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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Characterizing Walk Trips in communities by Using Data from 2009 National Household Travel Survey, American Community Survey, and Other Sources

Non-motorized travel (i.e. walking and bicycling) are of increasing interest to the transportation profession, especially in context with energy consumption, reducing vehicular congestion, urban development patterns, and promotion of healthier life styles. This research project aimed to identify factors impacting the amount of travel for both walk and bike trips at the Census block group or tract level, using several public and private data sources. The key survey of travel behavior is the 2009 National Household Travel Survey (NHTS) which had over 87,000 walk trips for persons 16 and over, and over 6000 bike trips for persons 16 and over. The NHTS, in conjunction with the Census Bureau s American Community Survey, street density measures using Census Bureau TIGER, WalkScore , Nielsen Claritas employment estimates, and several other sources were used for this study. Stepwise Logistic Regression modeling techniques as well as Discriminant Analysis were applied using the integrated data set. While the models performed reasonably well for walk trips, travel by bike was abandoned due to sparseness of data. This paper discusses data sources utilized and modeling processes conducted under this study. It also presents a summary of findings and addresses data challenges and lesson-learned from this research effort.

NHTS↗
DOE OSTI2018

Raising the Rent Premium: Moving Green Building Research Beyond Certifications and Rent

Advocates for high-performing, energy efficient buildings understand the hurdles to proving the business case to spur development and investment decisions among commercial real estate owners and investors. Although there is significant evidence that green buildings exhibit increased financial performance via decreased operating expenses and non-energy benefits such as workplace comfort and employee productivity, green building practitioners still face an uphill battle when asked to prove the profits, most frequently pointing to rent premium as an indicator to attract investment. This paper documents a recently conducted literature review commissioned by the US Department of Energy (DOE) of 39 peer-reviewed and published academic papers pertaining to green or energy-related building attributes and financial performance. The review (first published here) indicated that 27 papers found a positive correlation between green building certifications and rents. If this is the case, then why are green building development and energy efficiency retrofits not the norm for the industry? This paper first synthesizes the recent literature review to illustrate that the existing body of empirical evidence supports green building investment. Next, it describes a workshop of leading academics in real estate sustainability and finance who met in late 2017 to discuss gaps in green building research and assess data challenges in their fields. Insights synthesized the workshop were used to drive new DOE led research that can provide value and understanding to investors and builders of high performing buildings. Finally, the paper offers insights into why the market is not properly pricing building efficiency, with factors ranging from improper mitigation of risks associated with energy and lack of transparency and responsibility felt by building end users, and what further steps are needed to transform green building investment practices in the commercial real estate industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

STAR'S Approach to Highly Efficient End-To-End Grid Production

STAR's RHIC computing facility provides over 15K dedicated slots for data reconstruction. This number of slots is not always sufficient to satisfy an ambitious and data-challenging physics program, and harvesting resources from outside facilities is paramount to scientific success. However, constraints of remote sites (e.g. CPU time limits) do not always provide the flexibility of a dedicated farm. STAR has a breadth of smaller datasets (both in runtime and size) that can be easily offloaded to remote facilities with many such limits. Scavenged resources can be run with efficiency comparable to that of the local production and contributes additional computing time to an experiment that runs every year and therefore needs fast turnaround. We will discuss STAR's software stack of our grid production framework including features dealing with multi-site submission, automated re-submission, job tracking, as well as new challenges and possible improvements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2013

Progress in Offline Software for the GlueX Experiment at Jefferson Lab

The GlueX Experiment will begin taking data next year at CEBAF at Jefferson Lab. CEBAF is being upgraded to deliver 12 GeV electrons and GlueX will receive a secondary, tagged photon beam with linear polarization in a new experimental hall, Hall D. Over the past few years the collaboration has worked preparing offline software to do event simulation, event reconstruction, and data analysis in preparation for the beginning of operations. Progress in these areas will be presented along with a description of data challenges completed to test scaling of computing infrastructure.

