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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Detection of Isotopes in Urban Source Search Gamma Spectra Using Hopfield Neural Network

Source search campaigns involve the measurement of consecutive background gamma-ray spectra obtained with a mobile detector-spectrometer, such as tellurium-doped sodium iodide NaI(Tl) scintillator detector-spectrometer. Counts are measured with NaI(Tl) detector in short acquisition intervals, such as one second. Detection of isotopes in search data is challenging because of the low signal-to-noise ratio (S/N) of the spectral lines and highly varying background due to naturally occurring radioactive material (NORM). In this work, we investigate the performance of the detection of orphan Cs-137, Am-241, and I-131 isotopes in a gamma ray data set obtained in a search survey. Isotopic detection is performed with a Hopfield Neural Network (HNN), which implements an auto-associative memory by matching incoming measurements to the closest one of its memory-stored signature patterns. HNN is trained on examples of spectra of calibrated isotopic sources with well-resolved spectral lines. During testing, HNN processes spectra with partially complete or distorted isotopic lines. The performance of the HNN detection algorithm is benchmarked with Precision, Recall, and F1 scores. Test results demonstrate the potential of HNN to detect the isotope of interest with high accuracy (F1 > 90%) for all isotopes in this study.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Building Monitoring and Energy Efficiency Training for Small and Mid-Sized Commercial Buildings in Non-Urbanized Areas of Alaska

The goal of this project was to create an effective training that increases understanding, competency and capacity in monitoring critical building data. Mentorship, verification and follow-up ensured participants now have necessary skills to install and maintain building monitoring systems. This training sought to empower building managers and maintenance staff to properly and efficiently operate their buildings for energy savings and increased lifespan of equipment. Through the project, participants’ knowledge of the building was increased through hands-on training using a familiar building environment and eliminating common barriers to effective training through peer-to peer delivery. In successful cases the host organization will save money and resources due to more efficient operation of building equipment already in place.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Understanding Commercial Building Energy Use in Small Urban Northern New York Areas: Building Stock Segmentation for Retrofit Planning

This report is part of the second phase of a publication series focusing on approximately 100 different local geographies, or "clusters." Each report provides characteristic features and energy data for commercial buildings in a specific area to help policy makers at the city, county, and state level better understand building energy use and emissions. This report breaks down the energy consumption and emissions of the building stock in the counties shown in Figure 2 by building type, building size, end use, energy consumption, emissions, and segment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Understanding Commercial Building Energy Use in Small Urban Areas in the Dakotas, Idaho, Montana, and Wyoming: Building Stock Segmentation for Retrofit Planning

This report is part of the second phase of a publication series focusing on approximately 100 different local geographies, or "clusters." Each report provides characteristic features and energy data for commercial buildings in a specific area to help policy makers at the city, county, and state level better understand building energy use and emissions. This report breaks down the energy consumption and emissions of the building stock in the counties shown in Figure 2 by building type, building size, end use, energy consumption, emissions, and segment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Understanding Multifamily Energy Use in Small Urban Northern New York Areas: Building Stock Segmentation for Retrofit Planning

This report is an addendum to a publication series that focuses on approximately 100 different local geographies, or "clusters". This addendum expands the report series to include large multifamily building characteristics as well as energy and emission data for each local geography. The intention of this addendum is to help policymakers at the city, county, and state levels better understand building energy use and emissions in large multifamily buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Understanding Multifamily Energy Use in Small Urban Areas in the Dakotas, Idaho, Montana, and Wyoming: Building Stock Segmentation for Retrofit Planning

This report is an addendum to a publication series that focuses on approximately 100 different local geographies, or "clusters". This addendum expands the report series to include large multifamily building characteristics as well as energy and emission data for each local geography. The intention of this addendum is to help policymakers at the city, county, and state levels better understand building energy use and emissions in large multifamily buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Applying Transfer Learning for Street-Scale Nuisance Flood Forecasting in Coastal-Urban Cities

