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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

The MPI_T events interface: An early evaluation and overview of the interface

Understanding the behavior of parallel applications that use the Message Passing Interface (MPI) is critical for optimizing communication performance. Performance tools for MPI currently rely on the PMPI Profiling Interface or the MPI Tool Information Interface, MPI_T, for portably collecting information for performance measurement and analysis. While tools using these interfaces have proven to be extremely valuable for performance tuning, these interfaces only provide synchronous information, i.e., when an MPI or an MPI_T function is called. There is currently no option for collecting information about asynchronous events from within the MPI library. Here, we propose a callback-driven interface for event notification from MPI implementations. Our approach is integrated in the existing MPI_T interface and provides a portable API for tools to discover and register for events of interest. We implement our MPI_T Events interface in Open MPI and demonstrate its functionality and usability with a small logging tool (MEL) as well as an early integration into the comprehensive measurement infrastructure Score-P.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Adaptive ensemble simulations of biomolecules

Recent advances in both theory and computational power have created opportunities to simulate biomolecular processes more efficiently using adaptive ensemble simulations. Ensemble simulations are now widely used to compute a number of individual simulation trajectories and analyze statistics across them. Adaptive ensemble simulations offer a further level of sophistication and flexibility by enabling highlevel algorithms to control simulations-based on intermediate results. We review some of the adaptive ensemble algorithms and software infrastructure currently in use and outline where the complexities of implementing adaptive simulation have limited algorithmic innovation to date. In conclusion, we describe an adaptive ensemble API to overcome some of these barriers and more flexibly and simply express adaptive simulation algorithms to help realize the power of this type of simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities

Buildings account for 35% of the global final energy demand. Efficiency improvements and advanced control strategies have a significant impact in the reduction of energy costs and CO2 emissions. Building energy simulation is widely used to help planners, contractors, and building owners analyse diverse options regarding the planning and management of energy consumption in buildings. Furthermore, recent advances in data processing and computing have led to the development of sophisticated machine learning algorithms that can learn from large datasets, e.g., sensor data from buildings, and use them to develop building-specific adaptive and automatic energy controllers. Control algorithms, such as deep reinforcement learning can tune themselves, are model-free, and economical to implement. In this paper, we introduce an integrated simulation environment that combines CitySim, a fast building energy simulator, and TensorFlow, a platform for efficient implementation of advanced machine learning algorithms. The integration is achieved via Keras - an API for TensorFlow - and a set of text and csv files for data transfer between the applications. This new environment will allow researchers to investigate novel learning control algorithms, and demonstrate their robustness and potential for diverse applications in the built environment. We present two case studies for energy savings and demand response, respectively.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Localized Actual Meteorological Year File Creator (LAF): A tool for using locally observed weather data in building energy simulations

The Localized Actual Meteorological Year File Creator (LAF) application provides web-based access to real meteorological data and processes it into a weather file suitable for building energy modeling. Building energy consumption is affected by what is inside the building (such as occupants, appliances, HVAC systems, etc.) and by what is outside: the weather conditions the building is exposed to. However, freely available weather data files are limited to a number of specific locations, usually airports, which are often located away from city centers where buildings are concentrated. The authors have developed a new tool that supports the investigation and quantification of micro-climate conditions on building energy consumption. LAF is built on the Python open-source programming language and has a Graphical User Interface (GUI) that allows users to create custom weather data files for building energy simulations. Many sets of actual meteorological year weather data for long time periods are publicly available online (such as the MesoWest database) for thousands of locations in the US. LAF selects weather data according to user specifications and automatically processes it through an API across multiple weather stations and multiple time periods. The user may easily select a specific location, time frame, and time step that best meets their needs. This article presents a useful tool for energy modelers, building designers and operators to assist with building performance analysis and optimization.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

