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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

CHELAX-BNF: Turbulent Parameters by airborne measurements

The original data were collected on board the ARM Aerial Facility ArcticShark uncrewed aerial system (UAS; https://www.arm.gov/capabilities/observatories/aaf/uas ) during the “Characterizing HEterogeneous Land-Atmosphere eXchanges at BNF” field campaign (CHEAX-BNF; https://arm.gov/research/campaigns/aaf2025CHELAX-BNF ). The ARM Aerial Facility ArcticShark UAS was based at the public-use airport of Posey Field, AL (FAA LID: 1M4, 34.28027778° N, 87.60055556° W, 283m MSL) from May 28 through June 23, 2025. The ArcticShark UAS performed 5 flights, including 4 research flights over the BNF Main Site (ARM Mobile Facility 3, https://arm.gov/capabilities/observatories/amf ) and Supplemental Facilities to measure atmospheric state, turbulence, surface IR temperature and imagery, aerosol number concentration, and aerosol size distribution. The current data set presents a collection of turbulent parameters in the atmospheric boundary layer or lower free troposphere based on airborne measurement throughout the field campaign. The primary instruments used to create the current data set were the Aircraft Integrated Meteorological Measurement System (AIMMS-30) and the fine-wire thermocouple probe.

Aircraft Integrated Meteorological Measurement Sys↗
DOE OSTI2020

Artificial neural network based isotopic analysis of airborne radioactivity measurement for radiological incident detection

Responders need tools to rapidly detect and identify airborne alpha radioactivity during consequence management scenarios. Traditional continuous air monitor systems used for this purpose compute the net counts in various energy windows to determine the presence of specified isotopes, such as 235U, 239Pu, and 241Am. These calculations rely on having a well-calibrated detector, which is challenging in low-background environments. Here an alternative approach of using artificial neural networks to classify alpha spectra is presented. Two network architectures, fully connected and convolutional networks, were trained to classify alpha spectra into four categories: background and background plus the three isotopes above. Sources were injected into measured background at various fractions of the derived response level (DRL) corresponding to early-phase Protective Action Guides. The convolutional network identifies all sources at 1% of the DRL with average probability of detection of 95% and false alarm probability of 1%. Further, the network identifies sources ranging between 0.25% and 1% of the DRL with higher than 80% probability of detection and lower than 7% false alarm probability. Most significantly, the network performance improves in low-count background conditions, increasing its minimum probability of detection to 93% and reducing the false alarm probabilities to lower than 0.25%. These results show that, once trained on datasets representing a range of detection scenarios, artificial neural networks can accurately identify alpha isotopes of interest without the need for detector calibration.

Woldegiorgis, Surafel F.↗
DOE OSTI2021

Metrics for Interzonal Dispersion Assessment of Airborne SARS-CoV-2 Within Office Buildings

High ventilation rate is increasingly considered as a mitigation strategy in the rapid spread of COVID-19 in existing buildings in addition to other options like UVGI and added filtration. However, this and other similar measures may also impact energy use and the efficiency of the buildings in which they are installed. Without concrete metrics, the impact of all these potential changes to the system cannot be properly assessed relative to the other impacts the measures might have, including additional energy use, added operation and maintenance costs, and reduced system life. Multizone airflow and contaminant transport simulations involving parameters such as infiltration rate, system configuration, and level of occupancy can provide critical information on relative risk distribution within a building. In this study, prototype airflow models are used to assess the degree to which ventilation-related measures can mitigate the spread of a virus like SARS-CoV-2 in a similar way that prototype energy models are used to study energy efficiency measures. A new detailed medium-sized office building prototype model is developed using the CONTAM software to represent contaminant transport in buildings like those modeled by the U.S. Department of Energy’s detailed medium office prototype model. The goal of this study is to generate a set of suitable metrics that will give a whole-building picture of airborne SARS-CoV-2 virus distributions across the different zones of the building under different parameters and scenarios.

DeGraw, Jason↗
DOE OSTI2025

Large Area Airborne Contamination Monitoring

The individual components of a Large Area Airborne Contamination Monitoring (LAACM) system have been demonstrated as functional and the concept of a low-cost, rapidly deployable, and rapidly expandable system has been validated. All components of a previously demonstrated system were re-evaluated. Based on this evaluation, recommendations were made for air collection equipment, scintillating material was synthesized and optimized for application, and improved images were acquired. A preliminary concept of operations was developed, outlining the need for an automated scintillation application system and software for data acquisition and analysis. The work conducted under this program in FY24 has raised the Technology Readiness Level (TRL) of the LAACM from TRL 3 to TRL 4.

