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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Representativeness and repeatability of microenvironmental personal and head exposures to radio-frequency electromagnetic fields

Highlights: • First time assessment of head exposure to RF-EMF in uncontrolled environments, in 15 microenvironments in Melbourne, Australia. • We show that our measurement protocol is repeatable over time. • We show that the paths selected in our protocol are representative for the larger areas in which they are defined. • Exposure to RF-EMF is higher in more dense urban areas than in less populated suburban areas in the outskirts of the city. The aims of this study were to: i) investigate the repeatability and representativeness of personal radio frequency-electromagnetic fields (RF-EMFs) exposure measurements, across different microenvironments, ii) perform simultaneous evaluations of personal RF-EMF exposures for the whole body and the head, iii) validate the data obtained with a head-worn personal distributed exposimeter (PDE) against those obtained with an on-body worn personal exposimeter (PEM). Data on personal and head RF-EMF exposures were collected by performing measurements across 15 microenvironments in Melbourne, Australia. A body-worn PEM and a head-worn PDE were used for measuring body and head exposures, respectively. The summary statistics obtained for total RF-EMF exposure showed a high representativeness (r{sup 2} > 0.66 for two paths in the same area) and a high repeatability over time (r{sup 2} > 0.87 for repetitions of the same path). The median head exposure in the 900 MHz downlink band ranged between 0.06 V/m and 0.31 V/m. The results obtained during simultaneous measurements using the two devices showed high correlations (0.42 < r{sup 2} < 0.94). The highest mean total RF-EMF exposure was measured in Melbourne's central business district (0.89 V/m), whereas the lowest mean total exposure was measured in a suburban residential area (0.05 V/m). This study shows that personal RF-EMF microenvironmental measurements in multiple microenvironments have high representativeness and repeatability over time. The personal RF-EMF exposure levels (i.e. body and head exposures) demonstrated moderate to high correlations.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2021

An overview of data tools for representing and managing building information and performance data

Building information modeling (BIM) has been widely adopted for representing and exchanging building data across disciplines during building design and construction. However, BIM's use in the building operation phase is limited. With the increasing deployment of low-cost sensors and meters, as well as affordable digital storage and computing technologies, growing volumes of data have been collected from buildings, their energy services systems, and occupants. Such data are crucial to help decision makers understand what, how, and when energy is consumed in buildings—a critical step to improving building performance for energy efficiency, demand flexibility, and resilience. However, practical analyses and use of the collected data are very limited due to various reasons, including poor data quality, ad-hoc representation of data, and lack of data science skills. To unlock value from building data, there is a strong need for a toolchain to curate and represent building information and performance data in common standardized terminologies and schemas, to enable interoperability between tools and applications. This study selected and reviewed 24 data tools based on common use cases of data across the building life cycle, from design to construction, commissioning, operation, and retrofits. The selected data tools are grouped into three categories: (1) data dictionary or terminology, (2) data ontology and schemas, and (3) data platforms. The data are grouped into ten typologies covering most types of data collected in buildings. This study resulted in five main findings: (1) most data representation tools can represent their intended data typologies well, such as Green Button for smart meter data and Brick schema for metadata of sensors in buildings and HVAC systems, but none of the tools cover all ten types of data; (2) there is a need for data schemas to represent the basis of design data and metadata of occupant data; (3) standard terminologies such as those defined in BEDES are only adopted in a few data tools; (4) integrating data across various stages in the building life cycle remains a challenge; and (5) most data tools were developed and maintained by different parties for different purposes, their flexibility and interoperability can be improved to support broader use cases. Finally, recommendations for future research on building data tools are provided for the data and buildings community based on the FAIR principles to make data Findable, Accessible, Interoperable, and Reusable.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Assessing Spatial Representativeness of Global Flux Tower Eddy-Covariance Measurements Using Data from FLUXNET2015

