bluuebunny/crossref_metadata_2025_split huggingface dataset:
Numerical and experimental examination of performance curve instabilities of a centrifugal fan
Authors: Förster S , Neff F , Nikolajew E , Sterle L , Baum A , Kunz J , Böhle M 
Published: October 2024

Abstract
This paper investigates the performance curve instability of centrifugal fans used in industrial applications such as sintering or pelletizing plants. The study focuses on experimental and numerical analyses of the fan’s performance curve during operation. Model measurements of an optimized fan reveal instabilities in the characteristic curve around 73 % and 42 % of the volume flow at best efficiency point (Q/Qref) resulting in a sudden drop in pressure coefficient and the onset of low-frequency noise emission. Numerical investigations using ANSYS CFX predict the flow pattern accurately. Excellent agreement is observed between measured and calculated data for flow conditions, with deviations below 1 % in pressure coefficient. Notably, the calculated instability range occurs earlier at lower flow rates, leading to a 6 % shift in the data. The flow exhibits a detachment zone on the blade suction side and a complete blockage of a single impeller passage, corresponding to the pressure drop in the performance curve. Further investigations reveal that rotor-stator interaction causes the abrupt impeller rotating stall phenomena, significantly impacting pressure generation at lower flow rates. A FFT analysis confirms the presence of rotating stall, with its dominant frequency observed and multiple harmonics. The comparison of separation frequency with existing literature supports the assumption that rotating stall is the cause of the instability.
Multiscale modeling of cavitation around high-speed object based on Euler-Lagrange model and overset mesh
Authors: Li Linmin , Chen Mingming , Zhu Zuchao , Zhang Junhao 
Published: October 2024

Abstract
Cavitation is a common phenomenon, which usually occurs inside the high-speed liquid or at the liquid-solid interface. The unsteady behaviors of cavitating flow, such as shedding and collapse, can cause intense pressure fluctuations and reduce the stability of devices such as under-water high-speed vehicles. In addition, cavitating flow changes the dynamic characteristics of the liquid and causes vibration of the structure as well, making it extremely difficult in achieving precise control. Using OpenFOAM, the present work employs the large eddy simulation (LES) approach to investigate the characteristics of the unsteady cavitating flow around a high-speed under-water object with using the overset mesh technology. The volume of fluid (VOF) approach jointed with the Lagrangian discreate bubble model (DBM) is used for capturing the multiscale cavitation features. The Schnerr & Sauer model is applied for the mass transfer between water and vapor in macroscale, while the Reynolds-Pel equation is incorporated for the growing and collapsing of microscale bubbles. Due to the difficulty in incorporating the multi-scale method into the problems with mesh motion, the present work employs the overset mesh method. Compared with the published small-scale water tank experiment, the LES simulation results fit well with the experimental phenomena, and the DBM can capture the small bubbles accurately. On the basis of model verification, the mechanisms of cavitation forming and collapsing is investigated and the effect of cavitation shedding on the motion of the object is also studied. Moreover, the nucleation, growth, collapse of bubbles, and the interaction between discrete bubbles and large cavities can be well revealed. Further, the multi-scale Euler-Lagrange model is also applied in the rotating propeller, the generation of tip vortex cavitation which is hard to be captured by traditional models is well presented.
Comparative analysis of performance of different types of Tesla valves
Authors: Liu Wenhui , Zhang Xinchen , Cao Zhengqing , Fang Kaiyue , Yang Xuelong 
Published: October 2024