An important challenge with Machine Learning (ML) is its transferability; that is, whether a ML model trained on one set of data can be applied to a second set of data without requiring a full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained on data collected for one set of streets can effectively forecast flooding for another set of streets in the same city using TL. The envisioned use case is a city deploying a new flood depth monitoring sensor on a street and using TL to apply a ML model, trained on sensor data from an existing flood depth sensor network, to this new street. Eventually, the new flood depth sensor will have a sufficient dataset for training its own ML model, but TL can be used to fill the gap in time while this new dataset is being generated. This method is explored using a Long Short-Term Memory (LSTM) model trained on data for the flood-prone streets of Norfolk City, Virginia. The data used for training includes environmental time series (rainfall, tide), topographic features (Digital Elevation Model (DEM), Topographic Wetness Index (TWI), Depth To Water (DTW)), and street-scale flood depth time series obtained from a high-fidelity physics-based model, acting as a synthetic street-scale stream depth sensor dataset since actual stream depth sensor data is generally unavailable for most cities. A set of 180 flood-prone streets was used to train a base model, while another set of 180 flood-prone streets was used to re-train that model using different TL strategies. The results show that full-weight re-training proved most effective and minimal re-training of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As target data increased beyond 18 flood events, the benefit of TL diminished relative to training a ML model directly on the local flood events. These findings can assist cities as they implement street-scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.

Roy, Binata [Univ. of Virginia, Charlottesville, V↗
DOE OSTI2024

Simulative Prediction of Solar Illuminance and Application of the Du-Sharples Model in Estimating Adapted Daylighting Metrics for an Urban Environment

The practice of daylighting in indoor spaces can significantly reduce electricity consumption and carbon emissions, improve human productivity, and enhance mood and cognitive perception. This work discussed the recent developments in daylighting science and practice, computed the periodic variations in average diurnal daylight levels for each month, quantified in terms of global horizontal illuminance and diffuse horizontal illuminance, for Kolkata, India, a city with tropical wet and dry climate, with two empirical luminous efficacy models of estimating solar illuminance, and assessed daylighting metrics with the Du-Sharples model. A program was formulated that could compute and generate daylight data with monthly-hourly solar irradiation data and the Du-Sharples model was utilized to predict dirt-corrected daylighting metrics for three glazing transmittance values and five elemental carbon deposition levels on glazing material. The highest monthly average global horizontal illuminance is recorded in April (64.05 klx for Littlefair model and 66.82 klx for Muneer-Kinghorn model) and the highest monthly average diffuse horizontal illuminance is recorded in July (33.23 klx for Littlefair model and 30.63 klx for Muneer-Kinghorn model). Further, the computed yearly average global and diffuse horizontal illuminance levels agree well with a previous study that applied the Perez model. Yearly average horizontal work surface illuminance level remained >1.5 klx for window-towall area ratio >30 %. The approach adopted in this work and the temporal variation charts of computed exterior daylight level data may assist building service engineers, architects, and indoor lighting practitioners in making informed policy decisions at different stages of building planning.

Engineering↗
DOE OSTI2023

SARS-CoV-2 raw wastewater surveillance from student residences on an urban university campus

The COVID-19 pandemic brought about an urgent need to monitor the community prevalence of infection and detect the presence of SARS-CoV-2. Testing individual people is the most reliable method to measure the spread of the virus in any given community, but it is also the most expensive and time-consuming. Wastewater-based epidemiology (WBE) has been used since the 1960s when scientists implemented monitoring to measure the effectiveness of the Polio vaccine. Since then, WBE has been used to monitor populations for various pathogens, drugs, and pollutants. In August 2020, the University of Tennessee-Knoxville implemented a SARS-CoV-2 surveillance program that began with raw wastewater surveillance of the student residence buildings on campus, the results of which were shared with another lab group on campus that oversaw the pooled saliva testing of students. Sample collection began at 8 am, and the final RT-qPCR results were obtained by midnight. The previous day’s results were presented to the campus administrators and the Student Health Center at 8 am the following morning. The buildings surveyed included all campus dormitories, fraternities, and sororities, 46 buildings in all representing an on-campus community of over 8,000 students. The WBE surveillance relied upon early morning “grab” samples and 24-h composite sampling. Because we only had three Hach AS950 Portable Peristaltic Sampler units, we reserved 24-h composite sampling for the dormitories with the highest population of students. Samples were pasteurized, and heavy sediment was centrifuged and filtered out, followed by a virus concentration step before RNA extraction. Each sample was tested by RT-qPCR for the presence of SARS-CoV-2, using the CDC primers for N Capsid targets N1 and N3. The subsequent pooled saliva tests from sections of each building allowed lower costs and minimized the total number of individual verification tests that needed to be analyzed by the Student Health Center. Our WBE results matched the trend of the on-campus cases reported by the student health center. The highest concentration of genomic copies detected in one sample was 5.06 × 10 7 copies/L. Raw wastewater-based epidemiology is an efficient, economical, fast, and non-invasive method to monitor a large community for a single pathogen or multiple pathogen targets.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Community engagement in the development of bioenergy projects from cellulosic urban waste feedstock in Hawaii for sustainable aviation fuel

