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At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Bayesian Cloud Property Retrievals from ARM Active and Passive Measurements

The optimum use of the continuous measurements of thermodynamics, radiation, aerosols, clouds and precipitation from the DOE Atmospheric Radiation Measurement (ARM) program is key to achieve the DOE Atmospheric System Research (ASR)’s objectives. One of the key mission requirements is to retrieve cloud and precipitation properties, as well as vertical motion parameters, along the vertical cross- section defined by the profiling active sensors. Such retrievals are challenging to perform continuously in the entire spectrum of cloud and precipitation conditions due to the large natural microphysical and dynamical variability, the often-limited information content in the measurements, and the lack of proper characterization of measurement quality and uncertainty. Today, the acquisition of new remote and in-situ sensors by the ARM program creates opportunities to address the microphysical retrieval problem by exploiting new, more robust retrieval techniques and integrating various scattered advancements in both sensor techniques and retrieval algorithms. During this project, we constructed a robust Bayesian Markov chain Monte Carlo (MCMC) cloud property retrieval algorithm that includes a state of the art radar forward model. Our MCMC-based retrieval produces both the best estimate of height-resolved cloud and precipitation properties in the radar profile, as well as an estimate of the in-cloud vertical motion and turbulence. In addition, the MCMC algorithm automatically produces robust and flexible estimates of retrieval uncertainty. We tested the algorithm on several synthetic cloud profiles obtained from large eddy simulation (LES) models with bin-resolved microphysics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Physics-Based Method for Generating Fully Synthetic IV Curve Training Datasets for Machine Learning Classification of PV Failures

Classification machine learning models require high-quality labeled datasets for training. Among the most useful datasets for photovoltaic array fault detection and diagnosis are module or string current-voltage (IV) curves. Unfortunately, such datasets are rarely collected due to the cost of high fidelity monitoring, and the data that is available is generally not ideal, often consisting of unbalanced classes, noisy data due to environmental conditions, and few samples. In this paper, we propose an alternate approach that utilizes physics-based simulations of string-level IV curves as a fully synthetic training corpus that is independent of the test dataset. In our example, the training corpus consists of baseline (no fault), partial soiling, and cell crack system modes. The training corpus is used to train a 1D convolutional neural network (CNN) for failure classification. The approach is validated by comparing the model’s ability to classify failures detected on a real, measured IV curve testing corpus obtained from laboratory and field experiments. Results obtained using a fully synthetic training dataset achieve identical accuracy to those obtained with use of a measured training dataset. When evaluating the measured data’s test split, a 100% accuracy was found both when using simulations or measured data as the training corpus. When evaluating all of the measured data, a 96% accuracy was found when using a fully synthetic training dataset. The use of physics-based modeling results as a training corpus for failure detection and classification has many advantages for implementation as each PV system is configured differently, and it would be nearly impossible to train using labeled measured data.

Hopwood, Michael W. (ORCID:0000000161901767)↗
DOE OSTI2020

Deep Learning Prediction of Interspecies Interactions from Self-organized Spatiotemporal Patterns of Co-evolving Organisms

Microorganisms colonizing natural habits such as soils co-evolve to form specific spatial patterns through interspecies interactions. These self-organized patterns are a key ecological phenotype, which provides critical information on their interaction mechanisms. However, conventional network inference techniques that analyze species population data in bulk have yet to be extended to account for such spatial heterogeneity. Here we proposed supervised deep learning as a new network inference tool for predicting interspecies interactions from spatiotemporal patterns of microbial evolution. Due to lack of biological imaging data that can be used for training deep learning networks, we used in silico data generated from high-fidelity agent-based models to determine model structure and parameters. Even though networks were trained under simple configurations where interaction coefficients are assumed to be spatially invariant, we demonstrated that the resulting model can be utilized to successfully predict spatial variation of interactions in more complex domains (i.e., configured with a context-dependent mixture of interaction coefficients) as well as in simple domains without further training. In the further test against real biological data obtained through imaging experiments of a binary consortium (Pseudomonas fluorescens and a mutant of Escherichia coli), our model also predicted the dramatic shifts in interactions of the two organisms across different environmental contexts. Through various successful demonstrations in this work, the combined use of the agent-based model and machine learning algorithm provides a means to use new type of data - microscopic images - for extracting microbial interactions, therefore presenting itself as a useful tool for the analysis of more complex microbial community interactions.

