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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

A Systems Approach to Lower Cost Missions: Following the Rideshare Paradigm

Small-satellite rideshare capabilities and opportunities for low-cost access to space have been evolving over the past 10 years. Small space launch vehicle technology is rapidly being developed and demonstrated, including the Minotaur series and the Space X Falcon, among others, along with the lower cost launch facilities at Alaska's Kodiak Launch Complex, NASA's Wallops Flight Facility, and the Reagan Test Site in the Pacific. Demonstrated capabilities for the launch of multiple payloads have increased (and continue to increase) significantly. This will allow more efficient and cost-effective use of the various launch opportunities, including utilizing the excess capacity of the emerging Evolved Expendable Launch Vehicle (EELV)-based missions. The definition of standardized interfaces and processes, along with various user guides and payload implementation plans, has been developed and continues to be refined. Top-level agency policies for the support of low-cost access to space for small experimental payloads, such as the DoD policy structure on auxiliary payloads, have been defined and provide the basis for the continued refinement and implementation of these evolving technologies. Most importantly, the coordination and cooperative interfaces between the various stakeholders continues to evolve. The degree of this coordination and technical interchange is demonstrated by the wide stakeholder participation at the recent 2008 Small Payload Rideshare Workshop, held at NASA's Wallops Flight Facility. This annual workshop has been the major platform for coordination and technical interchange within the rideshare community and with the various sponsoring agencies. These developments have provided the foundation for a robust low-cost small payload rideshare capability. However, the continued evolution, sustainment, and utilization of these capabilities will require continued stakeholder recognition, support, and nourishing. Ongoing, coordinated effort, partnering, and support between stakeholders is essential to acquire the improved organizational processes and efficiencies required to meet the needs of the growing small payload community for low-cost access to space. Further, a mix of capabilities developed within the space community for Operationally Responsive Space, an international committee investigating space systems cross-compatibility, and an industry-based organization seeking small satellite "standardization" all work toward a new paradigm: sharing or leveraging resources amongst multiple users. The challenge: where are those users, and what is the best way to leverage them? What is leveraged-mass, power, cost-sharing? And how does one sort through these options? What policies may prevent the use of some options? Who are the "other users" that might share or leverage capabilities? This paper presents a systematic look at both the users and the launch options, and suggests a way forward.

access to space↗
DOE OSTI2024

The CMS Statistical Analysis and Combination Tool: Combine

This paper describes the Combine software package used for statistical analyses by the CMS Collaboration. The package, originally designed to perform searches for a Higgs boson and the combined analysis of those searches, has evolved to become the statistical analysis tool presently used in the majority of measurements and searches performed by the CMS Collaboration. It is not specific to the CMS experiment, and this paper is intended to serve as a reference for users outside of the CMS Collaboration, providing an outline of the most salient features and capabilities. Readers are provided with the possibility to run Combine and reproduce examples provided in this paper using a publicly available container image. Since the package is constantly evolving to meet the demands of ever-increasing data sets and analysis sophistication, this paper cannot cover all details of Combine. However, the online documentation referenced within this paper provides an up-to-date and complete user guide.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Object manipulation in immersive virtual environments: Hand Motion tracking technology and snap-to-fit function

There have been recent efforts to use virtual reality and manipulation to train construction workers and inspection. However, there is a lack of research efforts comparing and evaluating virtual manipulation hardware for construction tasks. Moreover, the current practice of virtual manipulation has limited functionality to guide users with the placement of objects in virtual environments. To address these issues, this paper presents 1) a detailed case study that compares three types of manipulation hardware (image-based, infrared-based, and magnetic-based) for construction applications and 2) a snap-to-fit method that improves the VM through solving limitations of the advanced virtual reality interaction metaphors. The latter enhances the placement process for manipulation by comparing two models (e.g., scan and BIM models) for proper placement in assembly scenarios. Finally, the case study results show that magnetic-based systems outperform others in construction scenarios. Lastly, the snap-to-fit function was validated in terms of accuracy and time performance.