Whiteside, Tad S. [Savannah River National Laborat↗
DOE OSTI2024

Multiaxis sensing system for detection of airborne molecules and radionuclides

Various technologies presented herein relate to a method and equipment for detecting both airborne radioisotope and molecular effluent gases. Multi-Axis Sensing can be conducted by utilizing a pressurized MOF sorbent, such as a scintillating Metal-Organic Frameworks (S-MOFs). These MOFs are crystalline nanoporous materials that have synthetic versatility that allow adjustment of pore size, chemical environment, and luminescence properties. A method for detecting an analyte in a fluid sample is provided that comprises: loading a sorbent with a sample fluid, wherein the sorbent comprises a MOF material; pressurizing the sample fluid to increase the fluid in the sorbent thereby making a pressurized sorbent; and detecting ionizing radiation or a chemical property of the analyte in the pressurized sorbent.

Allendorf, Mark D.↗
DOE OSTI2023

Quantifying spatiotemporal variability in occupant exposure to an indoor airborne contaminant with an uncertain source location

Well-mixed zone models are often employed to compute indoor air quality and occupant exposures. While effective, a potential downside to assuming instantaneous, perfect mixing is underpredicting exposures to high intermittent concentrations within a room. When such cases are of concern, more spatially resolved models, like computational-fluid dynamics methods, are used for some or all of the zones. But, these models have higher computational costs and require more input information. A preferred compromise would be to continue with a multi-zone modeling approach for all rooms, but with a better assessment of the spatial variability within a room. Here to do so, we present a quantitative method for estimating a room's spatiotemporal variability, based on influential room parameters. Our proposed method disaggregates variability into the variability in a room's average concentration, and the spatial variability within the room relative to that average. This enables a detailed assessment of how variability in particular room parameters impacts the uncertain occupant exposures. To demonstrate the utility of this method, we simulate contaminant dispersion for a variety of possible source locations. We compute breathing-zone exposure during the releasing (source is active) and decaying (source is removed) periods. Using CFD methods, we found after a 30 minutes release the average standard deviation in the spatial distribution of exposure was approximately 28% of the source average exposure, whereas variability in the different average exposures was lower, only 10% of the total average. We also find that although uncertainty in the source location leads to variability in the average magnitude of transient exposure, it does not have a particularly large influence on the spatial distribution during the decaying period, or on the average contaminant removal rate. By systematically characterizing a room's average concentration, its variability, and the spatial variability within the room important insights can be gained as to how much uncertainty is introduced into occupant exposure predictions by assuming a uniform in-room contaminant concentration. We discuss how the results of these characterizations can improve our understanding of the uncertainty in occupant exposures relative to well-mixed models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Review of particle deposition to and removal from clothing, skin, and hair after a radioactive airborne dispersal event

Explosive Radiological Dispersal Devices (RDD) – aka dirty bombs – are seen as a credible method to carry out a radiological terror attack. After exploding a radioactive source, the radionuclide-laden plume will be blown downwind of ground zero, with particles falling out and potentially depositing on people caught in and under the cloud. Some of these people may not show any sign of radiation sickness and therefore not realize they have been contaminated and may take the radioactive particulate with them on their daily activities, thus spreading the radioactive particulate outside the initially contaminated area. This paper reviews the scientific literature to better understand the rate at which particulate deposits on and is removed from the different “surfaces” of a person, i.e., hair, skin, and clothing. Prior research indicates that: 1) particle deposition is usually higher on skin than on hair and clothing; 2) particle deposition is greater for a person with higher skin moisture, 3) stronger wind increases the deposition flux onto a person, and 4) the fraction of particulate deposited on the hair, skin, and clothing respectively depends on the length of the hair, assuming all the hair surface is available for deposition. The studies taken into consideration show that the largest uncertainty in particulate deposition onto a person is due to clothing type because of the different possible weave arrangements and tightness which translate into differences in actual surface area and surface roughness. A factor of 2-to-20 variation in deposition rate was found. Removal of the particulate from the contaminated person may be due to wind, a person's movement, and/or contact transfer, i.e., by touching a different clean surface. Experiments show that the majority of the particulate is resuspended within 2–6 h mostly depending on the intensity of physical activity. The largest uncertainty in particulate removal from skin depends on the skin moisture, transfer rate of single-contact, and how many objects/people a person touches per hour. No data for hair were found for particle removal and resuspension. The studies considered did not utilize radionuclides directly; however, data on adhesion of radioactive vs. their non-radioactive counterpart have shown that the uncertainty due to the radioactivity of the particles is lower than that due to other factors. In conclusion, an idealized scenario involving a single building in the path of the cloud showed the impact of building-influenced flow on the cloud transport path and mixing, which affects the radiological dose the downwind population is exposed to and consequently the health effects.

