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Article
Publication date: 18 August 2023

Yi-Kang Liu, Xin-Yuan Liu, E. Deng, Yi-Qing Ni and Huan Yue

This study aims to propose a series of numerical and surrogate models to investigate the aerodynamic pressure inside cracks in high-speed railway tunnel linings and to predict the…

165

Abstract

Purpose

This study aims to propose a series of numerical and surrogate models to investigate the aerodynamic pressure inside cracks in high-speed railway tunnel linings and to predict the stress intensity factors (SIFs) at the crack tip.

Design/methodology/approach

A computational fluid dynamics (CFD) model is used to calculate the aerodynamic pressure exerted on two cracked surfaces. The simulation uses the viscous unsteady κ-ε turbulence model. Using this CFD model, the spatial and temporal distribution of aerodynamic pressure inside longitudinal, oblique and circumferential cracks are analyzed. The mechanism behind the pressure variation in tunnel lining cracks is revealed by the air density field. Furthermore, a response surface model (RSM) is proposed to predict the maximum SIF at the crack tip of circumferential cracks and analyze its influential parameters.

Findings

The initial compression wave amplifies and oscillates in cracks in tunnel linings, resulting from an increase in air density at the crack front. The maximum pressure in the circumferential crack is 2.27 and 1.76 times higher than that in the longitudinal and oblique cracks, respectively. The RSM accurately predicts the SIF at the crack tip of circumferential cracks. The SIF at the crack tip is most affected by variations in train velocities, followed by the depth and length of the cracks.

Originality/value

The mechanism behind the variation of aerodynamic pressure in tunnel lining cracks is revealed. In addition, a reliable surrogate model is proposed to predict the mechanical response of the crack tip under aerodynamic pressures.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 33 no. 12
Type: Research Article
ISSN: 0961-5539

Keywords

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Article
Publication date: 12 December 2024

Xiaoli Li and Qing Yi

We aim to determine the subsidy mechanism that can help participants of green supply chain financing (GSCF) maximize their benefits. Then, the optimal subsidy mechanism is…

14

Abstract

Purpose

We aim to determine the subsidy mechanism that can help participants of green supply chain financing (GSCF) maximize their benefits. Then, the optimal subsidy mechanism is designed to promote the development of GSCF.

Design/methodology/approach

To better understand the impact of different government subsidy measures on the optimal strategy for GSCF, we treat the motivation of the participants in the supply chain as a cost–benefit decision-making process. Then, a Stackelberg game model is developed that accounts for consumers' green preferences and government subsidies. In addition, the factors influencing supply chain members' earnings are analyzed via computational experiments.

Findings

(1) When consumers 2019 green sensitivity reaches a certain threshold relative to that of core enterprises (CEs), the optimal order quantity of these enterprises is greater when the government subsidizes small and medium-sized enterprises (SMEs). Conversely, the optimal order quantity is greater when CEs are subsidized. (2) When the government subsidizes CEs, financial institutions (FIs) and SMEs at the same time, these forms of subsidies have a cumulative effect on the supply chain, and the supply chain and all participants generate the highest earnings.

Originality/value

We analyze the benefits of each participant of GSCF under different government subsidies and then determine the optimal subsidy measures.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

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Article
Publication date: 28 May 2024

Guang-Zhi Zeng, Zheng-Wei Chen, Yi-Qing Ni and En-Ze Rui

Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of…

225

Abstract

Purpose

Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of fields in solving the Navier–Stokes equation and its variants. In view of the strengths of PINN, this study aims to investigate the impact of spatially embedded data distribution on the flow field results around the train in the crosswind environment reconstructed by PINN.

Design/methodology/approach

PINN can integrate data residuals with physical residuals into the loss function to train its parameters, allowing it to approximate the solution of the governing equations. In addition, with the aid of labelled training data, PINN can also incorporate the real site information of the flow field in model training. In light of this, the PINN model is adopted to reconstruct a two-dimensional time-averaged flow field around a train under crosswinds in the spatial domain with the aid of sparse flow field data, and the prediction results are compared with the reference results obtained from numerical modelling.

Findings

The prediction results from PINN results demonstrated a low discrepancy with those obtained from numerical simulations. The results of this study indicate that a threshold of the spatial embedded data density exists, in both the near wall and far wall areas on the train’s leeward side, as well as the near train surface area. In other words, a negative effect on the PINN reconstruction accuracy will emerge if the spatial embedded data density exceeds or slips below the threshold. Also, the optimum arrangement of the spatial embedded data in reconstructing the flow field of the train in crosswinds is obtained in this work.

