TOKAMAK INTEGRATED MODELING & DESIGN
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.
This foundation empowers end-to-end tokamak design and advanced predictive simulations, backed by extensive hands-on experience modeling more than 20 tokamaks across experiments, upgrades, and next-generation systems.
Explore the publications page to learn more about our research and work.
This foundation empowers end-to-end tokamak design and advanced predictive simulations, backed by extensive hands-on experience modeling more than 20 tokamaks across experiments, upgrades, and next-generation systems.
Explore the publications page to learn more about our research and work.
Case Studies
- [Coming soon] Fusion Neutron Source for Medical Isotopes Production
TOKAMAK DESIGN
From small university tokamaks to large-scale devices like ITER and industrial pilot plants.
Project vision and scope clarification
Feasibility study and pre-conceptual design
Conceptual design and engineering
ALL SERVICES
- 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.