Abstract
The Tesla valve is a valve with no moving parts. Its special configuration also allows the pressure drop of fluid passing through the Tesla valve from the reverse inlet to be much greater than the pressure drop of fluid passing through the Tesla valve from the forward inlet. Therefore, the Tesla valve has a certain degree of single pilot connectivity and pressure reduction capability. This article selected 8 common types of Tesla valves, By using numerical calculation method through Fluent simulation, the differences of resistance coefficient, pressure drop and Di value of 8 types of single-stage Tesla valves under three different working conditions were compared. In order to explore the performance of 8 kinds of Tesla valves under the three working conditions. At the same time, the relationship between the flow state and the structure of each part of the fluid before and after passing through the distributary pipe is analyzed. The simulation results show that due to the different structure of various Tesla valves, the flow rate, pressure and turbulent kinetic energy of the fluid inside the valve also have different changes. At the same time, there are many similarities in the flow state of some Tesla valves with similar structural types. Finally, according to the calculation results, the advantages and disadvantages of various types of Tesla valves and their applications are analyzed.
The impact of installation deviation on the lubricating characteristics of a 1000 MW giant hydropower unit’s tilting-pad journal bearing
Authors: Shi Yishu , Wang Zhengwei 
Published: October 2024

Abstract
With the trend towards larger and higher capacity units, tilting-pad journal bearings are being increasingly used in hydropower units. Through thermo-hydrodynamic (THD) analysis, it is possible to establish pressure differential equations that consider the change in oil viscosity with temperature, which is essential to ensure safe operation of the bearing. Further research on the flowing characteristics of tilting-pad journal bearings, especially about installation deviations, is essential for giant hydropower unit. This study establishes a three-dimensional model of a 1000 MW hydropower unit’s turbine journal bearing to investigate temperature, pressure distribution, and load under various installation deviation scenarios. The findings aim to optimize the design and engineering practices of tilting-pad bearings in rotating machinery, meeting the evolving needs of the power industry.
Investigation of Surge Phenomena in a Centrifugal Compressor by 3D-1D Coupling Approach
Authors: Li Peize , Katayama Yusuke , Miyagawa Kazuyoshi 
Published: October 2024

Abstract
Critical instability, such as surge phenomena, can occur at low flow rates for a compressor system, leading to flow rate fluctuations and potential damage. To study this, traditional numerical analysis requires a comprehensive approach encompassing the compressor and its piping system. This demands extensive computational resources and time due to the complexity of capturing internal flow during surge, including separation and vortex generation. To overcome these limitations, this research proposes a computational strategy combining 3D and 1D analysis for enhanced efficiency and reduced computational demands, which employs 1D analysis for more straightforward flow regions like piping and 3D analysis for the more complex compressor section. This approach is facilitated using AMESim for 1D and STAR-CCM+ for 3D simulations, enabling seamless integration and data exchange between different dimensional analyses. The method improves computational efficiency and reasonably approximates surge phenomena compared to experimental data. The 3D and 1D coupled analysis closely aligns with experimental data, providing a viable and more effective alternative method for surge analysis in compressor systems. This methodology promises to streamline the study of compressor surge phenomena, providing a practical tool for engineers and researchers.
Research on Purely Azimuth Passive Localization Model for UAV Formation Flight
Authors: Tang Chenchong , Ying Beining , Gu Ruoxuan , Yang Shengying 
Published: October 2024

Abstract
The practical applications of unmanned aerial vehicle (UAV) formation passive localization demand high accuracy and anti-interference ability. In this paper, a novel passive localization model based on analytical geometry and improved damped Gauss-Newton optimization method is proposed, which incorporates the improved damped Gauss-Newton algorithm and the error-based iterative algorithm for real-time coordinate correction of formation. Through Matlab simulation experiments, the proposed error-based iterative algorithm can stabilize the flight formation after only 24 iterations, and the distance error between each UAV and the specified position is only 1.2%. Moreover, in the case of signal loss, only four UAVs are required to transmit signals to localize a circular flying formation consisting of 10 UAVs. The model provides a theoretical method for passive localization and control of UAV formations in purely azimuthal flights.
Buried Dielectric Modulation in Tunnel Field-Effect Transistor
Authors: Su Chonghao , Jei Liou Juin , Yang Shanglin 
Published: October 2024

