scholarly journals Higher Order Rotating Cavitation in an Inducer

2004 ◽  
Vol 10 (4) ◽  
pp. 241-251 ◽  
Author(s):  
Akira Fujii ◽  
Seiji Azuma ◽  
Yoshiki Yoshida ◽  
Yoshinobu Tsujimoto ◽  
Hironori Horiguchi ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Transition Point ◽  
Pressure Fluctuations ◽  
Propagation Speed ◽  
Higher Order ◽  
Inlet Pressure ◽  
Speed Ratio ◽  
Stress Fluctuation ◽  
The Stability

In the present study, a higher order rotating cavitation predicted by the stability analysis was identified through the measurements of inlet pressure fluctuations and blade stress fluctuations. The propagation speed ratio of the higher order rotating cavitation is approximately 5, and the amplitude of the blade stress fluctuation caused by this rotating cavitation is the same level as that by the conventional rotating cavitation. In addition, a higher order cavitation surge was observed at the transition point from the conventional to the higher order rotating cavitation.

STABILITY ANALYSIS OF HIGHER-ORDER NEURAL NETWORKS FOR COMBINATORIAL OPTIMIZATION

2002 ◽  
Vol 12 (03n04) ◽  
pp. 177-186 ◽  
Author(s):  
BRENTON COOPER
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Combinatorial Optimization ◽  
Arbitrary Order ◽  
Higher Order ◽  
Hopfield Network ◽  
Trade Off ◽  
Higher Order Neural Networks ◽  
The Stability

Recurrent neural networks with higher order connections, from here on referred to as higher-order neural networks (HONNs), may be used for the solution of combinatorial optimization problems. In Ref. 5 a mapping of the traveling salesman problem (TSP) onto a HONN of arbitrary order was developed, thereby creating a family of related networks that can be used to solve the TSP. In this paper, we explore the trade-off between network complexity and quality of solution that is made available by the HONN mapping of the TSP. The trade-off is investigated by undertaking an analysis of the stability of valid solutions to the TSP in a HONN of arbitrary order. The techniques used to perform the stability analysis are not new, but have been widely used elsewhere in the literature.15–17 The original contribution in this paper is the application of these techniques to a HONN of arbitrary order used to solve the TSP. The results of the stability analysis show that the quality of solution is improved by increasing the network complexity, as measured by the order of the network. Furthermore, it is shown that the Hopfield network, as the simplest network in the family of higher-order networks, is expected to produce the poorest quality of solution.

scholarly journals The need for higher-order averaging in the stability analysis of hovering, flapping-wing flight

2015 ◽  
Vol 10 (1) ◽  
pp. 016002 ◽  
Author(s):  
Haithem E Taha ◽  
Sevak Tahmasian ◽  
Craig A Woolsey ◽  
Ali H Nayfeh ◽  
Muhammad R Hajj
Keyword(s):  
Stability Analysis ◽  
Higher Order ◽  
Flapping Wing ◽  
The Stability

Stability analysis and control of a flywheel energy storage rotor with rotational damping and nonsynchronous damping

2021 ◽  
pp. 107754632110212
Author(s):  
Huiwei Wang ◽  
Huichun Peng ◽  
Yaxin Zhen
Keyword(s):  
Stability Analysis ◽  
Dynamical Behavior ◽  
Linear Quadratic Regulator ◽  
Rotor System ◽  
Speed Ratio ◽  
Linear Quadratic ◽  
Coupling Effects ◽  
The Stability ◽  
Rotational Damping ◽  
Flywheel Rotor

Based on the principle of Lagrange mechanics, especially considering the effects of rotation damping and nonsynchronous damping, a radial 4-dimensional dynamic model of the flywheel bearing rotor system is proposed. Applying the Laplace eigenvalue method, the stability effects of rotational damping, nonsynchronous damping, and their coupling effects are investigated by means of root locus method. Under the control of the linear quadratic regulator, dynamical characteristics of the flywheel bearing rotor system with varied rotational damping and nonsynchronous damping are also studied. The results show that the rotation damping, nonsynchronous damping, and their coupling effects have vast and complex instability effects on high-speed flywheel bearing rotor system. However, there are three exceptions. The tiny proportional rotational damping, remaining below 12%, and the minuscule proportional co-nonsynchronous damping; the product of the nonsynchronous damping and the speed ratio below 5% both can enhance the stability of the system. Furthermore, in the situation that the counter-nonsynchronous damping is coupled with the large proportion of rotational damping, the stability of the system can also be boosted distinctly. On the other hand, the numerical experimental results show that the rotational damping and nonsynchronous damping have a beneficial effect on the flywheel system controlled by linear quadratic regulator. In addition, under the control of linear quadratic regulator, the transient dynamical behavior of the flywheel rotor system with rotational damping or co-nonsynchronous damping performed better than the flywheel rotor system with the coupled damping. The numerical simulations of the transient response of the flywheel rotor system under active control are consistent with some of the derived stability analysis results. The results about the stability analysis and the performance in vibration control give the suggestions for the instability control and fault detection of the system.

Stability Analysis of the Nanjung Water Diversion Twin Tunnels based on Convergence Measurement

2019 ◽  
Vol 1 (1) ◽  
pp. 49-60
Author(s):  
Simon Heru Prassetyo ◽  
Ganda Marihot Simangunsong ◽  
Ridho Kresna Wattimena ◽  
Made Astawa Rai ◽  
Irwandy Arif ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Convergence Rate ◽  
Critical Strain ◽  
Convergence Rates ◽  
Strain Analysis ◽  
Water Diversion ◽  
Tunnel Face ◽  
Tunnel Stability ◽  
The Stability ◽  
Twin Tunnels

This paper focuses on the stability analysis of the Nanjung Water Diversion Twin Tunnels using convergence measurement. The Nanjung Tunnel is horseshoe-shaped in cross-section, 10.2 m x 9.2 m in dimension, and 230 m in length. The location of the tunnel is in Curug Jompong, Margaasih Subdistrict, Bandung. Convergence monitoring was done for 144 days between February 18 and July 11, 2019. The results of the convergence measurement were recorded and plotted into the curves of convergence vs. day and convergence vs. distance from tunnel face. From these plots, the continuity of the convergence and the convergence rate in the tunnel roof and wall were then analyzed. The convergence rates from each tunnel were also compared to empirical values to determine the level of tunnel stability. In general, the trend of convergence rate shows that the Nanjung Tunnel is stable without any indication of instability. Although there was a spike in the convergence rate at several STA in the measured span, that spike was not replicated by the convergence rate in the other measured spans and it was not continuous. The stability of the Nanjung Tunnel is also confirmed from the critical strain analysis, in which most of the STA measured have strain magnitudes located below the critical strain line and are less than 1%.

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