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Porous non-woven fibrous media are widely used in various industrial applications such as filtration, insulation, and medical textiles due to their unique structural and functional properties. However, predicting the mechanical behavior of these materials is challenging due to their complex microstructure and anisotropic nature. In this study, a computational model is developed to simulate the mechanical response of porous non-woven fibrous media under external loading. The model is based on the finite element method and takes into account the geometric and material properties of the fibers and the void spaces between them. The effects of various factors such as fiber size, porosity, and fibers’ intersection ratio on the mechanical behavior of the material are investigated. The results reveal that the material’s porosity and fibers’ intersection ratio are the most significant factors influencing its mechanical properties. Additionally, the increase in fiber diameter has a relatively minor effect on the material’s elastic properties. However, such changes in elastic properties are primarily attributed to the increase in randomness within the fibrous network, which is directly related to the fiber diameter for the investigated structure. The proposed computational model predicts the mechanical properties of porous non-woven fibrous media and can provide invaluable insights into the design and optimization of porous non-woven fibrous media for various scientific and engineering applications. 

This perspective article presents the vision of combining findable, accessible, interoperable, and reusable (FAIR) Digital Objects with the National Science Data Fabric (NSDF) to enhance data accessibility, scientific discovery, and education. Integrating FAIR Digital Objects into the NSDF overcomes data access barriers and facilitates the extraction of machine-actionable metadata in alignment with FAIR principles. The article discusses examples of climate simulations and materials science workflows and establishes the groundwork for a dataflow design that prioritizes inclusivity, web-centricity, and a network-first approach to democratize data access and create opportunities for research and collaboration in the scientific community. 

In the era of big data, materials science workflows need to handle large-scale data distribution, storage, and computation. Any of these areas can become a performance bottleneck. We present a framework for analyzing internal material structures (e.g., cracks) to mitigate these bottlenecks. We demonstrate the effectiveness of our framework for a workflow performing synchrotron X-ray computed tomography reconstruction and segmentation of a silica-based structure. Our framework provides a cloud-based, cutting-edge solution to challenges such as growing intermediate and output data and heavy resource demands during image reconstruction and segmentation. Specifically, our framework efficiently manages data storage, scaling up compute resources on the cloud. The multi-layer software structure of our framework includes three layers. A top layer uses Jupyter notebooks and serves as the user interface. A middle layer uses Ansible for resource deployment and managing the execution environment. A low layer is dedicated to resource management and provides resource management and job scheduling on heterogeneous nodes (i.e., GPU and CPU). At the core of this layer, Kubernetes supports resource management, and Dask enables large-scale job scheduling for heterogeneous resources. The broader impact of our work is four-fold: through our framework, we hide the complexity of the cloud’s software stack to the user who otherwise is required to have expertise in cloud technologies; we manage job scheduling efficiently and in a scalable manner; we enable resource elasticity and workflow orchestration at a large scale; and we facilitate moving the study of nonporous structures, which has wide applications in engineering and scientific fields, to the cloud. While we demonstrate the capability of our framework for a specific materials science application, it can be adapted for other applications and domains because of its modular, multi-layer architecture.