Electronic Expansion Valve Experimental System Debugging Solution Based on PI Control Algorithm on Single-Tube Heat Exchange Experimental Platform

In this paper, the electronic expansion valve (EXV) on the single-tube heat exchange experimental platform was used as a research object. Firstly, the EXVs were selected according to the experimental requirements, and the functional parameters were set. Subsequently, the effective opening ranges of the EXVs were determined by manual control, and the control effects of the EXVs installed at the front and back ends of the test section were compared. Finally, by self-tuning and optimizing the best response curves, the proportional and integral coefficients suitable for the experimental platform were obtained; thus, the automatic intelligent control of EXV based on the proportional integral (PI) control algorithm was realized. From setting EXV functional parameters to realizing PI control, an appropriate experimental system-debugging solution for the whole process could be obtained. Based on the solution, the system stability could be improved, and the transition process time could be shortened. Furthermore, the solution also provided a method to guarantee the accuracy of experimental data and could be applied to the debugging of similar experimental systems.

A discrete event simulation-based methodology for building a digital twin of patient pathways in the hospital for near real-time monitoring and predictive simulation

Background: Discrete Event Simulation (DES) is one of the many tools and methods used in the analysis and improvement of healthcare services. Indeed, DES provides perhaps the most powerful and intuitive method for analyzing, evaluating, and improving complex healthcare systems. This paper highlights the process of developing a Digital Twin (DT) framework based on online DES to run the DES model in parallel with the real world in real-time. Methods: This paper suggests a new methodology that uses DES connected to the Internet of Things (IoT) devices to build a DT platform of patient pathways in a hospital for near real-time monitoring and predictive simulation. An experimental platform that mimics the behavior of a hospital has been used to validate this methodology. Results: The application of the proposed methodology allowed us to test the monitoring functionality in the DT. Therefore, we noticed that the DT behaves exactly as the emulator does in near real-time, we also tested the prediction functionality and we noticed that the DT provides us with a proactive overview for the near future of the patient pathways. The predictive functionality of this DT must be improved depending on the various reasons mentioned in this article. Conclusions: This paper presents a new methodology called HospiT'Win that uses DES and IoT devices to develop a DT of patient pathways in hospitals. This DT consists of two real-time models, a DT for Monitoring (DTM) and a DT for Predicting (DTP). An experimental platform with an emulator of a real hospital was used to validate this methodology before connecting to the real hospital. In the DTP, "dynamic" empirical distributions were used to perform a predictive simulation for the near future. In future research, some additional features and machine learning algorithms will be used to improve the proposed DT models.

Underwater Localization and Mapping Based on Multi-Beam Forward Looking Sonar

SLAM (Simultaneous Localization And Mapping) plays a vital role in navigation tasks of AUV (Autonomous Underwater Vehicle). However, due to a vast amount of image sonar data and some acoustic equipment's inherent high latency, it is a considerable challenge to implement real-time underwater SLAM on a small AUV. This paper presents a filter based methodology for SLAM algorithms in underwater environments. First, a multi-beam forward looking sonar (MFLS) is utilized to extract environmental features. The acquired sonar image is then converted to sparse point cloud format through threshold segmentation and distance-constrained filtering to solve the calculation explosion issue caused by a large amount of original data. Second, based on the proposed method, the DVL, IMU, and sonar data are fused, the Rao-Blackwellized particle filter (RBPF)-based SLAM method is used to estimate AUV pose and generate an occupancy grid map. To verify the proposed algorithm, the underwater vehicle is equipped as an experimental platform to conduct field tasks in both the experimental pool and wild lake, respectively. Experiments illustrate that the proposed approach achieves better performance in both state estimation and suppressing divergence.

Research on acoustic behaviors of water pipelines with guide vanes

In this paper, the pipeline with guide vanes was taken as the research object, the flow noise was studied based on the hybrid calculation method, then the acoustic-structure coupling method was introduced to study the vibration and radiation noise, and then explored the best position of the guide vanes. Based on the pipeline experimental platform and improved experimental methods, it was found that the guide vanes had a better noise reduction effect on the elbows; based on that, a simulation study was carried out on the elbow with guide vanes, and the mechanism of the guide vanes on the velocity field and pulsating pressure of the pipeline was explored. Finally, the noise reduction effect at different positions of the guide vanes under different flow speeds was studied. The results indicated that the guide vane at the middle of the elbow had the best effect on improving the flow field and reducing noise in the working conditions studied in this article, providing a calculation basis for the design of the guide vane.

