Search results

To compare slip surface and moving band techniques for modelling movement in 3D with FEM.

The slip surface and moving band techniques are used to model the rotation of electrical machines in 3D with FEM. The proposed techniques are applied to a permanent magnet synchronous machine. The comparison is carried out at no‐load for the electromotive force (EMF) and the cogging torque. The torque is also compared for the short circuit case.

For both the locked‐step and moving band approaches there is no difficulty in establishing the scalar potential and potential vector formulations. However, if step displacement is not equal to the mesh step, the results can show numerical irregularities. Some improvements have been proposed in order to limit this problem.

The results of the EMF and the cogging torque are improved.

This paper aims to study the influence of cylindrical texture parameters on the lubrication performance of static and dynamic pressure thrust bearings (hereinafter referred to as thrust bearings) and to optimize their lubrication performance using multiobjective optimization.

The influence of texture parameters on the lubrication performance of thrust bearings was studied based on the modified Reynolds equation. The objective functions are predicted through the BP neural network, and the texture parameters were optimized using the improved multiobjective ant lion algorithm (MOALA).

Compared with smooth surface, the introduction of texture can improve the lubrication properties. Under the optimization of the improved algorithm, when the texture diameter, depth, spacing and number are approximately 0.2 mm, 0.5 mm, 5 mm and 34, respectively, the loading capacity is increased by around 27.7% and the temperature is reduced by around 1.55°C.

This paper studies the effect of texture parameters on the lubrication properties of thrust bearings based on the modified Reynolds equation and performs multiobjective optimization through an improved MOALA.

The purpose of this paper is to solve the problems of poor mechanical properties, high surface roughness and waste support materials of thin-walled parts fabricated by flat-layered additive manufacturing process.

This paper proposes a curved-layered material extrusion modeling process with a five-axis motion mechanism. This process has advantages of the platform rotating, non-support printing and three-dimensional printing path. First, the authors present a curved-layered algorithm by offsetting the bottom surface into a series of conformal surfaces and a toolpath generation algorithm based on the geodesic distance field in each conformal surface. Second, they introduce a parallel five-axis printing machine consisting of a printing head fixed on a delta-type manipulator and a rotary platform on a spherical parallel machine.

Mechanical experiments show the failure force of the five-axis printed samples is 153% higher than that of the three-axis printed samples. Forming experiments show that the surface roughness significantly decreases from 42.09 to 18.31 µm, and in addition, the material consumption reduces by 42.90%. These data indicate the curved-layered algorithm and five-axis motion mechanism in this paper could effectively improve mechanical properties and the surface roughness of thin-walled parts, and realize non-support printing. These methods also have reference value for other additive manufacturing processes.

Previous researchers mostly focus on printing simple shapes such as arch or “T”-like shape. In contrast, this study sets out to explore the algorithm and benefits of modeling thin-walled parts by a five-axis machine. Several validated models would allow comparability in five-axis printing.

The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm.

Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized.

This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm.

Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast.

As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization.

The purpose of this paper is to present a new approach for selecting the good heavy oil reservoirs to develop preferentially, which can avoid the huge economical loss resulted from wrong decision.

A new method of ranking the development priority of heavy oil reservoir is present, in which the neural network is applied for the first time to acquire reservoir parameters' weights through training samples and the genetic algorithm is used to optimize the joint weighs of neurons in case that neural network falling into local minimum. Additionally, the paper establishes subordinate function of every parameter. Eventually, comprehensive evaluation values of all heavy oil reservoirs are obtained.

The method can ensure the veracity and creditability of the parameters' weights, avoid the randomicity brought by experts.

Accessibility of the data of many heavy oil reservoirs is the main limitation.

A very useful and new method for the decision makers of heavy oil reservoirs development.

The new approach of ranking the development priority of heavy oil reservoir based on the neural network and the genetic algorithm. The paper is aimed at the leaders who manage the development of heavy oil reservoirs.

The purpose of this paper is to investigate the tribological properties of MoS₂ nano‐sized hollow spheres in liquid paraffin (LP) and the corresponding action mechanism. Morever, its feasibity of industrial application as an oil additive in the industrial lubrication field is also explored.

The tribological properties of MoS₂ nano‐sized hollow spheres (NH‐MoS₂) modified by Cyanex 301(di‐(2,4,4‐trimethylpentyl) dithiophosphinic acid) with size of 200 ∼ 300 nm in LP are studied and compared with those of the commercial colloidal MoS₂ (CC‐MoS₂) on a four‐ball tester and an Optimol SRV Oscillating friction and wear tester in a ball‐on disk configuration. The worn surfaces of the lower flat disc are examined with a scanning electron microscopy and an X‐ray photoelectron spectroscopy, respectively.

