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The quality of the unstructured mesh has a considerable impact on the stability and accuracy of aerodynamic simulation in computational fluid dynamics (CFD). Typically, engineers spend a significant portion of their time on mesh quality evaluation to ensure a valid, high-quality mesh. The extensive manual interaction and a priori knowledge required to undertake an accurate and timely evaluation process have become a bottleneck in the idealized efficient CFD workflow. This paper aims to introduce a neural network-based quality evaluation approach for unstructured meshes to enable higher efficiency and the level of automation.

The paper investigates the capability of deep neural networks for the quality evaluation of unstructured meshes. For training the network, we build a training dataset for mesh quality learning algorithms. The dataset contains a rich variety of unstructured aircraft meshes with different mesh sizes, densities, cell distribution, growth ratios and cell numbers to ensure its diversity and availability. We also design a neural network, AircraftNet, to learn the effect of mesh quality on the convergent properties of the numerical solutions. The proposed network directly manipulates raw point data in mesh source files rather than passing it to an intermediate data representation. During training, AircraftNet extracts non-linear quality features from high-dimensional data spaces and then automatically predicts the overall quality of the input unstructured mesh.

The paper provides a series of experimental results on GPUs. It shows that AircraftNet is able to effectively analyze the quality-related features like mesh density and distribution from the extracted features and achieve high prediction accuracy on the proposed dataset with even a small number of training runs.

Because of the limited training dataset, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further.

The paper publishes a benchmarking dataset for mesh quality learning algorithms and designs a novel neural network approach for unstructured mesh quality evaluation.

The paper aims to propose a generation approach for unstructured surface mesh to speed up mesh generation.

The paper proposes a lightweight interactive generation approach for unstructured surface mesh and presents several key technologies to support this approach.

The experimental results show that the proposed approach is feasible for unstructured meshes and it can accelerate the mesh generation process.

More geometric defects should be covered, and more convenient and efficient interactive means need to be provided.

The proposed approach and key technologies are implemented in NNW-GridStar.UG, which is the unstructured version of the mesh generation software of National Numerical Windtunnel (NNW).

This paper proposes a lightweight interactive approach for unstructured surface mesh generation, which can speed up mesh generation.

Megaproject knowledge innovation (MKI) is perceived as a critical strategy for engineering value co-creation and industrial chain upgrading. Ascertaining the impact mechanism of MKI is a crucial initial step towards improving management practices. Within the framework of complex systems in megaprojects, factors exhibit intricate interdependencies. However, the current domain of knowledge has either overlooked or oversimplified this relationship and therefore cannot propose pragmatic and efficacious strategies for enhancing MKI. To close this gap, this study develops a Bayesian network (BN) model aiming to investigate the interdependencies among MKI-related factors and their impact on MKI.

First, this study implements literature review, expert interview and field investigation to identify the influencing factor nodes for the network model development. Second, a Bayesian network was constructed by integrating the expert knowledge with Dempster-Shafer theory. Next, a MKI measurement model was established using 253 training samples. Finally, the factor significance and optimal MKI improvement strategies are identified from the sensitivity analysis and probabilistic reasoning within the BNs.

The results indicate that (1) the BN model exhibits significant reliability and holds promotion and application value in formulating MKI management strategies; (2) knowledge sharing, shared vision and leadership are the key influencing factors of MKI; and (3) simultaneously improving institutional pressure, leadership and knowledge sharing is the most optimal strategy to enhance MKI.

This study innovatively introduced the BN method into the domain of MKI management, providing an appropriate approach for modelling complex relationships among factors and investigate nonlinear influences. The developed model raises megaproject stakeholders’ awareness about factors influencing MKI and presents quantified strategies that increase the likelihood of maximising MKI levels. Its ease of generalisability positions it as a promising decision support tool, facilitating the implementation of sustainable MKI practices.

This study aimed to explore the relationship between supply chain resilience (SCR) and organizational performance (OP), with innovation (INN) serving as a mediator and information sharing (IS) acting as a moderator.

The study comprehensively examined the connections between SCR, OP, INN and IS. An exploratory approach and quantitative methods were employed. The data were collected from small and medium-sized manufacturing enterprises of three cities Xian, Hainan and Guangzhou of China via online questionnaire surveys conducted through Emails and WeChat. SmartPLS-4 was used for data analysis.

The findings indicated that SCR has a positive effect on sustainability efforts. Additionally, INN and effective IS both mediated and moderated this relationship, playing crucial roles in improving sustainability within the supply chain.

The study offered practical insights for businesses to enhance their sustainability efforts. Managers can use these findings to develop strategies that improve SCR, foster INN and encourage effective IS, ultimately resulting in a more sustainable supply chain.

This study enriched the existing knowledge base by investigating the intricate relationships among SCR, OP, INN and IS, all within the context of achieving sustainability. By exploring these elements holistically, the research introduced originality and highlighted effective strategies for sustainable supply chain management.

This study aimed to investigate the relationship between supply chain resilience and organizational performance with innovation as a mediator and information sharing as a moderator.

The study thoroughly explored how supply chain resilience, organizational performance, innovation and information sharing are connected. It used an exploratory approach and quantitative methods. Data were collected from large manufacturing firms through online questionnaire surveys using Google Forms, emails and WhatsApp.

The findings demonstrated that supply chain resilience positively impacts sustainability efforts. Furthermore, leveraging innovation and effective information sharing mediated and moderated the relationship, playing pivotal roles in enhancing sustainability within the supply chain.

The study provided actionable insights for businesses to strengthen their sustainability efforts. Managers could utilize these findings to implement strategies that enhance supply chain resilience, drive innovation and promote effective information sharing, ultimately leading to a more sustainable supply chain.

This study contributed to the existing body of knowledge by examining the complex relationships between supply chain resilience, organizational performance, innovation and information sharing in the context of achieving sustainability. The exploration of these components in a holistic manner added originality to the research and shed light on effective strategies for sustainable supply chain management.