scholarly journals France’s State of the Art Distributed Optical Fibre Sensors Qualified for the Monitoring of the French Underground Repository for High Level and Intermediate Level Long Lived Radioactive Wastes

Sensors ◽  
2017 ◽  
Vol 17 (6) ◽  
pp. 1377 ◽  
Author(s):  
Sylvie Delepine-Lesoille ◽  
Sylvain Girard ◽  
Marcel Landolt ◽  
Johan Bertrand ◽  
Isabelle Planes ◽  
...  
2020 ◽  
Vol 11 (2) ◽  
pp. 66-74
Author(s):  
A. A. Ekidin ◽  
◽  
K. L. Antonov ◽  

Generation of radioactive wastes (RW) is viewed a most urgent problem of radiation safety under normal operation of nuclear power plants (NPP). The paper demonstrates the application of a specifi c indicator (rate) of RW generation per unit of generated power (m3/GW·h) for a retrospective assessment and forecasting of RW generation volumes at Russian NPPs. Mean and median values of annual specifi c RW generation rates were calculated for each NPP based on published environmental reports of JSC Rosenergoatom Concern for the period of 2008—2018. Advantage of applying median values in retrospective and forecast assessments was shown. Medians for solid very low-level, low-level, intermediate-level and high-level radioactive waste amounted to 1.5·10−2 m3/GW·h, 3.3·10−2 m3/GW·h, 3.3·10−3 m3/GW·h and 2.8·10−4 m3/GW·h, respectively; for liquid low-level and intermediate-level waste these values accounted for 1.4·10−3 m3/GW·h, 2.5·10−3 m3/GW·h, respectively. NPPs with RBMK reactor units are characterized by the highest mean and median values of specifi c RW generation rates for all RW categories. Given various types of reactor facilities and their characteristic specifi c rates, retrospective estimates for the total volume of liquid RW was increased by 8 % and for solid RW — by 12 %. The forecast estimates based on specifi c rate medians, as well as on increased power generation planned for Russian NPPs indicates probable increase in RW generation volumes by 0.8—7.1 % (depending on waste category) from 2020 to 2027.


2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Xizi Sun ◽  
Zhisheng Yang ◽  
Xiaobin Hong ◽  
Simon Zaslawski ◽  
Sheng Wang ◽  
...  

AbstractDistributed optical fibre sensors deliver a map of a physical quantity along an optical fibre, providing a unique solution for health monitoring of targeted structures. Considerable developments over recent years have pushed conventional distributed sensors towards their ultimate performance, while any significant improvement demands a substantial hardware overhead. Here, a technique is proposed, encoding the interrogating light signal by a single-sequence aperiodic code and spatially resolving the fibre information through a fast post-processing. The code sequence is once forever computed by a specifically developed genetic algorithm, enabling a performance enhancement using an unmodified conventional configuration for the sensor. The proposed approach is experimentally demonstrated in Brillouin and Raman based sensors, both outperforming the state-of-the-art. This methodological breakthrough can be readily implemented in existing instruments by only modifying the software, offering a simple and cost-effective upgrade towards higher performance for distributed fibre sensing.


2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.


Author(s):  
yifan yang ◽  
Lorenz S Cederbaum

The low-lying electronic states of neutral X@C60(X=Li, Na, K, Rb) have been computed and analyzed by employing state-of-the-art high level many-electron methods. Apart from the common charge-separated states, well known...


2007 ◽  
Vol 18 (10) ◽  
Author(s):  
Julian D C Jones ◽  
Ralph P Tatam

Author(s):  
L. S. Pioro ◽  
I. L. Pioro

It is well known that high-level radioactive wastes (HLRAW) are usually vitrified inside electric furnaces. Disadvantages of electric furnaces are their low melting capacity and restrictions on charge preparation. Therefore, a new concept for a high efficiency combined aggregate – submerged combustion melter (SCM)–electric furnace was developed for vitrification of HLRAW. The main idea of this concept is to use the SCM as the primary high-capacity melting unit with direct melt drainage into an electric furnace. The SCM employs a single-stage method for vitrification of HLRAW. The method includes concentration (evaporation), calcination, and vitrification of HLRAW in a single-stage process inside a melting chamber of the SCM. Specific to the melting process is the use of a gas-air or gas-oxygen-air mixture with direct combustion inside a melt. Located inside the melt are high-temperature zones with increased reactivity of the gas phase, the existence of a developed interface surface, and intensive mixing, leading to intensification of the charge melting and vitrification process. The electric furnace clarifies molten glass, thus preparing the high-quality melt for subsequent melt pouring into containers for final storage.


1998 ◽  
Vol 68 (1-3) ◽  
pp. 320-323 ◽  
Author(s):  
S.F. Knowles ◽  
B.E. Jones ◽  
S. Purdy ◽  
C.M. France

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