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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

Geographic applications of ERTS-1 imagery to landscape change

The author has identified the following significant results. ERTS-1 has proven to be an effective earth-orbiting monitor of landscape change. Its regional coverage for large areal monitoring has been effective for the detection and mapping of agricultural plowing regions, for general forest cover mapping, for flood mapping, for strip mine mapping, and for short-lived precipitation mapping patterns. Paramount to the entire study has been the temporal coverage provided by ERTS. Without the cyclic coverage on an 18 day basis, temporal coverage would have been inadequate for the detection and mapping of strip mining landscape change, the analysis of agricultural landscape change based on plowing patterns, the analysis of urban-suburban growth changes, and the mapping of the Mississippi River floods. Cost benefits from ERTS are unquestionably superior to aircraft systems in regard to large regional coverage and cyclic temporal parameters. For the analysis of landscape change in large regions such as statewide areas or even areas of 10,000 square miles, ERTS is of cost benefit consideration. Not only does the cost of imagery favor ERTS but the reduction of man-hours using ERTS has been in the magnitude of 1:10.

Rehder, J. B.↗
DOE OSTI2020

Using Residential and Office Building Archetypes for Energy Efficiency Building Solutions in an Urban Scale: A China Case Study

Building energy consumption accounts for 36% of the overall energy end use worldwide and is growing rapidly as developing countries continue to urbanize. Understanding the energy use at urban scale will lay the foundation for identification of energy efficiency opportunities to be deployed at speed. China has almost half of global new constructions and plays an important role in building suitability. However, an open source national building energy consumption database is not available in China. To provide data support for building energy consumptions, this paper used a simulation method to develop an urban building energy consumption database for a pilot city in Wuhan, China. First, residential, small, and large office building archetype energy models were created in EnergyPlus to represent typical building energy consumption in Wuhan. The baseline reference model simulation results were further validated using survey data from the literature. Second, stochastic simulations were conducted to consider different design parameters and occupants’ energy usage intensity scenarios, such as thermal properties of the building envelope, lighting power density, equipment power density, HVAC (heating, ventilation and air conditioning) schedule, etc. A building energy consumption database was generated for typical building archetypes. Third, data-driven regression analysis was conducted to support quick building energy consumption prediction using key high- level building information inputs. Finally, a web-based urban energy platform and an interface were developed to support further third-party application development. The research is expected to provide fast energy efficiency building design solutions for urban planning, new constructions as well as building retrofits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Sensitivity of Fine‐Resolution Urban Heat Island Simulations to Soil Moisture Parameterization

ABSTRACT Urban areas experience the impact of natural disasters, such as heatwaves and flash floods, disparately in different neighbourhoods across a city. The demand for precise urban hydrometeorological and hydroclimatological modelling to examine this disparity, and the interacting challenges posed by climate change and urbanisation, has thus surged. The Weather Research and Forecasting (WRF) model has served such operational and research purposes for decades. Recent advancements in WRF, including enhanced numerical schemes and sophisticated urban atmospheric‐hydrological parameterizations, have empowered the simulation of urban geophysical processes at high resolution (~1 km), but even this resolution misses significant urban microclimate variability. This study applies the large‐eddy simulations (LES) mode within WRF, coupled with single‐layer urban canopy models (SLUCM), to enable even finer‐scale modelling (150 m) of the Urban Heat Island (UHI) effect in the Baltimore metropolitan area. We run nine scenarios to evaluate various methods of initializing soil moisture and various spinup lead times, and to assess the impact of WRF's Mosaic approach in depicting subgrid‐scale processes. We evaluate the scenarios by comparing the WRF simulated land surface temperature (LST) against Landsat LST and the WRF simulated hourly 2‐m air temperatures (AT) with observations from eight weather stations across the domain. Results underscore the paramount influence of the lead spinup time on the spatiotemporal distribution of simulated soil moisture, consequently shaping WRF's efficacy in predicting the UHI. Furthermore, interpolating soil moisture‐related parameters from the parent for child domain initialization yields a notable reduction in mean and root‐mean‐squared errors. This improvement was particularly evident in simulations with the longest spinup time, affirming the importance of carefully designing the initialization of soil moisture for improved urban temperature predictions.

