a methodology combining SysML with the variability concept and multi-objectives optimization techniques. An initial SysML model is completed with variability information to show up the different alternatives for component re-dundancy and selection from a library. The constraints and objective functions are also added to the initial SysML model, with an optimization context. Then a representation of a constraint satisfaction problem (CSP) is generated with an algorithm from the optimization context and solved with an existing solver. The paper illustrates our methodology by designing an Embedded Cognitive Safety System (ECSS). From a component repository and redundancy alternatives, the best design alternatives are generated in order to minimize the total cost and maximize the estimated system reliability

Combining mathematical programming and SysML for component sizing as applied to hydraulic systems

In this research, the focus is on improving a designer's capability to determine near-optimal sizes of components for a given system architecture. Component sizing is a hard problem to solve because of the presence of competing objectives, requirements from multiple disciplines, and the need for finding a solution quickly for the architecture being considered. In current approaches, designers rely on heuristics and iterate over the multiple objectives and requirements until a satisfactory solution is found. To improve on this state of practice, this research introduces advances in the following two areas: a.) Formulating a component sizing problem in a manner that is convenient to designers and b.) Solving the component sizing problem in an efficient manner so that all of the imposed requirements are satisfied simultaneously and the solution obtained is mathematically optimal. In particular, an acausal, algebraic, equation-based, declarative modeling approach is taken to solve component sizing problems efficiently. This is because global optimization algorithms exist for algebraic models and the computation time is considerably less as compared to the optimization of dynamic simulations. In this thesis, the mathematical programming language known as GAMS (General Algebraic Modeling System) and its associated global optimization solvers are used to solve component sizing problems efficiently. Mathematical programming languages such as GAMS are not convenient for formulating component sizing problems and therefore the Systems Modeling Language developed by the Object Management Group (OMG SysML ) is used to formally capture and organize models related to component sizing into libraries that can be reused to compose new models quickly by connecting them together. Model-transformations are then used to generate low-level mathematical programming models in GAMS that can be solved using commercial off-the-shelf solvers such as BARON (Branch and Reduce Optimization Navigator) to determine the component sizes that satisfy the requirements and objectives imposed on the system. This framework is illustrated by applying it to an example application for sizing a hydraulic log splitter.M.S.Committee Co-Chair: Paredis, Chris ; Committee Co-Chair: Schaefer, Dirk; Committee Member: Goel, Asho

Model-based systems engineering approach in phased antenna array design and optimization

Abstract. This thesis introduces the concept of Model-based systems engineering by providing an example on how the hardware aspects of a phased antenna array can be modeled with a system modeling language SysML in a modeling software application Cameo Systems modeler, and demonstrates how the resulting system model is used as a central hub for integration with the analysis of a phased antenna array. This thesis covers the creation of analysis models at three different fidelity levels for a phased antenna array that operates in the frequency range from 3.4 to 4 gigahertz. The models are created with the electromagnetic modeling and simulation software Ansys HFSS, and the programming and numeric computing platform MATLAB, which is used to create a script that handles post-processing of the simulation results. The lowest fidelity analysis model is automated in the Multi-disciplinary analysis and optimization tool ModelCenter. The result is an analysis workflow with configurable design parameters as its inputs and performance evaluation parameters as its outputs. The workflow combines and automates the consequent execution of the HFSS electromagnetic model and the post-processing MATLAB script. Afterwards, the workflow is integrated with the system model, which enables the use of requirements in the analysis, and the ability to upload designs achieved with the analysis to the system model. This connected workflow is used to perform a Design of experiments and a machine learning algorithm driven optimization on the phased antenna array, with the goal of finding the best possible spacings between the individual radiating elements in the array. The Design of experiments produces graphs that visualize statistical relationships between the antenna array’s design variables and its performance evaluation parameters. The optimization produces a graph that visualizes a pareto front between different performance evaluation parameters. In other words, the graph shows the design alternatives that cannot be further improved in any parameter without degrading another. This graph is used to make an informed decision on the best radiating element spacings in the antenna array. This results in 50 millimetres for the vertical spacing and 40 millimetres for the horizontal spacing in this example. Finally, the design option is uploaded to the system model, which concludes the demonstration of system and analysis modeling and their integrated usage in the design and optimization of a phased antenna array.Tiivistelmä. Tässä diplomityössä käydään läpi mallipohjaisen järjestelmäsuunnittelun konsepti, sekä osoitetaan esimerkin avulla, kuinka sitä käytetään vaiheistettujen antenniryhmien mallintamiseen SysML-järjestelmänmallinnuskielellä, Cameo Systems Modeler -työkalussa. Tämän lisäksi työssä esitetään, kuinka mallinuksesta syntyvää järjestelmämallia käytetään integroinnin keskuksena vaiheistetun antenniryhmän suunnittelun analysoinnille. Työssä käydään läpi kolmen eri tarkkuustason mallin luonti vaiheistetulle antenniryhmälle, jonka toimintataajuusalue ulottuu 3.4:stä gigahertsistä neljään gigahertsiin. Mallit luodaan käyttämällä sähkömagneettista mallinnus- ja simulointi ohjelmistoa nimeltään Ansys HFSS, sekä numeerista laskenta- ja ohjelmointialustaa nimeltään MATLAB, jolla luodaan skripti simuloinnin tulosten jälkikäsittelyä varten. Tämän jälkeen alimman tarkkuustason analyysimalli automatisoidaan monitieteisellä analyysi- ja optimointi työkalulla nimeltään ModelCenter. Tämä tehdään rakentamalla analyysin työnkulku, jonka tulona on antenniryhmän suunnittelumuuttujia ja lähtönä sen suorituskykyä kuvaavia parametreja. Analyysin työnkulku yhdistää sekä automatisoi sähkömagneettisen HFSS-mallin ja MATLAB-jälkikäsittelyskriptin peräkkäisen ajamisen ModelCenterissä. Tämän jälkeen analyysin työnkulku integroidaan järjestelmämallin kanssa. Tämä mahdollistaa vaatimusten käyttämisen analyyseissa sekä kyvyn ladata analyysin perusteella saatuja suunnitteluvaihtoehtoja järjestelmämalliin. Tätä kytkettyä analyysin työnkulkua käytetään vaiheistetun antenniryhmän kokeelliseen suunnitteluun sekä koneoppimisen algoritmeja käyttävään optimointiin, joiden tavoitteena on löytää parhaat mahdolliset antenniryhmän yksittäisten säteilevien elementtien väliset etäisyydet. Kokeellinen suunnittelu tuottaa kuvaajia, jotka visualisoivat antenniryhmän suunnittelumuuttujien ja suorituskykyä kuvaavien parametrien välisiä tilastollisia riippuvuussuhteita, tehden niiden ymmärtämisestä helppoa. Optimointi tuottaa kuvaajan, joka visualisoi eri suunnitteluvaihtoehdoilla saatavien suorituskykyä kuvaavien parametrien välistä pareto-tehokkuutta. Toisin sanoen kuvaajasta nähdään parhaat suunnitteluvaihtoehdot, joissa minkään suorituskykyä kuvaavan parametrin arvoa ei voida enää parantaa huonontamatta jonkun toisen arvoa. Tämän kuvaajan perusteella tehdään päätös parhaista mahdollisista antenniryhmän säteilevien elementtien välisistä etäisyyksistä. Tulos johon esimerkissä päädytään on 50 millimetriä korkeussuunnassa ja 40 millimetriä sivusuunnassa. Lopuksi tämä suunnitteluvaihtoehto ladataan järjestelmämalliin, joka päättää havainnollistavan esimerkin järjestelmä ja analyysimallinnuksesta, sekä niiden yhdistetystä käytöstä vaiheistettujen antenniryhmien suunnittelussa ja optimoinnissa