Abstract
The tunnel field-effect transistor (TFET) with gate-on-source-only and n+ pocket configurations achieves both a low subthreshold swing (SS) and a high drain-source current. In this brief, the buried dielectric modulation effect in such a tunnel field-effect transistor (TFET) is investigated by using TCAD simulation. It is observed that the characteristic of the proposed TFET is modulated by the buried dielectric. Using a low-k dielectric material the electric field near the tunneling junction is increased, the band-to-band tunneling is enhanced and the drain-source current is increased. The simulation results show that with a low-k buried dielectric the drain-source current can be increased by more than one order of magnitude. The impact of the key parameters on the device performance is also discussed.
Fault Detection of Rolling Bearings using Real NVP Transformation
Authors: Wu Zhenpeng , Lin Chunyu , Zhang Rongchang , Zhang Liangwei 
Published: October 2024

Abstract
The scarcity of rolling bearing fault data and the difficulty in annotating fault types prompt us to employ unsupervised fault detection methods. However, existing unsupervised fault detection methods rely on manual feature extraction or use indirect approaches such as reconstruction error or density approximation for fault detection. To address this issue, this paper proposes an unsupervised rolling bearing fault detection method based on a Normalizing flow model, namely the Real-Valued Non-Volume Preserving (Real NVP) model. Compared to existing methods, this approach utilizes vibration data from normal bearings for model training. It transforms the complex distribution of normal bearing data into a simple prior distribution through the Real NVP transformation. Subsequently, fault detection indicators are constructed within this simple prior distribution to achieve rolling bearing fault detection. Experiments on the benchmark bearing dataset demonstrate that the proposed method can effectively identify the fault states of rolling bearings and achieve fault detection results superior to other fault detection methods.
Advancements in marine atmospheric parameters measurement: integrated dual-channel LIDAR for temperature, humidity, and wind speed estimation
Authors: Cui Haipeng , Zhu Shilong 
Published: October 2024

Abstract
In the realm of marine meteorology, accurate measurement of sea surface temperature, humidity, and wind speed is crucial for understanding weather patterns and climate change and ensuring maritime safety. Traditional methods for these measurements face challenges in accuracy and adaptability to extreme conditions. This study introduces an innovative approach using an integrated dual-channel Light Detection and Ranging (LIDAR) system, which combines Pure Rotational Raman LIDAR and Coherent LIDAR. This method significantly improves the precision and reliability of data under various atmospheric conditions. By employing advanced algorithms for temperature and humidity retrieval and Doppler analysis for wind speed, the study demonstrates enhanced capabilities in atmospheric data collection and analysis. The findings indicate a substantial advancement over existing methods, offering promising applications in weather forecasting, climate research, and marine navigation.
Tower and power line segmentation method based on RandLA-Net
Authors: Zhu Sha , Li Lu , Zhao Jianwei , Zhang Chunguang , Ni Shaofeng , Chen Yiping 
Published: October 2024

Abstract
The scale of the power grid of China has gradually expanded, and the number of long-distance and high-level transmission lines has also increased. However, it is very hard to detect the power infrastructure transmission lines because of the complex laying environment and various ground features. Lines and towers in the power system are important components of power transmission, and their safety directly affects the operation of the entire power system. Reasonable management of lines and towers can reduce the occurrence of faults and system safety risks. At present, both manual inspection efficiency and visual inspection processing accuracy are low. An airborne LiDAR point cloud has become a key means for monitoring towers and power lines. In response to the above problems, we propose a tower and power line segmentation algorithm based on RandLA-Net. Experimental results demonstrate that the proposed method gets precise detection of power poles and lines, and enhances the efficiency in identifying hazardous areas along power lines. The highest Mean Intersection over Union (mloU) recorded is 95.34%, establishing a clear correlation between power towers and lines. Power line data holds significant importance for the power sector, facilitating the monitoring and management of transmission lines.
Realization of (400∼950) nm detector-based spectral radiance scale at NIM
Authors: Nan Xu , Zhiwei Liu , Yandong Lin , Weiqiang Zhao , Wende Liu , Haiyong Gan 
Published: October 2024

