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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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All sources NASA NTRS DOE OSTI

DOE OSTI2013

MPDV QUICKVIEW

QuickView is written in MATLAB® and runs as a stand-alone widget application. The application is typically deployed as a MATLAB executable or an application (for Mac OS X). The source code for stand-alone applications consists either entirely of MATLAB files or some combination of MATLAB files and MEX-files; as such, the end user must use the MATLAB Compiler Runtime (MCR) or have a full version of MATLAB with associated toolboxes. This program has been tested on both Microsoft Windows and MAC OS X. QuickView provides a unique capability for viewing multiple PDV data records. The user can select up to 40 .dig files and view them as spectrograms, eight per page. In addition to basic image exploration, i.e., zooming, panning, and pixel information, the user can change contrast, spectrogram parameters, save figures, and extract velocity profiles from the spectrograms.

Diaz, Abel↗

DOE OSTI2011

PhyCA-PhyloChip Analysis

PhyCA allows interpretation of a PhyloChip assay. It takes as input, a text file containing the fluorescent intensity from each feature on the microarray surface. PhyCA will determine the background and noise levels, compare probes against controls and determine response scores for each probe pair. It then summarizes a set of probe pair results to determine presence of an operational taxonomic unit as well as its abundance. The PhyCA software is novel from other microarray interpretation software from the following reasons: 1) It leverages spiked-in Quantitative Standards and non-spike-in Negative Controls to build a per-chip model of positive and non-positive responses. 2) It allows a refinement of Presence/Absence calls by evaluating each responding probe against a putative cross-hybridization network. 3) It can be used with phylogenetic microarrays designed for Bacteria, Archaea or Eukaryotes. Hardware req.: PC, MAC; Software req.: OS: Windows, Mac, Linux; Types of files: source code, control information; user guide. The software depends on other software to be installed on the same machine as PhyCA.

Desantis, Todd↗

DOE OSTI2018

Sandia Cloud Forensics and Incident Response Platform v 1.0

As systems and devices become virtualized and deployed in the cloud, the hypervisor becomes an increasingly appropriate place to collect performance data, system state, system landscape, function calls, transaction traces, and other characteristics. We developed a method by which an introspection application may be coupled with a hypervisor to “reach into” the VM with minimal intrusiveness to collect data critical to the reconstruction of events, files, and operations. Such a capability is required to take advantage of the hypervisor as an instrumentation platform and to integrate that data with more traditional collection mechanisms. The concept of a VM serviced by a lightweight hypervisor is a relatively new paradigm for forensic practitioners. Traditional forensic techniques, based on assumptions that the filesystem was directly interacting with the hardware through an abstraction, afforded the forensic practitioner the assumption that there was nothing controlling the application below the filesystem. This is not the case when using virtualized technologies. Hypervisors can covertly monitor, introspect and interact with the guest in a transparent fashion. The problems of storage and collection of actionable data are exhausting. The current challenge is most hypervisors do not expose a useful application programming interface (API) at a sufficient level to do transparent, fine-grained and customizable introspection. Scalable VM instrumentation and introspection at an in-depth level requires fast handling of events, as well as direct access to VM state. Furthermore, deep introspection benefits greatly from the ability to gather data from the hardware during the VM’s exit to the hypervisor. All of this requires identical access to the system as the hypervisor itself; improper use of this ability could easily cause system instability. It is for this reason we believe that the hypervisor developers have been hesitant to grant this much control through their APIs. However, our approach leverages other means to collect and monitor the guests in a targeted fashion. Virtual machine introspection (VMI) is a technique used to monitor the runtime state of a system-level virtual machine. The runtime state can be may include processor registers, memory, disk, network, and any other hardware-level events A review of research literature and current VMI technologies exposed several limitations and tradeoffs in VMI approaches, including: the use of in-guest agents; kernel to user space transitions (dramatically slowing down processing); VMI tool pre-configuration requirements; hypervisor version lock-in or source code patching; reliance on operating system symbols; limited processor features due to hypervisor (even if the hardware could do more). To address these constraints, a VMI tool was envisioned to provide the cloud forensic capabilities while having as few of these limitations as possible. The Kernel-based Virtual Machine Introspection (KVMi) tool was developed for hypervisors on Intel’s x86-64 architecture. To meet performance, scoping and use-case demands, the follow criteria were applied to KVMi to support live, forensic data collection: • Shall not require in-guest agents. • Shall work with any recent version of modern hypervisors. • Shall introspect upon any guest (VM) running modern versions of Windows, Linux and OS X. • Shall have the ability to find and track operating system artifacts • Shall not require operating system symbol files. • Shall be able to fully handle VM-exits, bypassing execution of the hypervisor if necessary, to facilitate new features the hypervisor may not support. • Shall be compilable/loadable on a running system with standard build tools.

