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Tokamak Integrated Modeling
Our team of plasma scientists and software engineers builds NSFsim, a tokamak simulator that serves as the computational core of a broader integrated modeling framework, coupling TRAVIS (ECRH and ECCD ray-tracing), ASCOT5 (NBI and fast-particles), TGLF (turbulent transport), MISHKA (NN surrogate model for pedestal region), and other codes for consistent, end-to-end physics calculations.
Services
Our validated integrated modeling framework enables end-to-end tokamak design and predictive simulations for devices ranging from small university tokamaks to large-scale systems like ITER and industrial pilot plants.
  • Tokamak physics, design, and operation. Expert support across all phases of tokamak conceptual design, engineering integration, commissioning, and experimental operation.
  • Integrated modeling. Development and deployment of own integrated modeling framework that couples 2D Grad-Shafranov and 1D transport solver with first principle transport models, heating and current drive, scrape-off-layer and divertor plasma, and MHD, enabling multi-physics, time-dependent scenario analysis with consistent inputs and outputs across codes.
  • Disruption modeling (including 3D). Simulation of plasma disruptions and other off-normal fast events, including three-dimensional effects relevant for runaway electrons, halo currents, electromagnetic loads, and mitigation strategy assessment.
  • SOL, divertor, and plasma-material interaction modeling. Simulation of edge plasma phenomena, divertor physics, and plasma-wall interaction, with particular emphasis on liquid lithium plasma-facing components.
  • Control-oriented modeling and software-in-the-loop infrastructure. Development of physics-based models and reduced-order representations exposed via Python and web API to support software-in-the-loop validation of plasma controllers.
  • Machine learning for physics acceleration and control. Application of modern machine learning methods to surrogate modeling of expensive physics codes, disruption prediction and classification, control policy learning and optimization, data-driven augmentation of transport and edge models, with tight coupling to physics-based simulations for interpretability and robustness.
Tokamak design
Project vision and scope clarification
Feasibility study and pre-conceptual design
Conceptual design and engineering
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Case studies