Lee, Joon-Yong↗
DOE OSTI2020

A Perspective on the Successes of the NNSS Underground Test Area (UGTA) Activity - 20221

Between 1951 and 1992, 828 underground nuclear tests were performed at the Nevada National Security Site (NNSS). Underground testing was done in five major testing areas, which included (1) Frenchman Flat, (2) Rainier Mesa/Shoshone Mountain, (3) Yucca Flat/Climax Mine (4) Central Pahute Mesa, and (5) Western Pahute Mesa. About one third of the underground tests were detonated near, at or below the water table, and thus radioactive contamination was introduced to the groundwater system. The U.S. Department of Energy's Underground Test Area (UGTA) Activity was established in the late 1990's to address the fate and movement of residual radioactivity in groundwater, and characterize the risk that it may pose to NNSS workers and the offsite public. It has accomplished this goal through a process of iterative groundwater sampling and numerical groundwater flow and transport modeling, and long-term monitoring. The UGTA Activity draws on the expertise of scientists in the fields of geology, hydrology, radiochemistry, and risk assessment from the U.S. DOE staff, the lead contractor (currently Navarro) and many other organizations, in cooperation with the governing regulatory body, the State of Nevada's Division of Environmental Protection (NDEP). Los Alamos National Laboratory (LANL) is one of the participating research organizations involved with the UGTA Activity. Under the direction of DOE and the lead contractor, LANL's role in UGTA has evolved over time from an initial focus on geologic characterization, to conducting field and laboratory experiments, and finally to its current role of providing modeling and geochemistry expertise to characterize the rates and directions of groundwater and radionuclide movement. In its current role, LANL has either developed or provided the numerical tools for developing flow and transport models in each of the four major testing areas. In addition, LANL has provided an independent assessment of future groundwater flow and transport behavior through the analysis of naturally-occurring geochemical and isotopic tracers in groundwater. The five major testing areas are now in different stages of investigation or closure: (1) Frenchman Flat is in its fifth year of post closure modeling; (2) The Rainier Mesa/Shoshone Mountain Closure Report has been submitted to NDEP and is awaiting approval; (3) The Yucca Flat/Climax Mine Closure Report is under development; and (4) Central and (5) Western Pahute Mesa is still undergoing investigation and flow and transport model development. The success of the UGTA Activity in reaching or approaching closure in three of the five major testing areas is primarily due to the DoE's focus on regulatory closure requirements, but also is in part attributable to a rigorous internal review process and the involvement of NDEP and Nye County water professionals as participants and observers in the reviews. County and state involvement in the internal review process has promoted trust that the U.S. DOE is deeply committed to ensuring the present and future safely of NNSS workers and the surrounding communities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Real-time PCR assays that detect genes for botulinum neurotoxin A–G subtypes

The role of Real-Time PCR assays for surveillance and rapid screening for pathogens is garnering more and more attention because of its versatility and ease of adoption. The goal of this study was to design, test, and evaluate Real-Time TaqMan PCR assays for the detection of botulinum neurotoxin (bont/A-G) genes from currently recognized BoNT subtypes. Assays were computationally designed and then laboratory tested for sensitivity and specificity using DNA preparations containing bont genes from 82 target toxin subtypes, including nine bivalent toxin types; 31 strains representing other clostridial species; and an extensive panel that consisted of DNA from a diverse set of prokaryotic (bacterial) and eukaryotic (fungal, protozoan, plant, and animal) species. In addition to laboratory testing, the assays were computationally evaluated using in silico analysis for their ability to detect bont gene sequences from recently identified toxin subtypes. Seventeen specific assays (two for each of the bont/C, bont/D, bont/E, and bont/G subtypes and three for each of the bont/A, bont/B, and bont/F subtypes) were designed and evaluated for their ability to detect bont genes encoding multiple subtypes from all seven serotypes. These assays could provide an additional tool for the detection of botulinum neurotoxins in clinical, environmental and food samples that can complement other existing methods used in clinical diagnostics, regulatory, public health, and research laboratories.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Testing, Calibration, and UxS Integration of the Kromek GR1 Plus CZT Gamma Spectrometer