Abstract
Detector-based radiometric measurement is advantageous in terms of measurement uncertainty. National Institute of Metrology China (NIM) has developed a facility for detector-based spectral radiance scale realization in the range of (400∼950) nm. The scale is realized based on a tunable-laser coupled integrating sphere source using galvanometer-driven mirror, and a silicon trap reference standard detector calibrated directly against NIM’s primary standard for spectral power responsivity and NIM’s effective area calibration facility for apertrue area. Sources and transfer standards are characterized, and spectral radiance responsivity can be measured with uncertainties at the (0.2∼0.4) % level.
Primary realization of spectral responsivity in the near infrared using a laser-driven light source
Authors: Gamouras A 
Published: October 2024

Abstract
The National Research Council (NRC) Canada has realized the spectral responsivity scale from 900 nm to 1600 nm using a Xe-based laser driven light source, double subtractive monochromator system, and an absolute cryogenic radiometer. An NRC-designed InGaAs sphere transfer standard radiometer was calibrated with combined relative standard uncertainties below 0.135 % (k=1). The scale realization was also performed with a 100 W tungsten lamp, as previously done at NRC. Calibration results and uncertainties in spectral responsivity measurements with each broadband light source are compared and presented.
Virtual energy dispatching method for hybrid microgrid demand response based on Lyapunov optimization
Authors: Gebrie Jember Adugna , Bao Ruiyu , Wu Wenqing , Wang Zhao , Zhou Zhenyu 
Published: October 2024

Abstract
The hybrid microgrid (HMG) possesses power generation, storage, and distribution capabilities, serving as an integrated foundational structure for advanced localized distribution networks. However, due to significant discrepancies in load demands and energy consumption among different users, the photovoltaic (PV) generation output and end-user load requirements exhibit unpredictability, resulting in relatively low self-sufficiency ratio (SSR) and self-consumption ratio (SCR) of the HMG PV system. To address this issue, this study proposes a virtual energy dispatching method for hybrid microgrid demand response based on Lyapunov optimization. Initially, a bidirectional virtual energy dispatching system model based on energy provider/buyer classification is constructed, optimizing load demands and energy management of both the load side and the main utility grid (MUG) while considering demand response. Subsequently, a Lyapunov optimization-based bidirectional virtual energy dispatching algorithm (LOBVED) is introduced, optimizing queueing systems in stochastic networks based on drift-plus-penalty technique to resolve the dynamic interactions among stability, performance, and energy self-sufficiency in the HMG environment, aiming to maximize the PV SSR and SCR of the HMG while ensuring the reliability of battery energy storage (BES) energy queues. Finally, the effectiveness of the proposed method is validated through MATLAB simulations.
Integrated thermo-optic phase shifters for laser-written photonic circuits operating at cryogenic temperatures
Authors: Ceccarelli Francesco , Rakonjac Jelena V , Grandi Samuele , Riedmatten Hugues de , Osellame Roberto , Corrielli Giacomo 
Published: October 2024

Abstract
Integrated photonics offers compact and stable manipulation of optical signals in miniaturized chips, with the possibility of changing dynamically their functionality by means of integrated phase shifters. Cryogenic operation of these devices is becoming essential for advancing photonic quantum technologies, accommodating components like quantum light sources, single photon detectors and quantum memories operating at liquid helium temperatures. In this work, we report on a programmable glass photonic integrated circuit (PIC) fabricated through femtosecond laser waveguide writing (FLW) and controlled by thermo-optic phase shifters both in a room-temperature and in a cryogenic setting. By taking advantage of a femtosecond laser microstructuring process, we achieved reliable PIC operation with minimal power consumption and confined temperature gradients in both conditions. This advancement marks the first cryogenically-compatible programmable FLW PIC, paving the way for fully integrated quantum architectures realized on a laser-written photonic chip.
Machine-learning-based diabetes classification method using blood flow oscillations and Pearson correlation analysis of feature importance
Authors: Jung Hanbeen , Yeo Chaebeom , Jang Eunsil , Chang Yeonhee , Song Cheol 
Published: October 2024