Modeling and Analysis of Unmanned Aerial Vehicle System Leveraging Systems Modeling Language (SysML)

The use of unmanned aerial vehicles (UAVs) has seen a significant increase over time in several industries such as defense, healthcare, and agriculture to name a few. Their affordability has made it possible for industries to venture and invest in UAVs for both research and commercial purposes. In spite of their recent popularity; there remain a number of difficulties in the design representation of UAVs, including low image analysis, high cost, and time consumption. In addition, it is challenging to represent systems of systems that require multiple UAVs to work in cooperation, sharing resources, and complementing other assets on the ground or in the air. As a means of compensating for these difficulties; in this study; we use a model-based systems engineering (MBSE) approach, in which standardized diagrams are used to model and design different systems and subsystems of UAVs. SysML is widely used to support the design and analysis of many different kinds of systems and ensures consistency between the design of the system and its documentation through the use of an object-oriented model. In addition, SysML supports the modeling of both hardware and software, which will ease the representation of both the system’s architecture and flow of information. The following paper will follow the Magic Grid methodology to model a UAV system across the SysML four pillars and integration of SysML model with external script-based simulation tools, namely, MATLAB and OpenMDAO. These pillars are expressed within standard diagram views to describe the structural, behavior, requirements, and parametric aspect of the UAV. Finally, the paper will demonstrate how to utilize the simulation capability of the SysML model to verify a functional requirement

Integrated System Model of District Cooling for Energy Consumption Optimization

The successful modeling of a multi-plant district cooling (DC) system presents several challenges in integrating system level requirements with engineering analysis for verification and optimization. Currently, the ability to predict the behavior and performance parameters such as chilled water temperature difference, annual energy consumption, and central chiller plant coefficient of performance (COP) of the dynamic system is limited. Effective modeling and efficient simulation are required when it comes to complex physical systems. This paper presents an integrated model that combines system architecture with physical modeling to represent and simulate a multi-plant district cooling system (DCS). We refer to this model as model-based systems engineering (MBSE) model of the DC system. A systems modeling language (SysML) model is created to develop a multi-domain architecture of the DC system that will satisfy stakeholder needs and requirements. This model is capable of executing behavior and parametric aspects (or “views”) of the system. A closed-loop of information flow was developed to map SysML constructs with their respective Modelica models to support the integration of simulated experiments with SysML construct. The integrated MBSE model is successfully implemented and the results show that the IPLV.SI value of the chiller model was 6.4157, which is in the acceptable range. Based on the initial conditions provided by the actual plant, the simulation results show that the chilled water temperature predictions by Modelica as 4.8℃ verify the corresponding stakeholders’ requirements captured in the SysML model

AADLib, A Library of Reusable AADL Models

The SAE Architecture Analysis and Design Language is now a well-established language for the description of critical embedded systems, but also cyber-physical ones. A wide range of analysis tools is already available, either as part of the OSATE tool chain, or separate ones. A key missing elements of AADL is a set of reusable building blocks to help learning AADL concepts, but also experiment already existing tool chains on validated real-life examples. In this paper, we present AADLib, a library of reusable model elements. AADLib is build on two pillars: 1/ a set of ready-to- use examples so that practitioners can learn more about the AADL language itself, but also experiment with existing tools. Each example comes with a full description of available analysis and expected results. This helps reducing the learning curve of the language. 2/ a set of reusable model elements that cover typical building blocks of critical systems: processors, networks, devices with a high level of fidelity so that the cost to start a new project is reduced. AADLib is distributed under a Free/Open Source License to further disseminate the AADL language. As such, AADLib provides a convenient way to discover AADL concepts and tool chains, and learn about its features