Collecting radiation measurements can be a hazardous task, especially in the presence of highly active sources. Various scenarios necessitate source search, classification, and quantification, often with limited or no a priori information. Activities such as disaster mitigation, emergency response, environmental monitoring, and site remediation may involve dangerous radioactive sources. In these situations, there is a pressing need for remote monitoring capabilities that protect human operators from potential harm and enhance adherence to the principle of “As Low as Reasonably Achievable” (ALARA) for radiation doses. The first step toward achieving remote radiation measurement capabilities is the remote operation of a radiation sensor. Once this milestone is reached, the next challenge is to integrate this remote sensing capability into suitable actuation agents, collectively referred to as uncrewed systems (UxS). These systems include familiar platforms such as robotic quadrupeds, aerial multirotor vehicles, and ground vehicles, any of which may be teleoperated, act autonomously, or utilize a combination of both. A critical factor in achieving remote radiation sensing is the availability of data from the appropriate sensor. Many commercially available radiation sensors have closed-source documentation for their communication protocols. Typical end-user products are often self-contained, handheld devices designed for manual measurement scenarios. While there are commercial off-the-shelf (COTS) integrations of radiation sensors with UxS available for purchase, these solutions are typically tailored for specific use cases and may not meet the requirements of different applications. This paper discusses efforts to remotely acquire radiation measurements from a small form-factor CZT gamma spectrometer. Sandia National Laboratories has successfully demonstrated the initial capability to integrate low size, weight, and power (SWaP) gamma spectroscopy into various UxS, alongside co-located GPS data logging and sensor calibration and qualification. With remote gamma spectroscopy achieved, the stage is set for UxS integration of this capability.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

A novel approach to mitigating the potential release of radioisotopes under fire conditions - enhancing fire resiliency of radiological contamination fixatives during deactivation & decommissioning activities

Savannah River National Laboratory (SRNL), in close collaboration with the Florida International University Applied Research Center (FIU ARC), successfully executed a technology development activity on behalf of the Department of Energy, Office of Environmental Management (DOE EM). The purpose of the activity was to improve the capability of fixative technologies in immobilizing residual contamination when exposed to thermal stressors as postulated in accident scenarios in Basis for Interim Operations (BIO) documents across the DOE EM complex. The effort resulted in the test and evaluation of a down-selected, commercial-off-the-shelf (COTS) intumescent technology in a radioactive environment at the Savannah River Site (SRS) Building 235-F Plutonium Fuel Form (PuFF) Facility and highlighted the potential use of this technology as a stand-alone, fire retardant fixative. Furthermore, this activity highlighted significant shortfalls in common fixatives currently used to support decommissioning activities and brought to the forefront the need for a methodical, uniformed approach to certifying fixative technologies for operational use in Deactivation and Decommissioning (D&D) activities. An essential component of this research was to evaluate the state of industry fixatives currently in use and set the foundation for comparison to alternative technological solutions. A baseline of five (5) commonly used fixatives and decontamination gels (hereafter collectively referred to as fixatives) was conducted, and notable shortfalls and deficiencies were revealed, particularly when exposed to thermal, water, and other environmental stressors. At temperatures as low as 300 °F - 400 °F (148.89 °C – 204.44 °C), all commonly used fixatives melted from the substrates within 3-5 minutes of exposure, resulting in contaminant transport. Significant degradation in terms of mass loss, desiccation, and off-gassing occurred, and the chemical breakdown of the polymer was so complete researchers assessed there would likely have been a release of residual contamination. Additional vulnerabilities became evident when exposed to water immersion and high humidity; existing fixatives took up water, swelling and frequently delaminating from the substrate, once again increasing the likelihood of a contaminant release.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Metal Bioavailability and Ecotoxicity of Bioremediated Oils and Tailings by BioTiger{sup TM}, a Microbial Consortium

