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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 sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Quantum Electrodynamics Coupled-Cluster at Scale: High-Performance Implementation for Complex Systems

Coupled-cluster theory (CC) is a highly accurate and versatile method for simulating complex interactions within quantum systems. The extension of CC theory to model mixed electron-photon processes with quantum electrodynamics (QED) has improved our capability to predict cavity-modified chemistry, a field where photons are used as cost-effective and eco-friendly alternatives to catalyze/inhibit chemical reactions. However, calculations with CC methods, even without incorporating QED effects, are often prohibitively expensive. Simulations of larger systems require scalable infrastructures that exist for traditional CC methods but not for QED-CC methods. As such, we present a GPU-enabled, high-performance, open-source implementation of the quantum electrodynamics coupled-cluster method with single and double excitations (QED-CCSD) within the ExaChem quantum chemistry software package. ExaChem relies on the Tensor Algebra for Many-body Methods (TAMM) infrastructure: a parallel heterogeneous tensor library designed to achieve scalable performance on modern heterogeneous supercomputing platforms. Furthermore, we discuss theoretical foundations, algorithmic details, and numerical benchmarks to showcase the larger systems that ExaChem can simulate and how the integration of photonic degrees-of-freedom alters their ground-state properties.

Basis sets↗
DOE OSTI2023

An FPGA-Based Neuromorphic Processor with All-to-All Connectivity

Neuromorphic computing is a promising paradigm for future energy-efficient computing. At present, however, it is in its nascent stages—most hardware implementations are research-grade, commercial products are not available, and the software tools are not production-ready. The lack of hardware and software tools makes neuromorphic computing inaccessible to researchers around the globe. To this extent, we intend to build a low-cost, open-source, FPGA-based digital neuromorphic processor that can be used by researchers worldwide. In this paper, we present a preliminary implementation of the processor on a Xilinx Artix-7 FPGA using SystemVerilog. Our implementation supports the integrate-and-fire neuron with two parameters each for neurons and synapses. It also features all-to-all connectivity among neurons on the hardware. We test our implementation on four cases: bars and stripes datasets, shortest path algorithm, logic gates, and 8-3 encoder. We also perform a scalability study to understand the resource utilization of the FPGA as the number of all-to-all connected neurons increases. With our implementation, the Artix- 7 supports 65 neurons with all-to-all connectivity. Moreover, all the test cases mentioned above achieve 100% accuracy.

Maheshwari, Disha↗
DOE OSTI2014

MO-A-BRD-07: Feasibility of X-Ray Acoustic Computed Tomography as a Tool for Calibration and In Vivo Dosimetry of Radiotherapy Electron and Photon Beams

Purpose: This work simulates radiation-induced acoustic waves to assess the feasibility of x-ray acoustic computed tomography (XACT) as a dosimeter. XACT exploits the phenomenon that acoustic waves with amplitude proportional to the dose deposited are induced following a radiation pulse. After detecting these acoustic waves with an ultrasound transducer, an image of the dose distribution can be reconstructed in realtime. Methods: Monte Carlo was used to simulate the dose distribution for monoenergetic 6 MeV photon and 9 MeV electron beams incident on a water tank. The dose distribution for a prostate patient planned with a photon 4-field box technique was calculated using clinical treatment planning software. All three dose distributions were converted into initial pressure distributions, and transportation of the induced acoustic waves was simulated using an open-source toolkit. Ideal transducers were placed around the circumference of the target to detect the acoustic waves, and a time reversal reconstruction algorithm was used to obtain an XACT image of the dose for each radiation pulse. Results: For the photon water tank relative dosimetry case, it was found that the normalized acoustic signal amplitude agreed with the normalized dose at depths from 0 cm to 10 cm, with an average percent difference of 0.5%. For the reconstructed in-plane dose distribution of an electron water tank irradiation, all pixels passed a 3%–3 mm 2D gamma test. The reconstructed prostate dose distribution closely resembled the plan, with 89% of pixels passing a 3%–3 mm 2D gamma test. For all situations, the amplitude of the induced acoustic waves ranged from 0.01 Pa to 1 Pa. Conclusion: Based on the amplitude of the radiation-induced acoustic waves and accuracy of the reconstructed dose distributions, XACT is a feasible technique for dosimetry in both calibration and in vivo environments for photon and electron beams and merits further investigation. Funding from NSERC, CIHR and McGill University. SH acknowledges partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-CD-304-11: Veritas 2.0: A Cloud-Based Tool to Facilitate Research and Innovation

