Mass Measurement, Visualization and Signal Pattern Based Calibration of Capacitive Sensors for Measuring Void Fraction in Headers

This paper presents the calibration procedures of the capacitive sensors for measuring void fraction in headers with R134a. The sensors utilize the difference of dielectric constants between the liquid and vapor phases of the two-phase mixture. The 3D printed header has eleven microchannel-tube protrusions. Eleven capacitive sensors are located between protrusions. All sensors are made to be identical as much as possible in the manual fabrication procedures. Because the electric field within each sensor is not perfectly uniform, sensors need to be calibrated before measuring void fraction. Another transparent 3D printed header with the same inner geometry is installed at the same location of the facility for visualization and pressure drop measurement purposes. By controlling valves, the flow is directed to either header. The calibration is based on three methods: mass measurement (quick-closing valves, QCV), visualization (with a high-speed camera), and capacitive signals obtained from eleven individual sensors. After the calibration procedure, all sensors are ready to measure the void fraction in vertical headers

Design of Capacitive Sensors for Measuring Void Fraction in Headers of Microchannel Heat Exchangers

This paper presents the design and building of capacitive sensors for measuring void fraction in headers of microchannel heat exchangers with R134a. The capacitive sensors utilize different electrical properties (dielectric constant) between the liquid and vapor phases to measure the void fraction of two-phase flow. The test section (a header) is made of 3D printed partitions with a total length of 185.8 mm and an inner diameter of 15.8 mm (D). It has eleven microchannel-tube protrusions with a depth of half inner diameter. All microchannel tubes are capable of being connected to real aluminum tube-like real heat exchangers in the future study. Eleven capacitive sensors locate between protrusions. Each sensor has two concave-plate electrodes with the axial length of the half inner diameter (D/2). With these eleven sensors, void fractions along the header can be measured spontaneously. Preliminary validation of the sensors is also presented in this paper

Deep Reinforcement Learning in Surgical Robotics: Enhancing the Automation Level

Surgical robotics is a rapidly evolving field that is transforming the landscape of surgeries. Surgical robots have been shown to enhance precision, minimize invasiveness, and alleviate surgeon fatigue. One promising area of research in surgical robotics is the use of reinforcement learning to enhance the automation level. Reinforcement learning is a type of machine learning that involves training an agent to make decisions based on rewards and punishments. This literature review aims to comprehensively analyze existing research on reinforcement learning in surgical robotics. The review identified various applications of reinforcement learning in surgical robotics, including pre-operative, intra-body, and percutaneous procedures, listed the typical studies, and compared their methodologies and results. The findings show that reinforcement learning has great potential to improve the autonomy of surgical robots. Reinforcement learning can teach robots to perform complex surgical tasks, such as suturing and tissue manipulation. It can also improve the accuracy and precision of surgical robots, making them more effective at performing surgeries

Void Fraction and Flow Regimes of R134a In Horizontal and Vertical Round Tubes in Developed Adiabatic Conditions

This paper presents flow regimes and void fraction in horizontal and vertical round tubes ID 7 mm with R134a in the adiabatic conditions and low mass flux (40-150 kg/m2s for horizontal tubes and 65-115 kg/m2s for vertical tubes) captured by a high-speed camera. Horizontal flow patterns are compared to Wojtan-Ursenbacher-Thome flow regime map and some modifications are proposed. Void fraction results for both horizontal and vertical tubes are compared to some widely used correlations. Influences of tube orientation and mass flux on void fraction are discussed. At the same vapor quality condition, void fraction of horizontal tubes is larger than that of vertical tubes. Higher mass flux also results in larger void fraction compared that of lower mass flux

Void Fraction and Flow Regimes Determined by Visualization, Mass Measurement and New Capacitance Sensor

This paper presents void fraction and flow regimes determined by three methods: visualization (high speed camera), mass measurement (quick-closing valves) and a newly developed capacitance sensor. In a way, this is a calibration process for a capacitance sensor. It is shown that new sensor can characterize flow patterns in low mass flux range and measure void fraction for horizontal and vertical tubes. A calibration procedure of void fraction measurement is based on a mass measurement (quick-closing valves). Two calibration curves for measuring void fraction in horizontal and vertical tubes are developed. With calibration curves, sensors with similar configurations can be directly utilized to measure void fraction in further studies

