Magnetized Target Fusion: COMSOL Simulation & Inverse Problem Solving

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General Fusion Advances Fusion Energy with COMSOL® Modeling and Bayesian Inference

Achieving lasting fusion energy relies on overcoming complex physics and engineering challenges. General Fusion’s magnetized target fusion approach,utilizing a spherical tokamak plasma,is making notable strides,aided by advanced modeling techniques and data analysis. The company’s LM26 fusion exhibition, operational since February 2025, showcases the power of computational simulation in accelerating progress toward viable fusion power.

Modeling the Implosion: COMSOL® and Lithium Liner Compression

Initially, General Fusion leveraged the capabilities of COMSOL Multiphysics® software to model the intricate magnetomechanical compression of small-scale lithium components. These initial investigations focused on rings and cylinders, employing 2D axisymmetric models that concurrently accounted for nonlinear solid mechanics, magnetic field interactions, and heat transfer. According to a company release, these models were rigorously validated against high-speed imagery and laser diagnostics obtained from physical experiments.This validation process was crucial, as the refined models subsequently informed the design and operational parameters of the larger LM26 compressor.

The Challenge of Dynamic Adjustment

A key hurdle in achieving controlled fusion lies in the dynamic nature of the plasma and the surrounding materials. “Plasma equilibrium characteristics and lithium liner model parameters need to be adjusted during a compression shot,” a senior official stated. Traditional material testing on lithium, while valuable, proved insufficient to encompass the full spectrum of conditions experienced within the LM26 experiment. The limited range of testing data presented a significant challenge to accurately predicting and controlling the compression process.

Bayesian Inference: Reconstructing the Compression Sequence

To overcome this limitation, General Fusion implemented a elegant Bayesian inference reconstruction process to solve an inverse problem. This innovative approach involved recreating the lithium liner’s compression sequence through a parametric sweep of COMSOL Multiphysics models. These simulations were meticulously constrained by real-world data acquired from structured light reconstruction (SLR) and photon doppler velocimetry (PDV) measurements taken during LM26 experiments.

This method allowed General Fusion to precisely define magnetic flux boundary conditions for internal Grad-Shafranov magnetohydrodynamic (MHD) solvers. The MHD solvers,in turn,were used to reconstruct the plasma equilibrium and determine the crucial plasma density profiles necessary for calculating plasma temperature.

Towards 1 keV and Beyond

This work is fundamentally vital as General Fusion progresses toward its ambitious goals for LM26. The company is currently focused on achieving a plasma temperature of 1 keV, with a long-term objective of reaching 10 keV. Achieving these temperature milestones is critical for demonstrating the feasibility of magnetized target fusion as a clean and sustainable energy source.

reader question:– Did you know that Bayesian inference allows scientists to refine models using experimental data, even when complete data is unavailable? This is crucial for complex systems like fusion reactors.

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