The purpose of the research project was to reveal the perceptions of residents and communities of the City and County of Honolulu concerning bioenergy projects, feedstock, and sustainable aviation fuel. The perception study was conducted via community-scale surveys, interviews, and town meetings to gather feedback on the preliminary design of a proposed bioenergy project, including feedstock choice and the need for sustainable aviation fuel in Hawaii. Residents on the west side of Oahu were targeted due to their proximity to the proposed plant site location. Findings from this study aim to identify resident perceptions, understanding, and desire for bioenergy projects and new infrastructure to, improve the energy efficiency and sustainability of Hawaiian islands. While Hawaii has created policies and mandates to move toward 100% renewable energy sources by 2045, the focus has been predominately on renewable electricity, largely overlooking other sustainable energy options such as sustainable aviation fuel. There is a lack of research on the community engagement and perceptions of Hawaiian residents on bioenergy projects and sustainable aviation fuel, contributing to the low adoption of bioenergy projects on the islands. Insights from this study aim to add literature on the need for community engagement in the design process, the importance of accepting new sustainable infrastructure, and the production and use of sustainable aviation fuels.

09 BIOMASS FUELS↗
DOE OSTI2023

Source apportionment of soot particles and aqueous-phase processing of black carbon coatings in an urban environment

Abstract. The impacts of soot particles on climate and human health depend on the concentration of black carbon (BC) as well as the thickness and composition of the coating material, i.e., organic and inorganic compounds internally mixed with BC. In this study, the size-resolved chemical composition of BC-containing aerosol was measured using a high-resolution soot particle aerosol mass spectrometer (SP-AMS) during wintertime in Fresno, California, a location influenced by abundant combustion emissions and frequent fog events. Concurrently, particle optical properties were measured to investigate the BC absorption enhancement. Positive matrix factorization (PMF) analysis was performed on the SP-AMS mass spectral measurements to explore the sources of soot particles and the atmospheric processes affecting the properties of BC coatings. The analysis revealed that residential woodburning and traffic are the dominant sources of soot particles. Alongside primary soot particles originating from biomass burning (BBOABC) and vehicles (HOABC) two distinct types of processed BC-containing aerosol were identified: fog-related oxidized organic aerosol (FOOABC) and winter-background OOABC (WOOABC). Both types of OOABC showed evidence of having undergone aqueous processing, albeit with differences. The concentration of FOOABC was substantially elevated during fog events, indicating the formation of aqueous secondary organic aerosol (aqSOA) within fog droplets. On the other hand, WOOABC was present at a relatively consistent concentration throughout the winter and was likely related to the formation of secondary organic aerosol (SOA) in both the gas phase and aerosol liquid water. By comparing the chemical properties and temporal variations in FOOABC and WOOABC, we gain insights into the key aging processes of BC aerosol. It was found that aqueous-phase reactions facilitated by fog droplets had a significant impact on the thickness and chemical composition of BC coatings, thereby affecting the light absorption and hygroscopic properties of soot particles. These findings underscore the important role of chemical reactions occurring within clouds and fogs and influencing the climate forcing of BC aerosol in the atmosphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Urban Traveler - Changes and Impacts: Mobility Energy Productivity (MEP) Metric

For nearly a century, the automobile has been the primary mode of personal transportation in American life. This remains true today as millions of people rely heavily on cars to connect suburbs with cities or to travel long distances—often out of routine or convenience. However, advances in technology are fueling an era of transportation transformation, with the potential to transform a system that has remained virtually unchanged for decades. Aspiring smart cities are wrestling with questions such as: How does mobility impact a person’s quality of life? Would people make different travel choices if they were presented with better information about their mobility options? The ability to quantify the mobility potential of a given location is the first step toward answering these questions. In response, an interdisciplinary team at the National Renewable Energy Laboratory (NREL) has developed the Mobility-Energy Productivity (MEP) metric. The MEP metric provides an avenue to not only measure the mobility potential at a specific location in its current configuration, but also to test how various technological advances (e.g., connected and automated vehicles, plug-in electric vehicles, shared mobility) and infrastructure investments (e.g., building an additional highway lane, constructing a new shopping mall, implementing a transit-oriented development) impact the mobility of that location over time. This presentation details the FY19 progress on the MEP metric project funded through DOE VTO's SMART Mobility Consortium.

47 OTHER INSTRUMENTATION↗