Study and optimization of magnetized ICRF discharges for tokamak wall conditioning and assessment of the applicability to ITER

Het ambitieuze ITER-tokamakproject is momenteel het toonaangevende onderwerp in het wereldwijde onderzoek naar magnetische fusie. In deze experimentele tokamak zullen brandstofmengsels van deuterium en tritium sterk verhit worden tot de vereiste fusietemperatuur van ongeveer 150 miljoen graden. Bij deze hoge temperaturen verkrijgt men een plasma. De prestaties van fusieplasmas zijn sterk afhankelijk van de interactie tussen het plasma en de reactorwandcomponenten. Een noodzakelijke methode om de gevolgen van de plasma-wand-interactie te controleren bestaat uit het optimaliseren van de staat van de reactorwandoppervlakken, namelijk wandconditionering, met behulp van specifieke plasma-ontladingen met lage temperaturen. De kwalificatie van routine wandconditioneringsontladingen toepasbaar in de tokamak ITER heeft een hoge prioriteit binnen het magnetische fusieonderzoek. Dit proefschrift kadert in het internationale R&D-programma rond de wandconditioneringstechniek “Ion Cyclotron Wall Conditioning” (ICWC) dat als doel heeft de ICWC-techniek te consolideren en de toepasbaarheid ervan op ITER te kwalificeren. Het omvat zowel experimenteel werk op vier Europese tokamaks, nl. TORE SUPRA, TEXTOR, ASDEX Upgrade en JET, waarbij de efficientie van ICWC voor specifieke conditioneringsdoelstellingen werd nagegaan en geoptimaliseerd, als het modeleren van de ICWC-conditioneringsplasmas en de plasma-wand-interactie tijdens ICWC. De toepasbaarheid van de techniek voor specifieke conditioneringsdoelstellingen op ITER werd aangetoond, en verdere onderzoeksdoelstellingen werden geïdentificeerd

Multi-objective optimization of a wing fence on an unmanned aerial vehicle using surrogate-derived gradients

In this paper, the multi-objective, multifidelity optimization of a wing fence on an unmanned aerial vehicle (UAV) near stall is presented. The UAV under consideration is characterized by a blended wing body (BWB), which increases its efficiency, and a tailless design, which leads to a swept wing to ensure longitudinal static stability. The consequence is a possible appearance of a nose-up moment, loss of lift initiating at the tips, and reduced controllability during landing, commonly referred to as tip stall. A possible solution to counter this phenomenon is wing fences: planes placed on top of the wing aligned with the flow and developed from the idea of stopping the transverse component of the boundary layer flow. These are optimized to obtain the design that would fence off the appearance of a pitch-up moment at high angles of attack, without a significant loss of lift and controllability. This brings forth a constrained multi-objective optimization problem. The evaluations are performed through unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations. However, since controllability cannot be directly assessed through computational fluid dynamics (CFD), surrogate-derived gradients are used. An efficient global optimization framework is developed employing surrogate modeling, namely regressive co-Kriging, updated using a multi-objective formulation of the expected improvement. The result is a wing fence design that extends the flight envelope of the aircraft, obtained with a feasible computational budget

A machine learning-based framework for preventing video freezes in HTTP adaptive streaming

HTTP Adaptive Streaming (HAS) represents the dominant technology to deliver videos over the Internet, due to its ability to adapt the video quality to the available bandwidth. Despite that, HAS clients can still suffer from freezes in the video playout, the main factor influencing users' Quality of Experience (QoE). To reduce video freezes, we propose a network-based framework, where a network controller prioritizes the delivery of particular video segments to prevent freezes at the clients. This framework is based on OpenFlow, a widely adopted protocol to implement the software-defined networking principle. The main element of the controller is a Machine Learning (ML) engine based on the random undersampling boosting algorithm and fuzzy logic, which can detect when a client is close to a freeze and drive the network prioritization to avoid it. This decision is based on measurements collected from the network nodes only, without any knowledge on the streamed videos or on the clients' characteristics. In this paper, we detail the design of the proposed ML-based framework and compare its performance with other benchmarking HAS solutions, under various video streaming scenarios. Particularly, we show through extensive experimentation that the proposed approach can reduce video freezes and freeze time with about 65% and 45% respectively, when compared to benchmarking algorithms. These results represent a major improvement for the QoE of the users watching multimedia content online

Ion cyclotron resonance heating scenarios for DEMO

International audienceThe present paper offers an overview of the potential of ion cyclotron resonance heating (ICRH) or radio frequency (RF) heating for the DEMO machine. It is found that various suitable heating schemes are available. Similar to ITER and in view of the limited bandwidth of about 10M Hz that can be achieved to ensure optimal functioning of the launcher, it is proposed to make core second harmonic tritium heating the key ion heating scheme, assisted by fundamental cyclotron heating 3 He in the early phase of the discharge; for the present design of DEMO-with a static magnetic field strength of B o = 5.855T-that places the T and 3 He layers in the core for f = 60M Hz and suggests to center the bandwidth around that main operating frequency. In line with earlier studies for hot, dense plasmas in large-size magnetic confinement machines it is shown that good single pass absorption is achieved but that the size as well as operating density and temperature of the machine cause the electrons to absorb a non-negligible fraction of the power away from the core when core ion heating is aimed at. Current drive and alternative heating options are briefly discussed and a dedicated computation is done for the traveling wave antenna, proposed for DEMO in view of its compatibility with substantial antenna-plasma distances. The various tasks that ICRH can fulfill are briefly listed. Finally, the impact of transport and the sensitivity of the obtained results to changes in the machine parameters is commented on

IPTV deployment - Trigger for advanced network services!

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An HTTP/2 push-based approach for low-latency live streaming with super-short segments

Over the last years, streaming of multimedia content has become more prominent than ever. To meet increasing user requirements, the concept of HTTP Adaptive Streaming (HAS) has recently been introduced. In HAS, video content is temporally divided into multiple segments, each encoded at several quality levels. A rate adaptation heuristic selects the quality level for every segment, allowing the client to take into account the observed available bandwidth and the buffer filling level when deciding the most appropriate quality level for every new video segment. Despite the ability of HAS to deal with changing network conditions, a low average quality and a large camera-to-display delay are often observed in live streaming scenarios. In the meantime, the HTTP/2 protocol was standardized in February 2015, providing new features which target a reduction of the page loading time in web browsing. In this paper, we propose a novel push-based approach for HAS, in which HTTP/2's push feature is used to actively push segments from server to client. Using this approach with video segments with a sub-second duration, referred to as super-short segments, it is possible to reduce the startup time and end-to-end delay in HAS live streaming. Evaluation of the proposed approach, through emulation of a multi-client scenario with highly variable bandwidth and latency, shows that the startup time can be reduced with 31.2% compared to traditional solutions over HTTP/1.1 in mobile, high-latency networks. Furthermore, the end-to-end delay in live streaming scenarios can be reduced with 4 s, while providing the content at similar video quality