Oil Sands and Mature Fine Tailings: Oil sand reserves are a major source of oil for the United States. Oil sands are a mixture of sand, clay, water, and bitumen. The refining process requires large volumes of water and generates hazardous Mature Fine Tailings (MFTs) that are stored in engineered settling ponds. They are of major environmental concern due to their persistence and difficulty to naturally biodegrade. MFT Contaminants of Concern: Naphthenic Acids (NAs), Polycyclic Aromatic Hydrocarbons (PAHs), Benzene, Toluene, Ethylbenzene and Xylene (BTEX), Metals, Residual Bitumen. BioTiger{sup TM} (BT{sup TM}), the SRNL patented 12 component microbial consortia was found to cometabolically degrade some NAs and PAHs. Previous studies performing short term exposure (48 hours and 7 days) to BT{sup TM} have resulted in increased toxicity due to the partial degradation of PAHs forming toxic intermediates. Objective: Evaluate the ecotoxicity of BT{sup TM} remediated MFTs exposed to a 2 week period w/ yeast (Y) extract. This work will determine BT{sup TM}'s remediation of MFTs from Fort McMurray, Alberta. Acute toxicity tests will be performed under section 9 of EPA's Method for Measuring Acute Toxicity using the freshwater organism, Ceriodaphnia dubia. Monitor BT{sup TM} growth through Most Probable Number (MPN) counts, pH, and metal bioavailability will also be evaluated. Preparing tailing solutions for biodegradation: BT{sup TM} components were grown in R2A media and combined. Mother BT{sup TM} was centrifuged at 7000 rpm for 20 minutes and resuspended in Bushnell Haus. 33 g of tailings were added to 1100 mL Bushnell Haus along with yeast extract (11 g) to create a 3% tailing solution. Treatments were performed in triplicate. Preparing solutions for toxicity tests using C. dubia: Supernatant was collected immediately after centrifuging at 7000 rpm for 20 min. Supernatant was filtered via a 0.22 μm sterile system. All treatments except MFT, Y peaked at T=4. This decrease in microbial growth could be associated with toxicity from intermediate byproducts. Treatments containing only MFTs decreased in the initial 11 days before growing significantly. Final solution for MFT, Y treatment had the highest pH. This could potentially be due to increased microbial activity linked with yeast consumption. All treatments except MFT, Y peaked at T=4. This decrease in microbial growth could be associated with toxicity from intermediate byproducts. Treatments containing only MFTs decreased in the initial 11 days before growing significantly. Future Direction: Await results from Acute Toxicity Tests and Metal Bioavailability Quantification of biosurfactant production. Evaluate hydrocarbon degradation byproducts using an analytical chemistry approach. More toxicity experiments with variations in time and conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A building performance-based approach to determining energy resilience for grocery stores in the event of a power outage

Evaluating progress toward a built environment that is best equipped to serve communities during a regional power outage will require metrics that capture the energy resilience of the unique buildings and businesses most crucial to the well-being of those nearby. We focused on grocery stores as key buildings for which access, and thus energy resilience, is critical during a disaster when power is unavailable. We evaluated the energy resilience of these buildings by offering and testing building-scale metrics that assess business continuity potential during a power outage. Metrics proposed in this study are calculated based on the unique power loads characteristic to grocery stores, primarily refrigeration and maintaining safe indoor environmental conditions. Building simulations based on varying levels of backup power were carried out against occupant safety and comfort parameters to apply these metrics, with additional criteria imposed on grocery stores to capture the inventory and sales loss from food spoilage resulting from a lack of refrigeration power. Findings from this study demonstrate the feasibility of our proposed metrics and methodology to serve as a low-data burden means for stakeholders to evaluate the energy resilience of grocery stores, with greater implications in helping to understand the impact on community-scale energy resilience.

Siegel, Lino Sanchez↗
DOE OSTI2024

Machine Learning-Driven Reliability Estimation of PV Inverters Considering Alert-Ambient Variability

Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.