Results show that NH‐MoS₂ is a better extreme‐pressure additive and anti‐wear (AW) and friction‐reducing additive in LP than CC‐MoS₂. Under the optimum concentration of 0.5 per cent for both NH‐MoS₂ and CC‐MoS₂ and the load of 400 N, the friction coefficient of NH‐MoS₂ + LP and CC‐MoS₂ + LP decreases about 43.8 and 6.3 per cent, and the wear volume loss decreases about 60.3 and 12.0 per cent compared with the pure LP. The boundary lubrication mechanism for NH‐MoS₂ + LP can be deduced as the effective chemical adsorption protective film formed by the long chain alkyls R and active elements (S and P) in the modification layer and tribochemical reaction film containing the tribochemical products of the additive. Moreover, sliding and rolling frictions co‐exist in NH‐MoS₂ + LP, doing contributions to the good tribological properties as well.

In this paper, the Cyanex 301‐modified MoS₂ nano‐sized hollow spheres with diameter of 200 ∼ 300 nm are firstly added into LP to investigate its tribological properties. The excellent AW and friction‐reducing properties indicate that this MoS₂ hollow spheres product is a good oil additive, and the fundamental data presented here will be useful for its further industrial application in the future.

This paper aims to study the synergistic effect of graphene sponge on the thermal properties and shape stability of composite phase change material (PCM).

Graphene oxide sponge is first prepared from an aqueous solution of graphene oxide by freeze-drying method. The oxidized graphene sponge is reduced by hydrazine hydrate. Finally, use vacuum impregnation method to introduce paraffin into graphene sponge to prepare composite PCM.

Graphene sponge is used to improve the shape stability of paraffin wax and improves the thermal conductivity and latent heat of the composite PCM. The thermal conductivity increases by 200 per cent and the composite PCM has excellent reliability in 100 melt-freezing cycles.

A simple way for fabricating composite PCM with high thermal conductivity and latent heat which has the potential to be used as thermal storage materials without container encapsulation has been developed by using graphene sponge and paraffin.

The materials and preparation methods with special structure and properties in this paper provide a new idea for the research of PCM, which can be widely used in the fields of energy conversion and storage.

This study aims to take advantage of exporters’ product codes and examine the effects of government subsidization on corporate product strategies by focusing on the dimension of product differentiation.

This study uses harmonized system (HS) product codes to construct a novel measure of product differentiation among a sample of Chinese exporters during 2000–2012. It uses propensity score matching to construct a comparable sample of control firms for exporters receiving government subsidies, and then a difference-in-differences (DID) analysis is conducted.

This study finds that product differentiation decreases immediately upon receiving a government subsidy. This finding suggests that in an emerging market, firms use their subsidy to imitate competitors rather than increase innovation. Further analyses show that this effect is concentrated among wholly foreign-owned enterprises and firms that focus on general trade rather than processing trade. In addition, the authors find some evidence that government subsidization leads to an increase in the number of product lines and decreases in domestic value added and export product quality.

This study constructs a novel measure of product differentiation for a large sample of Chinese exporters and provides insights that government subsidization can affect corporate product strategies.

The torque ripple and fault-tolerant capability are the two main problems for the switched reluctance motors (SRMs) in applications. The purpose of this paper, therefore, is to propose a novel 16/10 segmented SRM (SSRM) to reduce the torque ripple and improve the fault-tolerant capability in this work.

The stator of the proposed SSRM is composed of exciting and auxiliary stator poles, while the rotor consists of a series of discrete segments. The fault-tolerant and torque ripple characteristics of the proposed SSRM are studied by the finite element analysis (FEA) method. Meanwhile, the characteristics of the SSRM are compared with those of a conventional SRM with 8/6 stator/rotor poles. Finally, FEA and experimental results are provided to validate the static and dynamic characteristics of the proposed SSRM.

It is found that the proposed novel 16/10 SSRM for the application in the belt-driven starter generator (BSG) possesses these functions: less mutual inductance and high fault-tolerant capability. It is also found that the proposed SSRM provides lower torque ripple and higher output torque. Finally, the experimental results validate that the proposed SSRM runs with lower torque ripple, better output torque and fault-tolerant characteristics, making it an ideal candidate for the BSG and similar systems.

This paper presents the analysis of torque ripple and fault-tolerant capability for a 16/10 segmented switched reluctance motor in hybrid electric vehicles. Using FEA simulation and building a test bench to verify the proposed SSRM’s superiority in both torque ripple and fault-tolerant capability.