Fusion energy: what’s the hold up?

Adam Gainford – a.gainford@pgr.reading.ac.uk

Unless you missed the news late last year that scientists at the National Ignition Facility (NIF) in California reported the first successful ignition experiments, you may be thinking that the world’s energy woes are over, that fusion energy will soon be a common and cheap alternative to fossil fuels, and that the grid will soon be almost fully carbon neutral. Well, it’s not quite that simple. It’s undeniably a huge achievement that the heralded break-even barrier has finally been breached, and the promise of fusion powered reactors are still as tantalising as ever, but even this hasn’t stopped the age-old joke that inertial fusion energy is always ten years away. So why has ignition taken this long to achieve, and why should we be cautious about proclaiming that the world’s energy problems have been solved?

Previous posts on the blog have focussed mostly on the more the traditional forms of clean energy which are already in widespread use throughout the world. But in this post, I’d like to introduce you to the source which some hope will be the future of clean energy production. Specifically, I’ll be explaining the basics behind inertial confinement fusion (ICF) reactions, and explain some of the challenges that researchers have been battling with for more than half a century.

Fusion Basics

After a nuclear reaction occurs, the combined mass of the reactants will always be different to the combined mass of the products. If the total reactant mass is larger than the total product mass, the deficit will be released as energy – this basic principle is the underlying mechanism behind both fission and fusion reactions. But while it’s quite easy to coax a heavy, unstable atom to decay into summatively lighter components, bringing two nuclei close enough together that they can fuse is a much tricker task. All nuclei are positively charged and will experience a repelling electromagnetic (EM) force which scales by the inverse square of the separation between them. But at femtometer (10-15) scales the strong force begins to dominate, and the nuclei will become bound. Creating the high-energy conditions necessary for nuclei to overcome the EM barrier, bind together, and release the excess mass as energy is the fundamental challenge to achieving fusion.

The only realistic way to do this is to heat the fusing material to a large enough temperature that the nuclei gain enough kinetic energy to approach such small separations. The choice of nuclei is also crucially important at this stage. Since the Coulomb barrier scales with the number of protons in the nuclei, using hydrogen isotopes is a necessity. A 50:50 mix of deuterium (3H) and tritium (2H), aka DT, provides the largest reaction cross section and best possible chance to achieve fusion. Each DT reaction releases 17.6 MeV of energy, and produces a helium nucleus and an extra neutron. The high energy neutron interacts weakly with its surroundings and will quickly escape the immediate environment, but the positively charged helium nucleus can scatter off other DT pairs and transfer energy, helping to kickstart further reactions. This extra self-heating is crucial for reaching a sufficient fuel burnup fraction and release more energy than was input to the system.

By considering the energy reabsorbed by helium self-heating against all radiation and conduction losses, the Lawson Criterion can be derived as a metric to assess the reaction performance. The criterion states that if the triple product of the particle number density (n), temperature (T) and confinement time (tau, the length of time over which fusion reactions can realistically occur) exceeds roughly 3 x 1028 Ksm-3, ignition will occur, and net energy gain will be achieved. If we fix the temperature to a realistic value for fusion (roughly 100 million Kelvin), we have a two parameter problem which can be solved in two ways. Either, we aim to compress the fuel to incredibly high densities for only a fraction of a second, as is the approach for inertial confinement fusion (ICF), or we keep the fuel at more manageable densities for a more extended period of time, as is the approach for magnetic confinement fusion (MCF). Historically, both methods have shown promise and have been making incremental progress towards net energy gain, but ultimately it was ICF that won the race to achieve first ignition.

The inertial confinement fusion (ICF) process

In ICF, a solid target of DT fuel surrounded by a plastic shell is irradiated by high-intensity lasers such that the inertia of the ablating material causes a rapid implosion of the interior fuel (fig 1a). As this fuel compresses (fig 1b), the central hotspot region reaches the required 10 keV temperature to begin DT fusion and initiate a burn wave which propagates throughout the rest of the target (fig 1c, d). Confinement of the plasma is entirely due to this inward inertia and lasts for only a few nanoseconds.

The diagram below shows this process in more detail and highlights some of the problems which can arise during the implosion. During the early stages (fig 1a), the interaction between the high-intensity lasers and the coronal plasma can generate laser-plasma instabilities which compromises the implosion by transferring large amounts of energy to electrons in the plasma. These “hot electrons” may penetrate into the DT ice and gas, depositing large amounts of energy. While this may initially sound useful for reaching ignition temperatures, instead, this fuel preheat increases the pressure inside the capsule, meaning that the inward compression is less efficient, and smaller hotspot temperatures are reached. Interestingly though, if these hot electrons have just the right temperature, they may instead be stopped closer to the imploding shell and contribute to the ablation pressures which drive compression.

The other major problem with ICF is ensuring a perfectly symmetric compression, as shown in fig 1b and 1c. Any deformities in the shell or asymmetry in the laser profiles can preferentially deposit more energy on one side of the target than the other, limiting the maximum achievable compression. Rayleigh-Taylor instability can also become a large problem in the inner DT-shell boundary, as mixing of the cold shell and hot fuel will reduce maximum temperatures. This is such a large problem in ICF that it has motivated a shift towards an alternative approach – “Indirect drive ICF”. Instead of irradiating the target directly, the capsule is contained inside a gold hohlraum which emits x-rays when heated by the lasers. The x-rays bathe the target in a more uniform glow, reducing the asymmetry impacts, though this does come at the expense of much smaller conversion efficiency between the laser and the target. The indirect-drive approach ultimately won out over direct-drive, and has shown the world that fusion energy is possible.

The ICF implosion process broken down into four stages.

