Graph Convolutional Neural Network Modeling of Vacancy Formation for Materials Discovery in Solar Thermochemical Water Splitting.
Abstract not provided.
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.
Abstract not provided.
Abstract not provided.
Coherent second phases can have a profound effect on the properties and performance of martensitic materials, and we lack a comprehensive, mechanistic understanding of the underlying processes. This limits our ability to rationally design second phases to tailor the response of martensitic materials. To address this situation, this project sought to develop a mechanistic, predictive understanding of the thermal and mechanical response of martensitic materials with nanoscale coherent heterogeneities. A synergistic combination of atomistic simulations and experiments was used to relate the local properties of each phase (in particular, their free energy landscape) and the material nanostructure (volume fraction, shape and size of second phase precipitates, and defects that may disrupt coherency) to the overall materials response. The focus was on second phases expected to induce phenomena or properties not otherwise achievable: second order martensitic transformation in SMAs, ultra-low stiffness, increased control of transition temperatures, and fatigue resistance.
In this report we present our findings and outcomes of the NNRDS (analysis of Neural Networks as Random Dynamical Systems) project. The work is largely motivated by the analogy of a large class of neural networks (NNs) with a discretized ordinary differential equation (ODE) schemes. Namely, residual NNs, or ResNets, can be viewed as a discretization of neural ODEs (NODEs) where the NN depth plays the role of the time evolution. We employ several legacy tools from ODE theory, such as stiffness, nonlocality, autonomicity, to enable regularization of ResNets thus improving their generalization capabilities. Furthermore, armed with NN analysis tools borrowed from the ODE theory, we are able to efficiently augment NN predictions with uncertainty overcoming wellknown dimensionality challenges and adding a degree of trust towards NN predictions. Finally, we have developed a Python library QUiNN (Quantification of Uncertainties in Neural Networks) that incorporates improved-architecture ResNets, besides classical feed-forward NNs, and contains wrappers to PyTorch NN models enabling several major classes of uncertainty quantification methods for NNs. Besides synthetic problems, we demonstrate the methods on datasets from climate modeling and materials science.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Data presented in poster is sourced from the Electrochemical Catalyst Sensitivity Analysis Tool. This tool comprises a material balance model with cost estimation to estimate the levelized cost of product for CO production via CO2 electrolysis. A set of sensitivity analyses on key system and financial parameters is included with results so that users can test the impacts of these parameters on LCOP.
This session will cover: (1) How models simulate material recovery, recycling, product life cycles and waste management. (2) Using life cycle analysis (LCA) to measure environmental impacts across product life cycles and guide decision-making. (3) Optimizing resource use, reducing energy consumption and minimizing waste in recycling systems. (4) Evaluating EPR and other interventions through scenario analysis to make informed decisions.
Proof-of-concept computational study of shock in granular materials of various shapes using LAMMPS
Graphite is widely used as moderator in many fission reactors and other criticality systems. The understanding of its short- and long-term behavior in fission environments is crucial not only for the operation and optimization of existing reactors but also for the efficient design and safe deployment of future advanced reactors. It is thus of utmost importance to incorporate in nuclear data libraries accurate evaluations of the interaction of thermal neutrons with graphite used in applications. This is however a complex task as there are challenges in defining the detailed characterizations of the studied material, appropriate modeling, and optimal validation suite. This will only be achieved by the concerted effort of experts of all relevant areas.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Structural mechanics of deformation and fracture - responses of model viscoelastic materials to impact
Heat transfer considerations in studying mechanical behavior using simple model composite material
Full scale models, nonflammable materials and tests for manned space expandable structures including airlocks, transfer tunnels, station modules, lunar shelters and flexible windows
Some results are presented of a two year investigation on microwave emissions from snow. Included in the investigation were: theoretical studies; laboratory measurements of electrical properties of snow and similar materials; numerical modelling of hypothetical and real snowpacks; analysis of microwave data obtained on everflights; and quantative measurements of natural and artificial snowpacks made in the field. Results indicate that a fairly simple combination of microwave polarizations and frequencies can ultimately be used to monitor the water equivalent and free-water content as well as the distribution of snow. This understanding may also shed light on the physics of microwave emission from other wet, granular, layered media.
Explore the source record for details and available documents.