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Article
Publication date: 9 February 2024

Chengpeng Zhang, Zhihua Yu, Jimin Shi, Yu Li, Wenqiang Xu, Zheyi Guo, Hongshi Zhang, Zhongyuan Zhu and Sheng Qiang

Hexahedral meshing is one of the most important steps in performing an accurate simulation using the finite element analysis (FEA). However, the current hexahedral meshing method…

130

Abstract

Purpose

Hexahedral meshing is one of the most important steps in performing an accurate simulation using the finite element analysis (FEA). However, the current hexahedral meshing method in the industry is a nonautomatic and inefficient method, i.e. manually decomposing the model into suitable blocks and obtaining the hexahedral mesh from these blocks by mapping or sweeping algorithms. The purpose of this paper is to propose an almost automatic decomposition algorithm based on the 3D frame field and model features to replace the traditional time-consuming and laborious manual decomposition method.

Design/methodology/approach

The proposed algorithm is based on the 3D frame field and features, where features are used to construct feature-cutting surfaces and the 3D frame field is used to construct singular-cutting surfaces. The feature-cutting surfaces constructed from concave features first reduce the complexity of the model and decompose it into some coarse blocks. Then, an improved 3D frame field algorithm is performed on these coarse blocks to extract the singular structure and construct singular-cutting surfaces to further decompose the coarse blocks. In most modeling examples, the proposed algorithm uses both types of cutting surfaces to decompose models fully automatically. In a few examples with special requirements for hexahedral meshes, the algorithm requires manual input of some user-defined cutting surfaces and constructs different singular-cutting surfaces to ensure the effectiveness of the decomposition.

Findings

Benefiting from the feature decomposition and the 3D frame field algorithm, the output blocks of the proposed algorithm have no inner singular structure and are suitable for the mapping or sweeping algorithm. The introduction of internal constraints makes 3D frame field generation more robust in this paper, and it can automatically correct some invalid 3–5 singular structures. In a few examples with special requirements, the proposed algorithm successfully generates valid blocks even though the singular structure of the model is modified by user-defined cutting surfaces.

Originality/value

The proposed algorithm takes the advantage of feature decomposition and the 3D frame field to generate suitable blocks for a mapping or sweeping algorithm, which saves a lot of simulation time and requires less experience. The user-defined cutting surfaces enable the creation of special hexahedral meshes, which was difficult with previous algorithms. An improved 3D frame field generation method is proposed to correct some invalid singular structures and improve the robustness of the previous methods.

Details

Engineering Computations, vol. 41 no. 1
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 5 November 2024

Weijun Liu, Mengzhen Cao and Wojciech J. Florkowski

This study aims to assess the effects of risk perception and management subject satisfaction on consumers' online meal food safety self-protection behavior during the COVID-19…

36

Abstract

Purpose

This study aims to assess the effects of risk perception and management subject satisfaction on consumers' online meal food safety self-protection behavior during the COVID-19 pandemic.

Design/methodology/approach

This study uses 742 questionnaires collected via a two-stage online survey conducted during the COVID-19 pandemic, between December 2021 and January 2022. The entropy method, descriptive statistics, ordered logit model, stepwise regression models, interaction terms and decentralization method were used in the quantitative analysis. Respondents’ written responses to self-protection behavior were categorized into five groups.

Findings

Less than half of consumers were aware that online food products carry the risk of SARS-COV-2 (44.48%). Between 30 and 40% of consumers took insufficient or no self-protection measures. Risk perception significantly and positively affected self-protection behavior during the COVID-19 pandemic. Consumers' management subject satisfaction has a positive moderating effect on risk perception, with the moderating effect of the satisfaction of online retailers being significant at the 5% level. Risk perception significantly and positively influences consumer self-protection behavior in provinces not affected by the pandemic.

Originality/value

The findings stress the benefits of synergistic interventions by consumers and management subject to food safety measures and the inclusion of tailored interventions during events threatening public health to effectively address food safety. The study offers valuable insights contributing to the improvement of public health outcomes, customer trust and service quality within the online food delivery industry.

Details

British Food Journal, vol. 127 no. 1
Type: Research Article
ISSN: 0007-070X

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