42 ENGINEERING↗
DOE OSTI2024

Using stable isotopes to inform water resource management in forested and agricultural ecosystems

Present and future climatic trends are expected to markedly alter water fluxes and stores in the hydrologic cycle. In addition, water demand continues to grow due to increased human use and a growing population. Sustainably managing water resources requires a thorough understanding of water storage and flow in natural, agricultural, and urban ecosystems. Measurements of stable isotopes of water (hydrogen and oxygen) in the water cycle (atmosphere, soils, plants, surface water, and groundwater) can provide information on the transport pathways, sourcing, dynamics, ages, and storage pools of water that is difficult to obtain with other techniques. However, the potential of these techniques for practical questions has not been fully exploited yet. Here, we outline the benefits and limitations of potential applications of stable isotope methods useful to water managers, farmers, and other stakeholders. We also describe several case studies demonstrating how stable isotopes of water can support water management decision-making. Finally, we propose a workflow that guides users through a sequence of decisions required to apply stable isotope methods to examples of water management issues. We call for ongoing dialogue and a stronger connection between water management stakeholders and water stable isotope practitioners to identify the most pressing issues and develop best-practice guidelines to apply these techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluating Multimodel Ensemble Seasonal Climate Forecasts on Rangeland Plant Production in the California Annual Grassland

Seasonal precipitation and temperature directly affect total plant production in the California Annual Grassland (CAG). Technological advances have resulted in skillful seasonal climate forecasts (i.e., significant correlations between actual and forecasted climate), which could be input into plant production models to inform stocking and other rangeland management decisions. This study presents a procedure for forecasting plant production in the CAG ecosystem to predict annual plant production for grazing, restoration, or other rangeland management practices using a combination of historical gridMET climate data and seasonal hindcasts (i.e., retrospective forecasts, from the North American Multi-Model Ensemble program). Further, the results of this study first confirmed high forecast skill, throughout the growing season at all sites. We also identified skillful plant production forecasts across most of the growing season at two sites and in three of the seven forecasting months at one study site. Forecasting climate and end-of-year plant production across the growing season at three CAG sites allowed us to identify the places and times in the growing season when forecasting might be most helpful in informing management decisions. Integrating plant production forecasting into rangeland management practices could significantly improve rangeland management outcomes. These procedures provide a user guide for creating plant production forecasts for any given area of interest and may be applicable across a wide range of other agricultural and rangeland management systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Tutorial: Machine-Learning-Based CREASE-2D Analysis of 2D SAXS Profiles to Characterize Anisotropic Nanostructures in Soft Materials

We present a tutorial to guide users on how to extend the Computational Reverse Engineering Analysis of Scattering Experiments-2D (CREASE-2D) framework to interpret their experimental two-dimensional small-angle scattering (SAS) data from soft materials (e.g., polymers, peptide amphiphiles, biomolecular fibrils). Unlike most traditional SAS analysis approaches, which typically rely on azimuthally averaged onedimensional (1D) profiles, CREASE-2D utilizes the complete 2D scattering profile to reveal information about anisotropy in the structure. In past applications, CREASE has provided insights into complex structural features, including the cross-sectional shapes of assembled nanostructures and dispersity in these features, which are difficult to discern with existing analytical models. While (1D- ) CREASE has been applied to SANS and SAXS data, this tutorial shares the steps for implementing CREASE-2D using an example of a dipeptide solution system, for which we have SAXS data. We present details for these steps involved in using CREASE-2D to interpret SAXS profiles: how to preprocess SAXS data, define relevant structural features, generate three-dimensional real-space structures for specific values of these features, train a machine learning (ML) surrogate model to predict scattering profiles for given structural features, and optimize these features using genetic algorithms (GA). Then, we use these steps to interpret complex 2DSAXS data collected from dipeptide solutions that, in microscopy images, exhibit nanoscale structures that could be elliptical tubes/ flat tapes/cylinders or a combination of these cross sections. Open-source codes, computational hardware, and software requirements, as well as the strengths and limitations of this protocol, are also presented. We expect researchers working with (soft) biomaterials, peptide amphiphiles, amphiphilic polymer solutions, polymer nanocomposites, and blends of particles/polymers will find this CREASE-2D method and this tutorial of use.