Ito, Mark↗
DOE OSTI2020

Machine Learning in Nuclear Thermal Hydraulics, Part 2: Opportunities and Perspectives

This paper summarizes the issues in nuclear thermal hydraulics (NTH), mainly from the three named aspects, and illustrates potential opportunities of machine learning (ML) applications in dealing with these issues. These issues are problems in simultaneously realizing computational efficiency and accuracy of system thermal hydraulic (STH) modeling and simulation, uncertainty in the scaling analysis for the best-estimate calculations of transient and accident analysis, and difficulties in optimal data production and usage for the development and assessment of evaluation models. Due to ML uncertainty, data insufficiency, and lack of cognition about how to efficiently incorporate knowledge and data, challenges of adapting ML techniques still exist. Accordingly, several perspectives are proposed to provide insights for ML applications in solving these issues.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1989

Summary of longitudinal stability and control parameters as determined from Space Shuttle Challenger flight test data

Estimates of longitudinal stability and control parameters for the space shuttle were determined by applying a maximum likelihood parameter estimation technique to Challenger flight test data. The parameters for pitching moment coefficient, C(m sub alpha), (at different angles of attack), pitching moment coefficient, C(m sub delta e), (at different elevator deflections) and the normal force coefficient, C(z sub alpha), (at different angles of attack) describe 90 percent of the response to longitudinal inputs during Space Shuttle Challenger flights with C(m sub delta e) being the dominant parameter. The values of C(z sub alpha) were found to be input dependent for these tests. However, when C(z sub alpha) was set at preflight predictions, the values determined for C(m sub delta e) changed less than 10 percent from the values obtained when C(z sub alpha) was estimated as well. The preflight predictions for C(z sub alpha) and C(m sub alpha) are acceptable values, while the values of C(z sub delta e) should be about 30 percent less negative than the preflight predictions near Mach 1, and 10 percent less negative, otherwise.

Suit, William T.↗
DOE OSTI2020

Automatic Classification of Biological Targets in a Tidal Channel Using a Multibeam Sonar

Multibeam sonars are widely used for environmental monitoring of fauna at marine renewable energy sites. However, they can rapidly accrue vast volumes of data, which poses a challenge for data processing. Here, using data from a deployment in a tidal channel with peak currents of 1–2 m s –1 , we demonstrate the data-reduction benefits of real-time automatic classification of targets detected and tracked in multibeam sonar data. First, we evaluate classification capabilities for three machine learning algorithms: random forests, support vector machines, and k-nearest neighbors. For each algorithm, a hill-climbing search optimizes a set of hand-engineered attributes that describe tracked targets. Here, the random forest algorithm is found to be most effective—in postprocessing, discriminating between biological and nonbiological targets with a recall rate of 0.97 and a precision of 0.60. In addition, 89% of biological targets are correctly classified as either seals, diving birds, fish schools, or small targets. Model dependence on the volume of training data is evaluated. Second, a real-time implementation of the model is shown to distinguish between biological targets and nonbiological targets with nearly the same performance as in postprocessing. From this, we make general recommendations for implementing real-time classification of biological targets in multibeam sonar data and the transferability of trained models.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

DOE Cold Climate Heat Pump Challenge Field Validation: Data Collection and Analysis Plan

This document details the data collection, storage, and analysis plan and methodologies for conducting the field validation portion of DOE’s Cold Climate Heat Pump (CCHP) Challenge. The study is focused on validating the in-field heating performance of prototypical CCHPs that have successfully demonstrated that they meet or exceed the Challenge specification in a laboratory setting. The units that have passed laboratory testing will be installed in real homes along with sensors and loggers for monitoring performance over an entire heating season. Data collected through monitoring will be cleaned and stored in a secure database. The data will be analyzed for calculating the key metrics defined by the Challenge including heating capacities at low outdoor air temperatures (below 32 °F), efficiency in terms of the Coefficient of Performance (COP), switchover temperatures and auxiliary heat staging. Demand Response (DR) capabilities will also be tested using specially designed DR events. In most cases, shoulder season and cooling season performance will also be determined and reported.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