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

Measured influence of overhead HVAC on exposure to airborne contaminants from simulated speaking in a meeting and a classroom

Tracer gas experiments were conducted in a 158 m 3 room with overhead supply diffusers to study dispersion of contaminants from simulated speaking in physically distanced meeting and classroom configurations. The room was contained within a 237 m 3 cell with open plenum return to the HVAC system. Heated manikins at desks and a researcher operating the tracer release apparatus presented 8-9 thermal plumes. Experiments were conducted under conditions of no forced air and neutral, cooled, or heated air supplied at 980-1100 cmh, and with/out 20% outdoor air. CO 2 was released at the head of one manikin in each experiment to simulate small (< 5 µm diameter) respiratory aerosols. The metric of exposure relative to perfectly mixed (ERM) is introduced to quantify impacts, based on measurements at manikin heads and at three heights in the center and corners of the room. Chilled or neutral supply air provided good mixing with ERMs close to one. Thermal stratification during heating produced higher ERMs at most manikins: 25% were ≥ 2.5 and the highest were > 5× perfectly mixed conditions. Operation of two within-zone air cleaners together moving ≥400 cmh vertically in the room provided enough mixing to mitigate elevated exposure variations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Marine Boundary Layer Cloud Boundaries and Phase Estimation Using Airborne Radar and In Situ Measurements During the SOCRATES Campaign over Southern Ocean

The Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) was an aircraft-based campaign (15 January–26 February 2018) that deployed in situ probes and remote sensors to investigate low-level clouds over the Southern Ocean (SO). A novel methodology was developed to identify cloud boundaries and classify cloud phases in single-layer, low-level marine boundary layer (MBL) clouds below 3 km using the HIAPER Cloud Radar (HCR) and in situ measurements. The cloud base and top heights derived from HCR reflectivity, Doppler velocity, and spectrum width measurements agreed well with corresponding lidar-based and in situ estimates of cloud boundaries, with mean differences below 100 m. A liquid water content–reflectivity (LWC-Z) relationship, LWC = 0.70Z0.29, was derived to retrieve the LWC and liquid water path (LWP) from HCR profiles. The cloud phase was classified using HCR measurements, temperature, and LWP, yielding 40.6% liquid, 18.3% mixed-phase, and 5.1% ice samples, along with drizzle (29.1%), rain (3.2%), and snow (3.7%) for drizzling cloud cases. The classification algorithm demonstrates good consistency with established methods. This study provides a framework for the boundary and phase detection of MBL clouds, offering insights into SO cloud microphysics and supporting future efforts in satellite retrievals and climate model evaluation.

MBL clouds over Southern Ocean↗
DOE OSTI2020

Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning

Kites can be used to harvest wind energy at higher altitudes while using only a fraction of the material required for conventional wind turbines. In this work, we present the kite system of Kyushu University and demonstrate how experimental data can be used to train machine learning regression models. The system is designed for 7 kW traction power and comprises an inflatable wing with suspended kite control unit that is either tethered to a fixed ground anchor or to a towing vehicle to produce a controlled relative flow environment. A measurement unit was attached to the kite for data acquisition. To predict the generated tether force, we collected input–output samples from a set of well-designed experimental runs to act as our labeled training data in a supervised machine learning setting. We then identified a set of key input parameters which were found to be consistent with our sensitivity analysis using Pearson input–output correlation metrics. Finally, we designed and tested the accuracy of a neural network, among other multivariate regression models. The quality metrics of our models show great promise in accurately predicting the tether force for new input/feature combinations and potentially guide new designs for optimal power generation.

17 WIND ENERGY↗
DOE OSTI2025

Biofilm growth in water-cooling towers as collection platforms for airborne radionuclides

Given its history of nuclear material processing, the Savannah River Site (SRS) was used to evaluate whether biofilms growing in water-cooling towers (WCTs) are effective passive collection platforms for environmental radionuclide surveillance. Uranium and plutonium analyses suggest that WCT-sourced biofilms are efficient, indigenous, constantly running samplers that can be used for environmental monitoring, as their isotopic compositions are distinct from atmospheric fallout and representative of SRS historical activities. Further, the ubiquity of WCTs worldwide and demonstrated ability to detect nuclear material processing and constrain specific activities based on biofilm actinide isotopic compositions make WCT biofilms a promising means to improve monitoring.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