ExactPack: A python library of exact analytic solutions

Verification of multi-physics simulation software against problems with known analytic or semi-analytic solutions is an important aspect of research into a wide variety of fields involving the motion of fluids, shock physics and other dynamic material properties. Previous work comparing simulation results against analytic solutions has been ad-hoc, with developers frequently writing their own analytic solvers. This has resulted in a large amount of duplicated effort. The python library ExactPack has been developed as a collection of analytic and semi-analytic solvers to a variety of multi-physics problems, providing a consistent API to a set of well-tested solver implementations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Novel Solid Form of Erlotinib: Synthesis by Heterogeneous Complexation and Characterization by NMR Crystallography

We describe the synthesis of a novel complex of the anticancer "active pharmaceutical ingredient erlotinib (ERL) via heterogeneous nucleation on polished zinc tiles. The resulting product, ERL 2 ·ZnCl 2 , is characterized by single-crystal X-ray diffraction, multinuclear solid-state NMR (ssNMR) spectroscopy, and density functional theory (DFT) calculations. Also characterized are the hydrochloride salt (ERL·HCl) and monohydrate free base (ERL·H 2 O) forms of erlotinib. 13 C ssNMR spectroscopy is useful for site-by-site assignment and rapid fingerprinting, while also providing preliminary structural interpretations, such as the number of molecules in the asymmetric unit. 35 Cl ssNMR can readily differentiate between the chloride ions in ERL·HCl and the covalently bonded chlorine in ERL 2 ·ZnCl 2 . 15 N ssNMR proves to be critical here because of the large isotropic chemical shift differences between ERL·H 2 O, ERL·HCl, and ERL 2 ·ZnCl 2 . The 15 N chemical shift tensors are linked directly to differences in structure and bonding with the aid of DFT calculations. Together, these results demonstrate the utility of multinuclear NMR crystallography for the characterization of solid forms of APIs, especially when other analytical techniques face significant challenges.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Lab and Reservoir Study of Produced Hydrocarbon Molecular Weight Selectivity during CO 2 Enhanced Oil Recovery

Two laboratory experiments were performed to investigate the molecular weight selectivity of crude oil hydrocarbons mobilized into CO 2 at the reservoir temperature (42 °C) and pressures ranging from below the minimum miscibility pressure (MMP) of 9.69 MPa (1406 psi) to pressures well above the MMP (8.27–27.58 MPa, 1200–4000 psi). The hydrocarbon composition at equilibrium in the CO 2 -rich upper “miscible” phase was measured, as was the ability of four pore volumes of CO 2 to recover crude oil hydrocarbons from a sand bed. Both experiments showed significant selectivity against producing higher molecular weight hydrocarbons at lower pressures. In addition, the bias against higher molecular weight hydrocarbons continued even at pressures well above the 9.69 MPa MMP. For example, both the total hydrocarbon concentration in the CO 2 -rich “miscible” phase and the fraction of C 21 –C 36 increased dramatically from 10.34 to 21.13 MPa (from 1500 to 3500 psi), even though both pressures were above the MMP. In addition, when crude oil was first equilibrated at 20.68 MPa (3000 psi) and then lowered before sampling the “miscible” phase, substantial deposition of the higher molecular weight hydrocarbons occurred with the result that the hydrocarbon composition and concentrations were similar to those when the crude oil was exposed to only the lower CO 2 pressures without first being exposed to any higher pressure. The sand flush experiments showed similar results to the “miscible” phase samplings in that both the total hydrocarbons and the fraction of C 21 –C 36 recovered increased dramatically with pressure, regardless of whether the pressures were below or more than double the MMP. The crude oil samples remaining after CO 2 exposures also showed large increases in molecular weight as well as higher viscosity and lower American Petroleum Institute (API) gravity than the original crude oil. As predicted by both lab experiments, produced crude oil samples collected from two wells before and after CO 2 breakthrough showed significant bias against high-molecular-weight hydrocarbons, with the fraction of C 21 –C 36 produced dropping by as much as 60–80% after CO 2 breakthrough compared to crude oil samples collected from the same wells before CO 2 breakthrough. Furthermore, these results verify that crude oil during a CO 2 flood does not move as a homogeneous “miscible” phase and that hydrocarbon dissolution (vaporization into the “miscible” phase) of lower molecular weight hydrocarbons dominates oil recovery in both the field and lab experiments.

