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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 865 records · Page 48

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NASA NTRS2000

A Real-Time Method for Estimating Viscous Forebody Drag Coefficients

This paper develops a real-time method based on the law of the wake for estimating forebody skin-friction coefficients. The incompressible law-of-the-wake equations are numerically integrated across the boundary layer depth to develop an engineering model that relates longitudinally averaged skin-friction coefficients to local boundary layer thickness. Solutions applicable to smooth surfaces with pressure gradients and rough surfaces with negligible pressure gradients are presented. Model accuracy is evaluated by comparing model predictions with previously measured flight data. This integral law procedure is beneficial in that skin-friction coefficients can be indirectly evaluated in real-time using a single boundary layer height measurement. In this concept a reference pitot probe is inserted into the flow, well above the anticipated maximum thickness of the local boundary layer. Another probe is servomechanism-driven and floats within the boundary layer. A controller regulates the position of the floating probe. The measured servomechanism position of this second probe provides an indirect measurement of both local and longitudinally averaged skin friction. Simulation results showing the performance of the control law for a noisy boundary layer are then presented.

Whitmore, Stephen A.↗

DOE OSTI2012

Search for a heavy top-quark partner in final states with two leptons with the ATLAS detector at the LHC

CERN-LHC. Search for direct pair production of heavy top-quark partners in proton-proton interactions of centre-of-mass energy of 7 TeV with a final state containing 2 leptons. The data sample taken during 2011 has a total integrated luminosity of 4.7 fb-1. Two models are compared with the data, one with a spin 1/.2 heavy top-quark and a heavy neutral partner and the other a supersymmetric scalar top quark and a massless neutralino. See the text of the paper for more details.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

C3F: Collaborative Container-based Model Coupling Framework

Solving complex real-world grand challenge problems requires in-depth collaboration of researchers from multiple disciplines. Such collaboration often involves harnessing multiscale and multi-dimensional data and combining models from different fields to simulate systems. However, the progress on this front has been limited mainly due to significant gaps in domain knowledge and tools that are typically employed in silos of the domains. Researchers from different fields face considerable barriers to understanding and reusing each other’s data/models in order to collaborate effectively. For example, in solving the global sustainability problems, researchers from hydrology, climate science, agriculture, and economics need to run their respective models to study different components of the global and local food, energy and water systems while, at the same time, need to interact with other researchers and integrate the results of one model with another. Developing this kind of model coupling workflow calls for (1) a large amount of data being processed and exchanged across domains and organizations, (2) identifying and processing the output of one model to make it ready for integration into another model, (3) controlling the workflow dynamically so that it runs until a certain convergence condition or other criteria is met, and (4) close collaboration among the modelers to explore, tune, and test the configuration and data transformation needed to link the models. We have developed C3F, a flexible collaborative model coupling framework to help researchers accelerate their model integration and linking efforts by leveraging advanced cyberinfrastructure such as high-performance computing and virtual containers. In this paper, we describe our experience and lessons learned in developing this cyberinfrastructure solution to support the linking of Water Balance Model (WBM) and SIMPLE-G agricultural economic model in an NSF funded INFEWS project and a DOE-funded Program on Coupled Human and Earth Systems (PCHES) to study the implications of groundwater scarcity for food-energy-water systems. The C3F model coupling framework can be extended to facilitate other model linkages as well.

containerization↗

NASA NTRS2024

Crew Health and Performance Integrated Data Architecture Project Updates

Future Human Exploration missions will face new constraints as crews move further from terrestrial communication, resupply, and the real-time support enjoyed by Low Earth Orbit missions today. Exploration crews will need to be more self-reliant and able to respond to emergencies without immediate support from ground-based personnel. A new generation of technologies, employing advanced analytical and predictive modeling techniques, is needed to assist the crew’s work, help maintain their health, and inform the decisions they make on these future Exploration missions. The Crew Health and Performance Integrated Data Architecture (CHP-IDA) project is laying a foundation for these future technologies by integrating sources of data generated by and around the crew and then providing them though common data models and Application Programming Interfaces to external systems. The combined data model makes comprehensive Crew Health and Performance data accessible and more meaningful to the decision-making process. This presentation will describe the currently ongoing effort to develop and evaluate a path-to-flight concept of the CHP-IDA software, current integrations, updates to the architecture, and examples of exploration scenarios in which CHP-IDA would be used.

