Dr. Bentley Oakes

With Digital Twin (DT) construction and evolution occurring over time, stakeholders require tools to understand the current characteristics and conceptual architecture of the system at any time. DTInsight is a systematic and automated tool and methodology for producing continuous reporting for DTs. DTInsight offers three key features: (a) an interactive conceptual architecture visualization of DTs; (b) generation of summaries of DT characteristics based on ontological data; and (c) integration of these outputs into a reporting page within a continuous integration and continuous deployment (CI/CD) pipeline. The tool enables stakeholders to generate detailed, up-to-date reports when provided with a modeled description of the Digital Twin that conforms to the proposed DT Description Framework.

The systematic engineering of Digital Twins (DTs) requires the establishment of clear methodologies supported by intelligent tooling. We propose an approach to guide the user in the creation and deployment of services for DTs utilizing ontologies and workflows. In our approach, the user selects a desired DT service from an array of options. This selection is then used to suggest a) enablers and models to place in the DT, and b) development and deployment workflows for the DT service. The aim is to provide DT engineering guidance to assist non-software engineering experts to develop DT services more rapidly with less effort. We describe our initial work on applying this approach to a derived version of an industrial wind turbine generator case study, utilizing openCAESAR for ontology definition and enacting the workflows with Jupyter notebooks.

Digital Twins (DTs) can be constructed for many different applications, leading to substantial differences between different case studies. To be able to learn from the challenges and lessons learned by other DT practitioners, it is important that experience reports be consistent to facilitate comparisons. In this paper, we merge three reference description frameworks for DTs, one generated from a systematic mapping study, one generated from an analysis of experience reports, and one from a systematic literature review, to come up with a unified characterization of DT applications. This analysis has identified six non-overlapping and three cross-cutting characteristics in the reference frameworks. This paper showcases the unified characterization with 21 characteristics to report on a DT case study called the Flex-cell, a manufacturing cell with two robotic arms used for cooperative assembly. The generalizability of this unified characterization is validated using a multi-case approach with another case study in robotics and another in the food industry. We call on the DT community to integrate these systematic reporting principles in their future DT experience reports such that other practitioners can learn from each other more effectively.

Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents to software engineering researchers the six key challenges that a domain expert faces in addressing their problem with a computational workflow, and the underlying executable implementation. These challenges arise out of our conceptual framework which presents the "route" of transformations that a domain expert may choose to take while developing their solution. To ground our conceptual framework in the state of the practice, this article discusses a selection of available textual and graphical workflow systems and their support for the transformations described in our framework. Example studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain specificity and machine learning usage of their problem, workflow, and implementation. The state of the practice informs our discussion of the six key challenges, where we identify which challenges and transformations are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains.

Model-Based System Engineering (MBSE) employs models and formal languages to support development of complex (systems-of-) systems. NASA Jet Propulsion Laboratory (JPL) sees MBSE as a key approach to managing the complexity of system development. However, balancing agility and rigor in MBSE has been reported as a challenging task not yet addressed by modeling tools and frameworks. This is because existing MBSE approaches may enable agility but compromise rigor, or enhance rigor but impede agility. We discuss the challenges of balancing agility and rigor in MBSE across seven systems engineering architectural functions defined by the JPL Integrated Model-Centric Engineering (IMCE) initiative. We demonstrate how openCAESAR, an open-source MBSE methodology and framework created at JPL, can strike a balance between agility and rigor through a case study of the Kepler16b project and discussion of lessons learned from past projects.