Ignition at the NIF

Even before the news broke of successful ignition at the NIF, there were hints that a breakthrough was close. A paper published in August 2022 detailed the first experiments to reach the Lawson criterion using indirect drive ICF but only managed to reach target gain (ratio of laser input energy to neutron output energy) of 0.72. Ignition was finally achieved later in the year when a 2.05 MJ laser ignited a target to produce 3.15 MJ of energy, implying a net gain of just over 1.5.

But we are still a long way from being able to hook up a fusion reactor to the grid. Shot cycles still take half a day or more to complete as lasers power up and cool down – in an ideal setting, this would be reduced to mere seconds. And there is still a large amount of additional energy required to cool and operate the lasers which typically is not included in calculations of scientific breakeven. But perhaps the most serious argument restricting ubiquitous fusion energy is an economic one. The UK’s first tokamak for energy production, STEP, is expected to be completed by 2040 for a staggering £10 billion. (As a quick aside, this is expected to achieve ignition through MCF simply by being the biggest tokamak ever built.) This is a huge sum of money, with a large potential for the project to run over-budget, and with large risk involved for investors. In comparison, decentralised renewables like wind and solar offer a much less risky investment with technology that is proven to work, and which is becoming less expensive by the day. Fusion power may once have been the future of energy production, but in my view, these results have come 20 years too late.

Tiger Teams: Using Machine Learning to Improve Urban Heat Wave Predictions

Adam Gainford a.gainford@pgr.reading.ac.uk

Brian Lobrian.lo@pgr.reading.ac.uk

Flynn Ames – f.ames@pgr.reading.ac.uk

Hannah Croad – h.croad@pgr.reading.ac.uk  

Ieuan Higgs  – i.higgs@pgr.reading.ac.uk

What is Tiger Teams?  

You may have heard the term Tiger Teams mentioned around the department by some PhD students, in a SCENARIO DTP weekly update email or even in the department’s pantomime. But what exactly is a tiger team? It is believed the term was coined in a 1964 Aerospace Reliability and Maintainability Conference paper to describe “a team of undomesticated and uninhibited technical specialists, selected for their experience, energy, and imagination, and assigned to track down relentlessly every possible source of failure in a spacecraft subsystem or simulation”.  

This sounds like a perfect team activity for a group of PhD students, although our project had less to do with hunting for flaws in spacecraft subsystems or simulations. Translating the original definition of a tiger team into the SCENARIO DTP activity, “Tiger Teams” is an opportunity for teams of PhD students to apply our skills to real-world challenges supplied by industrial partners.   

The project culminated in a visit to the Met Office to present our work.

Why did we sign up to Tiger Teams?  

In addition to a convincing pitch by our SCENARIO director, we thought that collaborating on a project in an unfamiliar area would be a great way to learn new skills from each other. The cross pollination of ideas and methods would not just be beneficial for our project, it may even help us with our individual PhD work.  

More generally, Tiger Teams was an opportunity to do something slightly different connected to research. Brainstorming ideas together for a specific real-life problem, maintaining a code repository as a group and giving team presentations were not the average experiences one could have as a PhD student. Even when, by chance, we get to collaborate with others, is it ever that different to our PhD? The sight of the same problems …. in the same area of work …everyday …. for months on end, can certainly get tiring. Dedicating one day per week on an unrelated, short-term project which will be completed within a few months helps to break the monotony of the mid-stage PhD blues. This is also much more indicative of how research is conducted in industry, where problems are solved collaboratively, and researchers with different talents are involved in multiple projects at once.

What did we do in this round’s Tiger Teams?  

One project was offered for this round of Tiger Teams: “Crowdsourced Data for Machine Learning Prediction of Urban Heat Wave Temperatures”. The bones of this project started during a machine learning hackathon at the Met Office and was later turned into a Tiger Teams proposal. Essentially, this project aimed to develop a machine learning model which would use amateur observations from the Met Offices Weather Observation Website (WOW), combined with landcover data, to fine-tune model outputs onto higher resolution grids.   

Having various backgrounds from environmental science, meteorology, physics and computer science, we were well equipped to carry out tasks formulated to predict urban heat wave temperatures. Some of the main components included:  

  • Quality control of data – as well as being more spatially dense, amateur observation stations are also more unreliable  
  • Feature selection – which inputs should we select to develop our ML models  
  • Error estimation and visualisation – How do we best assess and visualise the model performance  
  • Spatial predictions – Developing the tools to turn numerical weather prediction model outputs and high resolution landcover data into spatial temperature maps.  

Our supervisor for the project, Lewis Blunn, also provided many of the core ingredients to get this project to work, from retrieving and processing NWP data for our models, to developing a novel method for quantifying upstream land cover to be included in our machine learning models. 

An example of the spatial maps which our ML models can generate. Some key features of London are clearly visible, including the Thames and both Heathrow runways.

What were the deliverables?  

For most projects in industry, the team agrees with the customer (the industrial partner) on end-products to be produced before the conclusion of the project. Our two main deliverables were to (i) develop machine learning models that would predict urban heatwave temperatures across London and (ii) a presentation on our findings at the Met Office headquarters.  

By the end of the project, we had achieved both deliverables. Not only was our seminar at the Met Office attended by more than 120 staff, we also exchanged ideas with scientists from the Informatics Lab and briefly toured around the Met Office HQ and its operational centre. The models we developed as a team are in a shared Git repository, although we admit that we could still add a little more documentation for future development.  

As a bonus deliverable, our supervisor (and us) are consolidating our findings into a publishable paper. This is certainly a good deal considering our team effort in the past few months. Stay tuned for results from our paper perhaps in a future blog post!