14 SOLAR ENERGY↗
DOE OSTI2022

Training data selection for event classification in a highly variable environment

A problem of interest for nuclear nonproliferation is monitoring activities at nuclear facilities, where proliferation events may only take place a few times and often under variable conditions. Machine learning has revolutionized data analytics by enabling the use of measurable signatures to generate predictive models of facility operations. However, traditional methods for training these models require large, reliable data sets with labeled observations, a challenge for nonproliferation. Highly variable conditions further complicate this as events from training data may have occurred in conditions quite different from the event of interest. Our hypothesis is that when events occur in a highly variable environment, careful training data selection for each test event could outperform the standard approach of using all available training data. We developed a method to optimize training data selection for the given test event and applied it to predicting the power level of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In this study, the reactor startup exhibits variability between occurrences due to natural variability in environmental conditions and operational procedures. Using a combination of analysis techniques, a similitude assessment was performed on data collected from HFIR to isolate clusters that were optimal for training a predictive model. Concepts such as dynamic time warping and Jaccard similarity were used in conjunction with clustering analysis. In order to validate this approach, the model was trained on every combination of unique training events and the predictive performance was compared to the performance using a subset of the training data selected by isolated clusters found through the similitude assessment.

Iyer, A↗
DOE OSTI2020

Marine Corps Base Camp Lejeune Climate Forecasts

Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of encroachment pressures in surrounding areas, impairments due to other anthropogenic disturbances, and changes in climate and sea level. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and also optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the second 5 years of the program (DCERP2) are presented in the DCERP2 Final Report.There were four overarching objectives of DCERP2. The first objective was to understand the effects of climate change impacts, including warming temperatures, variability in the hydrological cycle, storm events, and sea level rise on the coastal ecosystems at MCBCL from observations and measurements made over the 10-year program. The second objective was to understand the carbon cycle of the coastal and terrestrial ecosystems at MCBCL through a highly integrated sampling program. The third objective was to develop models, tools, and indicators to evaluate current and projected future ecosystem state changes and translate scientific findings into actionable information for installation managers. The last objective was to recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.There are 252 mean change files represented in this dataset (12 months * 3 future time periods * 7 variables ). The mean change is calculated by taking the mean of the differences between the future projected average (e.g., 2046-2065) and the climate model baseline (e.g., 1979-1999). The differences were calculated for each downscaled GCM and then averaged across all models to obtain the mean change. This mean of the differences was calculated for each month, each future time period, and each variable.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Onslow Beach Washover Fan Morphology from 2012 to 2015

Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of encroachment pressures in surrounding areas, impairments due to other anthropogenic disturbances, and changes in climate and sea level. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and also optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the second 5 years of the program (DCERP2) are presented in the DCERP2 Final Report.There were four overarching objectives of DCERP2. The first objective was to understand the effects of climate change impacts, including warming temperatures, variability in the hydrological cycle, storm events, and sea level rise on the coastal ecosystems at MCBCL from observations and measurements made over the 10-year program. The second objective was to understand the carbon cycle of the coastal and terrestrial ecosystems at MCBCL through a highly integrated sampling program. The third objective was to develop models, tools, and indicators to evaluate current and projected future ecosystem state changes and translate scientific findings into actionable information for installation managers. The last objective was to recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.These grid files were created from data collected using a Riegl 3-D laser scanner. Millions of x, y, and z points from the F2 Washover Fan on Onslow Beach were processed using the MARS software package to define a bare-earth model. Grid files were imported into Golden Software Surfer version 19 for editing and exported from that application as .grd files. Files can be opened in Surfer or ArcMap, but the coordinate system information will be lost if opened in ArcMap. These data were collected to quantify the morphological changes that occur on a washover fan through time.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Predicting Pit Stability On Additively Manufactured SS316 Via Finite Element Modeling [Slides]

Pit growth and repassivation are complex, with many interconnecting geometric and environmental parameters to consider. Experimentally, it is difficult to isolate these individual parameters to study their effect on the stability of pits. To enable these studies, a finite element modeling approach has been developed to allow systematic testing of parameters that impact a pit’s stability. The specific parameters studied were the cathode diameter, the pit diameter and shape, and the water layer thickness. Hemispherical and rectangular-based pits were studied to determine the impact of the overall pit shape. Pit stability results were compared with mathematical calculations based on the Maximum Pit Model, for both 50% saturation and 100% saturated salt film coverage. Further studies expanded the range of pit geometry to those relevant to additively manufactured surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Aging of Concrete for the Evaluation of Repair Materials to Protect the Walls of the HCAEX Tunnel at Savannah River - 20301