B Schmitt↗

DOE OSTI2016

Integrating copper toxicity and climate change to understand extinction risk to two species of pond‐breeding anurans

Abstract Chemical contamination is often suggested as an important contributing factor to amphibian population declines, but direct links are rarely reported. Population modeling provides a quantitative method to integrate toxicity data with demographic data to understand the long‐term effects of contaminants on population persistence. In this study we use laboratory‐derived embryo and larval toxicity data for two anuran species to investigate the potential for toxicity to contribute to population declines. We use the southern toad ( Anaxyrus terrestris ) and the southern leopard frog ( Lithobates sphenocephalus ) as model species to investigate copper (Cu) toxicity. We use matrix models to project populations through time and quantify extinction risk (the probability of quasi‐extinction in 35 yr). Life‐history parameters for toads and frogs were obtained from previously published literature or unpublished data from a long‐term (>35 yr) data set. In addition to Cu toxicity, we investigate the role of climate change on amphibian populations by including the probability of early pond drying that results in catastrophic reproductive failure ( CRF , i.e., complete mortality of all larval individuals). Our models indicate that CRF is an important parameter for both species as both were unable to persist when CRF probability was >50% for toads or 40% for frogs. Copper toxicity alone did not result in significant effects on extinction risk unless toxicity was very high (>50% reduction in survival parameters). For toads, Cu toxicity and high probability of CRF both resulted in high extinction risk but no synergistic (or greater than additive) effects between the two stressors occurred. For leopard frogs, in the absence of CRF survival was high even under Cu toxicity, but with CRF Cu toxicity increased extinction risk. Our analyses highlight the importance of considering multiple stressors as well as species differences in response to those stressors. Our models were consistently most sensitive to juvenile and adult survival, further suggesting the importance of terrestrial stages to population persistence. Future models will incorporate multiple wetlands with different combinations of stressors to understand if our results for a single wetland result in a population sink within the landscape.

Weir, Scott M.↗

NASA NTRSDate not supplied

Development Testing of the Gateway Integrated Bipropellant Refueling Subsystem

The Lunar Gateway is a deep space orbiting outpost being developed by the National Aeronautics and Space Administration (NASA) in partnership with the European Space Agency (ESA) and other domestic and international partners. Because Gateway is a vital component of NASA’s Artemis program supporting long-term human exploration of the moon, it is designed for on-orbit refueling to allow for a longer life performance. The reaction control system (RCS) bipropellant refueling system onboard the station will have the capability to transfer propellants, monomethyl hydrazine (MMH) and mixed oxides of nitrogen-3 (MON-3), under controlled conditions and will span across three modules: the European System Providing Refueling Infrastructure and Telecommunication Refueling Module (ESPRIT-RM or ERM), the Habitation and Logistics Outpost (HALO), and the Propulsion and Power Element (PPE). These propellant transfers are complex operations with known hazards, and one of the risk areas is exceeding the flight system’s maximum design pressure (MDP) in priming and refueling pause/stop operations. In priming, liquid propellant is transferred from a pressurized source tank to evacuated transfer lines, which could result in excessive transient surge pressures and potentially damage hardware. In refueling pause/stop operations, fast-acting isolation valves (IV) are closed, which could lead to damaging water hammer. To mitigate these risks and develop the system, a collaborative NASA/ESA/Thales Alenia Space – United Kingdom (TAS-UK) test program was completed at TAS-UK on a simplified refueling breadboard representing ERM, HALO, and PPE bipropellant transfer systems. The objectives of the integrated breadboard testing were to (1) gather performance data to characterize and demonstrate critical refueling operations, (2) help inform flight designs, and (3) to validate numerical models that can be extended to predicting flight system performance. Integrated breadboard test data has shown the architected system performance is closing initial design assumptions. Further testing with propellant on a higher fidelity fluid simulator and analysis are planned.

