Experimental study of the Couple Characteristics of the Refrigerants and Vortex Tube

Vortex tube is a simple energy separation device, also known as Ranque tube or Hilsch tube, which can separate a high-pressure stream into two different hot and cold streams. Since its simple structure and unique temperature separation characteristics, vortex tube has been widely used in various industries. In recent years, with the in-depth study of the vortex tube, it has been found that compared with the conventional expansion expander and the throttle valve, the vortex tube is much more structurally simple and efficient, respectively. Researchers have proposed the use of the vortex tube in the refrigeration system in order to reduce the throttling loss and improve system efficiency. This has important implications for improving the performance of the system, to achieve energy saving and emission reduction. However, due to the different physical properties of the different working fluid, energy separation in the vortex tube are not the same. In the existing studies on the vortex tube, the working fluid mainly used air, nitrogen, carbon dioxide and other natural refrigerants, the research about the influence of refrigerants is few. Due to the fact that the vortex tube is increasingly used in refrigeration and heating system, it is urgent to study the coupling characteristics between vortex tube and refrigerants and find optimal conditions in different systems. The different temperature separation effect of the refrigerants in the vortex tube in the low inlet pressure(300kPa) have been studied in our previous study and three fluid characteristics (specific heat ratio, kinematic viscosity, thermal conductivity) were considered as main influencing factors of energy separation. The influence of different working fluid in high pressure conditions has not been considered ,which is part of research work in this paper. The coupling characteristic between vortex tube and refrigerants wais studied and the closed loop system was constructed. R134a, R744, R32, R227ea were selected as the working fluids, experiments were carried out in different inlet pressure(500kPa?850kPa), different inlet temperature (308.15K?333.15K), different cold flow ratio (20%?97%). The temperature separation of different working fluids under different conditions were explored and the influences of different characteristics of the working fluids on the temperature separation process were discussed. These studies can help more profound understanding of the vortex tube temperature separation process, and also has certain significance on the applications of the vortex tube in the refrigeration system

Turbulent Flow Overtopping a Dam - A CFD Modeling Study

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Reliability-based cleaning of noisy training labels with inductive conformal prediction in multi-modal biomedical data mining

Accurately labeling biomedical data presents a challenge. Traditional semi-supervised learning methods often under-utilize available unlabeled data. To address this, we propose a novel reliability-based training data cleaning method employing inductive conformal prediction (ICP). This method capitalizes on a small set of accurately labeled training data and leverages ICP-calculated reliability metrics to rectify mislabeled data and outliers within vast quantities of noisy training data. The efficacy of the method is validated across three classification tasks within distinct modalities: filtering drug-induced-liver-injury (DILI) literature with title and abstract, predicting ICU admission of COVID-19 patients through CT radiomics and electronic health records, and subtyping breast cancer using RNA-sequencing data. Varying levels of noise to the training labels were introduced through label permutation. Results show significant enhancements in classification performance: accuracy enhancement in 86 out of 96 DILI experiments (up to 11.4%), AUROC and AUPRC enhancements in all 48 COVID-19 experiments (up to 23.8% and 69.8%), and accuracy and macro-average F1 score improvements in 47 out of 48 RNA-sequencing experiments (up to 74.6% and 89.0%). Our method offers the potential to substantially boost classification performance in multi-modal biomedical machine learning tasks. Importantly, it accomplishes this without necessitating an excessive volume of meticulously curated training data

Spherical Frustum Sparse Convolution Network for LiDAR Point Cloud Semantic Segmentation

LiDAR point cloud semantic segmentation enables the robots to obtain fine-grained semantic information of the surrounding environment. Recently, many works project the point cloud onto the 2D image and adopt the 2D Convolutional Neural Networks (CNNs) or vision transformer for LiDAR point cloud semantic segmentation. However, since more than one point can be projected onto the same 2D position but only one point can be preserved, the previous 2D image-based segmentation methods suffer from inevitable quantized information loss. To avoid quantized information loss, in this paper, we propose a novel spherical frustum structure. The points projected onto the same 2D position are preserved in the spherical frustums. Moreover, we propose a memory-efficient hash-based representation of spherical frustums. Through the hash-based representation, we propose the Spherical Frustum sparse Convolution (SFC) and Frustum Fast Point Sampling (F2PS) to convolve and sample the points stored in spherical frustums respectively. Finally, we present the Spherical Frustum sparse Convolution Network (SFCNet) to adopt 2D CNNs for LiDAR point cloud semantic segmentation without quantized information loss. Extensive experiments on the SemanticKITTI and nuScenes datasets demonstrate that our SFCNet outperforms the 2D image-based semantic segmentation methods based on conventional spherical projection. The source code will be released later.Comment: 17 pages, 10 figures, under revie

A Thermoplastic Elastomer Belt Based Robotic Gripper

Novel robotic grippers have captured increasing interests recently because of their abilities to adapt to varieties of circumstances and their powerful functionalities. Differing from traditional gripper with mechanical components-made fingers, novel robotic grippers are typically made of novel structures and materials, using a novel manufacturing process. In this paper, a novel robotic gripper with external frame and internal thermoplastic elastomer belt-made net is proposed. The gripper grasps objects using the friction between the net and objects. It has the ability of adaptive gripping through flexible contact surface. Stress simulation has been used to explore the regularity between the normal stress on the net and the deformation of the net. Experiments are conducted on a variety of objects to measure the force needed to reliably grip and hold the object. Test results show that the gripper can successfully grip objects with varying shape, dimensions, and textures. It is promising that the gripper can be used for grasping fragile objects in the industry or out in the field, and also grasping the marine organisms without hurting them

Assessment of Changes of Complex Shoreline from Medium‑Resolution Satellite Imagery

The imagery collected by medium-resolution earth-observing satellites is a powerful and cost-effective tool for the quantitative assessment of shoreline dynamics for water bodies of different spatial scales. In this study, we utilize imagery collected in 1984–2021 on the Middle Peninsula, Virginia, bordering the Chesapeake Bay, USA, by medium-resolution (10–30 m) satellites Landsat-5/7/8 and Sentinel-2A/B. The data was managed in the Earth Analytics Interoperability Lab (EAIL) Data Cube built and configured by the Commonwealth Scientific and Industrial Research Organization (CSIRO, Australia and Chile). The assessments of shoreline change demonstrate adequate agreement with assessments based on aerial photography collected during 1937–2009 by the Virginia Institute of Marine Science, with reasonable disagreement attributed to the differences in the analyzed periods and in the accuracy of land/ water edge detection. Most of the studied coastline was subject to land loss (erosion), in some locations exceeding 3 m year− 1, usually along low-lying sandy beaches. The shoreline segments with man-made structures such as marinas, bulkheads, revetments, and offshore breakwaters demonstrated a significantly lower range of changes as compared to natural reaches. Regular analysis of medium resolution satellite imagery appears to be an effective method for routine assessment of shoreline changes along the land/water edge