Presentation Information
[.s6T5WKf-02]From observations to lessons learned; Implememting Improved Digital Twins to Support Offshore Energy Decision Making
*Thomas Mansfield1 (1. Plymouth Marine Laboratory (UK))
Environmental digital twins are increasingly supporting evidence-based decision-making across the lifecycle of offshore energy platforms. As these technologies mature, there is a growing need to move beyond proof-of-concept deployments to digital twins with improved reliability, transparency and long-term sustainability.
This paper builds on from the SyncED-Ocean digital twin deployment (Mansfield et al., 2025), which identified three key observations: applying engineering rigour to digital twin development, effective processes for balancing data timeliness with data quality, and maintaining appropriate human oversight despite increasing automation. The UK's EQUIFy and VALMAS programmes provide an opportunity for lessons to be learnt from these observations within active offshore energy initiatives.
To address the first observation, systems engineering approaches, particularly Model-Based Systems Engineering (MBSE), are being introduced. MBSE enables researchers, data managers, and domain experts to contribute to a shared system model, creating a single source of truth for requirements, interfaces, design decisions, and dependencies. This improves traceability throughout the digital twin lifecycle and supports the adoption of engineering best practice in environmental science projects.
The second observation is being explored through collaboration with environmental data-provider communities. Within initiatives such as the Western Channel Observatory, ERDDAP services and interactive visualisation tools support community-led quality assurance processes. Contributors can review datasets, apply SeaDataNet quality flags, increasing transparency around data quality while maintaining timely access to observations.
To progress the third observation, VALMAS's broad links to industry, academic and government organisations are helping shape future human-in-the-loop capabilities, recognising that trust in environmental digital twins depends not only on automation, but also on mechanisms for oversight, intervention, and accountability.
This work demonstrates how lessons from an operational digital twin deployment can be learned through an iterative process. Supported through a DAFNI Fellowship, this work continues to collect evidence on how environmental digital twins can evolve towards more robust, transparent, and sustainable socio-technical systems.
This paper builds on from the SyncED-Ocean digital twin deployment (Mansfield et al., 2025), which identified three key observations: applying engineering rigour to digital twin development, effective processes for balancing data timeliness with data quality, and maintaining appropriate human oversight despite increasing automation. The UK's EQUIFy and VALMAS programmes provide an opportunity for lessons to be learnt from these observations within active offshore energy initiatives.
To address the first observation, systems engineering approaches, particularly Model-Based Systems Engineering (MBSE), are being introduced. MBSE enables researchers, data managers, and domain experts to contribute to a shared system model, creating a single source of truth for requirements, interfaces, design decisions, and dependencies. This improves traceability throughout the digital twin lifecycle and supports the adoption of engineering best practice in environmental science projects.
The second observation is being explored through collaboration with environmental data-provider communities. Within initiatives such as the Western Channel Observatory, ERDDAP services and interactive visualisation tools support community-led quality assurance processes. Contributors can review datasets, apply SeaDataNet quality flags, increasing transparency around data quality while maintaining timely access to observations.
To progress the third observation, VALMAS's broad links to industry, academic and government organisations are helping shape future human-in-the-loop capabilities, recognising that trust in environmental digital twins depends not only on automation, but also on mechanisms for oversight, intervention, and accountability.
This work demonstrates how lessons from an operational digital twin deployment can be learned through an iterative process. Supported through a DAFNI Fellowship, this work continues to collect evidence on how environmental digital twins can evolve towards more robust, transparent, and sustainable socio-technical systems.
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