426 research outputs found
Finite Volume Graph Network(FVGN): Predicting unsteady incompressible fluid dynamics with finite volume informed neural network
In recent years, the development of deep learning is noticeably influencing
the progress of computational fluid dynamics. Numerous researchers have
undertaken flow field predictions on a variety of grids, such as MAC grids,
structured grids, unstructured meshes, and pixel-based grids which have been
many works focused on. However, predicting unsteady flow fields on unstructured
meshes remains challenging. When employing graph neural networks (GNNs) for
these predictions, the message-passing mechanism can become inefficient,
especially with denser unstructured meshes. Furthermore, unsteady flow field
predictions often rely on autoregressive neural networks, which are susceptible
to error accumulation during extended predictions. In this study, we integrate
the traditional finite volume method to devise a spatial integration strategy
that enables the formulation of a physically constrained loss function. This
aims to counter the error accumulation that emerged in autoregressive neural
networks during long-term predictions. Concurrently, we merge vertex-center and
cell-center grids from the finite volume method, introducing a dual
message-passing mechanism within a single GNN layer to enhance the
message-passing efficiency. We benchmark our approach against MeshGraphnets for
unsteady flow field predictions on unstructured meshes. Our findings indicate
that the methodologies combined in this study significantly enhance the
precision of flow field predictions while substantially minimizing the training
time cost. We offer a comparative analysis of flow field predictions, focusing
on cylindrical, airfoil, and square column obstacles in two-dimensional
incompressible fluid dynamics scenarios. This analysis encompasses lift
coefficient, drag coefficient, and pressure coefficient distribution comparison
on the boundary layers
Systematic Analysis of Impact of Sampling Regions and Storage Methods on Fecal Gut Microbiome and Metabolome Profiles.
The contribution of human gastrointestinal (GI) microbiota and metabolites to host health has recently become much clearer. However, many confounding factors can influence the accuracy of gut microbiome and metabolome studies, resulting in inconsistencies in published results. In this study, we systematically investigated the effects of fecal sampling regions and storage and retrieval conditions on gut microbiome and metabolite profiles from three healthy children. Our analysis indicated that compared to homogenized and snap-frozen samples (standard control [SC]), different sampling regions did not affect microbial community alpha diversity, while a total of 22 of 176 identified metabolites varied significantly across different sampling regions. In contrast, storage conditions significantly influenced the microbiome and metabolome. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles. Sample storage in RNALater showed a significant level of variation in both microbiome and metabolome profiles, independent of the storage or retrieval conditions. The effect of RNALater on the metabolome was stronger than the effect on the microbiome, and individual variability between study participants outweighed the effect of RNALater on the microbiome. We conclude that homogenizing stool samples was critical for metabolomic analysis but not necessary for microbiome analysis. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles and is recommended for short-term fecal sample storage. In addition, our study indicates that the use of RNALater as a storage medium of stool samples for microbial and metabolomic analyses is not recommended.IMPORTANCE The gastrointestinal microbiome and metabolome can provide a new angle to understand the development of health and disease. Stool samples are most frequently used for large-scale cohort studies. Standardized procedures for stool sample handling and storage can be a determining factor for performing microbiome or metabolome studies. In this study, we focused on the effects of stool sampling regions and stool sample storage conditions on variations in the gut microbiome composition and metabolome profile
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