Large datasets of carbon dioxide, energy, and water fluxes were measured with the eddy-covariance (EC) technique, such as FLUXNET2015. These datasets are widely used to validate remote-sensing products and benchmark models. One of the major challenges in utilizing EC-flux data is determining the spatial extent to which measurements taken at individual EC towers reflect model-grid or remote sensing pixels. To minimize the potential biases caused by the footprint-to-target area mismatch, it is important to use flux datasets with awareness of the footprint. This study analyze the spatial representativeness of global EC measurements based on the open-source FLUXNET2015 data, using the published flux footprint model (SAFE-f). The calculated annual cumulative footprint climatology (ACFC) was overlaid on land cover and vegetation index maps to create a spatial representativeness dataset of global flux towers. The dataset includes the following components: (1) the ACFC contour (ACFCC) data and areas representing 50%, 60%, 70%, and 80% ACFCC of each site, (2) the proportion of each land cover type weighted by the 80% ACFC (ACFCW), (3) the semivariogram calculated using Normalized Difference Vegetation Index (NDVI) considering the 80% ACFCW, and (4) the sensor location bias (SLB) between the 80% ACFCW and designated areas (e.g. 80% ACFCC and window sizes) proxied by NDVI. Finally, we conducted a comprehensive evaluation of the representativeness of each site from three aspects: (1) the underlying surface cover, (2) the semivariogram, and (3) the SLB between 80% ACFCW and 80% ACFCC, and categorized them into 3 levels. The goal of creating this dataset is to provide data quality guidance for international researchers to effectively utilize the FLUXNET2015 dataset in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Is the BPD Nationally Representative? A Comparison of the Building Performance Database to the Commercial Buildings Energy Consumption Survey

The Building Performance Database (BPD) contains building characteristics and energy use data that can be used to benchmark buildings against their peers. However, the BPD contains data from any source that was willing to contribute data, regardless of whether that dataset is representative of the national building stock. This report explores the representativeness of the BPD. Namely, we compared the BPD to the Commercial Buildings Energy Consumption Survey (CBECS), which was designed to be a nationally representative sample of buildings. We divided the datasets into subgroups based on building type and location, we focused on three key variables (floor area, site energy use intensity, and source energy use intensity), and we compared their statistical properties in the BPD to the CBECS. We found that while the BPD contains significantly more buildings with larger floor areas, the BPD is reasonably representative in terms of site and source energy use intensity and does not appear to have a systemic bias towards energy efficient buildings.

42 ENGINEERING↗
DOE OSTI2022

Representative-cell-based Modeling of HTTF in SAM

Although the current nuclear power market is primarily occupied by the light water reactors (LWRs), the TRISO-fueled helium-cooled graphite-moderated high temperature gas reactors (HTGRs) are drawing growing attention as the nuclear power industry marches towards more advanced systems. Compared to that of conventional LWRs, the thermal hydraulics of HTGR cores show extra complexity from their multi-scale heat transfer mechanisms with varying importance during different operation modes or transient stages. The modeling of HTGR cores for the system analysis purpose therefore faces the challenge of reaching reasonable fidelity and accuracy while maintaining sufficient simplicity. Previous development and validation efforts have demonstrated that a “2-D ring model” with reasonable performance can be implemented in the System Analysis Module (SAM) for prismatic HTGR cores. In the current project, an alternative modeling methodology based on “representative cells” is proposed for the typical prismatic core of HTGRs. Different from the previous ring model approach, the proposed methodology first separately models and then combines the small- and large-scale thermal conductions, by connecting representative local heat transfer units (cells) with effective core- wise thermal resistance. A modeling practice for an integral high-temperature test facility (HTTF) elaborates the modeling details. Steady-state validation of the resultant candidate model is performed against a higher-resolution benchmark from a 3D-1D coupled simulation, which shows satisfactory prediction with reasonably captured global parameters and well-represented temperature fields. A postulated pressurized conduction cooldown (PCC) is also simulated and analyzed, demonstrating the model’s capability of transient prediction with physically captured phenomena resolved in both small and large temporal and spatial scales. In general, the current work achieves a preliminary success in proposing a methodology using representative cells to model prismatic HTGR cores in SAM. Future efforts are envisioned with more validation activities and with potential modeling improvements to eventually achieve the high confidence on a high-fidelity robust modeling methodology with reasonable accuracy.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Development of 80- and 100- Mile Work Day Cycles Representative of Commercial Pickup and Delivery Operation

