Numerical and experimental evaluations of a glass-epoxy composite material under high velocity oblique impacts
Not Available
SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Not Available
A composite HDPE/PCM filament for 3D printing thermal energy storage systems is naturally hydrophobic.
Abstract not provided
Abstract not provided
Abstract not provided
Explore the source record for details and available documents.
Abstract not provided.
Abstract not provided.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Prognosis techniques for prediction of remaining useful life (RUL) are of crucial importance to the management of complex systems for they can lead to appropriate maintenance interventions and improvements in reliability. While various data-driven methods have been introduced to predict the remaining useful life (RUL) of machinery systems or batteries, no research has been reported on the remaining useful strength (RUS) prediction of silicon carbide fiber reinforced silicon carbide matrix (SiCf-SiCm) materials with pivotal role in its potential usage as a structural material in nuclear reactors and turbine engines. Knowledge of its degradation process is of the utmost importance to the manufacturers. For this purpose, two approaches based on the machine-learning techniques of random-forest (RF) and convolutional neural network (CNN) are proposed to predict the RUS of SiCf-SiCm using only acoustic emission (AE) signals generated during the material’s stress applying process. Experimental results show that the CNN models achieved better predictive performance than the RF models but the latter with expert-engineered features achieves better prediction for AE signals in the early stage of degradation. Additionally, our results demonstrate that both models can correctly predict the SiCf-SiCm RUS as evaluated by our robust testing method from which the best average root mean square error (RMSE) and Pearson correlation coefficient of 3.55 ksi units and 0.85 were obtained.
The present invention is directed to the use of silicic acid to transform biological materials, including cellular architecture into inorganic materials to provide biocomposites (nanomaterials) with stabilized structure and function. In the present invention, there has been discovered a means to stabilize the structure and function of biological materials, including cells, biomolecules, peptides, proteins (especially including enzymes), lipids, lipid vesicles, polysaccharides, cytoskeletal filaments, tissue and organs with silicic acid such that these materials may be used as biocomposites. In many instances, these materials retain their original biological activity and may be used in harsh conditions which would otherwise destroy the integrity of the biological material. In certain instances, these biomaterials may be storage stable for long periods of time and reconstituted after storage to return the biological material back to its original form. In addition, by exposing an entire cell to form CSCs, the CSCs may function to provide a unique system to study enzymes or a cascade of enzymes which are otherwise unavailable.
A rapid, scalable methodology for graphene dispersion and concentration with a polymer-organic solvent medium, as can be utilized without centrifugation, to enhance graphene concentration.
A rapid, scalable methodology for graphene dispersion and concentration with a polymer-organic solvent medium, as can be utilized without centrifugation, to enhance graphene concentration.
A rapid, scalable methodology for graphene dispersion and concentration with a polymer-organic solvent medium, as can be utilized without centrifugation, to enhance graphene concentration.
Abstract not provided.
The present disclosure relates to a system for optimizing an additive manufacturing (AM) process. The system may use a printing component for using a material to form a component in a layer-by-layer process. An electronic controller may control movement of one of the printing component or a substrate on which the component is being formed, in a manner to optimize a toolpath for the printing component as each layer of the component is formed. Optimization operations are performed using an optimization subsystem which enables manufacturability constraints, as well as optimized toolpaths for each layer of the component, to be defined using contours of a level-set function. The level-set function may be used to define the optimized toolpaths within a fixed, predetermined grid, and the optimized toolpaths then used to generate suitable code for controlling movement of the printing component relative to the substrate to create the part or structure in a layer-by-layer process, using the optimized toolpaths.