Knowledge, data and interests: Challenges in participation of diverse stakeholders in HIA

Stakeholder participation is considered an integral part of HIA. However, the challenges that participation implies in a multi-disciplinary and multi-ethnic society are less studied. This paper presents the manifestations of the multiplicity of sectors and population groups in HIA and discusses the challenges that such diversity imposes. Specifically, there is no common ground between participants, as their positions entail contradictory knowledge regarding the current situation, reliance on distinct data and conflicting interests. This entails usage of multiple professional and ethnic languages, disagreements regarding the definition of health and prioritizing health issues in HIA, and divergent perceptions of risk. These differences between participants are embedded culturally, socially, individually and, maybe most importantly, professionally. This complex picture of diverse stakeholder attributes is grounded in a case study of stakeholder participation in HIA, regarding zoning of a hazardous industry site in Israel. The implication is that participatory HIAs should address the multiplicity of stakeholders and types of knowledge, data and interests in a more comprehensive way. - Highlights: Black-Right-Pointing-Pointer This paper analyses challenges in participation of diverse stakeholders in HIA. Black-Right-Pointing-Pointer The multiplicity of disciplines and population groups raises fundamental challenges. Black-Right-Pointing-Pointer Stakeholders possess distinct and often contradictory knowledge, data and interests. Black-Right-Pointing-Pointer They speak different languages, and differ on approaches to health and risk perceptions. Black-Right-Pointing-Pointer Substantial amendments to diverse participation are needed, in HIA and generally.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Utilizing Novel Field and Data Exploration Methods to Explore Hot Moments in High-Frequency Soil Nitrous Oxide Emissions Data: Opportunities and Challenges

Soil nitrous oxide (N 2 O) emissions are an important driver of climate change and are a major mechanism of labile nitrogen (N) loss from terrestrial ecosystems. Evidence increasingly suggests that locations on the landscape that experience biogeochemical fluxes disproportionate to the surrounding matrix (hot spots) and time periods that show disproportionately high fluxes relative to the background (hot moments) strongly influence landscape-scale soil N 2 O emissions. However, substantial uncertainties remain regarding how to measure and model where and when these extreme soil N 2 O fluxes occur. High-frequency datasets of soil N 2 O fluxes are newly possible due to advancements in field-ready instrumentation that uses cavity ring-down spectroscopy (CRDS). Here, we outline the opportunities and challenges that are provided by the deployment of this field-based instrumentation and the collection of high-frequency soil N 2 O flux datasets. While there are substantial challenges associated with automated CRDS systems, there are also opportunities to utilize these near-continuous data to constrain our understanding of dynamics of the terrestrial N cycle across space and time. Finally, we propose future research directions exploring the influence of hot moments of N 2 O emissions on the N cycle, particularly considering the gaps surrounding how global change forces are likely to alter N dynamics in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Production and use of nuclear parameter covariance data: an overview of challenging cross cutting scientific issues

Nuclear data users’ requirements for uncertainty data started already in the seventies, when several fast reactor projects did use extensively “statistical data adjustments” to meet data improvement for core and shielding design. However, it was only ~20–30 years later that a major effort started to produce scientifically based covariance data and in particular since ~2005. Most work has been done since then with spectacular achievements and enhanced understanding both of the uncertainty evaluation process and of the data utilization in V&V. This paper summarizes some key developments and still open challenges.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Unblinded Data for PLAsTiCC Classification Challenge

For classification challenge (https://plasticc.org), the unblinded data files are included here. See PDF note above for more information. The original challenge (Sep 28, 2018 - Dec 17, 2018) was hosted at https://www.kaggle.com/c/PLAsTiCC-2018 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Real-time data reduction at 100 Tbps: Challenge and opportunity for AI-based data reduction for next-generation large-scale nuclear physics collider experiment