Talebpour, Mahdad↗
NASA NTRS2014

In-Situ and Remote-Sensing Data Fusion Using Machine Learning Techniques to Infer Urban and Fire Related Pollution Plumes

Airmass type characterization is key in understanding the relative contribution of various emission sources to atmospheric composition and air quality and can be useful in bottom-up model validation and emission inventories. However, classification of pollution plumes from space is often not trivial. Sub-orbital campaigns, such as SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) give us a unique opportunity to study atmospheric composition in detail, by using a vast suite of in-situ instruments for the detection of trace gases and aerosols. These measurements allow identification of spatial and temporal atmospheric composition changes due to various pollution plumes resulting from urban, biogenic and smoke emissions. Nevertheless, to transfer the knowledge gathered from such campaigns into a global spatial and temporal context, there is a need to develop workflow that can be applicable to measurements from space. In this work we rely on sub-orbital in-situ and total column remote sensing measurements of various pollution plumes taken aboard the NASA DC-8 during 2013 SEAC4RS campaign, linking them through a neural-network (NN) algorithm to allow inference of pollution plume types by input of columnar aerosol and trace-gas measurements. In particular, we use the 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) airborne measurements of wavelength dependent aerosol optical depth (AOD), particle size proxies, O3, NO2 and water vapor to classify different pollution plumes. Our method relies on assigning a-priori ground-truth labeling to the various plumes, which include urban pollution, different fire types (i.e. forest and agriculture) and fire stage (i.e. fresh and aged) using cluster analysis of aerosol and trace-gases in-situ and auxiliary (e.g. trajectory) data and the training of a NN scheme to fit the best prediction parameters using 4STAR measurements as input. We explore our misclassification rates as related to our ground-truth labels, and with multi-layered pollution plume cases. The next step in our analysis is to optimize parameter selection for a scheme that can be applied to space-borne aerosol and trace-gas observation platforms such as OMI, and future geostationary satellites such as TEMPO and GEO-CAPE.

Neural-network↗
DOE OSTI2022

Design and Performance of lithium-Ion Batteries for Achieving Electric Vehicle Takeoff, Flight, and Landing

Today, the burgeoning drive towards global urbanization with over half the earth’s population living in cities, has created major challenges with regards to intracity and intercity transit and mobility. This problem is compounded due to the fact that almost always urbanization and increase in standard of living drives individual automobile ownerships. Over 95% of automobiles are presently powered by some form of fossil fuel and as an unintended consequence, urban centers have also been centers for peak greenhouse gas emissions, a major contributor to global climate change. A revolutionary solution to this conundrum is flight capable electric automobiles or electric aerial vehicles that can tackle both urban mobility and climate change challenges. For such advanced electric platforms, energy storage and delivery component is the vital component towards achieving takeoff, flight, cruise, and landing. The requirements and duty cycle demands on the energy storage system is drastically different when compared to the performance metrics required for terrestrial electric vehicles. As the widely deployed lithium ion-based battery systems are often the primary go-to energy storage choice in electric vehicle related applications, it is imperative that performance metrics and specifications for such batteries towards areal electric vehicles need to be established. In this nascent field, there exists ample opportunities for battery material innovations, understanding degradation mechanism, battery design, development and deployment of battery control and management systems. Thus, this chapter comprehensively discusses battery requirements and identifies battery material chemistries suitable for handling aerial electric automobile duty cycles. The chapter also discusses the battery cell-level metrics pertaining to electrochemical, chemical, mechanical, and structural parameters. Furthermore, specific models for battery degradation, state of health (SOH), capacity and models for full cell performance and degradation are also discussed here. Finally, the chapter also discusses battery safety and future directions of batteries that would power these next generation urban electric aircrafts.