02 PETROLEUM↗
DOE OSTI2025

SparcleQC: Automated Input File Creation for QM/MM Studies of Protein:Ligand Complexes

SparcleQC is a Python package that, given a protein:ligand complex in the Protein Data Bank (PDB) file format, can create quantum mechanics/molecular mechanics (QM/MM)-like input files for the electronic structure theory packages PSI4, QChem, and NWChem. The resulting input files include quantum mechanical representations of the ligand and a small section of the protein, surrounded by point charges that represent the rest of the protein. Creation of these QM/MM input files includes cutting and capping the QM subregion, obtaining point charges for the protein, and adjusting charges at the QM/MM boundary; and each of these tasks are automated by the software. In this article, we describe the details of SparcleQC’s procedure, show examples of the Python API, and explain additional features that are helpful in protein:ligand interaction studies. Finally, we show that SparcleQC enables automated preparation of input files for QM/MM calculations, which can return can return accurate interaction energies in minutes, while a fully quantum mechanical computation on the protein:ligand complex could take days, if it is even possible.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Catalysis-Hub.org, an open electronic structure database for surface reactions

Abstract We present a new open repository for chemical reactions on catalytic surfaces, available at https://www.catalysis-hub.org . The featured database for surface reactions contains more than 100,000 chemisorption and reaction energies obtained from electronic structure calculations, and is continuously being updated with new datasets. In addition to providing quantum-mechanical results for a broad range of reactions and surfaces from different publications, the database features a systematic, large-scale study of chemical adsorption and hydrogenation on bimetallic alloy surfaces. The database contains reaction specific information, such as the surface composition and reaction energy for each reaction, as well as the surface geometries and calculational parameters, essential for data reproducibility. By providing direct access via the web-interface as well as a Python API, we seek to accelerate the discovery of catalytic materials for sustainable energy applications by enabling researchers to efficiently use the data as a basis for new calculations and model generation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Geolocated dataset of Chinese overseas development finance

China is now the world's largest source of bilateral development finance and will likely continue to play a prominent role in sovereign lending through its multi-billion-dollar Belt and Road Initiative. This paper introduces major methodological enhancements in tracking this finance: the use of an original application programming interface (API) to gathers news in multiple languages; double verification of every record to ensure every finance commitment has been formalized; and visual geo-location to trace the precise footprint of every project. The resulting dataset enables economic, environmental, and social analyses with high-precision spatial accuracy, as well as spatiotemporal monitoring by project stakeholders and enhanced planning by project managers. It covers the years 2008-2019 to enable analysis before and after the announcement of the Belt and Road Initiative. It includes 862 finance commitments, 669 of which have geographic location, to 94 countries across the world.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Georectified polygon database of ground-mounted large-scale solar photovoltaic sites in the United States.

Over 4,400 large-scale solar photovoltaic (LSPV) facilities operate in the United States as of December 2021, representing more than 60 gigawatts of electric energy capacity. Of these, over 3,900 are ground-mounted LSPV facilities with capacities of 1 megawatt direct current (MW dc ) or more. Ground-mounted LSPV installations continue increasing, with more than 400 projects appearing online in 2021 alone; however, a comprehensive, publicly available georectified dataset including spatial footprints of these facilities is lacking. The United States Large-Scale Solar Photovoltaic Database (USPVDB) was developed to fill this gap. Using US Energy Information Administration (EIA) data, locations of 3,699 LSPV facilities were verified using high-resolution aerial imagery, polygons were digitized around panel arrays, and attributes were appended. Quality assurance and control were achieved via team peer review and comparison to other US PV datasets. Data are publicly available via an interactive web application and multiple downloadable formats, including: comma-separated value (CSV), application programming interface (API), and GIS shapefile and GeoJSON.

