Nanofluids, the fluid suspensions of nanomaterials, have shown many interesting properties, and the distinctive features offer unprecedented potential for many applications. This paper summarizes the recent progress on the study of nanofluids, such as the preparation methods, the evaluation methods for the stability of nanofluids, and the ways to enhance the stability for nanofluids, the stability mechanisms of nanofluids, and presents the broad range of current and future applications in various fields including energy and mechanical and biomedical fields. At last, the paper identifies the opportunities for future research.

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A review on nanofluids: preparation, stability mechanisms, and applications

The special mechanical properties of nanoparticles allow for novel applications in many fields, e.g., surface engineering, tribology and nanomanufacturing/nanofabrication. In this review, the basic physics of the relevant interfacial forces to nanoparticles and the main measuring techniques are briefly introduced first. Then, the theories and important results of the mechanical properties between nanoparticles or the nanoparticles acting on a surface, e.g., hardness, elastic modulus, adhesion and friction, as well as movement laws are surveyed. Afterwards, several of the main applications of nanoparticles as a result of their special mechanical properties, including lubricant additives, nanoparticles in nanomanufacturing and nanoparticle reinforced composite coating, are introduced. A brief summary and the future outlook are also given in the final part. (Some figures may appear in colour only in the online journal)

https://iopscience.iop.org/article/10.1088/0022-3727/47/1/013001/pdf

Mechanical properties of nanoparticles: basics and applications

This book equips the reader to understand every important aspect of the dynamics of rotating machines. Will the vibration be large? What influences machine stability? How can the vibration be reduced? Which sorts of rotor vibration are the worst? The book develops this understanding initially using extremely simple models for each phenomenon, in which (at most) four equations capture the behavior. More detailed models are then developed based on finite element analysis, to enable the accurate simulation of the relevant phenomena for real machines. Analysis software (in MATLAB) is associated with this book, and novices to rotordynamics can expect to make good predictions of critical speeds and rotating mode shapes within days. The book is structured more as a learning guide than as a reference tome and provides readers with more than 100 worked examples and more than 100 problems and solutions.

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Dynamics of rotating machines

The prediction of chaotic time series with neural networks is a traditional practical problem of dynamic systems. This paper is not intended for proposing a new model or a new methodology, but to study carefully and thoroughly several aspects of a model on which there are no enough communicated experimental data, as well as to derive conclusions that would be of interest. The recurrent neural networks (RNN) models are not only important for the forecasting of time series but also generally for the control of the dynamical system. A RNN with a sufficiently large number of neurons is a nonlinear autoregressive and moving average (NARMA) model, with "moving average" referring to the inputs. The prediction can be assimilated to identification of dynamic process. An architectural approach of RNN with embedded memory, "Nonlinear Autoregressive model process with eXogenous input" (NARX), showing promising qualities for dynamic system applications, is analyzed in this paper. The performances of the NARX model are verified for several types of chaotic or fractal time series applied as input for neural network, in relation with the number of neurons, the training algorithms and the dimensions of his embedded memory. In addition, this work has attempted to identify a way to use the classic statistical methodologies (R/S Rescaled Range analysis and Hurst exponent) to obtain new methods of improving the process efficiency of the prediction chaotic time series with NARX.

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The use of NARX neural networks to predict chaotic time series

A necessary condition for double scroll attractor existence in fractional-order systems

Purpose – The present paper proposes a theoretical analysis of the stability characteristics of a rigid rotor‐hybrid bearing system. It is intended that on the basis of the numerical results drawn from this study, the optimal restriction parameter for stable operation can be determined for use in the bearing design process.Design/methodology/approach – A rigid rotor supported by hybrid oil film bearings with six recesses and capillary‐compensated restrictors is studied. In order to facilitate the calculation of film dynamics, using the perturbation method, the Reynolds equation was linearized and subsequently solved using finite difference techniques, whilst the stability maps were determined by the Routh‐Hurwitz method.Findings – The data reported here suggest that the stability characteristics of the rigid rotor‐bearing system could be improved by the use of shallow, dual‐recessed hybrid bearings with capillary compensation. For the same restriction parameter and the same land‐width ratio used in large ...