CREASE↗
DOE OSTI2023

All polymer microfluidic chips—A fixed target sample delivery workhorse for serial crystallography

The development of x-ray free electron laser (XFEL) light sources and serial crystallography methodologies has led to a revolution in protein crystallography, enabling the determination of previously unobtainable protein structures and near-atomic resolution of otherwise poorly diffracting protein crystals. However, to utilize XFEL sources efficiently demands the continuous, rapid delivery of a large number of difficult-to-handle microcrystals to the x-ray beam. A recently developed fixed-target system, in which crystals of interest are enclosed within a sample holder, which is rastered through the x-ray beam, is discussed in detail in this Perspective. The fixed target is easy to use, maintains sample hydration, and can be readily modified to allow a broad range of sample types and different beamline requirements. Recent innovations demonstrate the potential of such microfluidic-based fixed targets to be an all-around “workhorse” for serial crystallography measurements. In conclusion, this Perspective will summarize recent advancements in microfluidic fixed targets for serial crystallography, examine needs for future development, and guide users in designing, choosing, and utilizing a fixed-target sample delivery device for their system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Accessible, uniform protein property prediction with a scikit-learn based toolset AIDE

Summary Protein property prediction via machine learning with and without labeled data is becoming increasingly powerful, yet methods are disparate and capabilities vary widely over applications. The software presented here, “Artificial Intelligence Driven protein Estimation (AIDE)”, enables instantiating, optimizing, and testing many zero-shot and supervised property prediction methods for variants and variable length homologs in a single, reproducible notebook or script by defining a modular, standardized application programming interface (API), i.e. drop-in compatible with scikit-learn transformers and pipelines. Availability and implementation AIDE is an installable, importable python package inheriting from scikit-learn classes and API and is installable on Windows, Mac, and Linux. Many of the wrapped models internal to AIDE will be effectively inaccessible without a GPU, and some assume CUDA. The newest stable, tested version can be found at https://github.com/beckham-lab/aide_predict and a full user guide and API reference can be found at https://beckham-lab.github.io/aide_predict/. Static versions of both at the time of writing can be found on Zenodo.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Optimizing injection for the storage ring proton-EDM experiment

The proposed proton electric dipole moment (pEDM) experiment at Brookhaven National Laboratory (BNL), to be built inside the alternating gradient synchrotron (AGS) tunnel, aims to measure the proton’s electric dipole moment with a sensitivity of 10 −29 𝑒 cm. This paper presents the design of the injection line from the AGS booster to the pEDM storage ring, utilizing portions of the existing booster-to-AGS (BtA) transfer line. Building on the symmetric-hybrid lattice design [, Comprehensive symmetric-hybrid ring design for a proton-EDM experiment at below 10 −29 𝑒 cm, Phys. Rev. D 105, 032001 (2022)], our study emphasizes rigorous optics matching, detailed particle and spin tracking, and systematic error mitigation essential for achieving a target sensitivity of 10 −29 𝑒 cm. This design preserves the proton’s vertical spin orientation within ±20 mrad, a critical requirement for the pEDM measurement. Particle and spin tracking simulations using the ray-tracing code Zgoubi [F. Méot, Zgoubi users’ guide, Technical Report, Brookhaven National Laboratory (BNL), Relativistic Heavy Ion Collider (RHIC), Upton, NY, 2012] validate the design’s performance, demonstrating its feasibility for this precision experiment. The simulation results demonstrate that both clockwise (CW) and counterclockwise (CCW) injection lines meet the stringent beam envelope and polarization requirements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

GIWAXS-SIIRkit: scattering intensity, indexing and refraction calculation toolkit for grazing-incidence wide-angle X-ray scattering of organic materials

