Advancing tokamak research and development through tailored digital twins and Fusion Twin Software as a Service (SaaS)
ORAL
Abstract
Digital twins are often tailored to meet diverse stakeholder needs, from research to predictive maintenance. They offer precise insights and actionable data, improving decision-making and driving innovation across various domains.
To create digital twins of tokamaks, Next Step Fusion applies NSFsim, a flexible and fast control-oriented simulator. It solves 2D free-boundary plasma equilibrium (Grad-Shafranov equation) together with transport equations for a digital replica of a real device.
Precise simulations alone cannot address all needs. Fusion Twin SaaS was launched to bridge the gap between real machines, simulators, and digital twins. It provides cloud-based simulations of multiple tokamaks with NSFsim, plasma boundary reconstruction machine learning (ML) model, data management and visualization, Jupyter Notebooks, and collaboration features.
Future plans include creating an environment for training ML models, adding more digital replicas of real and virtual tokamaks, extending simulator capabilities with better transport and auxiliary heating and current drive models, and making available control, reconstruction, and prediction ML models. This will speed up research, reduce time to insights, simplify work for fusion professionals, and eventually bring fusion energy closer.
To create digital twins of tokamaks, Next Step Fusion applies NSFsim, a flexible and fast control-oriented simulator. It solves 2D free-boundary plasma equilibrium (Grad-Shafranov equation) together with transport equations for a digital replica of a real device.
Precise simulations alone cannot address all needs. Fusion Twin SaaS was launched to bridge the gap between real machines, simulators, and digital twins. It provides cloud-based simulations of multiple tokamaks with NSFsim, plasma boundary reconstruction machine learning (ML) model, data management and visualization, Jupyter Notebooks, and collaboration features.
Future plans include creating an environment for training ML models, adding more digital replicas of real and virtual tokamaks, extending simulator capabilities with better transport and auxiliary heating and current drive models, and making available control, reconstruction, and prediction ML models. This will speed up research, reduce time to insights, simplify work for fusion professionals, and eventually bring fusion energy closer.
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Presenters
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Alexei Zhurba
- Next Step Fusion