Abstract
The Atomic Weapons Establishment (AWE) is tasked with supporting Continuous At Sea Deterrence (CASD) by certifying the performance and safety of the national deterrent in the Comprehensive ...Test Ban Treaty (CTBT) era. This means that recourse to further underground testing is not possible, and certification must be achieved by supplementing the historical data with the use of computer calculation. In order to facilitate this, AWE operates some of the largest supercomputers in the UK. To validate the computer codes, and indeed the designers who are using them, it is necessary to carry out further experiments in the right regimes. An excellent way to meet many of the requirements for material property data and to provide confidence in the validity of the algorithms is through experiments carried out on high power laser facilities.
Inertial confinement fusion Randewich, Andrew; Lock, Rob; Garbett, Warren ...
Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences,
11/2020, Letnik:
378, Številka:
2184
Journal Article
The UMIST Linear System, or ULS, is a steady state linear plasma device. A Demirkhanov source injects plasma at a density of 2 x 10 18m-3, with an electron temperature of 12 eV, into a 1.5 m long ...cylindrical vacuum chamber. A set of coils supply a 0.1 Tesla axial magnetic held, which confines the plasma to a 1 cm diameter beam. A plate containing an aperture is inserted half way along the machine, dividing it into two chambers.A one dimensional fluid energy balance equation has been solved numerically to aid in the development of a new plasma source for the device. A reflex arc source has since been developed with the intention of producing a plasma with an axial temperature gradient facilitating experimental studies of thermal force.A kinetic model tracing individual ion trajectories in the ULS was developed to visualise ion orbits across the highly non-linear radial electric held in the core of the ULS plasma beam. By considering multiple ion trajectories in a non interacting Monte-Carlo code it was possible to explain the throughput dehcit at apertures placed in the beam path, and to understand the low value for the normalised sheath potential in the core of the plasma. By variation to reproduce the experimental density prohle, the transverse ion temperature was determined to be 50 eV.Experiments have been conducted on the ULS to investigate the interaction of a cool plasma beam with a neutral gas, with the aim of understanding physical processes in the gas target divertor region of tokamaks. The downstream chamber is hooded with hydrogen to a pressure of up to 8 x 10 -3 Torr. Ions enter this gas target chamber through a 14 mm diameter tapered orifice, with energies parallel to the magnetic held of approximately 70 eV. Considerable attenuation of plasma density is measured experimentally before impact on the end plate. Simple analytical models have failed to explain the ion loss mechanism, since cross held diffusion and relevant atomic process rates are apparently negligible.A numerical model aimed at improving understanding of the slowing down rate of ions in the ULS gas target chamber has been developed. The method of choice is a one dimensional particle-in-cell (PIC) code, designed to deal with a large range of ion velocities in a non-Maxwellian distribution. The model assumes a sea of background neutrals unaffected by neutral-ion interactions, consistent with the large neutral density in the ULS. Initial consistency tests of ion flux through the solved region in the absence of atomic processes have been carried out. Results following addition of charge exchange and elastic scattering as the dominant slowing down processes will be presented.
This paper proposes and evaluates, for the first time, a top-down (dorsal view), depth-only deep learning system for accurately identifying individual cattle and provides associated code, datasets, ...and training weights for immediate reproducibility. An increase in herd size skews the cow-to-human ratio at the farm and makes the manual monitoring of individuals more challenging. Therefore, real-time cattle identification is essential for the farms and a crucial step towards precision livestock farming. Underpinned by our previous work, this paper introduces a deep-metric learning method for cattle identification using depth data from an off-the-shelf 3D camera. The method relies on CNN and MLP backbones that learn well-generalised embedding spaces from the body shape to differentiate individuals -- requiring neither species-specific coat patterns nor close-up muzzle prints for operation. The network embeddings are clustered using a simple algorithm such as \(k\)-NN for highly accurate identification, thus eliminating the need to retrain the network for enrolling new individuals. We evaluate two backbone architectures, ResNet, as previously used to identify Holstein Friesians using RGB images, and PointNet, which is specialised to operate on 3D point clouds. We also present CowDepth2023, a new dataset containing 21,490 synchronised colour-depth image pairs of 99 cows, to evaluate the backbones. Both ResNet and PointNet architectures, which consume depth maps and point clouds, respectively, led to high accuracy that is on par with the coat pattern-based backbone.
PDF