Purpose: We introduce Veritas 2.0, a cloud-based, non-clinical research portal, to facilitate translation of radiotherapy research ideas to new delivery techniques. The ecosystem of research tools includes web apps for a research beam builder for TrueBeam Developer Mode, an image reader for compressed and uncompressed XIM files, and a trajectory log file based QA/beam delivery analyzer. Methods: The research beam builder can generate TrueBeam readable XML file either from scratch or from pre-existing DICOM-RT plans. DICOM-RT plan is first converted to XML format and then researcher can interactively modify or add control points to them. Delivered beam can be verified via reading generated images and analyzing trajectory log files. Image reader can read both uncompressed and HND-compressed XIM images. The trajectory log analyzer lets researchers plot expected vs. actual values and deviations among 30 mechanical axes. The analyzer gives an animated view of MLC patterns for the beam delivery. Veritas 2.0 is freely available and its advantages versus standalone software are i) No software installation or maintenance needed, ii) easy accessibility across all devices iii) seamless upgrades and iv) OS independence. Veritas is written using open-source tools like twitter bootstrap, jQuery, flask, and Python-based modules. Results: In the first experiment, an anonymized 7-beam DICOM-RT IMRT plan was converted to XML beam containing 1400 control points. kV and MV imaging points were inserted into this XML beam. In another experiment, a binary log file was analyzed to compare actual vs expected values and deviations among axes. Conclusions: Veritas 2.0 is a public cloud-based web app that hosts a pool of research tools for facilitating research from conceptualization to verification. It is aimed at providing a platform for facilitating research and collaboration. I am full time employee at Varian Medical systems, Palo Alto.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2011

JGI Genomic Single-cell Assembly Workflow

JIGSAW is a software package disigned to quality control and assemble genomic DNA sequences from single-cell bacterial and archaeal genomes. Amplification of singel-cell genomes using multiple displacement amplification technology presents challenges that magnify the amount of contaminants in the sample and produce non uniform depth of sequence coverage. these factors pose problems whan assembling the genomic data using currently availible short read assembles. The software addresses these problems by removing contaminants and normalizing the sequence read coverage prior to assemble. A hybrid assembly approach using two different open source genome assembly tools is then applied to piece together the DNA fragments. Additional reporting of QC metrics for the input sample and the genome assembly is provided for further analysis.

Trong, S.↗
DOE OSTI2024

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

This paper presents software advances to easily exploit computer architectures consisting of a multi-core CPU and CPU+GPU to accelerate diverse types of high-performance computing (HPC) applications using a single code implementation. The paper describes and demonstrates the performance of the open-source C++ matrix and array (MATAR) library that uniquely offers: (1) a straightforward syntax for programming productivity, (2) usable data structures for data-oriented programming (DOP) for performance, and (3) a simple interface to the open-source C++ Kokkos library for portability and memory management across CPUs and GPUs. The portability across architectures with a single code implementation is achieved by automatically switching between diverse fine-grained parallelism backends (e.g., CUDA, HIP, OpenMP, pthreads, etc.) at compile time. The MATAR library solves many longstanding challenges associated with easily writing software that can run in parallel on any computer architecture. This work benefits projects seeking to write new C++ codes while also addressing the challenges of quickly making existing Fortran codes performant and portable over modern computer architectures with minimal syntactical changes from Fortran to C++. We demonstrate the feasibility of readily writing new C++ codes and modernizing existing codes with MATAR to be performant, parallel, and portable across diverse computer architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Active learning for accelerated design of layered materials

Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric devices. Discovery of the optimal layered material for specific applications necessitates the estimation of key material properties, such as electronic band structure and thermal transport coefficients. However, screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing resources. Moreover, the functional dependence of material properties on the structures is often complicated, making simplistic statistical procedures for prediction difficult to employ without large amounts of data collection. Here, we present a Gaussian process regression model, which predicts material properties of an input hetero-structure, as well as an active learning model based on Bayesian optimization, which can efficiently discover the optimal hetero-structure using a minimal number of ab initio calculations. The electronic band gap, conduction/valence band dispersions, and thermoelectric performance are used as representative material properties for prediction and optimization. The Materials Project platform is used for electronic structure computation, while the BoltzTraP code is used to compute thermoelectric properties. Bayesian optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure by building a regression model to predict material properties. The models can be used for predictions with respect to any material property and our software, including data preparation code based on the Python Materials Genomics (PyMatGen) library as well as python-based machine learning code, is available open source.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Adapting federated cyberinfrastructure for shared data collection facilities in structural biology