When developing and designing new technology for integrated vehicle systems deployment, standard cycles have long existed for chassis dynamometer testing and tuning of the powertrain. However, to this day with recent developments and advancements in plug-in hybrid and battery electric vehicle technology, no true 'work day' cycles exist with which to tune and measure energy storage control and thermal management systems. To address these issues and in support of development of a range-extended pickup and delivery Class 6 commercial vehicle, researchers at the National Renewable Energy Laboratory in collaboration with Cummins analyzed 78,000 days of operational data captured from more than 260 vehicles operating across the United States to characterize the typical daily performance requirements associated with Class 6 commercial pickup and delivery operation. In total, over 2.5 million miles of real-world vehicle operation were condensed into a pair of duty cycles, an 80-mile cycle and a 100-mile cycle representative of the daily operation of U.S. class 3-6 commercial pickup and delivery trucks. Using novel machine learning clustering methods combined with mileage-based weighting, these composite representative cycles correspond to 90th and 95th percentiles for daily vehicle miles traveled by the vehicles observed. In addition to including vehicle speed vs time drive cycles, in an effort to better represent the environmental factors encountered by pickup and delivery vehicles operating across the United States, a nationally representative grade profile and key status information were also appended to the speed vs. time profiles to produce a 'work day' cycle that captures the effects of vehicle dynamics, geography, and driver behavior which can be used for future design, development, and validation of technology.

100 mile↗
DOE OSTI2021

Decision Tree Regression to Identify Representative Road Sections for Evaluating Performance of Connected and Automated Class 8 Tractors

Currently, connected and autonomous vehicle (CAV) technology is being developed for Class 8 tractor trucks aimed at improved safety and fuel economy and reduced CO2 emissions. Despite extensive efforts conducted across the world, the reported efficiency gains were varied from different research groups, raising concerns about the fidelity of models, the performance of control, and the effectiveness of the experimental validation. One root cause for this variation stems from the fact that the efficiency gain obtained from the CAV is sensitive to real-world conditions, including surrounding traffic and road grade. This study presents an approach aimed at identifying representative public road sections and facilitating CAV research from this perspective. By employing the decision tree regression (DTR) method to the Fleet DNA database, the most representative road sections can be identified. High-level metrics and detailed information of the derived road sections are also illustrated and discussed, which demonstrate their representativeness and the effectiveness of the approach. Meanwhile, the capability of this approach can be easily extended by integrating specific constraints into the DTR algorithm. As an example, a specific representative road section with an aggressive road grade profile was also provided via this approach.

27 ARPA - Advanced Research Projects Agency-Energy↗
NASA NTRS1997

An Arbitrary First Order Theory Can Be Represented by a Program: A Theorem

How can we represent knowledge inside a computer? For formalized knowledge, classical logic seems to be the most adequate tool. Classical logic is behind all formalisms of classical mathematics, and behind many formalisms used in Artificial Intelligence. There is only one serious problem with classical logic: due to the famous Godel's theorem, classical logic is algorithmically undecidable; as a result, when the knowledge is represented in the form of logical statements, it is very difficult to check whether, based on this statement, a given query is true or not. To make knowledge representations more algorithmic, a special field of logic programming was invented. An important portion of logic programming is algorithmically decidable. To cover knowledge that cannot be represented in this portion, several extensions of the decidable fragments have been proposed. In the spirit of logic programming, these extensions are usually introduced in such a way that even if a general algorithm is not available, good heuristic methods exist. It is important to check whether the already proposed extensions are sufficient, or further extensions is necessary. In the present paper, we show that one particular extension, namely, logic programming with classical negation, introduced by M. Gelfond and V. Lifschitz, can represent (in some reasonable sense) an arbitrary first order logical theory.