Adela D Han↗

NASA NTRSDate not supplied

Development Testing of the Gateway Integrated Bipropellant Refueling Subsystem

Adela D Han↗

DOE OSTI2018

AN INTEGRATED CO2 MONITORING SYSTEM: FIRST INVERSION RESULT

History-matching through inverse modeling of pressure monitoring data delivers a first-order approximation of a reservoir model at an industrial-scale CO2 injection site. The reservoir model forms the backbone of an integrated monitoring system (IMS) for subsurface CO2 storage, which will ultimately encompass the integrated analysis of pressure-temperature and geophysical seismic monitoring data. First inversion results confirm the existence of a hydraulically isolating thin layer in the lower injection interval, known as mudstone baffle.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2011

Incorporating JULES into NASA's Land Information System (LIS) and Investigations of Land-Atmosphere Coupling

NASA's Land Information System (LIS; lis.gsfc.nasa.gov) is a flexible land surface modeling and data assimilation framework developed over the past decade with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. LIS features a high performance and flexible design, and operates on an ensemble of land surface models for extension over user-specified regional or global domains. The extensible interfaces of LIS allow the incorporation of new domains, land surface models (LSMs), land surface parameters, meteorological inputs, data assimilation and optimization algorithms. In addition, LIS has also been demonstrated for parameter estimation and uncertainty estimation, and has been coupled to the Weather Research and Forecasting (WRF) mesoscale model. A visiting fellowship is currently underway to implement JULES into LIS and to undertake some fundamental science on the feedbacks between the land surface and the atmosphere. An overview of the LIS system, features, and sample results will be presented in an effort to engage the community in the potential advantages of LIS-JULES for a range of applications. Ongoing efforts to develop a framework for diagnosing land-atmosphere coupling will also be presented using the suite of LSM and PBL schemes available in LIS and WRF along with observations from the U. S .. Southern Great Plains. This methodology provides a potential pathway to study factors controlling local land-atmosphere coupling (LoCo) using the LIS-WRF system, which will serve as a testbed for future experiments to evaluate coupling diagnostics within the community.

Santanello, Joseph↗

NASA NTRS2010

Maintaining a Local Data Integration System in Support of Weather Forecast Operations

Since 2000, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) have used a local data integration system (LDIS) as part of their forecast and warning operations. Each has benefited from 3-dimensional analyses that are delivered to forecasters every 15 minutes across the peninsula of Florida. The intent is to generate products that enhance short-range weather forecasts issued in support of NWS MLB and SMG operational requirements within East Central Florida. The current LDIS uses the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS) package as its core, which integrates a wide variety of national, regional, and local observational data sets. It assimilates all available real-time data within its domain and is run at a finer spatial and temporal resolution than current national- or regional-scale analysis packages. As such, it provides local forecasters with a more comprehensive and complete understanding of evolving fine-scale weather features. Recent efforts have been undertaken to update the LDIS through the formal tasking process of NASA's Applied Meteorology Unit. The goals include upgrading LDIS with the latest version of ADAS, incorporating new sources of observational data, and making adjustments to shell scripts written to govern the system. A series of scripts run a complete modeling system consisting of the preprocessing step, the main model integration, and the post-processing step. The preprocessing step prepares the terrain, surface characteristics data sets, and the objective analysis for model initialization. Data ingested through ADAS include (but are not limited to) Level II Weather Surveillance Radar- 1988 Doppler (WSR-88D) data from six Florida radars, Geostationary Operational Environmental Satellites (GOES) visible and infrared satellite imagery, surface and upper air observations throughout Florida from NOAA's Earth System Research Laboratory/Global Systems Division/Meteorological Assimilation Data Ingest System (MADIS), as well as the Kennedy Space Center ICape Canaveral Air Force Station wind tower network. The scripts provide NWS MLB and SMG with several options for setting a desirable runtime configuration of the LDIS to account for adjustments in grid spacing, domain location, choice of observational data sources, and selection of background model fields, among others. The utility of an improved LDIS will be demonstrated through postanalysis warm and cool season case studies that compare high-resolution model output with and without the ADAS analyses. Operationally, these upgrades will result in more accurate depictions of the current local environment to help with short-range weather forecasting applications, while also offering an improved initialization for local versions of the Weather Research and Forecasting model.