The modern large-scale nuclear physics (NP) experiments in high-energy particle colliders utilize streaming-readout electronics to digitize detector response at O(100) Tbps bandwidth. Prominent examples at Brookhaven National Lab (BNL) include the sPHENIX experiment at Relativistic Heavy Ion Collider (RHIC), which is under construction, and the experiments proposed for the Electron-Ion Collider (EIC), planned for the 2030s . One of the main challenges for these streaming readout systems is to manage the data rate with sufficient data reduction in real time so the end-data fit persistent storage for offline analysis, which is typically at O(1000) times smaller and O(100) Gbps. Such data reduction traditionally is achieved via real-time high level triggers, which select and save a small subset of collisions of interest. Although triggering is applicable to high energy collider experiments such as those at the Large Hardron Collider at CERN, it is insufficient for these nuclear physics experiments which study diverse collision topologies. And traditional triggering approach is inefficient to preserve the max information harvested from the operation of colliders that costs O(100)M per year to DOE. Meanwhile, in recent years, ML-based high-throughput data reduction has emerged as a promising approach to efficiently preserve max information for a given space of persistent storage, e.g. via AI data compression, feature extraction, and noise filtering.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2008

Precision Measurement of Black Hole Binary Dynamics: Analyzing the LISA Data Stream

One of the richest potential sources of insight into fundamental physics that LISA will be capable of observing is the inspiral of supermassive black hole binaries (BHBs). However, the data analysis challenge presented by the LISA data stream is quite unlike the situation for present day gravitational wave detectors. In order to make the precision measurements necessary to achieve LISA's science goals, the BHB signal must be distinguished from a data stream that not only contains instrumental noise, but potentially thousands of other signals as well, so that the "background" we wish to separate out to focus on the BHB signal is likely to be highly nonstationary and nongaussian, as well as being of scientific interest in its own right. In addition, whereas the theoretical templates that we calculate in order to ultimately estimate the parameters can afford to be somewhat inaccurate and still be effective for present day and near future detectors, this is not the case for LISA, and extremely high fidelity of the theoretical templates for high signal-to-noise signals will be required to prevent theoretical errors from dominating the parameter estimates. NVe, will describe efforts in the community of LISA data analysts to address the challenges regarding the specific issue of BHB signals. These efforts include using a Markov Chain Monte Carlo approach with the freedom to model the BHB and the other signals present in the data stream simultaneously, rather than trying to remove other signals and risk biasing the remaining data. The Mock LISA Data Challenge is a community of LISA scientists who generate rounds of simulated LISA noise with increasingly difficult signal content, and invite the LISA data analysis community to exercise their methods, or develop new methods, in an attempt to extract the parameters for the signals embedded in the mock data. In addition to practical approaches such ,is this to assess the level of parameter accuracy, one can apply the Fisher matrix formalism to assess both the statistical errors from noise and the theoretical errors

McWilliams, Sean T.↗
DOE OSTI2025

Combining Observations and Models: A Review of the CARDAMOM Framework for Data‐Constrained Terrestrial Ecosystem Modeling

