Design Data Management in Model-Based Design

One of the keys to successfully harnessing the benefits of graphical modeling in ModelBased Design is the effective management of associated design data. Design data in the form of model parameters and simulation configuration settings are as important to the simulation as the algorithm itself. Together they determine the form and range of the simulation outputs and internal states of the model. However, control engineers have been inclined towards graphical editing and layout than design data management leading to several key design issues especially for large scale systems. These are separation, logical partitioning, change detection, dependency analysis and traceability. In this paper, we present an approach to address these issues using a demonstration within Simulink, an exemplary Model-Based Design environment. A tradeoff decision on embedding of design parameters in the graphical model as opposed to keeping them separate can determine the ease of carrying out a parameter sweep. Another advantage is in the case of modular design platforms where the control variables required to switch variant configurations must be separated out from the design data. Moreover, the use of multiple configuration settings can be used to optimize the performance of the simulation depending on the inherent dynamics present in the model. Lack of logical partitioning of the data can present understandability and data corruption issues within a collaborative environment. Parallel development of graphical components without clear data separation increases the risk of design error. Furthermore, innovative partitioning schemes require a careful understanding of sharing and logical relationships among the data which can be independent of the graphical model componentization. Another decision point in logical partitioning is regarding the storage of design data in a centralized or a decentralized repository system with each offering unique advantages. For example, a decentralized system consisting of multiple files can be put under source control and differenced to earlier versions to detect changes. A change detection workflow that integrates changes in the graphical design with those in the data is key to getting a complete understanding of the changes in the system. As the design begins to scale, it may become necessary to store the design data in a decentralized repository system. However, this does present additional challenges. For example, the tracking of the interdependencies between the graphical model components and the design data partitions becomes critical. An analysis of such dependencies must be carried out to identify any missing components from the project. At a lower level, the traceability of the individual design data to the components is critical to establish the necessary and sufficient conditions for executing a component. Such analysis can also help with the logical partitioning schemes and removing redundant data. One of the big challenges of utilizing the recommendations presented above is in migrating a legacy system to use them. Such migration presents constraints on how much porting can be done in practice. In this paper, we outline several recommendations for addressing these key questions. In conclusion, we offer a set of best practices that engineers can use to manage their data effectively within a team-based environment.