Fusion energy has the potential to be an effective clean energy source because its reactions generate incredibly large amounts of energy. Fusion reactors are intended to reproduce on Earth what happens in the core of the sun, where very light elements fuse and release energy. Engineers can harness this energy to heat water and generate electricity through a steam turbine, but the path to fusion is not entirely straightforward.
Controlled nuclear fusion has several advantages over other energy sources for generating electricity. First, the fusion reaction itself does not produce carbon dioxide. There is no risk of nuclear meltdown and the reaction does not generate long-lived radioactive waste.
I’m a nuclear engineer who studies materials that scientists can use in fusion reactors. Fusion takes place at incredibly high temperatures. To make fusion a viable energy source one day, reactors will need to be built with materials that can survive the heat and radiation generated by fusion reactions.
Fusion material challenges
During a fusion reaction, different types of elements can fuse together. The substance most scientists prefer is deuterium plus tritium. These two elements have the greatest chance of coalescing at temperatures that a reactor can maintain. This reaction generates a helium atom and a neutron, which transport most of the energy from the reaction.
Humans have been successfully generating fusion reactions on Earth since 1952 – some even in their garages. But the trick now is to make it worth it. You have to get more energy out of the process than you put in to get the reaction going.
Fusion reactions take place in a very hot plasma, a state of matter that resembles gas but consists of charged particles. The plasma must remain extremely hot – more than 100 million degrees Celsius – and condensed during the reaction.
To keep the plasma warm and condensed and create a reaction that can continue, you need special materials that make up the reactor walls. You also need a cheap and reliable fuel source.
While deuterium is very common and extracted from water, tritium is very rare. A 1 gigawatt fusion reactor is expected to burn 56 kilograms of tritium annually. But the world only has about 25 kilograms of tritium commercially available.
Researchers must find alternative sources for tritium before fusion energy can take off. One option is to have each reactor generate its own tritium through a system called the breeder blanket.
The grow blanket forms the first layer of the walls of the plasma chamber and contains lithium that reacts with the neutrons generated in the fusion reaction to produce tritium. The blanket also converts the energy of these neutrons into heat.
Fusion devices also require a divertor, which extracts the heat and ash released by the reaction. The divertor ensures that the reactions continue for longer.
These materials will be exposed to unprecedented levels of heat and particle bombardment. And there are currently no experimental facilities to reproduce these conditions and test materials in a real-world scenario. The focus of my research is therefore to bridge this gap using models and computer simulations.
From the atom to the complete device
My colleagues and I are working to produce instruments that can predict how the materials in a fusion reactor erode, and how their properties change when exposed to extreme heat and high levels of particle radiation.
As they are irradiated, defects can form and grow in these materials, affecting how well they respond to heat and stress. In the future, we hope that government agencies and private companies can use these tools to design fusion power plants.
Our approach, called multiscale modeling, involves looking at the physics in these materials over different time and length scales with a series of computer models.
We first study the phenomena occurring in these materials at the atomic scale through accurate but expensive simulations. For example, a simulation could investigate how hydrogen moves in a material during irradiation.
From these simulations we look at properties such as diffusivity, which tells us how much hydrogen can spread through the material.
We can integrate the information from these simulations at the atomic level into cheaper simulations that look at how the materials react on a larger scale. These large-scale simulations are cheaper because they model the materials as a continuum rather than considering each individual atom.
The atomic-scale simulations can take weeks to run on a supercomputer, while the continuum simulations will take only a few hours.
All this modeling work that takes place on computers is then compared with experimental results obtained in laboratories.
For example, if one side of the material contains hydrogen gas, we want to know how much hydrogen leaks to the other side of the material. If the model and experimental results agree, we can have confidence in the model and use it to predict the behavior of the same material under the conditions we would expect in a fusion device.
If they don’t match, we go back to the atomic-scale simulations to investigate what we missed.
Furthermore, we can link the material model to plasma models on a larger scale. These models can tell us which parts of a fusion reactor will be the hottest or experience the most particle bombardment. From there we can evaluate more scenarios.
For example, if too much hydrogen leaks through the material during operation of the fusion reactor, we may recommend making the material thicker in certain places or adding something to capture the hydrogen.
Designing new materials
As the search for commercial fusion energy continues, scientists will need to develop more resilient materials. The field of possibilities is daunting: engineers can fabricate multiple elements together in many ways.
You could combine two elements to create a new material, but how do you know what the correct ratio of each element is? And what if you want to combine five or more elements? It would take far too long to try to run our simulations for all these possibilities.
Fortunately, artificial intelligence is here to help. By combining experimental and simulation results, analytical AI can recommend combinations that are most likely to have the properties we are looking for, such as heat and stress resistance.
The goal is to reduce the number of materials an engineer must experimentally produce and test to save time and money.
This article is republished from The Conversation, an independent nonprofit organization providing facts and trusted analysis to help you understand our complex world. It was written by: Sophie Blondel, University of Tennessee
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Sophie Blondel receives funding from the US Department of Energy.