Grazing-incidence wide-angle X-ray scattering (GIWAXS) has become an increasingly popular technique for quantitative structural characterization and comparison of thin films. For this purpose, accurate intensity normalization and peak position determination are crucial. At present, few tools exist to estimate the uncertainties of these measurements. Here, a simulation package is introduced called GIWAXS-SIIRkit , where SIIR stands for scattering intensity, indexing and refraction. Furthermore, the package contains several tools that are freely available for download and can be executed in MATLAB. The package includes three functionalities: estimation of the relative scattering intensity and the corresponding uncertainty based on experimental setup and sample dimensions; extraction and indexing of peak positions to approximate the crystal structure of organic materials starting from calibrated GIWAXS patterns; and analysis of the effects of refraction on peak positions. Additionally, each tool is based on a graphical user interface and designed to have a short learning curve. A user guide is provided with detailed usage instruction, tips for adding functionality and customization, and exemplary files.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey

Here, the rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for system operators. The large-scale integration of distributed energy resources (DERs) and the rapid exchange of measurement data via communication networks present major opportunities for advancing grid operations but also introduce greater uncertainty, higher data dimensionality, more complex network and device models, and challenging control and optimization problems. Deep reinforcement learning (DRL) algorithms are promising in addressing these challenges. However, they have not been effectively adapted for power systems applications, requiring extensive customization for implementation and evaluation. This has resulted in reproducibility challenges and a steep learning curve for researchers new to applying DRL algorithms to the power systems domain. To bridge these gaps, this tutorial aims to serve as a valuable resource for researchers interested in exploring learning-based algorithms to operate active power distribution networks. Specifically, this work presents a generalized process for translating sequential decision-making problems in power distribution systems into Markov decision process (MDP) formulations, illustrated through concrete grid service examples. Additionally, we introduce a simple environment design strategy to develop and evaluate example DRL algorithms for distribution system applications, complete with an included code repository to guide users through environment construction.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Experiences Readying Applications for Exascale

The advent of Exascale computing invites an assessment of existing best practices for developing application readiness on the world's largest supercomputers. This work details observations from the last four years in preparing scientific applications to run on the Oak Ridge Leadership Computing Facility's (OLCF) Frontier system. This paper addresses a range of topics in software including programmability, tuning, and portability considerations that are key to moving applications from existing systems to future installations. A set of representative workloads provides case studies for general system and software testing. We evaluate the use of early access systems for development across several generations of hardware. Finally, we discuss how best practices were identified and disseminated to the community through a wide range of activities including user-guides and trainings. We conclude with recommendations for ensuring application readiness on future leadership computing systems.

exascale↗
DOE OSTI2022

R-Stream

R-Stream version 3.21.3, including its TensorFlow front-end (TFRCC) and LLVM 9 front-end. This version includes the features developed in the Portacy Phase II contract. R-Stream is a source-to-source automatic parallelization engine. For usage, please refer to the manuals (In particular, user guide and release notes) available in the package.

Meister, Benoit [Reservoir Labs, Inc., Portland, O↗
DOE OSTI2020

Auto-BLAST (AutoBLAST) [SWR-20-93]

