Remedy of Mixed Initiative Conflicts in Model-based System Engineering

SPACE is a technique for model-driven engineering of reactive distributedsystems. One of the strengths of its tool-set Arctis is that the system engineercan formally analyze the models for design errors such that these can becorrected early in the development process. In this paper, we go a step further andintroduce a technique that refines the fault detection and, in addition, offers a highlyautomatic mechanism to remedy the errors. For that, we combine model checking,the already existing analysis method of Arctis, with graph transformation. Usinggraph rewriting rules, we can analyze the state space graph of a system for the exact reason of an error as well as remove the erroneous parts of a model by changing themodel description. We exemplify the approach by envisaging the detection and remedyof mixed initiatives, a quite common cause for faulty behavior in event-drivensystems that often is overlooked in system development

Aerodynamic Optimization of Rocket Control Surface Geometry Using Cartesian Methods and CAD Geometry

Aerodynamic design is an iterative process involving geometry manipulation and complex computational analysis subject to physical constraints and aerodynamic objectives. A design cycle consists of first establishing the performance of a baseline design, which is usually created with low-fidelity engineering tools, and then progressively optimizing the design to maximize its performance. Optimization techniques have evolved from relying exclusively on designer intuition and insight in traditional trial and error methods, to sophisticated local and global search methods. Recent attempts at automating the search through a large design space with formal optimization methods include both database driven and direct evaluation schemes. Databases are being used in conjunction with surrogate and neural network models as a basis on which to run optimization algorithms. Optimization algorithms are also being driven by the direct evaluation of objectives and constraints using high-fidelity simulations. Surrogate methods use data points obtained from simulations, and possibly gradients evaluated at the data points, to create mathematical approximations of a database. Neural network models work in a similar fashion, using a number of high-fidelity database calculations as training iterations to create a database model. Optimal designs are obtained by coupling an optimization algorithm to the database model. Evaluation of the current best design then gives either a new local optima and/or increases the fidelity of the approximation model for the next iteration. Surrogate methods have also been developed that iterate on the selection of data points to decrease the uncertainty of the approximation model prior to searching for an optimal design. The database approximation models for each of these cases, however, become computationally expensive with increase in dimensionality. Thus the method of using optimization algorithms to search a database model becomes problematic as the number of design variables is increased

Recurrence network analysis of design-quality interactions in additive manufacturing

Powder bed fusion (PBF) additive manufacturing (AM) provides a great level of flexibility in the design-driven build of metal products. However, the more complex the design, the more difficult it becomes to control the quality of AM builds. The quality challenge persistently hampers the widespread application of AM technology. Advanced imaging (e.g., X-ray computed tomography scans and high-resolution optical images) has been increasingly explored to enhance the visibility of information and improve the AM quality control. Realizing the full potential of imaging data depends on the advent of information processing methodologies for the analysis of design-quality interactions. This paper presents a design of AM experiment to investigate how design parameters (e.g., build orientation, thin-wall width, thin-wall height, and contour space) interact with quality characteristics in thin-wall builds. Note that the build orientation refers to the position of thin-walls in relation to the recoating direction on the plate, and the contour space indicates the width between rectangle hatches. First, we develop a novel generalized recurrence network (GRN) to represent the AM spatial image data. Then, GRN quantifiers, namely degree, betweenness, pagerank, closeness, and eigenvector centralities, are extracted to characterize the quality of layerwise builds. Further, we establish a regression model to predict how the design complexity impacts GRN behaviors in each layer of thin-wall builds. Experimental results show that network features are sensitive to build orientations, width, height, and contour space under the significant level α = 0.05. Thin-walls with the width bigger than 0.1 mm printed under orientation 0° are found to yield better quality compared to 60° and 90°. Also, thin-walls build with orientation 60° are more sensitive to the changes in contour space compare to the other two orientations. As a result, the orientation 60° should be avoided while printing thin-wall structures. The proposed design-quality analysis shows great potential to optimize engineering design and enhance the quality of PBF-AM builds

Using domain specific languages to capture design knowledge for model-based systems engineering

Design synthesis is a fundamental engineering task that involves the creation of structure from a desired functional specification; it involves both creating a system topology as well as sizing the system's components. Although the use of computer tools is common throughout the design process, design synthesis is often a task left to the designer. At the synthesis stage of the design process, designers have an extensive choice of design alternatives that need to be considered and evaluated. Designers can benefit from computational synthesis methods in the creative phase of the design process. Recent increases in computational power allow automated synthesis methods for rapidly generating a large number of design solutions. Combining an automated synthesis method with an evaluation framework allows for a more thorough exploration of the design space as well as for a reduction of the time and cost needed to design a system. To facilitate computational synthesis, knowledge about feasible system configurations must be captured. Since it is difficult to capture such synthesis knowledge about any possible system, a design domain must be chosen. In this thesis, the design domain is hydraulic systems. In this thesis, Model-Driven Software Development concepts are leveraged to create a framework to automate the synthesis of hydraulic systems will be presented and demonstrated. This includes the presentation of a domain specific language to describe the function and structure of hydraulic systems as well as a framework for synthesizing hydraulic systems using graph grammars to generate system topologies. Also, a method using graph grammars for generating analysis models from the described structural system representations is presented. This approach fits in the context of Model-Based Systems Engineering where a variety of formal models are used to represent knowledge about a system. It uses the Systems Modeling Language developed by The Object Management Group (OMG SysML™) as a unifying language for model definition.M.S.Committee Chair: Paredis, Chris; Committee Member: McGinnis, Leon; Committee Member: Schaefer, Dir

