Optimisation of confinement in a fusion reactor using a nonlinear turbulence model

The confinement of heat in the core of a magnetic fusion reactor is optimised using a multidimensional optimisation algorithm. For the first time in such a study, the loss of heat due to turbulence is modelled at every stage using first-principles nonlinear simulations which accurately capture the turbulent cascade and large-scale zonal flows. The simulations utilise a novel approach, with gyrofluid treatment of the small-scale drift waves and gyrokinetic treatment of the large-scale zonal flows. A simple near-circular equilibrium with standard parameters is chosen as the initial condition. The figure of merit, fusion power per unit volume, is calculated, and then two control parameters, the elongation and triangularity of the outer flux surface, are varied, with the algorithm seeking to optimise the chosen figure of merit. A two-fold increase in the plasma power per unit volume is achieved by moving to higher elongation and strongly negative triangularity.Comment: 32 pages, 8 figures, accepted to JP

Direct Microstability Optimization of Stellarator Devices

Turbulent transport is regarded as one of the key issues in magnetic confinement nuclear fusion, both for tokamaks in stellarators. In this letter, we show that a significant decrease in the turbulent heat flux can be obtained in an efficient manner by coupling stellarator optimization with linear gyrokinetic simulations. This is accomplished by computing the quasi-linear heat flux at each step of the optimization process, as well as the deviation from quasisymmetry, and minimizing their sum, leading to a balance between neoclassical and turbulent transport.Comment: 6 pages, 3 figure

Post-Formation Sodium Loss on the Moon: A Bulk Estimate

The Moon and Earth are generally similar in terms of composition, but there exist variations in the abundance of certain elements among the two bodies. These differences are a likely consequence of differing physical evolution of the two bodies over the solar system's history. While previous works have assumed this may be due to conditions during the Moon"TM"s formation, we explore the likelihood that the observed depletion in Sodium in lunar samples may be partially due to post-formation mechanisms. Solar effects, loss from a primordial atmosphere and impacts are some of the dominant post-formation mechanisms that we examine. We describe how our past and current modeling efforts indicate that a significant fraction of the observed depletion of sodium in lunar samples relative to a bulk silicate earth composition may have been due to solar activity, atmospheric loss and impacts. Using profiles of sodium abundances from lunar crustal samples may thus serve as a powerful tool towards exploring conditions on the Moon's surface throughout solar system history. Conditions on the Moon immediately after formation may still be recorded in the lunar crust and may provide a window towards interpreting observations from some of the first rocky exoplanets that will be most amenable to characterization. Potential spatial variation of sodium in the lunar crust may be a relevant consideration for future sample return efforts. Sodium Depletion in the Lunar Crust: Luna

Particle Creation If a Cosmic String Snaps

We calculate the Bogolubov coefficients for a metric which describes the snapping of a cosmic string. If we insist on a matching condition for all times {\it and} a particle interpretation, we find no particle creation.Comment: 10 pages, MRC.PH.17/9

AI for the Public Sector: Opportunities and challenges of cross-sector collaboration

Public sector organisations are increasingly interested in using data science and artificial intelligence capabilities to deliver policy and generate efficiencies in high uncertainty environments. The long-term success of data science and AI in the public sector relies on effectively embedding it into delivery solutions for policy implementation. However, governments cannot do this integration of AI into public service delivery on their own. The UK Government Industrial Strategy is clear that delivering on the AI grand challenge requires collaboration between universities and public and private sectors. This cross-sectoral collaborative approach is the norm in applied AI centres of excellence around the world. Despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In this article we discuss the opportunities and challenges from AI for public sector. Finally, we propose a series of strategies to successfully manage these cross-sectoral collaborations