Momentum distribution and contacts of one-dimensional spinless Fermi gases with an attractive p-wave interaction

We present a rigorous study of momentum distribution and p-wave contacts of one dimensional (1D) spinless Fermi gases with an attractive p-wave interaction. Using the Bethe wave function, we analytically calculate the large-momentum tail of momentum distribution of the model. We show that the leading ($\sim 1/p^{2}$) and sub-leading terms ($\sim 1/p^{4}$) of the large-momentum tail are determined by two contacts $C_2$ and $C_4$, which we show, by explicit calculation, are related to the short-distance behaviour of the two-body correlation function and its derivatives. We show as one increases the 1D scattering length, the contact $C_2$ increases monotonically from zero while $C_4$ exhibits a peak for finite scattering length. In addition, we obtain analytic expressions for p-wave contacts at finite temperature from the thermodynamic Bethe ansatz equations in both weakly and strongly attractive regimes.Comment: 19 pages,2 figure

Robotized unplugging of a cylindrical peg press-fitted into a cylindrical hole

It is well accepted that remanufacturing, the returning of a product that has reached the end of its service life to its original condition, is economically and environmentally beneficial. Robotizing disassembly can make remanufacturing even more cost-effective by removing a substantial proportion of the labour costs associated with dismantling end-of-life products for subsequent processing. As unplugging of press-fitted components is a common operation in disassembly, it is appropriate to investigate how it can be robotized. This paper discusses an unplugging technique, twist-and-pull or twisting-pulling, to reduce the axial frictional resistance during the unplugging process and enable a robot to perform it easily. Through theoretical modelling, simulations, and experimental analysis, the paper explores the interaction between twisting, pulling and axial friction reduction during unplugging. Analysis of the experimental, simulation and theoretical results has confirmed that for a small radial interference, twist-and-pull reduces the axial friction and the maximum required unplugging force

Numerical Analysis on a Perforated Muffler Applied in the Discharge Chamber of a Twin Screw Refrigeration Compressor Based on Fluid-Acoustic Coupling Method

The twin screw compressor has been widely used in the refrigeration systems due to advantages such as compact structure, stable operation, high efficiency and good adaptability. Intermittent gas flow generates gas pulsation that cause serious problems such as structural vibration and noise in the twin screw refrigeration compressor. Because the mechanical noise can be controlled well with the improvement of machining and assembly accuracy, the aerodynamic noise induced by gas pulsation even has become the main noise source of the twin screw refrigeration compressor. In order to reduce the pressure pulsation, a broadband perforated panel muffler applied in the discharge chamber of the twin screw refrigeration compressor is proposed based on the noise spectrum and flow characteristics of the compressor. In order to obtain the noise spectrum of the twin screw refrigeration compressor, the pressure fluctuation in discharge chamber based on a three-dimensional CFD simulation model is calculated, and the acoustical model is established based on fluid-acoustic coupling method. Then the impacts of different structural parameters on the performance of a perforated panel muffler are investigated, including perforation rate, perforation diameter and panel thickness. Through the optimization of the perforated muffler, a better reduction effect of broadband noise can be achieved. Results of fluid-acoustic coupled analysis can provide guidance on the design and optimization of the perforated muffler and noise reduction of the twin screw refrigeration compressor

PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models

Generalizable 3D part segmentation is important but challenging in vision and robotics. Training deep models via conventional supervised methods requires large-scale 3D datasets with fine-grained part annotations, which are costly to collect. This paper explores an alternative way for low-shot part segmentation of 3D point clouds by leveraging a pretrained image-language model, GLIP, which achieves superior performance on open-vocabulary 2D detection. We transfer the rich knowledge from 2D to 3D through GLIP-based part detection on point cloud rendering and a novel 2D-to-3D label lifting algorithm. We also utilize multi-view 3D priors and few-shot prompt tuning to boost performance significantly. Extensive evaluation on PartNet and PartNet-Mobility datasets shows that our method enables excellent zero-shot 3D part segmentation. Our few-shot version not only outperforms existing few-shot approaches by a large margin but also achieves highly competitive results compared to the fully supervised counterpart. Furthermore, we demonstrate that our method can be directly applied to iPhone-scanned point clouds without significant domain gaps.Comment: CVPR 2023, project page: https://colin97.github.io/PartSLIP_page

OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding

We introduce OpenShape, a method for learning multi-modal joint representations of text, image, and point clouds. We adopt the commonly used multi-modal contrastive learning framework for representation alignment, but with a specific focus on scaling up 3D representations to enable open-world 3D shape understanding. To achieve this, we scale up training data by ensembling multiple 3D datasets and propose several strategies to automatically filter and enrich noisy text descriptions. We also explore and compare strategies for scaling 3D backbone networks and introduce a novel hard negative mining module for more efficient training. We evaluate OpenShape on zero-shot 3D classification benchmarks and demonstrate its superior capabilities for open-world recognition. Specifically, OpenShape achieves a zero-shot accuracy of 46.8% on the 1,156-category Objaverse-LVIS benchmark, compared to less than 10% for existing methods. OpenShape also achieves an accuracy of 85.3% on ModelNet40, outperforming previous zero-shot baseline methods by 20% and performing on par with some fully-supervised methods. Furthermore, we show that our learned embeddings encode a wide range of visual and semantic concepts (e.g., subcategories, color, shape, style) and facilitate fine-grained text-3D and image-3D interactions. Due to their alignment with CLIP embeddings, our learned shape representations can also be integrated with off-the-shelf CLIP-based models for various applications, such as point cloud captioning and point cloud-conditioned image generation.Comment: Project Website: https://colin97.github.io/OpenShape

Prediction of springback in multi-point forming

Flexible forming techniques, such as multi-point forming (MPF), are employed in manufacturing to reduce the time and cost of production. MPF uses a set of height-adjustable pins to construct free-form three-dimensional surfaces. Springback is a common phenomenon in forming including MPF which, if not properly catered for, will lead to parts that are out of specification. This paper introduces a detailed numerical approach for predicting springback in MPF. FE models were developed to simulate MPF of doubly curved panels in Aluminium alloy 5251-O. The Response Surface Method and the analysis of variance technique were employed to identify the most significant process parameters and to determine their optimal setting. The influence of these parameters on thickness variations across the formed panel and the subsequent effect of those variations on the amount of springback were investigated. It was found that the radius of curvature had the most significant effect on springback and thickness variation. Minimum springback can be achieved by introducing high strains through sheet stretching