Battery life modeling often involves a lot of manual parameter fitting and is not easy for users to adopt the model and use it. To reduce the difficulties for users to adopt battery lifetime models, an automatic battery lifetime modeling analysis and simulation tool suite, Auto-BLAST, has been developed. Auto-BLAST includes a lithium-loss-base life model, a battery electric model, and an algorithm which automatically fits all key parameters in the electric and life models using user provided data. The models and algorithm are packaged into two user-friendly GUIs, Auto-LifeMod, for easy battery life prognostic model fitting and Auto-LifeSim, for easy battery lifetime simulation. The lithium-loss-based life model adopts a similar model framework that models degradations using aging rate models, using battery cycling data to predict battery degradation and expected lifetime. The electric model is an equivalent circuit model which simulates battery voltage responses based on current/power demand profiles. The auto-fitting algorithm uses the user-input data to generate custom battery life model(s). Two GUIs, wrapping around the life model and the auto-fitting algorithm, provide friendly interfaces for users to generate a life model and use it for case study. One GUI requires summary data from accelerated battery life degradation tests as an input and produces battery life models predicting (a) capacity degradation, and (b) resistance growth of the battery. The GUI displays the electrical model response and the input experimental data against the life model predictions with fitted model parameters and fitting error. The GUI also generates an output file to save the fitted model parameters. The second GUI uses the life model from the first GUI and a user-defined battery cycling profile to predict battery lifetime degradation and expected lifetime. Predicted capacity and resistance vs. time are plotted in the GUI and saved to output file. There is a user guide for both GUIs

Mishra, Partha↗
DOE OSTI2023

Aquapv

This project brings together a multidisciplinary team to develop tools and analysis that will help U.S. industry accelerate deployment of floating PV on reservoirs and estuaries through producing: • A tool to assess potential environmental interactions of FPV on reservoirs and estuaries and thresholds for corresponding impacts. • A tailored FPV-specific techno-economic tool to guide users on financial performance and corresponding design considerations. • A synthesized tool, AquaPV, that combines the environmental and economic tools. • A set of case studies on an open-loop reservoir with a traditional hydropower plant (i.e., connected to a natural waterbody), closed-loop reservoir with a proposed pumped storage hydropower plant (i.e., manmade and disconnected from a natural waterbody), and a marine estuary (i.e., where a river meets the ocean), with an FPV system designed and modeled for each site. • A review of regulatory, environmental, and stakeholder opportunities and challenges to identify clear regulatory and environmental pathways to siting FPV on U.S. reservoirs. • A novel, high-fidelity geospatial resource assessment to estimate FPV technical potential on reservoirs in the United States.

Phillips, TylerB. [Idaho National Laboratory (INL)↗
DOE OSTI2024

RAIS Preliminary Remediation Goals for Chemicals

Chemical preliminary remediation goals (PRGs) (https://rais.ornl.gov/cgibin/prg/PRG_search?select=chem) are calculated by selecting the applicable chemical(s) together with the applicable media, land use, and exposure route combination. If “site-specific” is selected as the PRG type, the following page will show the equations and exposure parameters used for deriving the PRGs, and some of the parameter values may be changed as necessary. If “default” is selected as the PRG type, the RAIS will proceed directly to the results page. Multiple chemicals can be selected. Results can be downloaded in .xlsx or .pdf formats. Additionally, the session inputs for the PRG calculator can be saved for future use and recalled by the PRG calculator. The derivation of the selected PRG and the applicable equations and exposure parameters can be reviewed in more detail using the RAIS Chemical PRG Calculator User Guide (https://rais.ornl.gov/tools/rais_chemical_prg_guide.html).

Manning, Karessa [Oak Ridge National Laboratory (O↗
DOE OSTI2024

RAIS Preliminary Remediation Goals for Radionuclides

Radionuclide PRGs (https://rais.ornl.gov/cgi-bin/prg/PRG_search?select=rad) are calculated by selecting the applicable radionuclide(s) together with the applicable media, land use, and exposure route combination. If “site-specific” is selected as the PRG type, the following page will show the equations and exposure parameters used for deriving the PRGs, and some of the parameter values may be changed as necessary. If “default” is selected as the PRG type, the RAIS will proceed directly to the results page. Multiple radionuclides can be selected with this tool. Results can be downloaded in .xlsx or .pdf formats. Additionally, the session inputs for the PRG calculator can be saved for future use and recalled by the PRG calculator. The derivation of the selected PRG(s) and the applicable equations and exposure parameters can be reviewed in more detail using the RAIS Radionuclide PRG Calculator User Guide available here: https://rais.ornl.gov/tools/rais_rad_prg_guide.html. html.

Manning, Karessa [Oak Ridge National Laboratory (O↗