Amin, Ruhul↗
NASA NTRS2007

Reconstructed Historical Land Cover and Biophysical Parameters for Studies of Land-Atmosphere Interactions within the Eastern United States

The local environment where we live within the Earth's biosphere is often taken for granted. This environment can vary depending on whether the land cover is a forest, grassland, wetland, water body, bare soil, pastureland, agricultural field, village, residential suburb, or an urban complex with concrete, asphalt, and large buildings. In general, the type and characteristics of land cover influence surface temperatures, sunlight exposure and duration, relative humidity, wind speed and direction, soil moisture amount, plant life, birds, and other wildlife in our backyards. The physical and biological properties (biophysical characteristics) of land cover help to determine our surface environment because they directly affect surface radiation, heat, and soil moisture processes, and also feedback to regional weather and climate. Depending on the spatial scale and land use intensity, land cover changes can have profound impacts on our local and regional environment. Over the past 350 years, the eastern half of the United States, an area extending from the grassland prairies of the Great Plains to the Gulf and Atlantic coasts, has experienced extensive land cover and land use changes that began with land clearing in the 1600s, led to extensive deforestation and intensive land use practices by 1920, and then evolved to the present-day landscape. Determining the consequences of such land cover changes on regional and global climate is a major research issue. Such research requires detailed historical land cover data and modeling experiments simulating historical climates. Given the need to understand the effects of historical land cover changes in the eastern United States, some questions include: - What were the most important land cover transformations and how did they alter biophysical characteristics of the land cover at key points in time since the mid-1600s? - How have land cover and land use changes over the past 350 years affected the land surface environment including surface weather, hydrologic, and climatic variability? - How do the potential effects of regional human-induced land cover change on the environment compare to similar changes that are caused by the natural variations of the Earth's climate system? To help answer these questions, we reconstructed a fractional land cover and biophysical parameter dataset for the eastern United States at 1650, 1850, 1920, and 1992 time-slices. Each land cover fraction is associated with a biophysical parameter class, a suite of parameters defining the biophysical characteristics of that kind of land cover. This new dataset is designed for use in computer models of land-atmosphere interactions, to understand and quantify the effects of historical land cover changes on the water, energy, and carbon cycles

Steyaert, Louis T.↗
NASA NTRS1972

Assessment of the Impact of the California Water Project on the West Side of the San Joaquin Valley

Changes in the regional scale parameters used to characterize the nature of area transformation caused by the California State Water Project are analyzed. Data were taken from Mission 164 and ERTS high flight simulation imagery. Indicators considered cover general and urban oriented land use, general and problem oriented vegetation, irrigation systems, and identification of crops.

John E. Estes↗
NASA NTRS2022

Employing Relaxed Smoothness Constraints on Imaginary Part of Refractive Index in AERONET Aerosol Retrieval Algorithm