Early stage experimental data in structural biology is generally unmaintained and inaccessible to the public. It is increasingly believed that this data, which forms the basis for each macromolecular structure discovered by this field, must be archived and, in due course, published. Furthermore, the widespread use of shared scientific facilities such as synchrotron beamlines complicates the issue of data storage, access and movement, as does the increase of remote users. This work describes a prototype system that adapts existing federated cyberinfrastructure technology and techniques to significantly improve the operational environment for users and administrators of synchrotron data collection facilities used in structural biology. This is achieved through software from the Virtual Data Toolkit and Globus, bringing together federated users and facilities from the Stanford Synchrotron Radiation Lightsource, the Advanced Photon Source, the Open Science Grid, the SBGrid Consortium and Harvard Medical School. The performance and experience with the prototype provide a model for data management at shared scientific facilities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

GridAPPS-D™ a distribution management platform to develop applications for rural electric utilities

Rural electric utilities, many of which operate with a co-op management approach, have unique operational challenges not shared with their municipal or IOU counterparts. Rural electric utilities typically have fewer customers per feeder mile, and are generally less able to afford the costs, or provide robust support for today’s Advanced Distribution Management System (ADMS) offerings. Additionally, co-ops typically do not need the full suite of ADMS offerings but would benefit from a subset of applications such as OMS, Unbalanced Power-Flow, Volt-VAR optimization and FLISR (Fault Location Isolation and Service Restoration). Their needs, even within these applications, are simpler and more standardized. Lastly, due to their size, their IT capabilities and resources are commonly limited; hence hardware and software needs to be standards-based and easy to maintain with only most expansions of current capabilities. GridAPPS-D™ is an open-source, application development environment that’s part of the Department of Energy’s Grid Modernization Efforts, being led by the Pacific Northwest National Laboratory. The GridAPPS-D project is developing a reference architecture and implementation with the goal of reducing the time and cost to integrate distribution applications for both planning and operations through the use of standardized interfaces and data models. In this paper, we present a view of the reference architecture for GridAPPS-D and highlight its applicability to the rural electric utilities’ needs, including descriptions of applications being developed using this platform, and how they can be applied to other rural electric utilities.

distribution management, GridAPPS-D, rural electri↗
DOE OSTI2019

Face Recognition from Oak Ridge (FaRO)

Many universities have published highly accurate open source face recognition algorithms, but configuring and using those algorithms can be very difficult due to poor documentation and challenges with installing the right software and libraries. Comparing research results is also a problem due to uncontrolled research methods. Provide baseline face recognition implementations that can be used for evaluating algorithm accuracy. The software provides a consistent web service interface to face recognition algorithms that can be used to support research in biometrics. The software uses protobuf and grpc services to publish standardized interfaces to the algorithms. The software also provides Docker container support which allows services to be easily started and configured on new machines. Reproducible baseline configurations are provided to serve as bench marks for publications in this area.

Bolme, DavidS↗
DOE OSTI2013

Alquimia

Alquimia provides a generic interface between flow and transport simulators (drivers) and biogeochemical reaction capabilities (engines). Alquimia consists of two parts: an application programming interface, API, and a wrapper library. The API describes in detail how information is exchanged between the driver and the engine. This includes the function call signatures and data structures required for implementing alquimia in a mixed language (C/C++/Fortran) programming environment. The library is an implementation of the documented API, providing an interface into the biogeochemistry routines supplied by other codes: Alquimia does not do any geochemical calculations. Version 1.0 provides interfaces to the open source codes (BSD) CrunchFlow/CrunchTope and (LGPL) PFLOTRAN. Version 1.0 meets the policies set forth by (and is part of) the Extreme-scale Scientific Software Development Kit, xSDK version 0.6.0.

Andre, Benjamin [Lawrence Berkeley National Lab. (↗
DOE OSTI2017

Enhancing reproducibility in scientific computing: Metrics and registry for Singularity containers

Here we present Singularity Hub, a framework to build and deploy Singularity containers for mobility of compute, and the singularity-python software with novel metrics for assessing reproducibility of such containers. Singularity containers make it possible for scientists and developers to package reproducible software, and Singularity Hub adds automation to this workflow by building, capturing metadata for, visualizing, and serving containers programmatically. Our novel metrics, based on custom filters of content hashes of container contents, allow for comparison of an entire container, including operating system, custom software, and metadata. First we will review Singularity Hub’s primary use cases and how the infrastructure has been designed to support modern, common workflows. Next, we conduct three analyses to demonstrate build consistency, reproducibility metric and performance and interpretability, and potential for discovery. This is the first effort to demonstrate a rigorous assessment of measurable similarity between containers and operating systems. We provide these capabilities within Singularity Hub, as well as the source software singularity-python that provides the underlying functionality. Singularity Hub is available at https://singularity-hub.org, and we are excited to provide it as an openly available platform for building, and deploying scientific containers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Scientific Data Management Integrated Software Infrastructure Center (SDM/ISIC): Scientific Process Automation (SPA) (Final Report)

