Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “materials predictions”

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.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

A numerical procedure for predicting creep and delayed failures in laminated composites

A numerical procedure is described for predicting the viscoelastic response of general laminates. A nonlinear compliance model is used to predict the creep response of the individual laminae. A biaxial delayed failure model predicts ply failure. The numerical procedure, based on lamination theory, increases by increments through time to predict creep compliance and delayed failures in laminates. Numerical stability problems and experimental verification are discussed. Although the program has been quite successful in predicting creep of general laminates, the assumptions associated with lamination theory have resulted in erroneous bounds on the predicted material response. Delayed failure predictions have been conservative. Several improvements are suggested to increase the accuracy of the procedure.

Dillard, D. A.↗
NASA NTRS1993

Lifetime prediction of materials exposed to the natural space environment

The goal of this study is to model the lifetime of different types of seal materials based on results obtained from accelerated experiments. A semi-mechanistic approach was taken. Thermal aging data were taken from the literature whereas experiments were conducted at Auburn under this contract for selected environments. The seal materials of interest are Silicone 383, Silicone 650, Viton 835, and Viton 747. The relevant conditions include thermal, oxygen, inert gas, vacuum, and gamma radiation. Compression set data available from NASA were used to examine the thermal effect. Experiments were conducted at Auburn University and at NASA to isolate the role of thermal, oxygen, inert gas, vacuum, gamma irradiation, and proton irradiation. A simple discrete stress relaxation method was developed to determine the relaxation response of the elastomers. Dynamic mechanical thermal analysis was also used to characterize the mechanical response of the specimens. These provide a more meaningful correlation between mechanisms and degradation.

Zee, Ralph↗
NASA NTRS1994

Ceramic material life prediction: A program to translate ANSYS results to CARES/LIFE reliability analysis

This manual describes the use of the ANSCARES program to prepare a neutral file of FEM stress results taken from ANSYS Release 5.0, in the format needed by CARES/LIFE ceramics reliability program. It is intended for use by experienced users of ANSYS and CARES. Knowledge of compiling and linking FORTRAN programs is also required. Maximum use is made of existing routines (from other CARES interface programs and ANSYS routines) to extract the finite element results and prepare the neutral file for input to the reliability analysis. FORTRAN and machine language routines as described are used to read the ANSYS results file. Sub-element stresses are computed and written to a neutral file using FORTRAN subroutines which are nearly identical to those used in the NASCARES (MSC/NASTRAN to CARES) interface.

Vonhermann, Pieter↗
DOE OSTI2022

Machine learning molecular dynamics simulations toward exploration of high-temperature properties of nuclear fuel materials: case study of thorium dioxide

Predicting materials properties of nuclear fuel compounds is a challenging task in materials science. Their thermodynamical behaviors around and above the operational temperature are essential for the design of nuclear reactors. However, they are not easy to measure, because the target temperature range is too high to perform various standard experiments safely and accurately. Moreover, theoretical methods such as first-principles calculations also suffer from the computational limitations in calculating thermodynamical properties due to their high calculation-costs and complicated electronic structures stemming from f-orbital occupations of valence electrons in actinide elements. Here, we demonstrate, for the first time, machine-learning molecular-dynamics to theoretically explore high-temperature thermodynamical properties of a nuclear fuel material, thorium dioxide. The target compound satisfies first-principles calculation accuracy because f-electron occupation coincidentally diminishes and the scheme meets sampling sufficiency because it works at the computational cost of classical molecular-dynamics levels. We prepare a set of training data using first-principles molecular dynamics with small number of atoms, which cannot directly evaluate thermodynamical properties but captures essential atomistic dynamics at the high temperature range. Then, we construct a machine-learning molecular-dynamics potential and carry out large-scale molecular-dynamics calculations. Consequently, we successfully access two kinds of thermodynamic phase transitions, namely the melting and the anomalous λ transition induced by large diffusions of oxygen atoms. Furthermore, we quantitatively reproduce various experimental data in the best agreement manner by selecting a density functional scheme known as SCAN. Our results suggest that the present scale-up simulation-scheme using machine-learning techniques opens up a new pathway on theoretical studies of not only nuclear fuel compounds, but also a variety of similar materials that contain both heavy and light elements, like thorium dioxide.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Diagnostic and predictive capabilities of the TCR digital platform

The Transformational Challenge Reactor program is leveraging additive manufacturing technologies to fabricate the nuclear components required to assemble a microreactor core. Compared with traditional manufacturing processes, additive manufacturing allows for direct observation of the interior of the component during manufacturing. This unique capability promises significant possibilities for creating a new paradigm for nuclear component qualification by leveraging in-situ process data. This report describes FY21 efforts to predict material tensile properties based on data collected during the laser powder bed fusion printing process. The primary focus of this report is the test campaign designed to generate the large quantities of training data required to implement artificial intelligence algorithms that can predict these material properties. Preliminary prediction results and a demonstration of the overall data collection, analysis, and visualization pipeline are also provided.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