3-D Transient CFD Model For A Rolling Piston Compressor With A Dynamic Reed Valve

Rolling pistons are widely used as compressors in air conditioning systems due to their small size, low cost, and high performance. Reed valves, a special type of check valve driven by hydraulic forces, are commonly used with many types of compressors to restrict the flow to one direction. Reed valves are typically made of a thin layer of metal or plastic materials. The valve will bend in response to fluid pressure forces to open up the flow channel at the proper moment within the compression cycle. There are many things that could affect the performance and efficiency of a compressing system. Among them, the interaction between the compressor and the control valves is one of the most important factors. Due to the fully coupled dynamic nature of the problem, analysis of compressor/valve interaction is difficult. CFD simulation of combined compressor and valve systems can provide valuable insights regarding not only the performance of the compressor and valve themselves, but also the critical dynamic interaction between the compressor and the valve. In this paper, a full 3D transient CFD model for a rolling piston compressor with reed valve is described in detail. In the proposed model, a moving/deforming mesh algorithm is developed for the fluid pockets of the rolling piston. The bending reed valve is modeled as a rotational structure with moving/deforming mesh for the affected fluid volume. The motion of the valve is solved using a one dimensional rotational ordinary differential equation. Based on bending cantilever beam theory, important parameters of the rotational dynamics, such as torsion constant, are carefully derived to accurately model the behavior of the bending reed valve. The meshing and re-meshing algorithms for the fluid volume of rolling piston and the rotational valve are implemented in the CFD solver PumpLinx. The test simulation of a real compressor and valve system demonstrates that the algorithms and the implementations are robust, fast, and user friendly, and can be readily applied to industrial rolling piston compressor systems

A distributed cooperative control scheme with optimal priority assignment and stability assessment

International audienceIn this paper, a distributed partially cooperative control framework is proposed for a network of linear interconnected subsystems. It is assumed that each subsystem in the network possesses its own objective and a corresponding nominal interaction-free state feedback law. The proposed framework enables each subsystem to compute an additional control term in order to help maintaining the integrity of the overall network. As this cooperation-like behavior involves relative priority assignment, a communication aware heuristic is proposed with an associated stability assessment that is based on the closed-loop network matrix's spectrum monitoring. Illustrative examples are used to assess the effectiveness of the proposed scheme including a distributed load frequency problem

To Achieve Security and High Spectrum Efficiency: A New Transmission System Based on Faster-than-Nyquist and Deep Learning

With the rapid development of various services in wireless communications, spectrum resource has become increasingly valuable. Faster-than-Nyquist (FTN) signaling, which was proposed in the 1970s, has been a promising paradigm to improve the spectrum utilization. In this paper, we try to apply FTN into secure communications and propose a secure and high-spectrum-efficiency transmission system based on FTN and deep learning (DL). In the proposed system, the hopping symbol packing ratio with random values makes it difficult for the eavesdropper to obtain the accurate symbol rate and inter-symbol interference (ISI). While the receiver can use the blind estimation to choose the true parameters with the aid of DL. The results show that without the accurate symbol packing ratio, the eavesdropper will suffer from severe performance degradation. As a result, the system can achieve a secure transmission with a higher spectrum efficiency. Also, we propose a simplified symbol packing ratio estimation which has bee employed in our proposed system. Results show that the proposed simplified estimation achieves nearly the same performance as the original structure while its complexity has been greatly reduced

Adaptive time-switching based energy harvesting relaying protocols

Considering a dual-hop energy-harvesting (EH) relaying system, this paper advocates novel relaying protocols based on adaptive time-switching (TS) for amplify-and-forward and decode-and-forward modes, respectively. The optimal TS factor is first studied, which is adaptively adjusted based on the dual-hop channel state information (CSI), accumulated energy, and threshold signal-to-noise ratio (SNR), to achieve the maximum throughput efficiency per block. To reduce the CSI overhead at the EH relay, a low-complexity TS factor design is presented, which only needs single-hop CSI to determine the TS factor. Theoretical results show that, in comparison with the conventional solutions, the proposed optimal/low-complexity TS factor can achieve higher limiting throughput efficiency for sufficiently small threshold SNR. As the threshold SNR approaches infinity, the throughput efficiency of the proposed optimal/low-complexity TS factor tends to zero in a much slower pace than that of the conventional solutions. Simulation results are presented to corroborate the proposed methodology

CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds

We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D. Our proposed method first generates some high-quality 3D proposals by leveraging the class-aware local group strategy on the object surface voxels with the same semantic predictions, which considers semantic consistency and diverse locality abandoned in previous bottom-up approaches. Then, to recover the features of missed voxels due to incorrect voxel-wise segmentation, we build a fully sparse convolutional RoI pooling module to directly aggregate fine-grained spatial information from backbone for further proposal refinement. It is memory-and-computation efficient and can better encode the geometry-specific features of each 3D proposal. Our model achieves state-of-the-art 3D detection performance with remarkable gains of +\textit{3.6\%} on ScanNet V2 and +\textit{2.6}\% on SUN RGB-D in term of [email protected]. Code will be available at https://github.com/Haiyang-W/CAGroup3D.Comment: Accept by NeurIPS202