Experimental and analytical study on heat generation characteristics of a lithium-ion power battery

This document is the Accepted Manuscript version of the following article: Yongqi Xie, Shang Shi, Jincheng Tang, Hongwei Wu, and Jianzu Yu, ‘Experimental and analytical study on heat generation characteristics of a lithium-ion power battery’, International Journal of Heat and Mass Transfer, Vol. 122: 884-894, July 2018. Under embargo until 20 February 2019. The final, definitive version is available online via: https://doi.org/10.1016/j.ijheatmasstransfer.2018.02.038A combined experimental and analytical study has been performed to investigate the transient heat generation characteristics of a lithium-ion power battery in the present work. Experimental apparatus is newly built and the investigations on the charge/discharge characteristics and temperature rise behavior are carried out at ambient temperatures of 28 °C, 35 °C and 42 °C over the period of 1 C, 2 C, 3 C and 4 C rates. The thermal conductivity of a single battery cell is experimentally measured to be 5.22 W/(m K). A new transient model of heat generation rate based on the battery air cooling system is proposed. Comparison of the battery temperature between simulated results and experimental data is performed and good agreement is achieved. The impacts of the ambient temperature and charge/discharge rate on the heat generation rate are further analyzed. It is found that both ambient temperature and charge/discharge rate have significant influences on the voltage change and temperature rise as well as the heat generation rate. During charge/discharge process, the higher the current rate, the higher the heat generation rate. The effect of the ambient temperature on the heat generation demonstrates a remarkable difference at different charge states.Peer reviewe

Clustering Analysis of User Loyalty Based on K-means

In recent years, the rise of machine learning has made it possible to further explore large data in various fields. In order to explore the attributes of loyalty of public transport travelers and divide these people into different clustering clusters, this paper uses K-means clustering algorithm (K-means) to cluster the holding time, recharge amount and swiping frequency of bus travelers. Then we use Kernel Density Estimation Algorithms (KDE) to analyze the density distribution of the data of holding time, recharge amount and swipe frequency, and display the results of the two algorithms in the way of data visualization. Finally, according to the results of data visualization, the loyalty of users is classified, which provides theoretical and data support for public transport companies to determine the development potential of users

What Makes Good Open-Vocabulary Detector: A Disassembling Perspective

Open-vocabulary detection (OVD) is a new object detection paradigm, aiming to localize and recognize unseen objects defined by an unbounded vocabulary. This is challenging since traditional detectors can only learn from pre-defined categories and thus fail to detect and localize objects out of pre-defined vocabulary. To handle the challenge, OVD leverages pre-trained cross-modal VLM, such as CLIP, ALIGN, etc. Previous works mainly focus on the open vocabulary classification part, with less attention on the localization part. We argue that for a good OVD detector, both classification and localization should be parallelly studied for the novel object categories. We show in this work that improving localization as well as cross-modal classification complement each other, and compose a good OVD detector jointly. We analyze three families of OVD methods with different design emphases. We first propose a vanilla method,i.e., cropping a bounding box obtained by a localizer and resizing it into the CLIP. We next introduce another approach, which combines a standard two-stage object detector with CLIP. A two-stage object detector includes a visual backbone, a region proposal network (RPN), and a region of interest (RoI) head. We decouple RPN and ROI head (DRR) and use RoIAlign to extract meaningful features. In this case, it avoids resizing objects. To further accelerate the training time and reduce the model parameters, we couple RPN and ROI head (CRR) as the third approach. We conduct extensive experiments on these three types of approaches in different settings. On the OVD-COCO benchmark, DRR obtains the best performance and achieves 35.8 Novel AP$_{50}$, an absolute 2.8 gain over the previous state-of-the-art (SOTA). For OVD-LVIS, DRR surpasses the previous SOTA by 1.9 AP$_{50}$ in rare categories. We also provide an object detection dataset called PID and provide a baseline on PID

Investigation of Electron-Phonon Coupling in Epitaxial Silicene by In-situ Raman Spectroscopy

