Yuan Kang

Bio: Yuan Kang is an academic researcher from Chung Yuan Christian University. The author has contributed to research in topics: Bearing (mechanical) & Rotor (electric). The author has an hindex of 20, co-authored 78 publications receiving 1280 citations. Previous affiliations of Yuan Kang include National Chiao Tung University.

Size effects of SiO2 nanoparticles as oil additives on tribology of lubricant

Abstract: Purpose – The purpose of this paper is to investigate the tribological properties of liquid paraffin with SiO2 nanoparticles additive made by a sol‐gel method.Design/methodology/approach – The tribological properties of the SiO2 nanoparticles as an additive in liquid paraffin are measured using a ball‐on‐ring wear tester to determine the optimal additive concentration. The mechanism that wear and friction are reduced is studied using scanning electron microscopy (SEM), energy dispersive spectrometry (EDS), and atomic force microscope (AFM).Findings – Experimental results indicate that the sizes of the synthesized SiO2 nanoparticles are distributed uniformly and that the optimal concentrations of SiO2 nanoparticles in liquid paraffin is associated with better tribological properties than pure paraffin oil, and an anti‐wear (AW) ability that depends on the particle size.Originality/value – It is shown in the paper that by reducing friction and AW, the lubricant prepared by the methods described can prolong ...

Chaos in the Newton–Leipnik system with fractional order

Abstract: The dynamics of fractional-order systems has attracted increasing attention in recent years. In this paper, the dynamics of the Newton–Leipnik system with fractional order was studied numerically. The system displays many interesting dynamic behaviors, such as fixed points, periodic motions, chaotic motions, and transient chaos. It was found that chaos exists in the fractional-order system with order less than 3. In this study, the lowest order for this system to yield chaos is 2.82. A period-doubling route to chaos in the fractional-order system was also found.

Applications of fault diagnosis in rotating machinery by using time series analysis with neural network

Abstract: The common diagnosis method of time series analysis is an autoregressive (AR) method, which is a kind of math model that can be established by time difference and vibration amplitude. As the AR model utilized the math method for fitting the variable, the AR coefficients represent the signal features and can be used to determine fault types. This study proposed the difference values of AR coefficients, which indicated that the AR coefficients of ideal signal for normal machine are deducted from faulty machines. It is convention that the relationship between the difference values of AR coefficients and fault types as trained by using back-propagation neural network (BPNN). The new fault diagnosis method by using the difference of AR coefficients with BPNN was proposed in this study. The diagnosis results were obtained and compared with the three methods, which include the difference of AR coefficients with BPNN, the AR coefficients with BPNN and the distance of AR coefficients method for 23 samples. And the diagnosis results obtained by using the difference of AR coefficients with BPNN were superior to AR coefficients with BPNN and distance of AR coefficients methods.

Chaotic convection of viscoelastic fluids in porous media

Abstract: Buoyancy-induced convection in a viscoelastic fluid-saturated porous medium was analyzed using an Oldroydian-type constitutive relation. An autonomous system with four differential equations was deduced by applying the truncated Galerkin expansion to the momentum and heat transfer equations. The four-dimensional system can be reduced to many systems provided in the literature such as the Lorenz system, Vadasz system, Khayat system, and Akhatov system. Depending on the flow parameters, the asymptotic behavior can be stationary, periodic, or chaotic. Generation of a four-scroll, or two-“butterfly”, chaotic attractor was observed. Results also show that stress relaxation tends to precipitate the onset of chaos.

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

Abstract: 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.

Mechanical properties of nanoparticles: basics and applications

Abstract: 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)

Dynamics of rotating machines

Abstract: 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.

The use of NARX neural networks to predict chaotic time series

Abstract: 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.