The KEPLER project has made a significant impact and established a sustained leadership in the field of scientific workflows. The open source KEPLER project started in 2003 when members of the SciDAC-SDM Center/SPA Team, sponsored by this DOE-funded project (DE-FC02-01ER25486) and members of the SEEK project (NSF/ITR awards DBI-0225674 and DBI-0533368) decided to collaborate and jointly develop a scientific workflow system based on the open source PTOLEMY II system (Ludäscher was a co-PI on both projects). In the first project phase, between 2001 and 2003, the SDSC team (Altintas, Ludäscher) worked closely with a domain scientist (Matt Coleman, LLNL) and created early versions of a Promoter Identification Workflow (PIW). Towards the end of that period, the open source PTOLEMY II system was adopted by SciDAC-SDM and SEEK as the basis for a general scientific workflow system and problem-solving environment to design and execute scientific workflows, giving rise to Kepler. Once started through SciDAC-SDM and SEEK, the KEPLER leadership team was able to attract further support from funding agencies including from DOE (as part of SciDAC-2), NSF, NIH, and the Gordon and Betty Moore Foundation. Important advances to KEPLER were made by the SciDAC/SDM-SPA team during the report period (2001–2007), in particular by the SDSC team (and after Ludäscher's move) the UC Davis team, including (i) research and development of the workflow infrastructure, (ii) library (actor) development, and (iii) development of concrete scientific workflows, in collaboration with scientists. Years later, the KEPLER system was listed as one of the prominent “Big Data” research outcomes of DOE in the White House Big Data Factsheet, announced as part of the White House Big Data press release.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Portrait and Classification of Individual Haze Particulates

Haze (known as “Mai” in Chinese) threatens the health of billions of people across the globe. To begin solving this problem without severely slowing down the economy, one has to mechanistically and geographically pinpoint the sources of these pollutants, the key of which is to thoroughly characterize and fingerprint the particulates. Here we present a broad survey and classification of thousands of individual airborne particu-lates by using the Scanning Electron Microscope (SEM) to measure their diverse mor-phologies and chemistries, which could eventually be organized into a “haze finger-print database”. For instance, one collection in Xi’an City, China during March-April 2014 yielded 494 airborne particulates that settled on silicon wafers placed outside the window of a 3rd floor office. These 494 particulates were manually imaged with high resolution (down to 2 nm), elementally mapped using Energy-dispersive X-ray Spec-troscopy (EDS), and were identified and categorized into presumed source classes such as construction activities (Ca, Al, Si-O), coal burning (sulfates), biologic (pollen, bac-teria), automotive, mining, steel making, and etc. About 20% of the particulates have mysterious origins, as it is still unclear how they were formed, and a fraction of them contained clearly hazardous elements such as lead and chromium. For future work, we propose using unmanned aerial vehicles with a special “rolling film” substrate that can autonomously collect airborne particulates, a customized SEM auto-imaging system, and machine learning software to establish an online open-access database. The end goal would be to monitor and analyze the particulate pollutants that are pumped into our atmosphere every day, and precisely track down their sources so we can better model and police the quality of the air around us.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Creating a Simulation Platform for Research and Development of Advanced Control Methods

