Jason Ghidella

Requirements-Based Testing in Aircraft Control Design

To be competitive, Model-Based Design can be applied to help bring down the cost of system design and faster time to market. Model-based approaches are especially effective in the design stages and are increasingly tied in with the requirements capture and code generation and testing phases. The infrastructure for this allows linking requirements to parts of the system model as well as automatically generating references to the original requirements in the code. In addition, test vectors that are derived from the requirements are part of the system model and can be linked to the requirements they represent. This paper describes how this infrastructure can be combined with coverage analyses for model verification and validation to aid in making requirements consistent and unambiguous, while ensuring the set of test vectors is complete and the design minimal (i.e., no superfluous elements exist).

The Use of Computing Clusters and Automatic Code Generation to Speed Up Simulation Tasks

This paper studies a number of different techniques that can be used to reduce the amount of time needed to run block diagram simulations. The first is automatic code generation techniques used to create simulation exe cutables from graphical block diagram models. A number of alternative techniques are studied, highlighting increases in simulation speed that can be achieved at the expense of intera ctivity with the graphical model. This paper will discuss at which stages of modeling and simulation code generation should be considered. The second technique that is studied is the use of computing clusters to distribute a number of simulation runs across a number of processors. With the advent of the multicore processor this technique has become a ccessible to many more engineers than in the past.

Model Reuse for the Training of Fault Scenarios in Aerospace

The use of models has become common in aircraft development. Many different models of aircraft subsystems are designed to analyze, optimize, and synthesize the subsystem behavior. An important behavior in training simulators is the aircraft behavior when failures occur and it would be beneficial to re-use the subsystem models that were designed in the development process. One of these models captures the behavior of the redundancy management system that operates on aircraft elevators. This paper shows how this model can be connected to another model that captures the dynamics of the actuator hydraulics and elevator mechanics to study transient effects of loss of control. The same model of the redundancy management system is then included by reference in an aircraft model for training purposes to investigate transient effects of fault scenarios. Finally, the paper discusses how the real-time code that is used in training simulators can be automatically generated from the same model, using customization features to ensure that the generated code is compatible with the software with which it needs to be integrated.

Creating Flight Simulator Landing Gear Models Using Multidomain Modeling Tools

This paper uses commercial off-the-shelf (COTS) domain specific modeling software to create a high fidelity plant model of an aircraft’s landing gear for inclusion into a full aircraft flight simulator. The use of domain specific modeling software enables detailed modeling of the physics and facilitates accurate computational simulation of the aerodynamic and mechanical loads that occur when the landing gear are deployed and retracted during landing and take-off operations. The parameter design space is easily searched by considering a number of different landing scenarios including touching down on one wheel first, to optimize the design.

Team-Based Collaboration in Model-Based Design

In this paper, a new tool is introduced that enables team-based collaboration in Model-Based Design. The use of this tool enhances the engineer’s productivity by maintaining primary focus on the design tasks, encourages the adoption of configuration management tools through the abstraction of complex file and source control tasks, and improves knowledge transfer across the organization. The key challenges engineers face today and how this new tool addresses these challenges are described in this paper. Best practices for motivating organizations, large and small, to jumpstart team-based development initiatives are also described within the context of the tool’s capabilities.

Model-based design for test vector verification

To meet the needs of a competitive marketplace, model-based design is increasingly being adopted by companies. The use of computational models throughout the system design process accelerates development and increases quality by executable specifications, broader and automatic exploration of the design space, and high-level verification and validation. This paper shows how coverage technologies used in model-based design can be exploited in the verification and selection of test vectors used to troubleshoot faulty equipment

Model coverage as a quality measure and teaching Tool for embedded control system design

To systematically establish that a design satisfies its requirements, the design model is analyzed with respect to a set of test cases to establish a measure of so-called model coverage. If less than 100% coverage of model behavior is achieved, the design contains unintended functionality or there may be lacking test cases, which in turn may be because of missing requirements. This paper presents the use of model coverage in education, illustrated by the design of an aircraft attitude control system. Model coverage provides a measure of quality of a design task performed by a student while it can help obtain insight into details of critical behavior of a design and how to correct problems discovered.

Enabling Modular Design Platforms for Complex Systems

In recent times, an emerging trend in several industries that have adopted Model-Based Design has been holistic product platforms where a single systems design is reused and customized to meet diverse customer requirements such as application, cost, and operational considerations. Many of these dynamic changes in nature have required system design component variations referred to as “variants” on top of a fixed master design. One approach to realize this is to create copies of the original design for each variant combination. Additionally, this requires a sophisticated traceability mechanism to propagate any changes in the design to the various implementations. An alternative approach is to design a modular architecture that can reference all the product variations within a single file. Different implementations can then be realized by selecting different system components through a scripting language. This approach promotes design reuse and provides a powerful mechanism to implement traceability. However, such a paradigm requires core tool functionality similar to those available in various UML/SysML implementations before being applied to a systems development process. In this paper, we introduce variant semantics for complex systems design for use within the Simulink modeling environment. We discuss their attributes which can be parametric or structural that can be used throughout the development process. In addition to improving the efficiency and development of product variations, variants present a variety of uses in the context of systems engineering workflows. For example, design exploration, where several alternatives exist for a component, can now be managed efficiently to simulate every design possibility in a combinatorial fashion for a given test suite. For large-scale problems, these simulations could be distributed to a high performance computing cluster for overall speedup through a scripting methodology. Design elaboration and integration is a challenging activity that can also be improved through the use of variants, where low fidelity components are replaced by more specialized one’s going from mathematical equations to physical or software elements. Since the order in which these components are integrated influence design quality and subsequent iterations, it is possible to carry out several separate integrations that increase confidence. Since there are a number of ways to modularize a design, we also outline a set of best practices for partitioning the design variations for scalability and maintainability. Using Simulink-based examples, we illustrate the above scenarios and outline strategies on how organizations can leverage these possibilities to reuse while enhancing their existing knowledge to meet system design challenges of the future.