The large hadron collider

Brüning, Oliver; Burkhardt, H.; Myers, S.

doi:10.1016/j.ppnp.2012.03.001

Cited by 44 publications

(29 citation statements)

References 18 publications

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“…Superconducting high-field magnets have become indispensable in high-energy particle physics accelerators to accurately guide the particle beams, such as at the LHC at CERN (Brüning et al, 2012), and in fusion reactors to confine and control the plasma, such as ITER (Mitchell et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…The magnet systems, as they are used in CERN's LHC or ITER, contain coils made of niobium-titanium (NbTi) or niobium-tin (Nb 3 Sn) conductors, that need to be cooled to cryogenic temperatures to achieve the superconducting state (Brüning et al, 2012;Apollinari et al, 2015;Mitchell et al, 2008). Superconducting cables allow to apply very high currents that induce strong magnetic fields causing high magnetic forces within the magnet system.…”

Section: Introductionmentioning

confidence: 99%

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Flow and fracture of austenitic stainless steels at cryogenic temperatures

Fernández-Pisón

Rodríguez-Martínez

García‐Tabarés

et al. 2021

Engineering Fracture Mechanics

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In this paper, we have characterized the microstructural evolution and the plastic flow and fracture behaviours of AISI 304L and AISI 316LN stainless steel grades at liquid nitrogen temperature (77 K) and at liquid helium temperature (4 K). Uninterrupted tensile experiments, where the sample is continuously deformed under quasi-static loading conditions until fracture, have been carried out with a Single-Section Sample to obtain the stress-strain characteristics of the two grades. Interrupted tensile experiments, in which the sample is unloaded before fracture, have been performed with a novel Double-Section Sample to later characterize the strain-induced martensitic transformation at different levels of deformation. The content of martensite has been determined post-mortem, using magnetic induction, electron backscatter diffraction and quantitative light optical micrography. The results obtained with the three methods show quantitative agreement, and reveal that the martensitic transformation in AISI 304L occurs faster and to a greater extent than in AISI 316LN both at 77 K and at 4 K. To the authors' knowledge, in this paper we provide the first experimental results for the evolution of the content of strain-induced martensite in AISI 304L and AISI 316LN samples tested at liquid helium temperature. In addition, the experimental data for the evolution of the martensite volume fraction with the strain have been used to identify the temperature-dependent parameters of the martensitic transformation kinetic models proposed by Olson and Cohen (1975) and Garion and Skoczeń (2002). Moreover, Mode I fracture tests with fatigue-precracked Compact Samples have been carried out to determine the fracture properties of the two investigated materials using the "resistance curve procedure" (ASTM-E1820-20a, 2020). The crack-growth resistance curves have been obtained with four different methods here referred to as ASTM Compliance Method, W-N Compliance Method, Modified W-N Compliance Method and ASTM Normalization Method, which is an original methodological contribution of this paper. While the four approaches yield similar results for the fracture toughness, only the W-N Compliance Method and the Modified W-N Compliance Method, the latter being proposed in this paper, fulfil all the requirements of the standard ASTM-E1820-20a (2020) so that the calculated fracture toughness can be accepted as a material property. The comparison of results for both materials and testing temperatures shows that the AISI 316LN displays higher fracture toughness than the AISI 304L. Moreover, post-mortem microstructural analysis of the Compact Samples near the fracture surface has revealed that the content of martensite is greater in AISI 304L than in AISI 316LN. Furthermore, for AISI 304L more martensite is formed in the sample tested at 77 K because the plastic deformation near the crack is greater than at 4 K.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flow and fracture of austenitic stainless steels at cryogenic temperatures

Fernández-Pisón

Rodríguez-Martínez

García‐Tabarés

et al. 2021

Engineering Fracture Mechanics

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“…A two-stage procedure has been adopted for the validation of the proposed method: (i) the individual steps of the proposed method, i.e., the classification using the CS-SVM, the feature ranking provided by the Relief algorithm, and the method for the quantification of the information content of a subset of features have been verified considering 15 benchmark labeled datasets from the Knowledge Extraction based on Evolutionary Learning (KEEL) repository [25,27]; (ii) the identification of the critical components in CTIs has been validated by means of a real case study involving 260 monitoring signals of the LHC particle accelerator of CERN, which is a large-scale and complex system of systems, in which the failure of individual components/units directly influences the accelerator performance and its total availability for physics experiments [28,29]. Since the ground-truth importance ranking of the CTI components is not available, the obtained results have been directly validated by expert judgment of CERN operators knowledgeable of the CTI behavior and its malfunctioning.…”

Section: Introductionmentioning

confidence: 99%

Identification of Critical Components in the Complex Technical Infrastructure of the Large Hadron Collider Using Relief Feature Ranking and Support Vector Machines

et al. 2021

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This work proposes a data-driven methodology for identifying critical components in Complex Technical Infrastructures (CTIs), for which the functional logic and/or the system structure functions are not known due the CTI’s complexity and evolving nature. The methodology uses large amounts of CTI monitoring data acquired over long periods of time and under different operating conditions. The critical components are identified as those for which the condition monitoring signals permit the optimal classification of the CTI functioning or failed state. The methodology includes two stages: in the first stage, a feature selection filter method based on the Relief technique is used to rank the monitoring signals according to their importance with respect to the CTI functioning or failed state; the second stage identifies the subset of signals among those highlighted by the Relief technique that are most informative with respect to the CTI state. This identification is performed on the basis of evaluating the performance of a Cost-Sensitive Support Vector Machine (CS-SVM) classifier trained with several subsets of the candidate signals. The capabilities of the methodology proposed are assessed through its application to different benchmarks of highly imbalanced datasets, showing performances that are competitive to those obtained by other methods presented in the literature. The methodology is finally applied to the monitoring signals of the Large Hadron Collider (LHC) of the European Organization for Nuclear Research (CERN), a CTI for experiments of physics; the criticality of the identified components has been confirmed by CERN experts.

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“…O detector de partículas ATLAS (A Toroidal LHC Appa-ratuS) [1] opera no grande colisionador de hádrons (do inglês Large Hadron Collider) (LHC) [2] com o objetivo de mensurar os fenômenos físicos de interesse gerados nas colisões entre prótons. Atualmente, existem três sub-detectores no ATLAS: o detector de traços, o calorímetro e o detector de múons, responsáveis pela detecção da trajetória das partículas, da energia depositada e da trajetória dos múons, respectivamente [1].…”

Section: Introductionunclassified