Materials and Processes
Parts, Manufacturing, Production Processes, Composites
Acoustic Emission Analysis Applet (AEAA) Software
Post-processing software has been developed at NASA that is tailored for novel analysis of composite pressure vessels acoustic emission (AE) data. The software can be used with data acquired from Digital Wave, Inc. and Mistras Group (Physical Acoustics, Inc.) hardware
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MicroStructPy - A Microstructure Mesh Generator for Heterogeneous Materials
This technology is software that generates meshes of materials composed of dissimilar constituents. Example materials include carbon fiber composites, metallic alloys, ceramics, and rocks. These meshes are unstructured, meaning the mesh elements are triangular in 2D and tetrahedral in 3D. The mesh generator has the following capabilities -2D and 3D domains -Mesh quality controls -Multiple constituents/phases -User-defined grain size distributions -Highly eccentric grain shapes -Preferred grain orientation -Preferred grain positions -Multiple instances of the same microstructure
Tool for Analysis of Surface Cracks (TASC)
Created using the commercial math analysis software MATLAB, TASC enables the easy computation of nonlinear J-integral solutions for surface-cracked plates in tension by accessing and interpolating between the 600 nonlinear surface crack solutions documented in NASA/TP-2011-217480. The only required inputs to the program are the surface crack dimensions, plate cross-section dimensions, and material properties. TASC provides a convenient and easy-to-use interface for the solution set that allows a novice user to obtain a fast and reliable fracture toughness solution.
Scalable Implementation of Finite Elements by NASA (ScIFEN)
The Scalable Implementation of Finite Elements by NASA (ScIFEN) package is a parallel finite element analysis code written in C++. It is designed to enable scalable solutions to computational mechanics problems by leveraging several open-source high performance computing libraries for numerical linear algebra routines and parallel input/output. ScIFEN supports several different finite element types, nonlinear material models, and boundary conditions and contains both implicit and explicit time integration procedures called ScIFEi and ScIFEx, respectively.
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Abaqus User Subroutine Verification (abaverify)
abaverify is a collection of Python scripts that is used for testing and verifying the behavior of user subroutines for the commercial finite element code Abaqus.
AladynPi Adaptive Neural Network Molecular Dynamics Simulation Code with Physically Informed Potential: Computational Materials Mini-Application
AladynPi is a basic molecular dynamics codes written in FORTRAN 2008, which is designed to demonstrate the use of artificial neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. An input for the ANN is a set of structure coefficients, characterizing the local atomic environment of each atom, for which the atomic energy is obtained in the ANN inference process. The ANN gives optimized parameters for a predefined empirical function, known as bond-order-potential (BOP). Thus parameterized BOP function is then used to calculate the energy of an atom. AladynPi code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multiprocessor computers or computers equipped with graphic processing units (GPUs).
Scalable Implementation of Finite Elements by NASA (ScIFEN) v.2
Scalable Implementation of Finite Elements by NASA (ScIFEN) is a three-dimensional solid mechanics code that provides a parallel implementation of the finite element method (FEM). Generally speaking, the FEM is a numerical technique for finding approximate solutions to partial differential equations (PDEs). In particular, ScIFEN uses the FEM to solve the PDEs that govern the motion and deformation of solid materials under the action of external loads. Thus, ScIFEN uses input information describing a solid's geometry, material properties, applied forces, etc. to compute and return the resulting mechanical response (e.g. displacements, stresses, etc.). ScIFEN is a parallel code written in C++ and designed to scale to large problems that can be run on supercomputers across many processors. This scalability and efficiency is enabled through the utilization of several open-source high performance computing libraries. The most heavily used libraries include: 1) Portable Extensible Toolkit for Scientific Computation (PETSc) for the parallel solution of linear/non-linear systems of equations, 2) Hierarchical Data Format (HDF5) for general parallel input/output, and 3) Mesh Orientated Database (MOAB) for storage and managing finite element mesh data in parallel. ScIFEN includes several standard linear and non-linear material models including linear elastic isotropic, linear hardening plasticity, and crystal plasticity models. It supports various applied loadings including prescribed displacements and forces, surface tractions and pressures, and body forces. ScIFEN supports both implicit time integration (ScIFEi driver) and explicit time integration (ScIFEx driver).
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Microscopy Segmentation Models
A new technique was developed for creating highly accurate microscopy image segmentation models with less training data. This technique uses transfer learning from classification models that were pretrained on the massive microscopy image database from the NASA ASG lab. Experiments show that transferring the features learned from pretraining on large microscopy datasets to segmentation tasks leads to accurate segmentation models with significantly less training data and the models generalize to unseen data better. This is significant because training data for segmentation tasks is expensive and in limited availability and this technique reduces the required training data. In addition, the labeled training data cannot account for all possible imaging and sample conditions that the model should be expected to perform accurately on, and experiments showed that this technique improves model accuracy on data outside the training distribution. Developing accurate segmentation models is significant because it is the first and hardest step in automatically quantifying microstructure features which is critical to linking the processing-structure-property relationships of materials. By quantitatively understanding these relationships, one may discover and develop new materials through traditional or data-driven methods.
Parallel Grand Canonical Monte Carlo Simulation Code - ParaGrandMC - V.2.0
This is version 2 of the previously reported Parallel Grand Canonical Monte Carlo simulation code - ParaGrandMC (LAR-18773). ParaGrandMC is a highly parallelized code in FORTRAN for simulating the thermodynamic evolution of metal alloy systems at atomic level, and predicting their thermodynamic state, phase diagram, chemical composition and mechanical properties. The approach taken is based on evolving an initially given atomic system (defined through a list of atomic coordinates of all participating atoms) using Monte Carlo and Molecular Dynamics algorithms. Atomic configurations, in terms of coordinates of all atoms, are stored periodically for a post-processing analysis, such as phase identification, lattice parameter estimates, free energy integration, etc. The numerical implementation is highly parallelized allowing simulations of multimilion atom systems. The current release has two new interatomic potentials added: Bond Order Potential (BOP), and Physically Informed Neural Network Potential (PINN). In addition, capabilities to compute diffusivity, thermal conductivity, and viscosity are also introduced.
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