Effect mechanism of cavitation on the hump characteristic of a pump-turbine

Positive slopes in the performance characteristics of pump-turbines are an indicative feature of unstable behaviour in pump mode operation, and are additionally conducive to instability problems during pump start-up. With the rapid development of pumped-storage power plants, this instability has become increasingly critical to the safe and stable operation of the entire unit. Thus, this topic has been attracting attention in the pump-turbine design and research fields. In this study, the relative achievements with regard to positive slopes, which have been obtained by various investigators over the last two decades, were reviewed. Such studies, including flow characteristics, and influence parameter, related to positive slopes were summarized. The generating mechanism, accompanying unsteady phenomena, and corresponding hysteresis phenomena, are also discussed. Investigations on the influence of geometric and operating unit parameters are presented. Finally, precautions and countermeasures for the improvement of positive slopes, and various perspectives regarding future work are proposed. The present review provides theoretical guidance for the design and operation of pump-turbines, in terms of the stability of the pumped-storage powers plants.

Review of positive slopes on pump performance characteristics of pump-turbines

Experimental evidence of cavitation influences on the positive slope on the pump performance curve of a low specific speed model pump-turbine

This paper presents an adaptive hysteresis compensation approach for a piezoelectric actuator (PEA) using single-neuron adaptive control. For a given desired trajectory, the control input to the PEA is dynamically adjusted by the error between the actual and desired trajectories using Hebb learning rules. A single neuron with self-learning and self-adaptive capabilities is a non-linear processing unit, which is ideal for time-variant systems. Based on the single-neuron control, the compensation of the PEA’s hysteresis can be regarded as a process of transmitting biological neuron information. Through the error information between the actual and desired trajectories, the control input is adjusted via the weight adjustment method of neuron learning. In addition, this paper also integrates the combination of Hebb learning rules and supervised learning as teacher signals, which can quickly respond to control signals. The weights of the single-neuron controller can be constantly adjusted online to improve the control performance of the system. Experimental results show that the proposed single-neuron adaptive hysteresis compensation method can track continuous and discontinuous trajectories well. The single-neuron adaptive controller has better adaptive and self-learning performance against the rate-dependence of the PEA’s hysteresis.

https://www.mdpi.com/2072-666X/11/1/84/pdf

Single-Neuron Adaptive Hysteresis Compensation of Piezoelectric Actuator Based on Hebb Learning Rules.

A modeling and parameter identification method for rate dependent hysteresis of piezoelectric actuated nano-stage is presented in this work. A system level quasi-static hysteresis model is employed to construct a neural network. To better describe the rate dependent behavior of hysteresis in piezoelectric actuated stage, a Nonlinear AutoRegressive Moving Average with eXogenous input (NARMAX) based dynamic model is incorporated with the quasi-static hysteresis model, where the weights of specifically designed neural network corresponds to the model parameters. To handle the multivalued problem of hysteresis, generalized input gradient is proposed to convert multivalued mapping of hysteresis into one-to-one mapping. The parameters of the nonlinear rate dependent hysteresis in piezoelectric actuated stage is identified by neural network training, taking advantage of their universal function approximation capabilities. The proposed scheme is also compared with conventional black box and particle swarm optimization identification based methods, simulation and experimental results demonstrate significant performance improvement with an error of 20.77nm for proposed method whereas 96.56nm and 31.46nm for black box and particle swarm optimization respectively.

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Modeling and Identification of Rate Dependent Hysteresis in Piezoelectric Actuated Nano-Stage: A Gray Box Neural Network Based Approach

Piezoelectric actuators (PAs) require high precision positioning for the applications of micro electrical mechanical systems, but it exhibits hysteresis nonlinearity which deteriorates positioning accuracy if no proper compensation is given. Hysteresis nonlinear modeling of PAs is a prime choice for hysteresis compensation. This paper proposes a novel intelligent positioning control algorithm based on Bouc-Wen (BW) model for the compensation of a bi-morph type piezoelectric actuator (PA) suffering rate-dependent hysteresis. A region based mixed-species swarm optimization (RMSO) algorithm is proposed for BW modeling to capture the dynamic nonlinearity of a piezoelectric actuator which exhibits rate-dependent hysteresis. Results of numerical simulations have been disclosed to illustrate the performance enhancement of RMSO over classical algorithm while they are applied to the parameter fitting problem of BW model for experimentally acquired datasets. An model based adaptive Fuzzy neural network (Fuzzy-NN) controller of PA is utilized to compensate the hysteresis for the positioning tracking control. Experimental results also illustrate the good performance of the proposed RMSO-BW based control scheme for the hysteresis compensation control of the PA.