Hosheleva, Olga↗
NASA NTRS1994

Representing Learning With Graphical Models

Probabilistic graphical models are being used widely in artificial intelligence, for instance, in diagnosis and expert systems, as a unified qualitative and quantitative framework for representing and reasoning with probabilities and independencies. Their development and use spans several fields including artificial intelligence, decision theory and statistics, and provides an important bridge between these communities. This paper shows by way of example that these models can be extended to machine learning, neural networks and knowledge discovery by representing the notion of a sample on the graphical model. Not only does this allow a flexible variety of learning problems to be represented, it also provides the means for representing the goal of learning and opens the way for the automatic development of learning algorithms from specifications.

Buntine, Wray L.↗
NASA NTRS2003

Representativeness Uncertainty in Chemical Data Assimilation Highlight Mixing Barriers

When performing chemical data assimilation the observational, representativeness, and theoretical uncertainties have very different characteristics. In this study we have accurately characterized the representativeness uncertainty by studying the probability distribution function (PDF) of the observations. The average deviation has been used as a measure of the width of the PDF and of the variability (representativeness uncertainty) for the grid cell. It turns out that for long-lived tracers such as N2O and CH4 the representativeness uncertainty is markedly different from the observational uncertainty and clearly delineates mixing barriers such as the polar vortex edge, the tropical pipe and the tropopause.

Lary, David John↗
DOE OSTI2024

Identification of Climatological Representative Days in the Mid-Atlantic for High-Fidelity Offshore Wind Energy Modeling

The goal of reaching 30 GW of offshore wind energy by 2030 becomes more realistic with the continued approval of offshore wind energy areas by the Biden Administration. In the Mid-Atlantic, where wind energy projects are in the most advanced stages of development, there is increased research focus on the eventual interaction of these wind farms. These interactions, in the form of wakes and cluster wakes, or wakes from multiple wind farms, could have detrimental effects on power production and forecastability for downwind wind farms (Pryor et al. 2022, Golbazi et al. 2022, Rosencrans et al. 2023). To help alleviate these issues, numerical simulations in the form of numerical weather prediction (NWP) and large eddy simulations (LES) can provide insight into when cluster wake situations may occur, but running such simulations can be expensive and difficult to run for multiple years. In this study, we leverage and build upon existing techniques in the literature (Fischereit et al. 2022) to identify climatologically representative days for wind energy areas in the Mid-Atlantic where conditions would promote cluster wake situations. We select meteorological variables (wind speed, wind direction, atmospheric stability, boundary-layer height, TKE) critical to understanding wind energy production and wake propagation. We then consider two different NWP datasets of varying spatial and temporal resolution: ERA5 provides data at hourly intervals from 1940 to present at 0.25 deg (31 km) spatial resolution (Hersbach et al. 2020), and the NOW-23 dataset provides data at 5-minute resolution for 21 years at 2-km spatial resolution (Bodini et al. 2020). Our first step is to compare these two datasets for an overlapping 21-year time period. Initial results show that the required number of days to represent the long-term climate increases with each additional variable considered. In their study of the German Bight, Fischereit et al. (2022) found that they could represent the long-term wind and wave climate in a "near-perfect" way with -180 days, by reaching a Perkins Skill Score (PSS) of 0.9; our investigation of the mid-Atlantic wind resource region with ERA5 and NOW-23 data suggests that we will need -100 days to reach a PSS of 0.9. As we expand our parameter space to include multiple variables, the number of required days will likely grow. These results will ultimately be used to select case studies to best represent cluster wake conditions that apply to this region for the lifetime of likely wind farms in this mid-Atlantic region.

clusterwakes↗
DOE OSTI2021

Development of heavy-duty vehicle representative driving cycles via decision tree regression