Stout, WilliamM.S.↗

DOE OSTI2019

ALFABET™ and nfp (Neural Fingerprint) [SWR-19-13]

Keras layers for end-to-end learning on molecular structure. Based on Keras, Tensorflow, and RDKit. Source code used in the study Message-passing neural networks for high-throughput polymer screening. SEE ALSO https://github.com/NREL/alfabet

St. John, Peter↗

DOE OSTI2019

Ompparser

ompparser is a standalone and unified OpenMP parser, which is motivated by the facts that: 1) The differences in terms of syntax and semantics of OpenMP constructs between C/C++ and Fortran are minor. 2) Current OpenMP compilers often develop their own parsers, which represent redundant work. The features of ompparser include: 1) ompparser can be used standalone for static source code analysis, e.g. tools for semantics checking or similarity analysis between C/C++ and Fortran programs 2) omppaser can be integrated into an OpenMP compiler implementation can reduce the development efforts, and there is no need to create and maintain two separate parsers for C/C++ and Fortran. 3) ompparser provides a complete reference OpenMP grammar in the Backus-Naur Form that formally describes all the latest OpenMP language constructs. This will help users understand the rules and restriction of the OpenMP standard, which no longer contains a reference grammar in its recent versions.

Wang, Anjia↗

DOE OSTI2020

Parameter Optimization Toolbox for NS-3 network optimization, NS-3 Parameter Optimization Framework [SWR-18-60]

This simulation-based parameter optimization framework is proposed to tune parameters of different types of communication networks using ns-3 to achieve the optimal network performance. It consists of three main components: an ns-3 packet reporting module; a sampler running simulations with all possible parameter sets for the input parameter variables by using a parallel executor at each generation; and a hybrid optimization algorithm for tuning configurable parameters of hybrid designs and application parameter variables. The proposed hybrid metaheuristic optimization algorithm combines an evolutionary algorithm with a gradient descent function to quickly achieve an approximate globally optimum solution. This software is designed to be used in a multi-core processing Linux environment and run over a long duration of time. The execution time varies depending mainly upon the nature of the ns-3 configuration being simulated. This software includes a custom ns-3 QoS measurement application which must be included with the ns-3 source code during installation of the software.

Hasandka, Adarsh↗

DOE OSTI2017

Sunlamp

The purpose of this model was to facilitate the design of a control system that uses fine grained control of residential and small commercial HVAC loads to counterbalance voltage swings caused by intermittent solar power sources (e.g., rooftop panels) installed in that distribution circuit. Included is the source code and pre-compiled 64 bit dll for adding building HVAC loads to an OpenDSS distribution circuit. As written, the Makefile assumes you are using the Microsoft C++ development tools.

Nutaro, JamesJ↗

DOE OSTI2018

DataXray

Lab experiments involving digital signal processing tend to capture heterogeneous data that is binned into individual files. The structure of the whole experiment tends to get lost in this process, generating a "mess" of data that is difficult to summarize, and even more difficult to analyze in a consistent way. DataXray is a set of python routines that wraps publicly available open-source codes to create a consistent workflow for data and experiments, involving imaging, spectral and hyperspectral data. Routines are provided for data translation, slicing, visualization, multivariate statistics, integral transforms, feature extraction and reporting in the form of publication-grade figures.