Sensor Data Fusion for a Mobile Robot Using Neural Networks

Mobile robots must be capable to obtain an accurate map of their surroundings to move within it. To detect different materials that might be undetectable to one sensor but not others it is necessary to construct at least a two-sensor fusion scheme. With this, it is possible to generate a 2D occupancy map in which glass obstacles are identified. An artificial neural network is used to fuse data from a tri-sensor (RealSense Stereo camera, 2D 360° LiDAR, and Ultrasonic Sensors) setup capable of detecting glass and other materials typically found in indoor environments that may or may not be visible to traditional 2D LiDAR sensors, hence the expression improved LiDAR. A preprocessing scheme is implemented to filter all the outliers, project a 3D pointcloud to a 2D plane and adjust distance data. With a Neural Network as a data fusion algorithm, we integrate all the information into a single, more accurate distance-to-obstacle reading to finally generate a 2D Occupancy Grid Map (OGM) that considers all sensors information. The Robotis Turtlebot3 Waffle Pi robot is used as the experimental platform to conduct experiments given the different fusion strategies. Test results show that with such a fusion algorithm, it is possible to detect glass and other obstacles with an estimated root-mean-square error (RMSE) of 3 cm with multiple fusion strategies.

A Real-Time Simulator for an Innovative Hybrid Thermal Management System Based on Experimental Verification

The performance and efficiency of green energy sources in electric vehicles (EVs) are significantly affected by operation temperatures. To maintain the optimal temperatures of a hybrid energy system (HES), an innovative hybrid thermal management system (IHTMS) was designed. The IHTMS contains a coolant pump, a heat exchanger, a proportional valve for hybrid flow rates, five coolant pipes, and three electromagnetic valves to form two mode-switch coolant loops. A Matlab/Simulink-based simulator of the IHTMS was constructed by formulating a set of first-ordered dynamics of temperatures of coolant pipes and energy bodies using the theories of Newton’s law of cooling and the lumped-parameter technique. Parameters were majorly derived by measured performance maps and data from the experimental platform of the IHTMS. To properly manage the optimal temperatures, four control modes were designed for inner-loop form and outer-loop form. For the experimental platform to verify the simulator, two power supplies generated the waste heat of dual energy sources calculated by the driving cycle and vehicle dynamics. Simulation results show that the temperatures were controlled at their optimal ranges by proper mode/loop switch. With the inner-loop mechanism, the rise time of optimal temperature decreased 27.4%. The average simulation-experiment temperature error of the battery was 0.898 °C; the average simulation-experiment temperature error of the PEMFC was 4.839 °C. The IHTMS will be integrated to a real HES in the future.

Research on Segmented Primary Consequent-Pole Permanent Magnet Linear Synchronous Motor with Symmetric and Periodic End Force

The end force of North-South permanent magnet linear synchronous motor (NS-PMLSM) fluctuates symmetrically with a period of one pole. Different from it, the end force’s fluctuation cycle of the consequent-pole permanent magnet linear synchronous motor (CP-PMLSM) is usually two poles and asymmetrical. Especially, within two half cycles, the end force presents complex asymmetric characteristics. In this paper, a piecewise function model of the end force of CP-PMLSM is established. Then a primary segmented design method for adjusting the period, symmetry and phase of the end force waveform is proposed. The two-segment and grouped four-segment modular distances are combined and matched to eliminate the odd-numbered harmonics of the end force and suppress the thrust ripple. A slotless primary core experimental platform and a slotted CP-PMLSM prototype are manufactured and tested to verify the theoretical analysis and simulation results.

Gravitational caustics in an atom laser

Typically discussed in the context of optics, caustics are envelopes of classical trajectories (rays) where the density of states diverges, resulting in pronounced observable features such as bright points, curves, and extended networks of patterns. Here, we generate caustics in the matter waves of an atom laser, providing a striking experimental example of catastrophe theory applied to atom optics in an accelerated (gravitational) reference frame. We showcase caustics formed by individual attractive and repulsive potentials, and present an example of a network generated by multiple potentials. Exploiting internal atomic states, we demonstrate fluid-flow tracing as another tool of this flexible experimental platform. The effective gravity experienced by the atoms can be tuned with magnetic gradients, forming caustics analogous to those produced by gravitational lensing. From a more applied point of view, atom optics affords perspectives for metrology, atom interferometry, and nanofabrication. Caustics in this context may lead to quantum innovations as they are an inherently robust way of manipulating matter waves.

Decomposition characteristics of SF6/N2 gas mixture with local overheating

In recent years, Chinese power enterprises have adopted SF6/N2 to replace SF6 gas in transmission electrical equipment. In this paper, an experimental platform was set up to simulate the local overheating fault of gas insulated electrical equipment. The relationship between the decomposition products of SF6/N2 gas and temperature, experimental time, gas pressure and SF6 content was studied. The results showed that the main products of SF6/N2 gas decomposition were SO2, SO2F2, N2O, C2F6, COS, CS2 and CF4. The content of SO2 increased nonlinearly with the increase of temperature, experimental time, gas pressure and SF6 content. However, the contents of SO2F2, N2O, C2F6, COS, CS2 and CF4 decreased first and remained unchanged with the increase of gas pressure, and increased with the increase of temperature, experimental time and SF6 content. And C element in metal also affected the type and quantity of gas products.