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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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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

LOGOS

LOGOS is a software package which contains a set of discrete optimization models that can be employed for capital budgeting optimization problems. More specifically, provided a set of items (characterized by cost and reward values) and constraints, these models select the best combination of items which maximizes overall reward and satisfies the provided constraints. The developed models are based on different versions of the knapsack optimization algorithms. Two main classes of optimization models have been initially developed: deterministic and stochastic. Stochastic optimization models evolve deterministic models by explicitly considering data uncertainties (associated to constraints or item cost and reward). These models can be employed as stand-alone models or interfaced with the INL developed RAVEN code to propagate data uncertainties and analyze the generated data (i.e., sensitivity analysis).

Wang, Congjian↗
DOE OSTI2021

Automated CT registration, segmentation, and quantification (AutoCT) v1.0

Processing and analyzing brain imaging is crucial in both scientific development and clinical field. In this software package, we build a pipeline that integrates automatic registration, segmentation, and quantitative analysis for subjects' CT scans. Leveraging diffeomorphic transformations, we enable optimized forward and inverse mappings between an image and the reference. Furthermore, we extract localized features from deformation field based on an online template process, which advances statistical learning downstream. The created templates, atlas as well as our methods provide the brain imaging community tools for AI implementations.

Essiari, Abdelilah↗
DOE OSTI2021

battery-data-toolkit

This software package contains utilities for ingesting battery cycling data from various equipment and converting it to a common file format including metadata. Further development of the package will include utilities for further processing the data for a variety of machine learning tasks.

PAULSON, NOAHHARRIS↗
DOE OSTI2020

XRF-MAPS

x-ray fluorescence (XRF) imaging typically involves the creation and analysis of 3D data sets, where at each scan position a full energy dispersive x-ray spectrum is recorded. This allows one to later process the data in a variety of different approaches, e.g., by spectral region of interest (ROI) summation with or without background subtraction, principal component analysis, or fitting. XRF-Maps is a C++ open source software package that implements these functions to provide a tool set for the analysis of XRF data sets.

GLOWACKI, ARTHURT↗
DOE OSTI2021

Radiological Anomaly Detection And Identification (RADAI) v1.0

The Radiological Anomaly Detection and Identification (RADAI) software package is a python library for implementing, training, and storing algorithms that detect and identify anomalies in gamma-ray spectra. The library defines a general framework for implementing detection (binary) and identification (classification) algorithms, objects to encapsulate the results of analyses, a variety of temporal filtering tools that can be used in constructing algorithms, and conceptual design that allows easy reading and writing of algorithms (and their time dependent state). In addition to this framework, the library includes implementations of a variety of algorithms from the scientific literature including: gross-counts k-sigma, SPRT, N-SCRAD, Region of Interest, and Censored Energy Window. The implementation of these algorithms within the RADAI package was done to facilitate user-initiated training and configuration to by applied to different gamma-ray detector types. Finally, benchmarked and synthetic datasets will be made available for standardized algorithm characterization with corresponding utilities in the RADAI package for data access and processing.

Joshi, Tenzing↗
DOE OSTI2021

Automated CT registration, segmentation, and quantification (AutoCT) v1.1

Processing and analyzing brain imaging is crucial in both scientific development and clinical field. In this software package, we build a pipeline that integrates automatic registration, segmentation, and quantitative analysis for subjects' CT scans. Leveraging diffeomorphic transofrmations, we enable optimized forward and inverse mappings between an image and the reference. Furthermore, we extract localized features from deformation field based on an online template process, which advances statistical learning downstream. The created templates, atlas as well as our methods provide the brain imaging community tools for AI implementations

Bai, Zhe↗
DOE OSTI2021

Bulb-style Kaplan Turbine - Run-of-the-river (ror) Hydro Governor And Turbine Model

This software package includes hydro governor and turbine models developed in Simulink (Mathworks Inc.) and RSCAD (RTDS Technologies) for low-head bulb-style Kaplan turbine hydropower plants. The model developed in Simulink has been designed and tuned to match the governor-turbine response for a real-world hydropower unit owned by Idaho Falls Power (IFP). The Simulink and RSCAD models enable real-time testing in a hardware-in-the-loop (HIL) implementation using OPAL-RT and RTDS digital real-time simulators, respectively. An automatic initialization for dynamic simulation has also been integrated to each model in both Simulink and RSCAD. The RSCAD initialization involves a novel python-based interfacing for automatic modification of the initial conditions in the model. The parameters of these models can be tuned to match other hydropower plants in this class.

Alam, SMShafiul↗
DOE OSTI2020

LIBlib v0.1.0

LIBlib is a Python 2 / Python 3 software package (library), built on top of the PyGDH package, for relatively simple and highly flexible implementation of macrohomogenous lithium ion battery models.

Higa, Kenneth↗
DOE OSTI2020

materialsCloudCompute

materialsCloudCompute (mCC) is a software package for running easily parallelizable materials science theoretical computations on Amazon Web Services (AWS) infrastructure. mCC provides tools to partition out computational problem into smaller subproblems which can be run in parallel, and ensure that each of those subproblems completes successfully. It utilizes the following AWS services: EC2, S3, and RDS. It operates by launch a managing EC2 instance, which can launch worker instances that perform the calculations. The Managing instance tracks the subproblems and ensures that the Worker instances continue to operation, killing and relaunching them if they fail.