In the Aerosol Robotic Network (AERONET) retrieval algorithm, smoothness constraints on the imaginary part of the refractive index provide control of retrieved spectral dependence of aerosol absorption by preventing the inversion code from fitting the noise in optical measurements and thus avoiding unrealistic oscillations of retrievals with wavelength. The history of implementation of the smoothness constraints in the AERONET retrieval algorithm is discussed. It is shown that the latest version of the smoothness constraints on the imaginary part of refractive index, termed standard and employed by Version 3 of the retrieval algorithm, should be modified to account for strong variability of light absorption by brown-carbon-containing aerosols in UV through mid-visible parts of the solar spectrum. In Version 3 strong spectral constraints were imposed at high values of the Ångström exponent (440–870 nm) since black carbon was assumed to be the primary absorber, while the constraints became increasingly relaxed as aerosol exponent deceased to allow for wavelength dependence of absorption for dust aerosols. The new version of the smoothness constraints on the imaginary part of the refractive index assigns different weights to different pairs of wavelengths, which are the same for all values of the Ångström exponent. For example, in the case of four-wavelength input, the weights assigned to short-wavelength pairs (440–675, 675–870 nm) are small so that smoothness constraints do not suppress natural spectral variability of the imaginary part of the refractive index. At longer wavelengths (870–1020 nm), however, the weight is 10 times higher to provide additional constraints on the imaginary part of refractive index retrievals of aerosols with a high Ångström exponent due to low sensitivity to aerosol absorption for longer channels at relatively low aerosol optical depths. The effect of applying the new version of smoothness constraints, termed relaxed, on retrievals of single-scattering albedo is analyzed for case studies of different aerosol types: black- and brown-carbon-containing fine mode aerosols, mineral dust coarse mode aerosols, and urban industrial fine mode aerosol. It is shown that for brown-carbon-containing aerosols employing the relaxed smoothness constraints resulted in significant reduction in retrieved single-scattering albedo and spectral residual errors (compared to standard) at the short wavelengths. For example, biomass burning smoke cases showed a reduction in single-scattering albedo and spectral residual error at 380 nm of ∼ 0.033 and ∼ 17 %, respectively, for the Rexburg site and ∼ 0.04 and ∼ 12.7 % for the Rimrock site, both AERONET sites in Idaho, USA. For a site with very high levels of black-carbon-containing aerosols (Mongu, Zambia), the effect of modification in the smoothness constraints was minor. For mineral dust aerosols at small Ångström exponent values (Mezaira site, UAE), the spectral constraint on the imaginary part of the refractive index was already relaxed in Version 3; therefore the new relaxed constraint results in minimal change. In the case of weakly absorbing urban industrial aerosols at the GSFC site, there are significant changes in retrieved single-scattering albedo using relaxed assumption, especially reductions at longer wavelengths: ∼ 0.016 and ∼ 0.02 at 875 and 1020 nm, respectively, for 440 nm aerosol optical depth (AOD) ∼ 0.3. The modification of smoothness constraints on the imaginary part of the refractive index has a minor effect on retrievals of other aerosol parameters such as the real part of the refractive index and parameters of the aerosol size distribution. The implementation of the relaxed smoothness constraints on the imaginary part of the refractive index in the next version of the AERONET inversion algorithm will produce significant impacts at some sites in short wavelength channels (380 and 440 nm) for some biomass burning smoke cases with significant brown carbon content and possibly in mid-visible channels (500 and 675 nm) to near-infrared channels (870 to 1020 nm) for some urban industrial aerosol types. However, most differences in single-scattering albedo retrievals between those applying the new relaxed constraint and the standard constraint will be within the uncertainty of the single-scattering albedo retrievals, depending on the level of aerosol optical depth, Ångström exponent, brown carbon content and wavelength.

Aliaksandr Siniuk↗
NASA NTRS1977

Application of LANDSAT to the surveillance of lake eutrophication in the Great Lakes basin

The author has identified the following significant results. A step-by-step procedure for establishing and monitoring the trophic status of inland lakes with the use of LANDSAT data, surface sampling, laboratory analysis, and aerial observations were demonstrated. The biomass was related to chlorophyll-a concentrations, water clarity, and trophic state. A procedure was developed for using surface sampling, LANDSAT data, and linear regression equations to produce a color-coded image of large lakes showing the distribution and concentrations of water quality parameters, causing eutrophication as well as parameters which indicate its effects. Cover categories readily derived from LANDSAT were those for which loading rates were available and were known to have major effects on the quality and quantity of runoff and lake eutrophication. Urban, barren land, cropland, grassland, forest, wetlands, and water were included.

Rogers, R. H.↗
NASA NTRS1993

Electromagnetic field strength prediction in an urban environment: A useful tool for the planning of LMSS

A model for the prediction of the electromagnetic field strength in an urban environment is presented. The ray model, that is based on the Uniform Theory of Diffraction (UTD), includes effects of the non-perfect conductivity of the obstacles and their surface roughness. The urban environment is transformed into a list of standardized obstacles that have various shapes and material properties. The model is capable of accurately predicting the field strength in the urban environment by calculating different types of wave contributions such as reflected, edge and corner diffracted waves, and combinations thereof. Also, antenna weight functions are introduced to simulate the spatial filtering by the mobile antenna. Communication channel parameters such as signal fading, time delay profiles, Doppler shifts and delay-Doppler spectra can be derived from the ray-tracing procedure using post-processing routines. The model has been tested against results from scaled measurements at 50 GHz and proves to be accurate.