Advanced nuclear reactors are essential to meet the changing energy requirements throughout both the United States and the rest of world. In addition to other features, they are designed to enable deployment in remote locations and operate in a fully (or near-fully) autonomous manner, which will require a new control paradigm. To realize autonomously operating reactors, the U.S. Department of Energy’s Nuclear Energy Enabling Technologies Advanced Sensors and Instrumentation (NEET ASI) program conducts research and development into the enabling technologies and methods needed, including digital twins, machine learning, and risk modeling, in addition to various types of control methods. These technologies and methods are the key foundations needed to achieve fully autonomous systems. To develop and evaluate the technologies and methods necessary for achieving autonomous operations, it is critical to identify a software tool capable of integrating all the required elements. In surveying the available solutions, no software platforms were identified that could accomplish what was needed without introducing drawbacks. This challenge was the motivation for the current effort: to develop a software platform that can seamlessly integrate autonomouscontrol-enabling technologies and methods, allowing for accelerated research and development and transfer of ideas. The resulting platform, known as the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND), is Python-based, and leverages open-source tools to provide flexibility and facilitate building upon prior research. It is designed to enable advanced reactor developers to deploy and test advanced control technologies and methods coupled with their own models, solutions, and hardware. Given the substantial undertaking of developing such a platform, the current effort focused on laying down scalable, flexible software foundations and infrastructure, then demonstrating the platform via a use case. These foundations included developing generic modules, which contain the base variable and system blocks (the information and functional building blocks, respectively, that can be used to design a simulation) and the data handling and storage blocks needed to exchange information between the various blocks; as well as enablingtechnology-specific modules. This platform was evaluated via a use case, which was to simulate and control a process for the Microreactor Automated Control System (MACS) test bed. While MACS is not currently directly coupled to any specific microreactor physics, it was initially developed in concert with the Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor, and so the MARVEL physics are used here. As part of this use case, several enabling-technology-specific blocks within COMMAND were integrated, including a proportional integral derivative (PID) control block, a Reactor Excursion and Leak Analysis Program (RELAP5-3D) block, and an anomaly detection block. The COMMAND software platform was successfully demonstrated to achieve the scalability and flexibility objectives of this effort and will be leveraged by the program’s research efforts to advance state of the art control methodologies towards autonomous operations of advanced reactors. As new use cases are created and implemented, it is anticipated that COMMAND will continue to grow and evolve to meet new requirements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Automated analysis of unlabeled PV data with Solar Data Tools software: Overview and feature updates

Distributed rooftop PV systems: ubiquitous, yet commonly have unlabeled data Difficult or impossible to form a performance index We developed Solar Data Tools (SDT), an open-source Python library for analyzing PV power (and irradiance) time-series data SDT enables analysis of unlabeled PV data—no model, no meteorological data, no performance index required Takes a statistical signal processing approach Data processing steps are largely pre-defined and automatic regardless of system type—from utility tracking systems to multi-pitch rooftop systems

Meyers-Im, Bennet E↗
DOE OSTI2017

Make safety awareness a priority: Use a login software in your research facility

We report on a facility login software, whose objective is to improve safety in multi-user research facilities. Its most important safety features are: 1) blocks users from entering the lab after being absent for more than a predetermined number of days; 2) gives users a random safety quiz question, which they need to answer satisfactorily in order to use the facility; 3) blocks unauthorized users from using the facility afterhours; and 4) displays the current users in the facility. Besides restricting access to unauthorized users, the software keeps users mindful of key safety concepts. In addition, integration of the software with a door controller system can convert it into an effective physical safety mechanism. Depending on DOE approval, the code may be available as open source.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

SALT3: An Improved Type Ia Supernova Model for Measuring Cosmic Distances

Abstract A spectral-energy distribution (SED) model for Type Ia supernovae (SNe Ia) is a critical tool for measuring precise and accurate distances across a large redshift range and constraining cosmological parameters. We present an improved model framework, SALT3, which has several advantages over current models—including the leading SALT2 model (SALT2.4). While SALT3 has a similar philosophy, it differs from SALT2 by having improved estimation of uncertainties, better separation of color and light-curve stretch, and a publicly available training code. We present the application of our training method on a cross-calibrated compilation of 1083 SNe with 1207 spectra. Our compilation is 2.5× larger than the SALT2 training sample and has greatly reduced calibration uncertainties. The resulting trained SALT3.K21 model has an extended wavelength range 2000–11,000 Å (1800 Å redder) and reduced uncertainties compared to SALT2, enabling accurate use of low- z I and iz photometric bands. Including these previously discarded bands, SALT3.K21 reduces the Hubble scatter of the low- z Foundation and CfA3 samples by 15% and 10%, respectively. To check for potential systematic uncertainties, we compare distances of low (0.01 < z < 0.2) and high (0.4 < z < 0.6) redshift SNe in the training compilation, finding an insignificant 3 ± 14 mmag shift between SALT2.4 and SALT3.K21. While the SALT3.K21 model was trained on optical data, our method can be used to build a model for rest-frame NIR samples from the Roman Space Telescope. Our open-source training code, public training data, model, and documentation are available at https://saltshaker.readthedocs.io/en/latest/ , and the model is integrated into the sncosmo and SNANA software packages.

79 ASTRONOMY AND ASTROPHYSICS↗