Forbes, David↗
DOE OSTI2021

Zero-Order Reaction Kinetics v3.0

Zero-RK is a software package that simulates chemically reacting systems using sparse, preconditioned, adaptive matrix methods to achieve orders-of-magnitude reduction in simulation time while maintaining accurate results.

Mcnenly, MatthewJ.↗
DOE OSTI2021

Highly Extensible X-ray Diffraction Toolkit

This code has been previous released as open source under LLNL-CODE-529294. The HEXRD program comprises a library of X-ray diffraction analysis tools and a stand-alone GUI application. While there are many software packages that provide similar capabilities, HEXRD was designed to provide an extremely flexible and extensible basis for describing a broad array of X-ray diffraction instruments; this includes both mono- and poly-chromatic modalities as well as poly- and single-crystal samples. The high-level objects include Material: an abstraction for defining crystalline materials, including unit cell parameters and anisotropic elastic moduli Imageseries: an interface for series of diffraction images containing one or more frames, including provisions for metadata. Instrument: an abstraction of an X-ray diffraction instrument including support for the definition of multiple detector elements as well as powder, Laue, and mono-chromatic single/multi-grain diffraction analysis modalities (including attendant calibration routines). The hexrdgui package is a standalone PySide2/Qt5 GUI built on top of hexrd. The 0.8.1 release includes various bug fixes as well as some new functionality in the GUI, including: o Enhancements to the import tools for LLNL diffraction instruemtns (PXRDIP and TARDIS) o The ability to save 1-d LeBail fits of integrated diffraction spectra.

Barton, NathanR.↗
DOE OSTI2020

Zero-Order Reaction Kinetics

Zero-RK is a software package that simulates chemically reacting systems using sparse, preconditioned, adaptive matrix methods to achieve orders-of-magnitude reduction in simulation time while maintaining accurate results

Lapointe, Simon↗
DOE OSTI2022

ENSIGN

ENSIGN is a data analytics software package offering a modern unsupervised machine learning solution for scalable discovery in Big Data. The analytics in ENSIGN are based on an advanced mathematical tool called tensor decomposition and they are optimized to run efficiently on a range of computing platforms (from small multicore Desktop platforms to large Supercomputing clusters and novel high-end memory-driven computing platforms such as HPE Superdome Flex). ENSIGN enables the user to extract deep insights from the entirety of massive-scale (100s of Gigabytes or Terabytes scale) multidimensional data. ENSIGN uncovers latent patterns in data without the user having to specify or describe what the patterns are; the user, in the first place, may not even know such patterns existed and that they have to look for such patterns. The insights gained from ENSIGN could be trailheads that can be used as starting points for deeper forensic investigation.

Baskaran, Muthu↗
DOE OSTI2022

NISQ Benchmarking

Test suite of quantum algorithms for Noisy Intermediate Scale Quantum (NISQ) computers. The test suite includes benchmark-style code for quantum volume circuits (QV), fairness sampling circuits, quantum telecloning circuits, and other NISQ benchmark style algorithms on small problems (i.e., up to 100 qubits), such as Variational Quantum Eigensolver (VQE), Hamiltonian Simulation, and Grover unstructured search example circuits. These benchmark-style applications are implemented in quantum software packages, mostly IBM's QISKIT, but may include vendor-specific frameworks, such as PyQuil (for Rigetti) or Q\# for Microsoft, or CirQ (for Google) as the test suite grows with the vendor sample. The test suite also includes numerical simulation code for Quantum Alternating Operator Ansatz (QAOA) algorithms, VQE, Hamiltonian Simulation and search examples. Numerical simulation code simulates quantum computers on classical computers, which is only possible for small problem instances; the implementation framework of choice is typically within Python, using the numpy/scipy libraries as well as extensions to the Julia language.

Pelofske, Elijah↗
DOE OSTI2022

MPC4CLR (Model-Predictive-Control-for-Critical-Load-Restoration-in-Power-Distribution-Systems) [SWR-22-24]

Model predictive control (MPC) is a system or process control technique for making decisions under uncertainty via rolling look-ahead optimizations at each control step where only the current step decisions are applied, and the rest are discarded. In this work, we developed an MPC for a critical load restoration (CLR) in power distribution systems to recover system service (electricity delivery) following an extreme event-triggered substation outage. The method considers the problem of controlling distributed energy resources (DERs) of the distribution system with the objective of achieving maximum load pick up while satisfying distribution network flow and voltage constraints. A linearized optimal power flow (OPF) model is employed to represent the physics of the network. The problem formulation is augmented with a ramping (up) reserve product for the DERs to ensure improved and upward monotonic load restoration as time evolves. Simulation analysis and performance tests are performed using a modified IEEE 13-bus test feeder integrated with wind, solar, microturbine, and energy storage battery. The software is developed using various software packages in Julia and Python. The MPC model is implemented using the JuMP optimization language in Julia while the data analytics including renewable generation and load demand forecasts, running the MPC simulation and visualizations is performed in Python.

Eseye, Abinet Tesfaye↗