Abstract
Diabetes is a global health issue affecting millions of people and is related to high morbidity and mortality rates. Current diagnostic methods are primarily invasive, involving blood sampling, which can lead to infection and increased patient stress. As a result, there is a growing need for noninvasive diabetes diagnostic methods that are both accurate and fast. High measurement accuracy and fast measurement time are essential for effective noninvasive diabetes diagnosis; these can be achieved using diffuse speckle contrast analysis (DSCA) systems and artificial intelligence algorithms. In this study, we use a machine learning algorithm to analyze rat blood flow signals measured using a DSCA system with simple operation, easy fabrication, and fast measurement for helping diagnose diabetes. The results confirmed that the machine learning algorithm for analyzing blood flow oscillation data shows good potential for diabetes classification. Furthermore, analyzing the blood flow reactivity test revealed that blood flow signals can be quickly measured for diabetes classification. Finally, we evaluated the influence of each blood flow oscillation data on diabetes classification through feature importance and Pearson correlation analysis. The results of this study should provide a basis for the future development of hemodynamic-based disease diagnostic methods.
Green Superpowers
Authors: Prontera Andrea 
Published: October 2024

AbstractThis book analyses, compares, and explains the emerging green foreign energy policies and green power strategies of China, the European Union, and the United States. Fossil fuels have long been key to major powers’ foreign policies and international affairs. In the context of the current global energy transition, renewables and low-carbon technologies are emerging as elements that can have a similarly important impact. By combining insights from international political economy, comparative public policy, international relations, and energy policy scholarship, this book presents an original framework for studying these dynamics. It then uses this framework to shed light on the recent green foreign energy policies and green power strategies of the three green superpowers. In doing so, it illustrates the links between the domestic green approaches that these major actors are promoting and their external actions regarding renewables and low-carbon technologies. It also draws attention to the limits and potential of Chinese, European, and US green foreign energy policy as well as to the influence of their competitive interactions on twenty-first-century world politics.
A comparison of drug information question responses by a drug information center and by ChatGPT
Authors: Triplett Samantha , Ness-Engle Genevieve Lynn , Behnen Erin M 
Published: October 2024

Abstract

Purpose
A study was conducted to assess the accuracy and ability of Chat Generative Pre-trained Transformer (ChatGPT) to systematically respond to drug information inquiries relative to responses of a drug information center (DIC).

Methods
Ten drug information questions answered by the DIC in 2022 or 2023 were selected for analysis. Three pharmacists created new ChatGPT accounts and submitted each question to ChatGPT at the same time. Each question was submitted twice to identify consistency in responses. Two days later, the same process was conducted by a fourth pharmacist. Phase 1 of data analysis consisted of a drug information pharmacist assessing all 84 ChatGPT responses for accuracy relative to the DIC responses. In phase 2, 10 ChatGPT responses were selected to be assessed by 3 blinded reviewers. Reviewers utilized an 8-question predetermined rubric to evaluate the ChatGPT and DIC responses.

Results
When comparing the ChatGPT responses (n = 84) to the DIC responses, ChatGPT had an overall accuracy rate of 50%. Accuracy across the different question types varied. In regards to the overall blinded score, ChatGPT responses scored higher than the responses by the DIC according to the rubric (overall scores of 67.5% and 55.0%, respectively). The DIC responses scored higher in the categories of references mentioned and references identified.

Conclusion
Responses generated by ChatGPT have been found to be better than those created by a DIC in clarity and readability; however, the accuracy of ChatGPT responses was lacking. ChatGPT responses to drug information questions would need to be carefully reviewed for accuracy and completeness.