Watson, Leela R.↗

DOE OSTI2017

Thermosyphon Cooler Hybrid System for Water Savings in an Energy-Efficient HPC Data Center: Modeling and Installation: Preprint

The Thermosyphon Cooler Hybrid System (TCHS) integrates the control of a dry heat rejection device, the thermosyphon cooler (TSC), with an open cooling tower. A combination of equipment and controls, this new heat rejection system embraces the 'smart use of water,' using evaporative cooling when it is most advantageous and then saving water and modulating toward increased dry sensible cooling as system operations and ambient weather conditions permit. Innovative fan control strategies ensure the most economical balance between water savings and parasitic fan energy. The unique low-pressure-drop design of the TSC allows water to be cooled directly by the TSC evaporator without risk of bursting tubes in subfreezing ambient conditions. Johnson Controls partnered with the National Renewable Energy Laboratory (NREL) and Sandia National Laboratories to deploy the TSC as a test bed at NREL's high-performance computing (HPC) data center in the first half of 2016. Located in NREL's Energy Systems Integration Facility (ESIF), this HPC data center has achieved an annualized average power usage effectiveness rating of 1.06 or better since 2012. Warm-water liquid cooling is used to capture heat generated by computer systems direct to water; that waste heat is either reused as the primary heat source in the ESIF building or rejected using evaporative cooling. This data center is the single largest source of water and power demand on the NREL campus, using about 7,600 m3 (2.0 million gal) of water during the past year with an hourly average IT load of nearly 1 MW (3.4 million Btu/h) -- so dramatically reducing water use while continuing efficient data center operations is of significant interest. Because Sandia's climate is similar to NREL's, this new heat rejection system being deployed at NREL has gained interest at Sandia. Sandia's data centers utilize an hourly average of 8.5 MW (29 million Btu/h) and are also one of the largest consumers of water on Sandia's site. In addition to describing the installation of the TSC and its integration into the ESIF, this paper focuses on the full heat rejection system simulation program used for hourly analysis of the energy and water consumption of the complete system under varying operating scenarios. A follow-up paper will detail the test results. The evaluation of the TSC's performance at NREL will also determine a path forward at Sandia for possible deployment in a large-scale system not only for data center use but also possibly site wide.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Delineating Hydrofacies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics

This study develops a new framework of facies-based data assimilation for characterizing spatial distribution of hydrofacies and estimating their associated hydraulic properties. This framework couples ensemble data assimilation with transition probability-based geostatistical model via a parameterization based on a level set function. The nature of ensemble data assimilation makes the framework efficient and flexible to be integrated with various types of observation data. The transition probability-based geostatistical model keeps the updated hydrofacies distributions under geological constrains. The framework is illustrated by using a two-dimensional synthetic study that estimates hydrofacies spatial distribution and permeability in each hydrofacies from transient head data. Our results show that the proposed framework can characterize hydrofacies distribution and associated permeability with adequate accuracy even with limited direct measurements of hydrofacies. Our study provides a promising starting point for hydrofacies delineation in complex real problems.