In this letter, we report that the special coupling between Dirac fermion and lattice vibrations, in other words, electron-phonon coupling (EPC), in silicene layers on Ag(111) surface was probed by an in-situ Raman spectroscopy. We find the EPC is significantly modulated due to tensile strain, which results from the lattice mismatch between silicene and the substrate, and the charge doping from the substrate. The special phonon modes corresponding to two-dimensional electron gas scattering at edge sites in the silicene were identified. Detecting relationship between EPC and Dirac fermion through the Raman scattering will provide a direct route to investigate the exotic property in buckled two-dimensional honeycomb materials.Comment: 15 pages, 4 figure

Experimental and numerical investigation on conjugate performance of fan and heat exchanger of helicopter oil cooling system

© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license. https://creativecommons.org/licenses/by/4.0/In this article, a combined experimental and numerical study has been performed to investigate the operating performance of axial fan and oil cooling system. A test rig was established, and six types of plate-fin heat exchangers (HEs) with offset strip and rectangular fins and three different flow lengths of 30 mm, 60 mm and 90 mm at air side were designed and manufactured. The performance of an axial fan with front guide vane was experimentally studied by two different adjusting modes: gradually increasing and decreasing air flow rate. The conjugate performances of the axial fan and different HEs were discussed in detail. Moreover, a three-dimensional (3D) model was developed to investigate the flow distribution of the system including fan and 30 mm offset strip fins HE at different flow rates. The results show that: (1) the total pressure performance curve of the axial fan under two adjusting modes could form a hysteresis region near the stall boundary. In the hysteresis region, the fan performance curves showed significant difference under both adjusting modes; (2) when the offset strip fins HE with large flow resistance is considered, the system could have two theoretical working points in the hysteresis region. For the case of HE with 90 mm offset strip fins, the flow rates of the system at two theoretical working points were 25.1 m³/min and 34.2 m³/min, and the heat transfer capacity of the HE were 23.1 kW and 27.5 kW, respectively. In the current experiment, it was found that the system operated at the point with smaller flow rate; (3) when the HE flow resistance exceeded a certain value, the boundary layer separation of the airflow could occur at the rotor blade. The separation had a small effect on the inlet airflow due to its turbulence kinetic energy was low and basically the same at each blade passage. Therefore, the system did not surge or stall at small flow rate.Peer reviewe

Recognition of Indoor Scenes using 3D Scene Graphs

Scene recognition is a fundamental task in 3-D scene understanding. It answers the question, 'What is this place?' In an indoor environment, the answer can be an office, kitchen, lobby, and so on. As the number of point clouds increases, using embedded point information in scene recognition becomes computationally heavy to process. To achieve computational efficiency and accurate classification, our idea is to use an indoor scene graph that represents the 3-D spatial structures via object instances. The proposed method comprises two parts, namely: 1) construction of indoor scene graphs leveraging object instances and their spatial relationships and 2) classification of these graphs using a deep learning network. Specifically, each indoor scene is represented by a graph, where each node represents either a structural element (like a ceiling, a wall, or a floor) or a piece of furniture (like a chair or a table), and each edge encodes the spatial relationship between these elements. Then, these graphs are used as input for our proposed graph classification network to learn different scene representations. The public indoor dataset, ScanNet v2, with 625.53 million points, is selected to test our method. Experiments yield good results with up to 88.00% accuracy and 82.30% F1 score in the fixed validation dataset and 90.46% accuracy and 81.45% F1 score in the ten-fold cross-validation method; moreover, if some indoor objects cannot be successfully identified, the scene classification accuracy depends sublinearly on the rate of missing objects in the scene.</p

Numerical and experimental investigations into protection net icing at the helicopter engine inlet

© 2020, Emerald Publishing Limited. This is the accepted manuscript version of an article which has been published in final form at . https://doi.org/10.1108/AEAT-09-2019-0190The ice shapes of the protection net at different times are firstly simulated by a 2D icing calculation, then the porous media parameters are calculated based on the 2D ice shapes. Afterwards, a three-dimensional (3D) flow fields of the engine inlet with the iced net are simulated using the porous media model instead of the real protection net. The transient pressure losses of the iced protection net are calculated and tested through an icing wind tunnel test rig under different icing conditions. Overall the numerical results and experimental data shows a good agreement. The effects of several control parameters such as liquid water contents (LWC), water droplet diameters, and airflow velocities on the pressure loss of the protection net during the icing process are analyzed in a systematic manner. The results indicate that the pressure loss increases with the increase of the LWC at the same icing time. The same trend occurs when the water droplet diameter and the airflow velocity increase.Peer reviewe