Tokamak Integrated Modeling
We built a tokamak integrated modeling framework for consistent, end-to-end physics calculations across devices ranging from small university tokamaks to ITER-scale systems and industrial pilot plants.
NSFsim and the Framework
NSFsim is an IMAS-compatible advanced Grad-Shafranov code for 1.5D axisymmetric tokamak plasma simulation. It supports time-dependent transport modeling with free-boundary evolution in external magnetic fields, and builds a tokamak digital replica from the device's magnetic system and conducting structure characteristics.
Capabilities include direct, inverse, and plasma-free calculations; discharge scenario development; disruption and VDE simulation; equilibrium reconstruction; and synthetic diagnostics. NSFsim has been verified many times and validated against other simulation codes and experimental data from many tokamaks.
NSFsim is available as an online simulation platform on the Fusion Twin Platform for DIII-D, ISTTOK, NSF NTT, SMART, and other tokamaks, with a public web API and Python and MATLAB examples on GitHub.
NSFsim sits at the core of an integrated framework coupled with TRAVIS (ECRH/ECCD ray-tracing), ASCOT5 (NBI and fast-particle physics), TGLF (turbulent transport), and MISHKA (neural network surrogate for the pedestal), enabling validated end-to-end predictive simulations across the full tokamak lifecycle.
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.
Tokamak Design
Project vision and scope clarification
Feasibility study and pre-conceptual design
Conceptual design and engineering
Case Studies
We use NSFsim and the framework around it for a wide variety of tasks, including tokamak feasibility study and design, solving difficult optimization and prediction problems, developing conventional and training ML-based real-time controllers of plasma shape, position, and other parameters, including multi-objective optimization of control.
Negative Triangularity Tokamak
Blog ↗Plasma Disruption in Tokamak
Blog ↗Training ML Models for Plasma Shape Control
Ready to Get Started?
Tell us about your device and modeling needs and we'll propose a workflow that fits your team and timeline.
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