Song, Xuehang↗

DOE OSTI2019

Machine learning framework for assessment of microbial factory performance

Metabolic models can estimate intrinsic product yields for microbial factories, but such frameworks struggle to predict cell performance (including product titer or rate) under suboptimal metabolism and complex bioprocess conditions. On the other hand, machine learning, complementary to metabolic modeling necessitates large amounts of data. Building such a database for metabolic engineering designs requires significant manpower and is prone to human errors and bias. We propose an approach to integrate data-driven methods with genome scale metabolic model for assessment of microbial bio-production (yield, titer and rate). Using engineered E. coli as an example, we manually extracted and curated a data set comprising about 1200 experimentally realized cell factories from ~100 papers. We furthermore augmented the key design features (e.g., genetic modifications and bioprocess variables) extracted from literature with additional features derived from running the genome-scale metabolic model iML1515 simulations with constraints that match the experimental data. Then, data augmentation and ensemble learning (e.g., support vector machines, gradient boosted trees, and neural networks in a stacked regressor model) are employed to alleviate the challenges of sparse, non-standardized, and incomplete data sets, while multiple correspondence analysis/principal component analysis are used to rank influential factors on bio-production. The hybrid framework demonstrates a reasonably high cross-validation accuracy for prediction of E.coli factory performance metrics under presumed bioprocess and pathway conditions (Pearson correlation coefficients between 0.8 and 0.93 on new data not seen by the model).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Assessment of age, origin, and sustainability of fossil aquifers: A geochemical and remote sensing-based approach

An integrated approach was developed to assess the sustainability, origin, age, evolution, and groundwater potential of large fossil aquifers using the Mega Aquifer System (MAS) (area: 2 x 10(6) km(2)) in the Arabian Peninsula as a test site. A two-step exercise was adopted, the sustainability of the MAS was first investigated through the analysis of GRACE data and land surface model (LSM) outputs. Then, integrated geochemical, remote sensing, and field studies were conducted to address the age, origin, hydrogeological setting, and evolution of the southern sections of the MAS (Rub Al Khali Aquifer System (RAKAS)), the area identified from the analysis of GRACE and ISMs as being the most suited for sustainable development. Analysis of GRACE and ISMs revealed: (1) the MAS central and northern sections are experiencing high groundwater extraction (6.6 km(3)/yr) and depletion rates (-2.8 +/- 0.8 km(3)/yr) with minimum balancing potential through aquifer capture processes, yet sustainability could be achieved by reducing annual extraction by 2.8 km(3), and (2) the MAS southern sections (Rub Al Khali Aquifer System (RAKAS)) are experiencing low groundwater depletion rates (eastern RAKAS: -1.8 +/- 1.4 mm/yr) to steady-state conditions (western RAKAS: -0.73 +/- 1.4 mm/yr). Geochemical, remote sensing, and field investigations over the RAKAS revealed: (1) the presence of west to east trending drainage networks and large-scale groundwater flow systems consistent with a meteoric source (precipitation over Red Sea Hills); (2) increasing Cl-36 model ages along groundwater flow directions (up to 970,000 years), indicating aquifer recharge in wet Pleistocene periods; (3) progressive depletion in the O and H stable isotopic compositions of aquifers with increasing distance from the Red Sea Hills basement outcrops, indicating modest recharge during prevailing dry conditions; and (4) the presence of relatively fresh (TDS: 800-2800 mg/L) and nonradioactive (Ra-226 + Ra-228 < 0.185 Bq/kg) water in western RAKAS. Findings suggest that sustainable agricultural development is achievable at current extraction rates in western RAKAS and provide a replicable and cost-effective model.

Fossil aquifers↗

NASA NTRS2001

Processors, Pipelines, and Protocols for Advanced Modeling Networks

Predictive capabilities arise from our understanding of natural processes and our ability to construct models that accurately reproduce these processes. Although our modeling state-of-the-art is primarily limited by existing computational capabilities, other technical areas will soon present obstacles to the development and deployment of future predictive capabilities. Advancement of our modeling capabilities will require not only faster processors, but new processing algorithms, high-speed data pipelines, and a common software engineering framework that allows networking of diverse models that represent the many components of Earth's climate and weather system. Development and integration of these new capabilities will pose serious challenges to the Information Systems (IS) technology community. Designers of future IS infrastructures must deal with issues that include performance, reliability, interoperability, portability of data and software, and ultimately, the full integration of various ES model systems into a unified ES modeling network.

