Imaging the surface of General Fusion’s liquid metal reactor

Liza Belskiy, Paul Graham, Laura Romer and Mack Wilson

Our project 

While fusion energy has the potential to produce zero-emission energy more efficiently than other methods, many engineering challenges need to be solved to get there. General Fusion is advancing technology to achieve fusion conditions of over 100 million degrees Celsius by 2025. The company’s approach is to inject plasma (electrically charged gas) into a fusion reactor where it is then compressed with a collapsing liquid metal wall to achieve the density needed for fusion to occur. 

General Fusion asked us to come up with a way to create an image of the liquid metal wall during the compression process. 

This is a two-year capstone project. Our goal for the first year of this project was to define and scope out the problem and then identify which potential design solution offers the most promise for us to advance to a prototype next year. 

General Fusion

Exploring potential design solutions 

General Fusion wants to know about any disturbances in the liquid metal wall during compression, as these disturbances lead to plasma instability and a decrease in energy output. 

Our design solution needs to work within a very constrained system to see this wall from the inside out – to look out from a central rod to image the wall and measure its depth profile. 

Any sensors and hardware need to be positioned at the central rod and be able to function within an environment of extreme heat and pressure. Imaging must happen from inside the cavity, with approximately 100 microseconds between samples. Another challenge is that the environment inside the reactor can cause oxidation of the liquid metal wall, which changes its surface properties and makes light interact differently with different parts of it. 

Because of the conditions inside the reactor, we are essentially limited to using electromagnetic waves to image the three-dimensional liquid lithium cavity. 

We looked at several different imaging options, including LiDAR, deflectometry, radio frequency and stimulated emission. 

Over the course of the year, we researched, modelled and evaluated each potential solution. We ruled out radio frequency and stimulated emission for reasons of complexity, high costs and equipment-related issues. At the end of the year, we’ve decided to continue advancing our work on the LiDAR and deflectometry solutions.

LiDAR offers high-precision depth information, has a small sensor footprint and can rapidly measure the full surface. Some of its disadvantages are that it is generally not used for specular surfaces, so there’s the potential for multiple bounces or lost bounces before return.

Deflectometry works well for specular reflection, offers high speed and accuracy and is a non-contact technique. However, it has a hard time with diffuse reflection and surface discontinuities and requires careful calibration, among other potential drawbacks.


The challenges we faced

This project requires a strong fundamental understanding of deep concepts in physics. 

One of the hardest things has been attaining the level of understanding needed to confidently apply our knowledge to this specific engineering challenge. 

It’s involved a lot of research time and talking with experts in the field, including regular meetings with our contact at General Fusion, the VP of Engineering. 

What we’re most proud of

Although this capstone project is very physics heavy, it is ultimately an engineering problem. The initial problem – come up with a way to image a liquid lithium wall during a fusion reaction – was both very specific and very daunting. It was up to us to figure out the scope and approach given that this has not ever been done before. 

We’re proud of our ability to scope out the problem and come up with and evaluate potential solutions. 


Our project’s future

This year we focused on scoping out the problem and identifying our most promising approach to the challenge. Our initial plan is to advance and continue to evaluate two approaches. 

For LiDAR, next year we’ll continue with our simulations, design and build a test apparatus, test our apparatus on a diffuse surface to develop algorithms and validate our design, and then test the apparatus on surfaces that are both diffuse and specular. For the deflectometry solution, we need to advance our Python simulation code, expand the simulation to determine solution breakpoint for smooth and rough surface combinations, and then potentially select, build and test a deflectometry test apparatus.

We’re very excited to be working on this project again next year! 

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