Super cavity model with the coupling reaction of slender body motion and water flow

 On the imperfect water entry, a high-speed slender body moving in the forward direction, rotates inside the cavity. The body's motion makes super cavity phenomena in the water flow. The water velocity and pressure fields interact during the body's motion. In this paper, the coupling simulation model is a combination of two sub-models: In the first sub-model, the motion of slender body running very fast underwater is simulated. The equation system of this sub-model is solved by Runge-Kutta method; In the second sub-model, the water flow and pressure field under reaction of very fast slender body motion are simulated by CFD model. The simulation results of this coupled model are compared with experiments based on magnitudes of velocity U by x0 direction and error percents for cavity diameter and length

The determinants of loyalty to ecotourism against the background of consumer satisfaction

The desire to gain knowledge about the cultural identity and nature of unexplored places has been inherent in humans since time immemorial. However, the emergence of motivation to care, preserve and increase the cultural, historical, natural and recreational values of destinations indicates a higher level of consciousness and responsibility of tourists. The commitment of tourists to this kind of travel is caused not only by marketing prerequisites, but also by socio-psychological factors different: responsibility, psychological ownership. Due to international competition, the relevance of improving the tourist image is increasing, for which it is necessary to develop the concept of socially and environmentally responsible tourism, develop useful behavioral attitudes in society, which increases the role of transfer of cultural and natural knowledge, consolidation of tourists and local residents in improving the tourist image. The study considers the construct of loyalty to a particular destination. The aim of the study is to deepen the analysis of socio-psychological factors of loyalty to ecotourism and develop a methodology for their study. On the basis of correlational, empirical and sociological methods the direct correlation between loyalty and responsibility, satisfaction, positive experience, psychological property and image of the destination was substantiated. It is proved that research activity, intellectual preparation, motivation, respect for nature, cultural involvement and involvement of local residents have a positive effect on the level of responsibility. Cultural involvement and protection of nature, psychological attachment and social interaction increase the sense of psychological ownership, in contrast to the massiveness factor of the place, which decreases it. © 2022, ASERS Publishing House. All rights reserved.IGA/FaME/2022/01

Common Mode Voltage Elimination for Quasi-Switch Boost T-Type Inverter Based on SVM Technique

In this paper, the effect of common-mode voltage generated in the three-level quasi-switched boost T-type inverter is minimized by applying the proposed space-vector modulation technique, which uses only medium vectors and zero vector to synthesize the reference vector. The switching sequence is selected smoothly for inserting the shoot-through state for the inverter branch. The shoot-through vector is added within the zero vector in order to not affect the active vectors as well as the output voltage. In addition, the shoot-through control signal of active switches of the impedance network is generated to ensure that its phase is shifted 90 degrees compared to shoot through the signal of the inverter leg, which provides an improvement in reducing the inductor current ripple and enhancing the voltage gain. The effectiveness of the proposed method is verified through simulation and experimental results. In addition, the superiority of the proposed scheme is demonstrated by comparing it to the conventional pulse-width modulation technique

POLYNOMIAL OBSERVER-BASED CONTROLLER SYNTHESIS AND FAULT-TOLERANT CONTROL FOR TRACKING OPTIMAL POWER OF WIND ENERGY CONVERSION SYSTEMS

This article proposes a new approach to design a fault-tolerant control (FTC) scheme for tracking the optimal power of wind energy conversion systems (WECSs). In this article, the considered fault will not only impact on actuator but also sensors. As the fault severely affects the performance of WECSs, the FTC are required to be worked accurately and effectively. The polynomial observer, as a part of the proposed FTC system, is synthesized to estimate the aerodynamic torque, electromagnetic torque, and fault simultaneously without using sensors to measure. The information of these parameters is sent back to the LQR (Linear Quadratic Regular) controller of WECSs. Both fault and aerodynamic torque in this study are unnecessary to fulfil any constraint. It should be noted that WECSs is reconstructed to a new form based on the descriptor technique, then the observer will design for this new form instead of the original system. Based on Lyapunov methodology and with the aid of SOS (Sum-Of-Square) technique, the conditions for polynomial observer design are derived in the main theorems. Finally, the simulation results have proved the effectiveness and merit of the proposed FTC method

SHREC\u2717: RgB-D to CAD Retrieval With ObjectNN Dataset

© 2017 The Eurographics Association. The goal of this track is to study and evaluate the performance of 3D object retrieval algorithms using RGB-D data. This is inspired from the practical need to pair an object acquired from a consumer-grade depth camera to CAD models available in public datasets on the Internet. To support the study, we propose ObjectNN, a new dataset with well segmented and annotated RGB-D objects from SceneNN [HPN∗16] and CAD models from ShapeNet [CFG∗15]. The evaluation results show that the RGB-D to CAD retrieval problem, while being challenging to solve due to partial and noisy 3D reconstruction, can be addressed to a good extent using deep learning techniques, particularly, convolutional neural networks trained by multi-view and 3D geometry. The best method in this track scores 82% in accuracy

RGB-D to CAD retrieval with objectNN dataset

The goal of this track is to study and evaluate the performance of 3D object retrieval algorithms using RGB-D data. This is inspired from the practical need to pair an object acquired from a consumer-grade depth camera to CAD models available in public datasets on the Internet. To support the study, we propose ObjectNN, a new dataset with well segmented and annotated RGB-D objects from SceneNN [HPN*16] and CAD models from ShapeNet [CFG*15]. The evaluation results show that the RGB-D to CAD retrieval problem, while being challenging to solve due to partial and noisy 3D reconstruction, can be addressed to a good extent using deep learning techniques, particularly, convolutional neural networks trained by multi-view and 3D geometry. The best method in this track scores 82% in accuracy