Previously, researchers who developed representative driving cycles mainly focused on light-duty vehicles and only considered vehicle speed and related derivations. In this paper, we propose a novel approach to develop representative cycles for heavy-duty vehicles. By implementing decision tree regression (DTR) to the Fleet DNA on-road vehicle data, a broader set of metrics, such as engine power and fuel consumption, can be used for more robust cycle development. Additionally, the influence of each metric on the regression target is also accounted for by a weighted number derived through the DTR to enhance the representativenss of the developed cycle. As case studies, we applied the proposed method to five heavy-duty vocations (drayage, long haul, regional haul, local delivery, and transit bus) and derived the most representative cycle, as well as four extreme cycles (maximal energy consumption, maximal power-weighted work, maximal fraction of high speed, and minimal fuel economy) to advance the related alternative powertrain design.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Determination of a most representative cycle from cylinder pressure ensembles via statistical method using distribution skewness

In internal combustion engine research, cylinder pressure measurements provide valuable information about the underlying thermodynamic and combustion processes, and are typically collected in ensembles of several 100 traces. Although in some particular fields of combustion research all traces are analyzed, in most cases only one trace is studied because analyzing all the traces is impractical due to the large number of collected samples. Instead, an ensemble-averaged pressure trace is commonly calculated and used for analysis. However, this pressure trace is highly smoothed and dynamic information is lost during the averaging process. With the average trace, pressure rise rates are lower and pressure oscillations such as the ones resulting from combustion knock are lost. In this work, a statistical method was developed to determine the “most representative cycle,” which is the cycle from the ensemble that has the pressure trace most representative of the engine operating condition. Eleven characteristic parameters are computed from each pressure trace and probabilistic distributions are obtained for each of the parameters using all the traces in the ensemble. Finally, the most representative cycle is selected by means of a cost function minimization. The benefits of this method are illustrated using experimental data from four very different engine platforms, under four different combustion modes and over a range of operating conditions.

42 ENGINEERING↗
DOE OSTI2014

Landscape Characterization and Representativeness Analysis for Understanding Sampling Network Coverage

Sampling networks rarely conform to spatial and temporal ideals, often comprised of network sampling points which are unevenly distributed and located in less than ideal locations due to access constraints, budget limitations, or political conflict. Quantifying the global, regional, and temporal representativeness of these networks by quantifying the coverage of network infrastructure highlights the capabilities and limitations of the data collected, facilitates upscaling and downscaling for modeling purposes, and improves the planning efforts for future infrastructure investment under current conditions and future modeled scenarios. The work presented here utilizes multivariate spatiotemporal clustering analysis and representativeness analysis for quantitative landscape characterization and assessment of the Fluxnet, RAINFOR, and ForestGEO networks. Results include ecoregions that highlight patterns of bioclimatic, topographic, and edaphic variables and quantitative representativeness maps of individual and combined networks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Modeling and Depletion Simulations for a High Flux Isotope Reactor Cycle with a Representative Experiment Loading

The purpose of this report is to document a high-fidelity VESTA/MCNP High Flux Isotope Reactor (HFIR) core model that features a new, representative experiment loading. This model, which represents the current, high-enriched uranium fuel core, will serve as a reference for low-enriched uranium conversion studies, safety-basis calculations, and other research activities. A new experiment loading model was developed to better represent current, typical experiment loadings, in comparison to the experiment loading included in the model for Cycle 400 (operated in 2004). The new experiment loading model for the flux trap target region includes full length 252 Cf production targets, 75 Se production capsules, 63 Ni production capsules, a 188 W production capsule, and various materials irradiation targets. Fully loaded 238 Pu production targets are modeled in eleven vertical experiment facilities located in the beryllium reflector. Other changes compared to the Cycle 400 model are the high-fidelity modeling of the fuel element side plates and the material composition of the control elements. Results obtained from the depletion simulations with the new model are presented, with a focus on time-dependent isotopic composition of irradiated fuel and single cycle isotope production metrics.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Investigation of representing hysteresis in macroscopic models of two-phase flow in porous media using intermediate scale experimental data

