Search

The consequences of tungsten (W) cracking on divertor lifetime and plasma operation are high priority issues for ITER. One actively cooled ITER-like plasma facing unit (PFU) has been pre-damaged in a High Heat Flux (HHF) facility before its installation in WEST in order to assess the damage evolution after tokamak plasma exposure. The resulting pre-damage exhibits micrometer-size crack network and high roughness on the tungsten monoblock (MB) top surface. A total of 10 MBs, equally distributed on the low and high field sides of the lower divertor, have been pre-damaged among the 35 radially aligned MBs characteristic of the WEST PFU. Subsequent plasma exposure was carried out, from the first breakdown achieved in WEST (in 2017) until the removal of the damaged PFU three years later (2020). On top of the whole WEST plasma exposure (covering C1-C4 experimental campaigns), a dedicated experiment has also been performed in the frame of the EU work program to maximize the power and energy loads on one of the damaged MBs featuring a “crack network” pattern. The MB top surface, including both “crack network” damage and “healthy” (undamaged) areas, was monitored with a high spatial resolution IR camera to detect any potential evolution of the damage pulse after pulse. This paper describes the full plasma exposure achieved in the WEST tokamak (including large number of steady-state and transient heat loading cycles), the dedicated “damaged PFU exposure” experiment together with the experimental results (heat loading on the damaged MBs). Post-mortem measurement reveals significant broadening of the cracks and new cracks in the electron beam loaded area only.

Abstract Background A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. Methods We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. Results Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. Conclusions Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.