AADL for Cyber-Physical Systems: Semantics and beyond, validate what's next

The SAE Architecture Analysis and Design Language is a design-by-committee standard promoted to help the space and avionics domain. It now extends to a much broader audience, and this language is used in many domains related to Cyber-Physical Systems. AADL is an ADL promoted in the context of Model-Driven Engineering which has now gained a significant momentum in the industry. Models are a valuable asset that should be used and preserved down to the construction of the final system; modeling time and effort should be reduced to focus directly on the system and its realization. Yet, validation & verification may require many different analysis models, involving a strong theoretical background to be mastered. The SAE AADL has been defined to match the concepts understood by any engineer (interface, software or hardware components, packages, generics). From these concepts, typical behavior elements (scheduling and dispatch, communication mechanisms) have been added using both formal and informal description, always bound to theoretical frameworks for V&V. In parallel, the AADL allows one to attach user-defined properties or languages for specific analysis. This enables the application of many different techniques for the analysis of AADL models, among which schedulability, safety, security, fault-propagation, model-checking, resource dimensioning, etc.; but also code generation. In this talk, we give an overview of the AADL, and discuss how to use its features to analyse in depth a CPS case study

A sequential sampling-based Bayesian numerical method for reliability-based design optimization

For efficiently solving the Reliability-Based Design Optimization (RBDO) problem with multi-modal, highly nonlinear and expensive-to-evaluate limit state functions (LSFs), a sequential sampling-based Bayesian active learning method is developed in this work. The penalty function method is embedded to transform the constrained optimization problem into a non-constrained one to reduce the model complexity. The proposed method for solving RBDO problems starts by training a Gaussian process (GP) model, in the augmented space of random and design variables. It is then based on an efficient sampling scheme for simulating the GP model, the adaptive Bayesian optimization (BO) and Bayesian reliability analysis (BRA) procedures are combined in a collaborative way for sequentially producing the joint training points. BO driven by expected improvement (EI) function is used for inferring the global optimum in the design space with global convergence, and the BRA equipped with U function is implemented for inferring the failure probabilities at the identified design points with the desired accuracy. The superiority of the proposed method is demonstrated with two numerical and two real-world engineering examples

Probabilistic Analysis of a Composite Crew Module

An approach for conducting reliability-based analysis (RBA) of a Composite Crew Module (CCM) is presented. The goal is to identify and quantify the benefits of probabilistic design methods for the CCM and future space vehicles. The coarse finite element model from a previous NASA Engineering and Safety Center (NESC) project is used as the baseline deterministic analysis model to evaluate the performance of the CCM using a strength-based failure index. The first step in the probabilistic analysis process is the determination of the uncertainty distributions for key parameters in the model. Analytical data from water landing simulations are used to develop an uncertainty distribution, but such data were unavailable for other load cases. The uncertainty distributions for the other load scale factors and the strength allowables are generated based on assumed coefficients of variation. Probability of first-ply failure is estimated using three methods: the first order reliability method (FORM), Monte Carlo simulation, and conditional sampling. Results for the three methods were consistent. The reliability is shown to be driven by first ply failure in one region of the CCM at the high altitude abort load set. The final predicted probability of failure is on the order of 10-11 due to the conservative nature of the factors of safety on the deterministic loads

Addressing the complexity of sustainability-driven structural design: Computational design, optimization, and decision making

Being one of the sectors with the largest environmental burden and high socio-economic impacts sets high requirements on the construction industry. At the same time, this provides the sector with great opportunities to contribute to the globally pursued sustainability transition. To cope with the increasing need for infrastructure and, at the same time, limit their sustainability impacts, changes and innovation in the construction sector are required. The greatest possibility to limit the sustainability impact of construction works is at the early design phase of construction projects, as many of the choices influencing sustainability are made at that point. Traditionally, an early choice of a preferred design is often made based on limited knowledge and past experience, considering only a handful of options. This preferred design is then taken on to the successive stages in the stepwise design process, leading to suboptimization.Alternatively, many different design choices could be considered and evaluated in a more holistic approach in order to find the most sustainable design for a particular application. However, finding design solutions that offer the best sustainability performance and fulfil all structural, performance and buildability requirements, require methods that allow considering different design options, analysing them, and assessing their sustainability. The aim of this thesis is to explore and develop methods enabling structural engineers to take sustainability objectives into account in the design of structures. Throughout this thesis, a number of methods have been explored to take sustainability aspects into account in the structural design process. As a first step, highly parameterized computer codes for sustainability-driven design have been developed. These codes interoperate with FE analysis software to automatically model and analyse design concepts over the whole design space and verify compliance with structural design standards. The codes were complemented with a harmonized method for life cycle sustainability performance assessment, in line with the state-of-the-art standards. Here, sustainability criteria were defined covering environmental, social, economic, buildability and structural performance for multi-criteria assessment of design concepts. To identify the most sustainable designs within the set, multi-objective optimization algorithms were used. Algorithms that address the high expense of constraint function evaluations of structural design problems were developed and integrated in the parameterized computer codes for sustainability-driven design. To ensure the applicability and validity of these methods, case studies based on real-world projects and common structural engineering problems were used in this thesis. Case studies for bridges and wind turbine foundations as well as a benchmark case of a reinforced concrete beam were investigated.The case studies highlight the potential of the methods explored to support the design of more sustainable structures, as well as the applicability of the methods in structural engineering practice. It is concluded that it is possible and beneficial to combine computational design, life cycle sustainability assessment, and multi-objective design optimization as a basis for decision making in the design phase of civil engineering projects. A wide adoption of such a sustainability-driven design optimization approach in structural engineering practice can directly improve the sustainability of the construction sector