Henry Broodney

Leveraging Domain Expertise 1in Architectural Exploration

Domain experience is a key driver behind design quality, especially during the early design phases of a product or service. Currently, the only practical way to bring such experience into a project is to directly engage subject matter experts, which means there is the potential for a resource availability bottleneck because the experts are not available when required. Whilst many domain specific tools have attempted to capture expert knowledge in embedded analytics thus allowing less experienced engineers to perform complex tasks, this is certainly not the case for highly complex systems of systems where their architectures can go far beyond what a single human being can comprehend. This paper proposes a new approach to leveraging design expertise in a manner that facilitates architectural exploration and architecture optimization by using pre-defined architecture patterns. In addition, we propose a means to streamline such a process by delineating the knowledge creation process and architectural exploration analytics with the means to facilitate information flow from the former to the latter through a carefuly designed integration framework.

Reusable Derivation of Operational Metrics for Architectural Optimization

Abstract Maintaining coherence between system functional, performance, production and operational requirements is a key to the ability to optimize the design of large-scale systems. Different architectural configurations entail significant differences in functionality, performance, ease of manufacturing/assembly and operational behavior. While the first two are the usual concerns in architectural tradeoff analysis, the last two, reflected by manufacturability and operational metrics, such as manufacturability and affordability, are often neglected in architectural optimization. In this work, we propose a methodology to derive the formal specification of operational metrics applicable to design optimization based on life cycle processes, such as “manufacturing”, “sunny day operation”, and “unplanned maintenance”. These operational metrics are presented in the context of an industrial case study for an Unmanned Underwater Vehicle (UUV) to provide context for the recommended approach. We suggest an approach to (1) define libraries of reusable operational metrics based on architectural properties, (2) build reusable data processing patterns to calculate these architectural properties, and (3) map calculated architectural parameters to a specific design model.