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Menon, Nandana

Publications and source records attributed to Menon, Nandana.

Fabrication of 8-Strand Rutherford Cables Using Roped Strands Made from Ultrafine Wires

Conventional Rutherford cables are typically made from solid round wire. While multi-stage cables have been made elsewhere, the purpose was usually to achieve a higher strand count and therefore a higher current carrying capability. In this work, we attempt to use two-stage roped strands made from ultrafine wires to fabricate Rutherford cables. The ultimate goal of this work is to obtain a very flexible cable that can wind accelerator magnet coils with a very tight bend radius in both the “easy way” (along the broad face of the cable) and the “hard way” (along the edge of the cable) in the wind-and-react manner, or wind coils with a radius typically used today but in the react and-wind manner with a much reduced degradation in critical current. We report our experience fabricating such Rutherford cables at the Lawrence Berkeley National Laboratory, and the initial findings from the analysis of the experimental cables made. We will discuss how the conventional wisdom and some rules of thumb for making Rutherford cables are no longer applicable or relevant, and the new thinking required in designing these cables.

Pong, Ian↗

Crack Identification and Characterization in Deformed Nb3Sn Rutherford Cable Stacks Using Machine Learning

An investigation of instance segmentation of cracks in Nb3Sn 4-stack 40-strand Rutherford cables using machine learning is presented. Three samples were uniaxially and biaxially loaded before metallographic inspections were performed. The Mask R-CNN model was used in the Detectron2 framework with pre-trained weights but fine-tuned to detect and segment cracks. The model detected cracks with bounding box and mask average precisions (AP) of 42.8 and 27.9, respectively, and was used for instance segmentation of all cracks in the three samples. More cracks were found in the sample pre-loaded along the z-axis (i.e., along the cable length). Pre-loading along the x-axis (i.e., on the cables edges) reduced the number of cracks and changed the crack orientation distribution, away from being highly aligned with the y-axis (i.e., normal to the cables broad faces), i.e., the direction with the highest applied load. Fine-tuning of the Segment Anything Model (SAM) was also studied but performed poorly without human-provided prompts. However, the zero-shot capability of SAM showed high promises to accelerate the image annotation process for applications beyond this study.

Croteau, Jean-Francois↗