Maksymovych, Petro↗

DOE OSTI2011

Bulk Data Mover

Bulk Data Mover (BDM) is a high-level data transfer management tool. BDM handles the issue of large variance in file sizes and a big portion of small files by managing the file transfers with optimized transfer queue and concurrency management algorithms. For example, climate simulation data sets are characterized by large volume of files with extreme variance in file sizes. The BDN achieves high performance using a variety of techniques, including multi-threaded concurrent transfer connections, data channel caching, load balancing over multiple transfer servers, and storage i/o pre-fetching. Logging information from the BDM is collected and analyzed to study the effectiveness of the transfer management algorithms. The BDM can accept a request composed of multiple files or an entire directory. The request also contains the target site and directory where the replicated files will reside. If a directory is provided at the source, then the BDM will replicate the structure of the source directory at the target site. The BDM is capable of transferring multiple files concurrently as well as using parallel TCP streams. The optimal level of concurrency or parallel streams depends on the bandwidth capacity of the storage systems at both ends of the transfer as well as achievable bandwidth of the wide-area network. Hardware req.-PC, MAC, Multi-platform & Workstation; Software req.: Compile/version-Java 1.50_x or above; Type of files: source code, executable modules, installation instructions other, user guide; URL: http://sdm.lbl.gov/bdm/

Natarajan, Vijaya↗

DOE OSTI2015

GnuForPlot Graphics

Gnuforplot Graphics is a Fortran90 program designed to generate two and three dimensional plots of data on a personal computer. The program uses calls to the open source code Gnuplot to generate the plots. Two Fortran90 programs have been written to use the Gnuplot graphics capabilities. The first program, named Plotsetup.f90 reads data from output files created by either the Stadium or LeachXS/Orchestra modeling codes and saves the data in arrays for plotting. This program then calls Gnuforplot which takes the data array along with user specified parameters to set plot specifications and issues Gnuplot commands that generate the screen plots. The user can view the plots and optionally save copies in jpeg format.

Flach, GregoryP.↗

DOE OSTI2017

Sparx PCA Module

Sparx, a new environment for Cryo-EM image processing; Cryo-EM, Single particle reconstruction, principal component analysis; Hardware Req.: PC, MAC, Supercomputer, Mainframe, Multiplatform, Workstation. Software Req.: operating system is Unix; Compiler C++; type of files: source code, object library, executable modules, compilation instructions; sample problem input data. Location/transmission: http://sparx-em.org; User manual & paper: http://sparx-em.org;

Yang, Chao↗

DOE OSTI2021

NRWAL (NLR formerly known as NREL Wind Analysis Library) [SWR-21-26]

NRWAL (NLR (National Laboratory of the Rockies) formerly known as NREL (National Renewable Energy Laboratory) Wind Analysis Library: A library of offshore wind cost equations (plus new energy technologies like marine hydro!) Easy equation manipulation without editing source code Full continental-scale integration with the NREL Renewable Energy Potential Model (reV) https://nrel.github.io/reV/ Ready-to-use configs for basic users Dynamic python tools for intuitive equation handling One seriously badass sea unicorn To get started with NRWAL, check out the NRWAL Config documentation or the NRWAL example notebook. You can also launch the notebook in an interactive jupyter shell right in your browser without any downloads or software using binder. Ready to build a model with NRWAL but don't want to contribute to the library? No problem! Check out the example getting started project here. Here is the important stuff: The NRWAL Equation Library. Default NRWAL Configs

Nunemaker, Jacob↗

DOE OSTI2021

beluga

beluga is an open-source optimal control software that uses publicly available, indirect methods from GitHub. beluga's indirect optimization engine has been reworked to follow a lazy functional paradigm. This improves the software's flexibility and enables users to create their own optimization processes without modification of the source code. beluga is general-purpose and therefore very broad in scope and application. SAND2020-12847 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Sparapany, Michael↗

DOE OSTI2021

Attend and Decode: 4D fMRI Task State Decoding Using Attention Models

Source code for Brain Attend and Decode paper. Functional magnetic resonance imaging (fMRI) is a neuroimaging modality that captures the blood oxygen level in a subject's brain while the subject either rests or performs a variety of functional tasks under different conditions. Given fMRI data, the problem of inferring the task, known as task state decoding, is challenging due to the high dimensionality (hundreds of million sampling points per datum) and complex spatio-temporal blood flow patterns inherent in the data. In this work, we propose to tackle the fMRI task state decoding problem by casting it as a 4D spatiotemporal classification problem. We present a novel architecture called Brain Attend and Decode (BAnD), that uses residual convolutional neural networks for spatial feature extraction and self-attention mechanisms for temporal modeling. We achieve significant performance gain compared to previous works on a 7-task benchmark from the large-scale Human Connectome Project-Young Adult (HCP-YA) dataset. We also investigate the transferability of BAnD's extracted features on unseen HCP tasks, either by freezing the spatial feature extraction layers and retraining the temporal model, or finetuning the entire model. The pre-trained features from BAnD are useful on similar tasks while finetuning them yields competitive results on unseen tasks/conditions.

