Accurate Modeling of Material Nonlinearities in a Wind Turbine Spar Cap.
Abstract not provided.
Ennis, Brandon↗
SEARCH · Engineering Papers
Results for “materials modelling”
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.
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Physics-informed Machine Learning for Development of Interpretable, Improved Material Damage Models.
Abstract not provided.
Birky, Donovan↗
Anisotropic Extension of a Model for Materials with Ferroelectric to Antiferroelectric Phase Transformation.
Abstract not provided.
Carleton, James↗
Microstructure-Sensitive UQ for Materials Constitutive Models in Crystal Plasticity Finite Element Method.
Abstract not provided.
Tran, Anh↗
Modular and Scalable Solutions of Machine Learned Models (for Material Science and Beyond)
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Wood, Mitchell Anthony↗
Machine learning driven discovery and modeling of materials for hydrogen storage and generation
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Witman, Matthew David↗
MatFold: systematic insights into materials discovery models' performance through standardized cross-validation protocols
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Witman, Matthew David↗
MatFold: Cross-validation Protocols to Systematically Evaluate Generalization Errors in Materials Discovery Models
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Schindler, Peter [Northeastern University]↗
Multiscale Modeling of Material Strength for the Shock-to-Detonation Behavior in Heterogeneous PETN
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Stewart, James Allen [Sandia National Laboratories↗
Structure-Property Defect Models for Materials Discovery in High-Temperature Energy Applications
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Witman, Matthew David [Sandia National Laboratorie↗
Structure-property defect models for materials discovery in high-temperature energy applications
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Witman, Matthew David [Sandia National Laboratorie↗
Generation of Models of Materials with Complex Structure
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Kraus, Julie Anne [Sandia National Laboratories (S↗
Atomistic Modeling of materials for fusion energy using machine learned interatomic potentials
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Cusentino, Mary Alice [Sandia National Laboratorie↗
An analytical model for material strength accounting for microstructural variability.
Abstract not provided.
Phung, Brian↗
Nonlinear Regression for Accurate and Efficient Surrogate Models of Materials.
Abstract not provided.
Logarzo, Hernan↗
Reference constitutive model for Alloy 617 and 316H stainless steel for use with the ASME Division 5 design by inelastic analysis rules
This report describes the development, calibration, and validation of an inelastic constitutive model describing the monotonic, cyclic, and creep behavior of Alloy 617 from room temperature to 983 °C . The model is intended for incorporation into a new Nonmandatory Appendix to Section III, Division 5, Subsection HB, Subpart B of the ASME Boiler & Pressure Vessel Code providing guidance on developing models for use with the ASME design by inelastic analysis provisions as well as a listing of acceptable material models for each Class A material that designers can use without further validation. The report described the development of the A617 model as well as providing an updated draft appendix, ready for ASME ballot, incorporating the A617 and and the previously-developed 316H model. The development of this appendix and the related material models will promote the widespread application of the generally more efficient inelastic analysis rules rules.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