Vandooren, G. A. J.↗
DOE OSTI2021

Optimizing green infrastructure placement under precipitation uncertainty

Increased urbanization, infrastructure degradation, and climate change threaten to overwhelm stormwater systems across the nation, rendering them ineffective. Green Infrastructure (GI) practices are low cost, low regret strategies that can contribute to urban runoff management. However, questions remain as to how to best distribute GI practices through urban watersheds given precipitation uncertainty and the variable hydrological responses to them. We develop stochastic programming models to determine the optimal placement of GI practices across a set of candidate locations in a watershed to minimize the total expected runoff under medium-term precipitation uncertainties. Specifically, we first develop a two-stage stochastic programming model. Next, we reformulate this model using perturbed parameters to reduce the requisite computational time and extend it to multi-stage. In addition, we introduce constraints that allow for incorporating sub-catchment-level runoff reduction considerations. Furthermore, we account for hydrological connectivity in the watershed using an underlying acyclic connectivity graph of sub-catchments and incorporate various practical considerations into the models. In addition, we develop a systemic approach to downscale the existing daily precipitation projections into hourly units and efficiently estimate the corresponding hydrological responses. These advancements are brought together in a case study for an urban watershed in a mid-sized city in the U.S., where we perform sensitivity analyses, evaluate the importance of the considered constraints, and provide insights.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

NATURF

The Neighborhood Adaptive Tissues for Urban Resilience Futures tool (NATURF) is a Python workflow that generates les readable by the Weather Research and Forecasting (WRF) model. The NATURF Python modules use shapefiles containing building footprint and height data as input to calculate 132 building parameters at any resolution and converts the parameters into a binary file format.

Allen-Dumas, MelissaRee [Oak Ridge National Lab. (↗
DOE OSTI2022

Correcting for filter-based aerosol light absorption biases at the Atmospheric Radiation Measurement program's Southern Great Plains site using photoacoustic measurements and machine learning

Abstract. Measurement of light absorption of solar radiation by aerosols is vital for assessing direct aerosol radiative forcing, which affects local and global climate. Low-cost and easy-to-operate filter-based instruments, such as the Particle Soot Absorption Photometer (PSAP), that collect aerosols on a filter and measure light attenuation through the filter are widely used to infer aerosol light absorption. However, filter-based absorption measurements are subject to artifacts that are difficult to quantify. These artifacts are associated with the presence of the filter medium and the complex interactions between the filter fibers and accumulated aerosols. Various correction algorithms have been introduced to correct for the filter-based absorption coefficient measurements toward predicting the particle-phase absorption coefficient (Babs). However, the inability of these algorithms to incorporate into their formulations the complex matrix of influencing parameters such as particle asymmetry parameter, particle size, and particle penetration depth results in prediction of particle-phase absorption coefficients with relatively low accuracy. The analytical forms of corrections also suffer from a lack of universal applicability: different corrections are required for rural and urban sites across the world. In this study, we analyzed and compared 3 months of high-time-resolution ambient aerosol absorption data collected synchronously using a three-wavelength photoacoustic absorption spectrometer (PASS) and PSAP. Both instruments were operated on the same sampling inlet at the Department of Energy's Atmospheric Radiation Measurement program's Southern Great Plains (SGP) user facility in Oklahoma. We implemented the two most commonly used analytical correction algorithms, namely, Virkkula (2010) and the average of Virkkula (2010) and Ogren (2010)–Bond et al. (1999) as well as a random forest regression (RFR) machine learning algorithm to predict Babs values from the PSAP's filter-based measurements. The predicted Babs was compared against the reference Babs measured by the PASS. The RFR algorithm performed the best by yielding the lowest root mean square error of prediction. The algorithm was trained using input datasets from the PSAP (transmission and uncorrected absorption coefficient), a co-located nephelometer (scattering coefficients), and the Aerosol Chemical Speciation Monitor (mass concentration of non-refractory aerosol particles). A revised form of the Virkkula (2010) algorithm suitable for the SGP site has been proposed; however, its performance yields approximately 2-fold errors when compared to the RFR algorithm. To generalize the accuracy and applicability of our proposed RFR algorithm, we trained and tested it on a dataset of laboratory measurements of combustion aerosols. Input variables to the algorithm included the aerosol number size distribution from the Scanning Mobility Particle Sizer, absorption coefficients from the filter-based Tricolor Absorption Photometer, and scattering coefficients from a multiwavelength nephelometer. The RFR algorithm predicted Babs values within 5 % of the reference Babs measured by the multiwavelength PASS during the laboratory experiments. Thus, we show that machine learning approaches offer a promising path to correct for biases in long-term filter-based absorption datasets and accurately quantify their variability and trends needed for robust radiative forcing determination.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