14 SOLAR ENERGY↗
DOE OSTI2024

An open source knowledge graph ecosystem for the life sciences

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Netting and pan traps fail to identify the pollinator guild of an agricultural crop

Pollinator decline is expected to cause significant reductions in food production and plant reproduction. Quantifying the impact of pollinator decline on food production requires survey methods that can identify insect and bee species responsible for pollination of specific crops. To address this issue, we compared the effectiveness of two survey methods, netting and pan traps, at capturing the pollinators of alfalfa, Medicago sativa. Alfalfa is a major component of forage for cows and an important ingredient in chicken feed. We also examined bee species richness and diversity with these two survey methods, and compared these measures among three different colors of pan traps. Netting was more effective at capturing known pollinators of alfalfa, especially those belonging to the Bombus and Apis genera. Pan traps captured a higher bee diversity relative to netting and, like previous studies, each survey method and each trap color was more efficient at capturing certain bee genera. However, without a priori knowledge of pollinators, neither survey method could identify which of the bee species captured could pollinate alfalfa. We therefore recommend direct observations when the goal of a study is to identify pollinators or link pollinator decline to food production.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Non-invasive authentication of mail packages using nuclear quadrupole resonance spectroscopy

The international postal network is one of the most widely used methods for correspondence throughout the world. Most postal traffic across the globe consists of legitimate interpersonal, business-consumer, and business-business communications. However, the global postal system is also utilized for criminal activity. In particular, it is often utilized to ship and distribute contraband, including illegal psychoactive drugs such as fentanyl and heroin, to consumers. Existing technological solutions are capable of identifying synthetic opioids and other illegal drugs within packages, but are accompanied by several disadvantages that make them unsuitable for large-scale authentication of international mail traffic. This paper presents a novel method for non-invasive authentication of mail packages that overcomes these challenges. The approach uses nuclear quadrupole resonance (NQR) spectroscopy to detect and quantify the presence of known active pharmaceutical ingredients (APIs) within the package. It has been experimentally demonstrated using a bench top prototype. Test results from a variety of package types demonstrate the effectiveness of the proposed authentication approach.

42 ENGINEERING↗
DOE OSTI2026

An end-to-end workflow for executing a classically bootstrapped variational quantum algorithm on an academic quantum computer

Academic quantum computing platforms often face unique challenges in executing quantum workloads due to fragmented software environments and limited engineering support. Unlike commercial ecosystems, academic devices typically evolve without full-stack integration in mind, making it difficult to run complex applications—such as variational quantum algorithms (VQA)—reliably and efficiently. Issues such as incompatible software layers and lack of automated job management significantly increase the overhead of theory-experiment collaboration. To address these challenges, we develop a modular, end-to-end workflow that decouples application-layer code from low-level hardware control, automates circuit submission and result collection, and supports fine-grained circuit-level job scheduling and recovery. The architecture employs a dual-end application programming interface (API) design, enabling robust operation across unstable or resource-constrained hardware backends. For practical use, the framework is lightweight and user-friendly, allowing rapid prototyping of full-stack workflows using basic Python tools. We validate this workflow on a high-fidelity trapped-ion quantum computer by demonstrating a variational quantum eigensolver (VQE) experiment with a classically bootstrapped ansatz initialization technique. The system successfully executed over 60,000 circuits across multiple molecular test cases with minimal human intervention, highlighting the framework’s effectiveness in enabling reproducible, resilient quantum experimentation in academic settings.

Clifford↗
DOE OSTI2018

Performance of the Belle II Conditions Database

The Belle II experiment at KEK observed its first collisions in the summer of 2018. Processing the large amounts of data that will be produced requires conditions data to be readily available to systems worldwide in a fast and efficient manner that is straightforward for both the user and maintainer. This was accomplished by relying on industry-standard tools and methods: the conditions database is built as an HTTP REST service using tools such as Swagger for the API interface development, Payara for the Java EE application server, and Squid for the caching proxy. This article presents the design of the Belle II conditions database production environment as well as details about the capabilities and performance during both Monte Carlo campaigns and data reprocessing.

Wood, Lynn S.↗