The influences of capillary restriction and journal eccentricity on the stability of the rigid rotor‐hybrid bearing system

In this paper, the mathematic model of the swashplate type variable displacement axial piston pump (VDAPP) is established. The VDAPP applied to an electro-hydraulic servo control system usually induces unstable performance, so that a servo controller is designed and analyzed to control the swashplate angular displacement and improve the stability and transient response of pump performance. The flow and pressure variations of pump with the proposed controller are investigated and analyzed by mathematic simulations and experiments. The simulation and experiment results show that the proposed controller can improve the stability of swashplate angular displacement, and easily applied to the energy saving hydraulic systems.

The Swashplate Angle Control of a Variable Displacement Pump with an Electro-Hydraulic Proportional Valve

To apply effective methods to analyze and distinguish all kinds of response patterns is an important issue of nonlinear dynamics. This paper contributes to develop an alternative Poincare section method to analyze and identify the whirl responses of a nonlinear oscillator. This integration for analyzing high-order harmonic and chaotic motions is used to integrate the distance between state trajectory and the origin in the phase plane during a specific period. This response integration process is based on the fact that the integration value would be constant if the integration interval is equal to the response period. It provides a quantitative characterization of system responses as the role of the traditional stroboscopic technique (Poincare section method) to observe bifurcations and chaos of the nonlinear oscillators. However, due to the signal response contamination of system, the section points on Poincare maps might be too close to distinguish, especially for a high-order subharmonic vibration. Combining the capability of precisely identifying period and constructing bifurcation diagrams, the advantages of the proposed method are shown by the simulations of a Duffing-Van der Pol oscillator. The simulation results show that the high-order subharmonic and chaotic responses and their bifurcations can be effectively observed.

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An alternative Poincaré section for steady-state responses and bifurcations of a Duffing-Van der Pol oscillator

A novel Chebyshev functional recurrent neuro-fuzzy (CFRNF) network is developed from a combination of the Takagi-Sugeno-Kang (TSK) fuzzy model and the Chebyshev recurrent neural network (CRNN). The CFRNF network can emulate the nonlinear dynamics of a servomechanism system. The system nonlinearity is addressed by enhancing the input dimensions of the consequent parts in the fuzzy rules due to functional expansion of a Chebyshev polynomial. The back propagation algorithm is used to adjust the parameters of the antecedent membership functions as well as those of consequent functions. To verify the performance of the proposed CFRNF, the experiment of the belt servomechanism is presented in this paper. Both of identification methods of adaptive neural fuzzy inference system (ANFIS) and recurrent neural network (RNN) are also studied for modeling of the belt servomechanism. The analysis and comparison results indicate that CFRNF makes identification of complex nonlinear dynamic systems easier. It is verified that the accuracy and convergence of the CFRNF are superior to those of ANFIS and RNN by the identification results of a belt servomechanism.

Modeling Belt-Servomechanism by Chebyshev Functional Recurrent Neuro-Fuzzy Network

The signal is processed by using empirical mode decomposition (EMD) and Hilbert transformation (HT), which can obtain instantaneous frequency, instantaneous amplitude and marginal spectrum as the basis of pattern matching. Simultaneously, the energy distribution of signal at each frequency domain can be used to train a neural network as fault diagnosis tool. However, the influence of noise on EMD of gear operation signal is large. The noise may disturb EMD and generate the mix mode and. In this study, wavelet packet de-noises and Ensemble EMD (EEMD) is used to reduce the influence of noise on EMD. The diagnosis results display these two methods can improve gear fault diagnosis.

Yuan Kang

Papers

The influences of capillary restriction and journal eccentricity on the stability of the rigid rotor‐hybrid bearing system

The Swashplate Angle Control of a Variable Displacement Pump with an Electro-Hydraulic Proportional Valve

An alternative Poincaré section for steady-state responses and bifurcations of a Duffing-Van der Pol oscillator

Modeling Belt-Servomechanism by Chebyshev Functional Recurrent Neuro-Fuzzy Network

Study on the Affection of Gear Fault Diagnosis Bases on HHT by Noises