Development of capacitive sensor to determine void fraction in horizontal and vertical tubes

This study first presents flow regimes and void fraction in horizontal and vertical tubes with R134a in the adiabatic condition and low mass flux range. Visualization results of horizontal flow patterns are compared to Wojtan-Ursenbacher-Thome flow regime map and have a good agreement. Both void fraction results for horizontal and vertical tubes are compared to some widely used correlations. Influences of tube orientation and mass flux on void fraction are discussed. At the same vapor quality condition, void fraction of horizontal tubes is larger than that of vertical tubes. Higher mass flux also results in larger void fraction compared that of lower mass flux. A new capacitive sensor aimed to measure void fraction is designed and built. With this capacitive sensor, characterizing flow patterns appear in low mass flux range and measuring void fraction for horizontal and vertical tubes can be achieved. Signal characterization for each flow pattern is proposed. To apply this sensor to more applications in HVAC&R systems, electrodes with different axial lengths are built together to be compared. The comparison of signals from them show the signals are independent on axial length. Due to the non-uniformity of the electric field between the electrodes of the sensor, a calibration procedure of void fraction measurement must be finished before utilizations. An experiment based (quick closing valves (QCV)) calibration procedure is conducted. Two calibrated curves for measuring void fraction in horizontal and vertical tubes are proposed. With calibrated curves, sensors with similar configurations can be directly utilized to measure void fraction in further studies

Application of fuzzy random finite element method on rotor dynamics

Fuzzy and stochastic characteristics of parameters exist widely in rotating machinery. To research the parameters characteristics is of great significance in rotor dynamics. Dynamic characteristics of rotor system are analyzed taking into account uncertain properties of fuzzy and stochastic coexisting. Fuzzy variables are transformed into stochastic variables based on information entropy theory. The Neumann stochastic finite element method based on Neumann expansion combined with Newmark-β method is used in linear and nonlinear rotor system within the frame work of Monte Carlo simulation. Critical speed and dynamic response of fuzzy stochastic rotor systems are described by the proposed method. The results show that the Neumann stochastic finite element method has good applicability and efficiency in rotor dynamics

Comparison of Common Methods in Dynamic Response Predictions of Rotor Systems with Malfunctions

The efficiency and accuracy of common time and frequency domain methods that are used to simulate the response of a rotor system with malfunctions are compared and analyzed. The Newmark method and the incremental harmonic balance method are selected as typical representatives of time and frequency domain methods, respectively. To improve the simulation efficiency, the fixed interface component mode synthesis approach is combined with the Newmark method and the receptance approach is combined with the incremental harmonic balance method. Numerical simulations are performed for rotor systems with single and double frequency excitations. The inherent characteristic that determines the efficiency of the two methods is analyzed. The results of the analysis indicated that frequency domain methods are suitable single and double frequency excitation rotor systems, whereas time domain methods are more suitable for multifrequency excitation rotor systems

Identifying Crypto Addresses with Gambling Behaviors: A Graph Neural Network Approach

The development of blockchain technology has brought prosperity to the cryptocurrency market and has made the blockchain platform a hotbed of crimes. As one of the most rampant crimes, crypto gambling has more high risk of illegal activities due to the lack of regulation. As a result, identifying crypto addresses with gambling behaviors has emerged as a significant research topic. In this work, we propose a novel detection approach based on Graph Neural Networks named CGDetector, consisting of Graph Construction, Subgraph Extractor, Statistical Feature Extraction, and Gambling Address Classification. Extensive experiments of large-scale and heterogeneous Ethereum transaction data are implemented to demonstrate that our proposed approach outperforms state-of-the-art address classifiers of traditional machine learning methods. This work makes the first attempt to detect suspicious crypto gambling addresses via Graph Neural Networks by all EVM-compatible blockchain systems, providing new insights into the field of cryptocurrency crime detection and blockchain security regulation