Abstract
Building robust and trustworthy digital twins that can be used for critical operational decisions is a key industrial challenge. As a response to this challenge DNV has developed a suite of recommended practices for development and assurance of digital twins, with the principal DNV-RP-A204 "Qualification and Assurance of Digital Twins" overarching.
The methodology presented in this paper shares the philosophy and key features of the methodology in DNV-RP-A204 which was developed in close collaboration with industry partners and is currently being used by several leading operators and suppliers. Our recommended practice proposes a definition of a digital twin and its capability levels: descriptive, diagnostic, predictive, or fully automated solutions. Further we have a framework to describe digital twin functional elements, which can help to introduce granular structure to the twin and to clearly define quality assurance requirements.
While there are industry-wide standards and commonly agreed best practices for how to assure components and systems of physical assets, there remain no common agreed standards against which to develop, deliver and operate digital twins. The digital twin framework developed by DNV addresses requirements for all aspects of a digital twin: functionality, operations, platform, data quality, cyber security, and organizational requirements. For each of these aspects, the framework clearly defines necessary requirements and assurance processes.
Assurance provides confidence in the digital services, which are increasingly deployed across the energy sector. Trustworthy digital twins can significantly increase efficiency in the project execution phase and the operational phase. A systematic way of addressing and assessing quality will increase trustworthiness of digital twins. The methodology and reflections presented in this paper should enable the user to develop trustworthy digital twins more easily.
Assurance of digital twins is a nascent concept for the energy sector, even though we are familiar with assurance in "traditional" applications, like product development and operation support. Here we demonstrate applications of the recommended practices for assurance, with examples from recent projects.