The rapid increase in the volume and variety of terrestrial biosphere observations (i.e., remote sensing data and in situ measurements) offers a unique opportunity to derive ecological insights, refine process‐based models, and improve forecasting for decision support. However, despite their potential, ecological observations have primarily been used to benchmark process‐based models, as many past and current models lack the capability to directly integrate observations and their associated uncertainties for parameterization. In contrast, data assimilation frameworks such as the CARbon DAta MOdel fraMework (CARDAMOM) and its suite of process‐based models, known as the Data Assimilation Linked Ecosystem Carbon Model (DALEC), are specifically designed for model‐data fusion. This review, motivated by a recent CARDAMOM community workshop, examines the development and applications of CARDAMOM, with an emphasis on its role in advancing ecosystem process understanding. CARDAMOM employs a Bayesian approach, using a Markov Chain Monte Carlo algorithm to enable data‐driven calibration of DALEC parameters and initial states (i.e., carbon pool sizes) through observation operators. CARDAMOM's unique ability to retrieve localized model process parameters from diverse datasets—ranging from in situ measurements to global satellite observations—makes it a highly flexible tool for analyzing spatially variable ecosystem responses to environmental change. However, assimilating these data also presents challenges, including data quality issues that propagate into model skill, as well as trade‐offs between model complexity, parameter equifinality, and predictive performance. We discuss potential solutions to these challenges, such as reducing parameter equifinality by incorporating new observations. This review also offers community recommendations for incorporating emerging datasets, integrating machine learning techniques, strengthening collaboration with remote sensing, field, and modeling communities, and expanding CARDAMOM's relevance for localized ecosystem monitoring and decision‐making. CARDAMOM enables a deep, mechanistic understanding of terrestrial ecosystem dynamics that cannot be achieved through empirical analyses of observational datasets or weakly constrained models alone.

Bayesian inference↗
DOE OSTI2011

Simulating the Impacts of Disturbances on Forest Carbon Cycling in North America: Processes, Data, Models, and Challenges

Disturbances disrupt the forest structures and alter forest resources, substrate availability, or the physical environment. Understanding disturbances and their impacts is critical for a better quantification of North American carbon dynamics. We reviewed the status and major challenges in simulating the impacts of disturbances on forest carbon cycling in North America from the perspectives of process understanding, disturbance characterization, and modeling, and found some major issues. First, we do not have adequate continentally-consistent disturbance databases to support the NACP missions. Community effort, in collaborating with the modeling community, should be designated to systematically define international standards for disturbance databases with explicit inclusion of uncertainty measurements, adequate spatial and temporal resolutions, and explicit and quantifiable linkages with model simulations. Second, there is a need to develop effective and comprehensive process-based procedures or algorithms that can be used to quantify the immediate and long-term impacts of disturbances on forest succession, soils, microclimate, and cycles of carbon, water, and nutrients. These algorithms should be evaluated not only on the carbon cycle but also on the simulations of vegetation succession, alterations of soil and microclimate, and water and nutrients dynamics because adequate simulations of carbon dynamics rely on the correct quantification of the changes of soil and vegetation conditions. Third, the scaling challenges have rarely been addressed at the continental scale. We do not understand which processes and properties at a given temporal or spatial scales are critical while others can be simplified when we try to balance the North American carbon budget. Previous and current model simulations at the continental scale did not incorporate a full suite of disturbances information (because they do not exist as described above), and therefore provided little insight on the relative importance of various disturbances at the continent and regional scales on the carbon cycle. Fourth, our capability of simulating the occurrences and severity of disturbances under climate change and management futures are very limited. Fifth, uncertainty exists in every step of the effort to simulating the impacts of disturbances on carbon dynamics. Procedures are needed to quantify the uncertainty of model inputs or data layers, model parameters, and model structures, and their impacts on model simulations. Working together, modelers and data layer developers can identify the most uncertain areas and develop working hypotheses to reduce the uncertainty.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2006

From Zero to Integration in Eight Months, the Dawn Ground Data System Engineering Challenge

The Dawn GDS Team met the SC Sim integration challenge in eight months. The GDS System Engineering approach in response to the SC Simintegration challenge, focused on a set of key practices: decomposition of project request into manageable requirements; integration of multiple ground disciplines and experts into a focused team effort; risk management thru management of expectations; and aggregation of intermediate products into a final product. By maintaining a a system-level focus, the overall systems engineering process unified team GDS Team members with a common goal: the success of the ground system as a whole and not just the success of their individual expert contributions. Incorporation of Agile-type development efforts were aligned with a risk strategy based on team-oriented principles and expectations management, thus achieving a more stable baseline solution without compromising the integrity of the GDS design.

Dawn Mission↗