Showing 11 - 20 of 260 Conference Paper Results
2023
UKAEA-STEP-CP(23)12
29th IAEA Fusion Energy Conference (FEC 2023), London, 16-21 October 2023
R. T. Osawa S. L. Newton D. Moulton S. S. Henderson V. Badicel A. Hudoba
In order to achieve a compatible solution between the divertors and the core, SOLPS-ITER simulations were performed on STEP (Spherical Tokamak for Energy Production) connected double-null geometry to investigate the possibility of using deuterium (D) fuelling puff locations as an actuator for divertor argon (Ar) retention. Significant reduction of …
Preprint2022
UKAEA-CCFE-CP(23)65
The Technology of Fusion Energy, Anaheim, California, USA, 12-16 June 2022
T. R. Barrett M. Bamford B. Chuilon T. Deighan T. Grant D. Horsley D. Johnstone M. Kovari M. Tindall
The CHIMERA fusion technology test facility is currently under construction at the UK Atomic Energy Authority site in South Yorkshire, UK. With the completion of the engineering design, including the design of a component mock-up to be used in full system commissioning, this paper presents in detail the features and capabilities of the CHIMERA faci…
Preprint Published2022
UKAEA-RACE-CP(23)11
23rd Towards Autonomous Robotic Systems Conference (TAROS), RACE, UKAEA, 7-9 September 2022
B. Tabia I. Zoulias G.Burroughes
The debris removal effectiveness is evaluated using visual inspection with three types of dry debris: flour, sand, and metallic swarf. These debris particles are chosen for their property mimicking contaminants found in real conditions. Brushing operational parameters such as the brush angle of attack and the brush penetration have been investig…
Purchase2023
UKAEA-CCFE-CP(23)66
48th EPS Conference on Plasma Physics, Maastricht (Netherlands), June 27 to July 1 2022
L. Horvath M. Maslov P.A. Schneider C.F. Maggi L.Frassinetti B. Lomanowski R.B. Morales D. Nina S. Saarelma J. Simpson G. Corrigan D.I. Refy M.Brix T. Pereira M. Vecsei JET Contributors
The relative role of particle transport and edge fuelling in setting the H-mode density pedestal is still a key open question [1]. Although reduced pedestal models have proven successful in predicting the pedestal pressure for a wide range of plasma scenarios [2,3,4], they lack a first principle based, predictive model for the edge density. Predict…
Preprint Published2023
UKAEA-RACE-CP(23)10
24th Towards Autonomous Robotic Systems Conference (TAROS), Cambridge, 13-15 September 2023
D. Batty L. Manes A. West M. Patel I. Caliskanelli P. Paoletti
In the nuclear industry, the need for improved reliability in current and future technology hinders the deployment of autonomous robotic systems. The following research aims to develop a method of reliably mapping a large environment and abstracting the map into a sparse node graph to create a more efficient data form. The proposed data form all…
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UKAEA-RACE-CP(23)09
24th Towards Autonomous Robotic Systems Conference (TAROS), Cambridge, 13-15 September 2023
Wenxing Liu Hanlin Niu Robert Skilton Joaquin Carrasco
When transferring a Deep Reinforcement Learning (DRL) model from simulation to the real world, the performance could be unsatisfactory since the simulation cannot imitate the real world well in many circumstances. This results in a long period of fine-tuning in the real world. This paper proposes a self-supervised vision-based DRL method that al…
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UKAEA-RACE-CP(23)08
24th Towards Autonomous Robotic Systems Conference (TAROS), Cambridge, 13-15 September 2023
B. Devlin-Hill R. Calinescu J. Cámara I. Caliskanelli
Many complex domains would benefit from the services of Large-scale, Safety-verified, Always-on (LSA) robotic systems. However, existing large-scale solutions often forego the complex reasoning required for safety verification and prescient reasoning in favour of scalability. We propose a method of partitioning the task domain to enable scalabil…
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UKAEA-RACE-CP(23)07
European Conference on Mobile Robots, University of Coimbra, Portugal, 4-7 September 2023
Michal Staniaszek Lara Brudermuller Raunak Bhattacharyya Bruno Lacerda Nick Hawes
We consider planning problems where a robot must visit a large set of locations to complete a task at each one. Our focus is problems where the difficulty of each task, and thus its duration, can be predicted, but not fully known in advance. We propose a general Markov decision process (MDP) model for difficulty-aware problems, and propose varia…
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UKAEA-RACE-CP(23)06
ICRA 2023- International Conference on Robotics and Automation, London, 29 May-2 June 2023
Wenxing Liu Hanlin Niu Wei Pan Guido Herrmann Joaquin Carrasco
Sim-and-real training is a promising alternative to sim-to-real training for robot manipulations. However, the current sim-and-real training is neither efficient, i.e., slow convergence to the optimal policy, nor effective, i.e., sizeable real-world robot data. Given limited time and hardware budgets, the performance of sim-and-real training is …
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UKAEA-CCFE-CP(23)59
13th International Tritium Science and Technology Conference, Radisson Blu Hotel, Bucharest, Romania, 16 - 21 October 2022