Ng, BrendaM.↗

DOE OSTI2022

LANL Contributions to pyiron: An integrated development environment for computational materials science

To support and accelerate the development of simulation protocols in atomistic modelling, we introduce an integrated development environment (IDE) for computational materials science called pyiron (http://pyiron.org). The pyiron IDE combines a web based source code editor, a job management system for build automation, and a hierarchical data management solution. The core components of the pyiron IDE are pyiron objects based on an abstract class, which links application structures such as atomistic structures, projects, jobs, simulation protocols and computing resources with persistent storage and an interactive user environment. The simulation protocols within the pyiron IDE are constructed using the Python programming language. To highlight key concepts of this tool as well as to demonstrate its ability to simplify the implementation and testing of simulation protocols we discuss two applications. In these examples we show how pyiron supports the whole life cycle of a typical simulation, seamlessly combines ab initio with empirical potential calculations, and how complex feedback loops can be implemented. While originally developed with focus on ab initio thermodynamics simulations, the concepts and implementation of pyiron are general thus allowing to employ it for a wide range of simulation topics.

Janssen, Jan↗

DOE OSTI2021

NEMA-Phase Compliant Traffic Signal Controller Module in SUMO

The controller modules in SUMO use a stage-based control structure. A phase is defined as a stage of all allowed movements at a time instance. However, traffic signal controllers used in North America widely use National Electrical Manufacturers Association (NEMA) phase definition. A NEMA phase is defined by a certain flow movement at an intersection. At one time, more than one NEMA phase could happen together as long as they do not conflict with each other. We can visualize the NEMA phases and timings in Ring-and-Barrier structured NEMA diagrams. For one controller, only one phase from a ring can be activated at a time. Phases from different rings could be activated together as long as they are not from the different sides of a barrier. When a controller is operated in fixed-time control mode, we can model the NEMA phase timing as a corresponding stage-based control timing without any issues. When introducing actuation into the signal control, a Ring-and-Barrier structured traffic signal controller can be more flexible than stage-based controller by allowing different possible phase combinations. We made two efforts in modeling Ring-and-Barrier structured controllers in SUMO. One is to translate a NEMA phases timing into SUMO-readable phases and timings as an additional file for SUMO. This translation worked well for fixed-time control. To model actuated control and coordinated actuated control, we augmented the SUMO source code by adding a Ring-and-Barrier structured controller module. This module could implement traffic signal timing from controllers using NEMA phases. We also augmented TraCI to be able to set new NEMA phase timings during simulations. We examined the Ring-and-Barrier structured traffic signal controller module by both visually observing the simulation animations and the simulation records. The developed control module can model the generalized Ring-and-Barrier structured traffic signal timing that is used in North America. SEE: https://github.com/eclipse/sumo/blob/main/src/microsim/traffic_lights/NEMAController.cpp

Wang, Qichao↗

DOE OSTI2017

ExaSat: An Exascale Static Analysis Tool (ExaSat) v1.0

The ExaSAT framework automates the extraction of parameterized performance models directly from source code using compiler analysis. Its parameterized analytic model enables quantitative evaluation of a broad range of hardware design trade-offs and software optimizations on a variety of different performance metrics, with a primary focus on data movement as a metric. ExaSAT analysis provides insights in the hardware and software tradeoffs and lays the groundwork for exploring a more targeted set of design points using cycle-accurate architectural simulators and emulators.

Chan, Cy↗

DOE OSTI2022

LBC (Learning Building Control)

LBC encompasses the source code and data to reproduce and extend results for a manuscript that compares demand responsive control schemes for multi-zone buildings. It includes examples of model predictive control (MPC), value function approximation via CVXPYLAYERS, and differentiable predictive control (DPC). The goal of the study is to evaluate state-of-the-art controllers and establish the efficacy (if any) of learning-based approaches that leverage deep neural networks in one way or another.

Zhang, Xiangyu↗