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In a boundary layer ingestion (BLI) propulsion system, the fan operates continuously under distorted inflow conditions, leading to an increment of aerodynamic loss and in turn impacting the potential fuel burn reduction of the aircraft. Usually, in the preliminary design stage of a BLI propulsion system, it is essential to assess the impact of fuselage boundary layer fluids on fan aerodynamic performances under various flight conditions. However, the hub region flow loss is one of the major loss sources in a fan and would greatly influence the fan performances. Moreover, the inflow distortion also results in a complex and highly nonlinear mapping relation between loss and local physical parameters. It will diminish the prediction accuracy of the commonly used low-fidelity computational approaches which often incorporate traditional physics-based loss models, reducing the reliability of these approaches in evaluating fan performances. Meanwhile, the high-fidelity full-annulus unsteady Reynolds-averaged Navier–Stokes (URANS) approach, even though it can give rather accurate loss predictions, is extremely time-consuming. This study aims to develop a fast and accurate hub loss prediction method for a BLI fan under distorted inflow conditions.

This paper develops a data-driven hub loss prediction method for a BLI fan under distorted inflows. To improve the prediction accuracy and applicability, physical understandings of hub flow features are integrated into the modeling process. Then, the key physical parameters related to flow loss are screened by conducting a sensitivity analysis of influencing parameters. Next, a quasi-steady assumption of flow is made to generate a training sample database, reducing the computational time by acquiring one single sample from the highly time-consuming full-annulus URANS approach to a cost-efficient single-blade-passage approach. Finally, a radial basis function neural network is used to establish a surrogate model that correlates the input parameters and the output loss.

The data-driven hub loss model shows higher prediction accuracy than the traditional physics-based loss models. It can accurately capture the circumferentially and radially nonuniform variation trends of the losses and the associated absolute magnitudes in a BLI fan under different blade load, inlet distortion intensity and rotating speed conditions. Compared with the high-fidelity full-annulus URANS results, the averaged relative prediction errors of the data-driven hub loss model are kept less than 10%.

The originality of this paper lies in developing a new method for predicting flow loss in a BLI fan rotor blade hub region. This method offers higher prediction accuracy than the traditional loss models and lower computational time cost than the full-annulus URANS approach, which could realize fast evaluations of fan aerodynamic performances and provide technical support for designing high-performance BLI fans.

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.

This paper aims to propose an effective method to verify poles polarities of switched reluctance motors (SRMs). Different from the ways of detection poles polarities by permanent magnet in SRMs, the difference of self-inductance between different winding connections is used to verify the pole polarity.

First, the winding connections with the forward and reverse series are proposed. The magnetic circuit models are established to analyze the flux linkage of different winding connections. Then, according to the difference of inductance characteristics, including the self-inductance and the mutual inductance affected by the adjacent poles, it is theoretically feasible to verify the polarity of each pole. Finally, the proposed method is verified by the simulation and experiment on a six-phase SRM.

First, compared to the reverse series, the forward series can produce larger self-inductance when one phase is excited at the same current excitation, which can be used to verify the poles polarities of one phase with different winding connection. Second, the mutual inductance can be used to distinguish the winding connections. Third, the difference of the maximum self-inductance of the winding, which is composed of two adjacent windings, can be used to verify the polarities of the adjacent poles.

This paper proposes an effective method to verify poles polarities of SRMs.

This paper's aim is to investigate how Chinese consumers' purchase intention of private label drugs is associated with price advantage, appearance, store trust, manufacturer trust, and drug quality.

A survey study was conducted in two large cities in China. Subjects were asked to decide whether they would purchase a private label drug over a national brand drug and to evaluate a set of related factors. Data were collected from 230 consumers.

A total of 45 percent of the variance in private label drug purchase intention is explained by the five predictors. Price advantage, store trust, manufacturer trust, and drug quality are all significantly related to purchase intention, whereas appearance is not. None of the control variables (age, gender, health literacy, and income) has a significant relationship with purchase intention. Product quality and service quality are significant predictors of store trust, accounting for 44 percent of its variance.

The private label drug market has great potential in China, yet little is known about what factors influence Chinese consumers' intention to purchase private label drugs.

This paper is one of the first attempts to achieve an in‐depth understanding in this area. The findings of this research will benefit drug retailers and manufacturers who are interested in the Chinese market.