Coughlan, Joseph↗

NASA NTRS2014

Application of the Tool for Turbine Engine Closed-loop Transient Analysis (TTECTrA) for Dynamic Systems Analysis

Systems analysis involves steady-state simulations of combined components to evaluate the steady-state performance, weight, and cost of a system; dynamic considerations are not included until later in the design process. The Dynamic Systems Analysis task, under NASAs Fixed Wing project, is developing the capability for assessing dynamic issues at earlier stages during systems analysis. To provide this capability the Tool for Turbine Engine Closed-loop Transient Analysis (TTECTrA) has been developed to design a single flight condition controller (defined as altitude and Mach number) and, ultimately, provide an estimate of the closed-loop performance of the engine model. This tool has been integrated with the Commercial Modular Aero-Propulsion System Simulation 40,000(CMAPSS40k) engine model to demonstrate the additional information TTECTrA makes available for dynamic systems analysis. This dynamic data can be used to evaluate the trade-off between performance and safety, which could not be done with steady-state systems analysis data. TTECTrA has been designed to integrate with any turbine engine model that is compatible with the MATLABSimulink (The MathWorks, Inc.) environment.

systems analysis↗

NASA NTRS2014

NMME Monthly / Seasonal Forecasts for NASA SERVIR Applications Science

This work details use of the North American Multi-Model Ensemble (NMME) experimental forecasts as drivers for Decision Support Systems (DSSs) in the NASA / USAID initiative, SERVIR (a Spanish acronym meaning "to serve"). SERVIR integrates satellite observations, ground-based data and forecast models to monitor and forecast environmental changes and to improve response to natural disasters. Through the use of DSSs whose "front ends" are physically based models, the SERVIR activity provides a natural testbed to determine the extent to which NMME monthly to seasonal projections enable scientists, educators, project managers and policy implementers in developing countries to better use probabilistic outlooks of seasonal hydrologic anomalies in assessing agricultural / food security impacts, water availability, and risk to societal infrastructure. The multi-model NMME framework provides a "best practices" approach to probabilistic forecasting. The NMME forecasts are generated at resolution more coarse than that required to support DSS models; downscaling in both space and time is necessary. The methodology adopted here applied model output statistics where we use NMME ensemble monthly projections of sea-surface temperature (SST) and precipitation from 30 years of hindcasts with observations of precipitation and temperature for target regions. Since raw model forecasts are well-known to have structural biases, a cross-validated multivariate regression methodology (CCA) is used to link the model projected states as predictors to the predictands of the target region. The target regions include a number of basins in East and South Africa as well as the Ganges / Baramaputra / Meghna basin complex. The MOS approach used address spatial downscaling. Temporal disaggregation of monthly seasonal forecasts is achieved through use of a tercile bootstrapping approach. We interpret the results of these studies, the levels of skill by several metrics, and key uncertainties.

Robertson, Franklin R.↗

DOE OSTI2025

Empowering Geothermal Research: The Geothermal Data Repository's New AI Research Assistant

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has integrated a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets to create an Artificially Intelligent (AI) research assistant. By leveraging work done to make GDR metadata machine-readable and an open-source LLM integration model called the Energy Language Model, developed by the National Renewable Energy Laboratory, AskGDR serves as a virtual research assistant to GDR users. It provides answers to a variety of user-provided questions using natural language processing and generative machine learning. Users can get answers to questions about specific datasets, including inquiries about the equipment, assumptions and methodologies used in the origination of the data; or more abstract questions, such as the applicability of data to specific research fields. AskGDR improves the discoverability of geothermal data by helping guide users to datasets beyond simple keyword searches. It enables users to find data based on properties of the data, discover information contained within supporting documents, and explore data from projects related to their research objectives. This paper will outline the development, integration, output, and efficacy of the AskGDR LLM, including adherence to scientific rigor through improvements designed to increase the accuracy of generated answers, avoid speculation, and provide proper references for all resources used.

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