Intelligent Rate-Dependent Hysteresis Control Compensator Design With Bouc-Wen Model Based on RMSO for Piezoelectric Actuator

Piezo-ceramic actuators are widely used in micro-electro-mechanical systems. An adaptive inverse neurocontrol design is proposed for positioning control of the piezo-ceramic actuator (PA). Piezo-ceramic actuators exhibit rate-dependent hysteresis which changes its hysteretic behavior when the rate or the frequency of its driving signal varies. Radial basis function neural network (RBFNN) is utilized to model the input/output relation of the PA with rate-dependent hysteresis to make it work as an accurate hysteretic model for positioning control. And an adaptive inverse nonlinear controller is introduced for the rate-dependent hysteresis compensation of PA. A novel membrane structure genetic algorithm (MSGA) is proposed for the adaptive inverse nonlinear neurocontrol design of PA. Compared with the classical genetic algorithm, the experimental results illustrate the performance of the proposed control system as well as the efficiency of adaptive membrane structure genetic algorithm for positioning control of PA system.

Adaptive novel MSGA-RBF neurocontrol for piezo-ceramic actuator suffering rate-dependent hysteresis

The current development of modern pumped storage plants aims towards a higher flexibility in operation, an extended operation range of the hydraulic machine (especially in the pumping mode), and a higher reliability. The pumping requirements are the crucial design drivers, since, even if the turbine mode performance is very sound, the success of a project depends also on the pump turbine delivering the required maximum pump head and starting reliably in pump mode. Pump hump (pump instability working points at highest head) which is an instability source to the pump-turbine vibration is a serious damage to the pump operation on high head. So the pump hump and cavitation number based on the numerical simulation and experiment results are shown in this paper. The pump hump is sensitive affected by the cavitation number. With the cavitation number decreasing, the hump on flow characteristic curve (e.g. head-flow rate curve, H-Q curve) is gradually decreasing until vanished. Predicting cavitating flows with multi-phase CFD computations is still a very challenging task. Some results of ongoing work in this field are presented. The hump on H-Q curve with cavitation number is discussed in this paper.

http://iopscience.iop.org/article/10.1088/1757-899X/72/3/032016/pdf

Pump hump characteristic research based on mass transfer equation

An optimization method of heart sound feature combination based on binary particle swarm optimization (BPSO) is proposed to analyze hypertrophic cardiomyopathy (HCM), which is a typical cardiovascular disease. The analysis procedure mainly includes feature extraction, feature combination optimization and HCM analysis. Firstly, 13-dimension energy features are extracted by the wavelet transformation. Secondly, feature combinations are selected by the proposed optimization method for classifying the normal and HCM heart sounds as well as the different types of HCM heart sounds. Lastly, 362 clinical cases including normal and HCM heart sounds are used to verify the validity of the proposed optimization method for heart sound feature combination, compared with principle component analysis (PCA), genetic algorithm (GA) and the analysis method without optimization of heart sound features. The average accuracy by our proposed optimization method, considering the number of features selected out, reaches to 20.91%, higher than the results utilizing feature combinations obtained by the other methods. In addition, the proposed optimization method of feature combination has potential for analyzing different kinds of HCM cases.

Optimization of heart sound feature combination for HCM analysis based on BPSO

NACA0015 is a commonly used hydrofoil for hydraulic machinery. Because the calculated flow characteristic is sensitive to the mesh density and distribution, a careful check of mesh independence should be performed before the CFD calculation. For this purpose, flow comparison was done between calculation and experimental test at the same operating condition with the Reynolds number equal to 130,000. When the span length is two times of the chord length, the ratio of lift to drag is 34.97, which is close to the test result of 36.47. The pressure coefficient and cavitation characteristic were also calculated. For example, the pressure coefficient was compared in different flow conditions with Reynolds number equal to 130,000, 200,000 and 400,000. The cavitation characteristic was calculated at different cavitation numbers, e.g. 0.5, 1.0 and 1.5. Based on these hydraulic and cavitation characteristic, a tidal turbine runner was developed by using hydrofoil NACA0015. According to our studies, the flow characteristic and cavitation index can satisfy the engineering requirements.

https://iopscience.iop.org/article/10.1088/1757-899X/72/4/042030/pdf

Dongbo Liu

Papers

Intelligent Rate-Dependent Hysteresis Control Compensator Design With Bouc-Wen Model Based on RMSO for Piezoelectric Actuator

Adaptive novel MSGA-RBF neurocontrol for piezo-ceramic actuator suffering rate-dependent hysteresis

Pump hump characteristic research based on mass transfer equation

Optimization of heart sound feature combination for HCM analysis based on BPSO

Flow analysis of NACA0015 and its application on tidal power