Incorporating hysteresis into models is important to accurately capture the two phase flow behavior when porous media systems undergo cycles of drainage and imbibition such as in the cases of injection and post-injection redistribution of CO 2 during geological CO 2 storage (GCS). In the traditional model of two-phase flow, existing constitutive models that parameterize the hysteresis associated with these processes are generally based on the empirical relationships. This manuscript presents development and testing of mathematical hysteretic capillary pressure—saturation—relative permeability models with the objective of more accurately representing the redistribution of the fluids after injection. The constitutive models are developed by relating macroscopic variables to basic physics of two-phase capillary displacements at pore-scale and void space distribution properties. The modeling approach with the developed constitutive models with and without hysteresis as input is tested against some intermediate-scale flow cell experiments to test the ability of the models to represent movement and capillary trapping of immiscible fluids under macroscopically homogeneous and heterogeneous conditions. The hysteretic two-phase flow model predicted the overall plume migration and distribution during and post injection reasonably well and represented the postinjection behavior of the plume more accurately than the nonhysteretic models. In conclusion, based on the results in this study, neglecting hysteresis in the constitutive models of the traditional two-phase flow theory can seriously overpredict or underpredict the injected fluid distribution during post-injection under both homogeneous and heterogeneous conditions, depending on the selected value of the residual saturation in the nonhysteretic models.

58 GEOSCIENCES↗
DOE OSTI2025

The Freundlich isotherm equation best represents phosphate sorption across soil orders and land use types in tropical soils of Puerto Rico

Biomass production in the lowland wet tropical forest is greater than in any other biome, and it is typically limited by soil phosphorus (P) availability. However, the mechanisms involved in the P cycle remain poorly represented in Earth System Models (ESMs). Soil P sorption processes are key in the P cycle and for understanding the extent of P limitation for plant productivity. Currently, a few ESMs include isotherm equations to model these processes. Although the Langmuir equation is widely cited, other isotherm equations may better describe sorption in tropical soils. Here, we use a diverse range of soil samples from Puerto Rico to test the validity of the Langmuir, Freundlich, and Temkin equation. We found that across four soil orders (Inceptisols, Mollisols, Oxisols, Ultisols), and forested and cultivated land use types, the Freundlich equation best represented soil P sorption. Furthermore, the Langmuir and the Temkin equations poorly represent soil P adsorption, especially at low P concentrations. Specifically, the Langmuir equation underestimated soil P adsorption by 40% and the Temkin equation overestimated adsorption by 76%. We also found, as expected, that soil clay content and pH were the most important parameters explaining the variability of the Freundlich (K f ) constant. Greater clay content and lower pH, common in highly weathered Ultisols and Oxisols which are abundant in the tropics, led to greater K f values. Overall, our results suggest that a diversity of soils can prompt underestimation of P sorption when using the Langmuir isotherm, which leads to an overestimation of available P that can have repercussions on ESM predictions of the P cycle and tropical forest productivity.

Earth system models↗
DOE OSTI2022

A hierarchical stochastic modeling approach for representing point bar geometries and petrophysical property variations

The flow of fluids in point bars is affected by the existence of heterogeneities like shale drapes that are found on the surfaces of inclined heterolithic stratifications. In fact, these shale drapes can act as fluid flow baffles; therefore, developing a framework for modeling point bars and their associated heterogeneities is vital. In this study, a stochastic process-based modeling approach is presented for capturing the main point bar heterogeneities: accretion surfaces (i.e., the aerial heterogeneity) and inclined heterolithic stratifications (i.e., the vertical heterogeneity). The former was modeled using a sine-generation function and the latter, with a sigmoidal function after which they were combined into a 3D point bar model. To ensure proper modeling of petrophysical properties, we developed a more representative gridding scheme which generates curvilinear grids representative of the point bar geometry. This grid was then transformed into a rectilinear grid to allow for geostatistical simulation after which all petrophysical properties were mapped back into the original curvilinear grid. An essential element of this modeling approach is the stochastic representation of shale drapes at the interface between successive accretion surfaces. The workflow was tested using a real field dataset for the Cranfield field, Mississippi. The constructed model was then subjected to a flow simulation study mimicking a CO 2 storage scenario. Various sensitivities were simulated to evaluate the effect of heterogeneities on CO 2 flow within the point-bar. Results demonstrate the importance of representing point-bar related heterogeneity and the spatial distribution of shale drapes on CO 2 plume migration and storage.

58 GEOSCIENCES↗