Study of annoyance due to urban automobile traffic

Continuous traffic noise measurements were carried out over 48 hour periods in front of a hundred buildings in different types of streets in Paris and its suburbs. Physical interpretation of the results provides a noise prediction formula for a traditional type of street. The noise at each point was characterized by a limited number of parameters by means of factor analysis, and their effect on the degree of disturbance by 700 individuals questioned near the measurement stations was studied. It was found that many additional factors affect disturbance besides noise, and that the dispersion of replies for any given noise situation is very wide. A disturbance index is proposed which takes into account the daylight level L(50), the position of rooms in the dwelling exposed to noise, and the individual degrees of satisfaction with the area.

Aubree, D.↗
NASA NTRS1982

Mapping urban land cover from space: Some observations for future progress

The multilevel classification system adopted by the USGS for operational mapping of land use and land cover at levels 1 and 2 is discussed and the successes and failures of mapping land cover from LANDSAT digital data are reviewed. Techniques used for image interpretation and their relationships to sensor parameters are examined. The requirements for mapping levels 2 and 3 classes are considered.

Gaydos, L.↗
NASA NTRS1978

Endurance testing of first generation (Block 1) commercial solar cell modules

To determine lifetimes of the first generation (Block 1) commercial solar cell modules used in solar cell arrays, a program was initiated to expose these modules to a range of environments. The conditions endured by these modules encompassed hot and dry, hot and humid, tropical rain forests, sea-air, urban industrial and urban clean. Exposures were for periods up to 1 year. The effect of outdoor exposure on the performance of the modules was determined using current-voltage curves. Short-circuit current (I sub sc) and maximum power (P sub max) were the parameters monitored. In all cases, there was a loss of performance of the modules with outdoor exposure.

Anagnostou, E.↗
NASA NTRS1984

Differential detection of Gaussian MSK in a mobile radio environment

Minimum shift keying with Gaussian shaped transmit pulses is a strong candidate for a modulation technique that satisfies the stringent out-of-band radiated power requirements of the mobil radio application. Numerous studies and field experiments have been conducted by the Japanese on urban and suburban mobile radio channels with systems employing Gaussian minimum-shift keying (GMSK) transmission and differentially coherent reception. A comprehensive analytical treatment is presented of the performance of such systems emphasizing the important trade-offs among the various system design parameters such as transmit and receiver filter bandwidths and detection threshold level. It is shown that two-bit differential detection of GMSK is capable of offering far superior performance to the more conventional one-bit detection method both in the presence of an additive Gaussian noise background and Rician fading.

Simon, M. K.↗
DOE OSTI2023

A Bayesian model for multivariate discrete data using spatial and expert information with application to inferring building attributes

When modeling sparsely observed multivariate data, strong prior information elicited from experts can be used to bolster predictive accuracy and counteract sampling bias. Similarly, modeling autocorrelation in space can help make use of co-occurrence patterns present in many types of spatial data. To make use of both expert prior information and spatial structure, we propose a novel graphical model for a spatial Bayesian network developed specifically to address challenges in inferring the attributes of buildings from geographically sparse observational data. This model is implemented as the sum of a spatial multivariate Gaussian random field and a tabular conditional probability function in real-valued space prior to projection onto the probability simplex. This modeling form is especially suitable for the usage of prior information in the form of sets of atomic rules obtained from experts. To perform inference with missing data, we implement a Markov chain Monte Carlo scheme composed of alternating steps of Gibbs sampling of missing entries and Hamiltonian Monte Carlo for model parameters. A case study in building attribution is presented to highlight the advantages and limitations of this approach.

97 MATHEMATICS AND COMPUTING↗