Originality/value

In this work, a strategy of reconstructing the time-averaged flow field of the train under crosswind conditions is proposed based on the physics-informed data-driven method, which enhances the scope of neural network applications. In addition, for the flow field reconstruction, the effect of spatial embedded data arrangement in PINN is compared to improve its accuracy.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 34 no. 8
Type: Research Article
ISSN: 0961-5539

Keywords

Available. Open Access. Open Access
Article
Publication date: 22 November 2023

En-Ze Rui, Guang-Zhi Zeng, Yi-Qing Ni, Zheng-Wei Chen and Shuo Hao

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural…

1741

Abstract

Purpose

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

Design/methodology/approach

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Findings

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

Originality/value

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 34 no. 1
Type: Research Article
ISSN: 0961-5539

Keywords

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Article
Publication date: 13 June 2019

Yi Qing, Moyu Chen, Yu Sheng and Jikun Huang

The purpose of this paper is to investigate the impact of mechanization services on farm productivity in Northern China from an empirical perspective, with the aim to identify the…

935

Abstract

Purpose

The purpose of this paper is to investigate the impact of mechanization services on farm productivity in Northern China from an empirical perspective, with the aim to identify the underlying market and institutional barriers.

Design/methodology/approach

The authors apply the regression method with the control of village fixed effects to examining the relationship between capital–labor ratio, mechanization service ratio and farm productivity, using the panel data collected in 2013 and 2015 by CCAP.

Findings

Mechanization services improve farm productivity through substituting labor, but it may generate a less positive impact on farms who do not have self-owned capital equipment.

Originality/value

It is the first study to investigate how mechanization services affect farm productivity for grain producers in Northern China.

Details

China Agricultural Economic Review, vol. 11 no. 3
Type: Research Article
ISSN: 1756-137X

Keywords

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Book part
Publication date: 2 April 2021

Shuhan Chen and Peter Lunt

Abstract

Details

Chinese Social Media
Type: Book
ISBN: 978-1-83909-136-0

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Article
Publication date: 25 November 2020

Carl Déry

This paper aims to explore various tensions related to the diffusion and reception of the New Qing History (NQH) in China, and more specifically, it aims at underlying a recurrent…

163

Abstract

Purpose

This paper aims to explore various tensions related to the diffusion and reception of the New Qing History (NQH) in China, and more specifically, it aims at underlying a recurrent tension within the core of this debate, between a Global and a Nationalist historical narrative.

Design/methodology/approach

The author’s focus is to analyze various texts published in China between 2006 and 2018.

Findings

The author argues that the intensity of the current debate is partly related on the one hand, to the fact that NQH undermines various aspects of China’s Nationalist teleology and territorial claims and, on the other hand, to the basic difficulty of accepting the coexistence of various historical representations that are risking to weaken contemporary’s justifications of its rising schemes.

Originality/value

The text presents an original reading of some important issues raised by the NQH debate.

Details

Social Transformations in Chinese Societies, vol. 16 no. 2
Type: Research Article
ISSN: 1871-2673

Keywords

Available. Content available
Book part
Publication date: 2 April 2021

Shuhan Chen and Peter Lunt

Free Access. Free Access

Abstract

Details

Chinese Social Media
Type: Book
ISBN: 978-1-83909-136-0

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Article
Publication date: 12 September 2008

Deng Shu‐hao, Yi Dan‐qing, Gong Zhu‐qing and Su Yu‐chang

To obtain an optimized microarc oxidation (MAO) coating on magnesium alloy from an environmentally‐friendly electrolyte free of Cr6 +  and PO43− and to investigate the influence…

452

Abstract

Purpose

To obtain an optimized microarc oxidation (MAO) coating on magnesium alloy from an environmentally‐friendly electrolyte free of Cr6 +  and PO43− and to investigate the influence of oxidation potential on the morphology, composition, structure, and other properties such as micro‐hardness and corrosion resistance.

Design/methodology/approach

A constant potential regime was applied to produce the coatings and scanning electron microscopy, energy dispersive spectroscope, X‐ray diffraction, hardness testing and electrochemical methods were used to study coating properties.

Findings

The results clearly show that oxidation potential plays an important role in the formation of coating structure and properties. The MAO coating is smooth and white and consists of two layers. The external layer is loose and porous and enriched in Al and Si. Moreover, the content of Al and Si increase with operated potential. The inner layer is compact and the content of Al and Si are lower than are those of the external layer. The coating is composed of several phases: the main phase is MgAl2O4/MgO, and the minor phase is Al2O3/SiO2 when the potential is higher. The micro‐hardness of the coating obtained a maximum at a potential of 45 V, as does the corrosion resistance.

Originality/value

This paper provides information relating to MAO technology and the morphology, structure and properties of MAO coatings.

Details

Anti-Corrosion Methods and Materials, vol. 55 no. 5
Type: Research Article
ISSN: 0003-5599

Keywords

Available. Content available
Book part
Publication date: 2 April 2021

Shuhan Chen and Peter Lunt

Free Access. Free Access

Abstract

Details

Chinese Social Media
Type: Book
ISBN: 978-1-83909-136-0

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