H-Canyon, located in the Savannah River site, is a unique facility for chemical reprocessing of plutonium, highly-enriched uranium, and other radioactive materials. The exhaust gases of the H-Canyon are sent through the H-Canyon Exhaust (HCAEX) tunnel for contamination removal. Robotic inspections of the tunnel revealed significant degradation of the reinforced concrete structure that was associated with acid attack, and could compromise the structural stability of the tunnel. Thus, the identification and evaluation of potential repair materials that could be applied on the degraded walls to mitigate and prevent further degradation is of significant interest to the Department of Energy and the Savannah River representatives. This research effort has been divided into two phases: 1) Development and evaluation of aged concrete under accelerated aging conditions (which is the focus of this paper) and 2) Evaluation of potential repair materials applied on aged and non-aged concrete under simulated aggressive conditions. In order to develop and evaluate concrete samples exposed to accelerated aging conditions in simulated aggressive environments, a literature review of the HCAEX tunnel was conducted that included 1) characterization and extent of the concrete damage, 2) environmental conditions inside the tunnel and 3) primary deterioration mechanisms. In addition, potential coatings and/or repair materials for degraded concrete surfaces exposed to aggressive environments (primarily acidic) were selected from the literature review and the most common testing and measurements for evaluating acid attack phenomena, erosion, etc. were reviewed. From the literature review findings, a preliminary bench-scale test plan for the concrete aging was developed, including accelerated aging tests with aggressive conditions (acid fumes, humidity, etc.). Concrete samples were exposed to the aging accelerated conditions (e.g. immersion in acid solutions) and visual inspection, mass loss and pH change (acid solution) were recorded over time. Correlations between the visual inspection, mass loss and pH changes results and the aging time or the aging conditions were developed. Specimens submitted to the highest acid concentration showed the fastest and most intense degradation. The type of coarse aggregate (limestone) used for the concrete seemed to be the cause of the fastest aging observed, compared to the cement paste. The research findings created the foundation for the ongoing investigation, in which new concrete samples with a mix design similar to the HCAEX tunnel will be tested and will serve as the substrate for testing the selected coatings and /or repair materials. In this paper, the literature review and preliminary results from the aging tests are provided. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Multicriteria-Based Selection of Microalgae Biorefineries: Biomass Composition Defines the Most Suitable Product Portfolios

The supply of microalgae-derived biofuels and bioproducts will be vital in a global-scale bioeconomy. As the diversity of microalgae strains and their compositional plasticity may yield a wide array of products of market interest, the design of effective biorefineries is a central aspect in the path to making microalgae-based products available in the market. In this way, the conversion of microalgae biomass should be planned to employ mature technologies, while maximizing economic and environmental benefits obtained from a diverse product portfolio. This study applied sequential, hybrid Multicriteria Decision Analyses (MCDA) to aid the decision-making process of outlining the most suitable biorefining pathways for specific compositional profiles. For this, the methodology simultaneously considered multiple technical, economic, and environmental criteria, such as market aspects for the main algae-derived products, potential reduction in greenhouse gas (GHG) emissions provided by the biobased alternatives, and technology readiness level of conversion routes, among others. The framework was tested using productivity and compositional data for 13 high-productivity summer strains cultivated under varying nutrient availability. The analysis pointed to compositional profile being a key driver in defining the core biorefining strategy of algae biomass, with lipid-to-carbohydrate ratios higher than roughly 1 warranting the preferential processing of lipids into hydrocarbon fuels with the remainder of the algae biomass compounds being sent to higher-value applications, such as carboxylic acids and renewable thermoplastic substitutes. An in-depth process simulation, techno-economic assessment (TEA), and life-cycle analysis (LCA) effort was carried out as a closing step to corroborate the results stemming from the proposed framework. This study validates the use of MCDAs as a screening method prior to implementing more time-consuming analysis techniques and makes a compelling case for this approach as a product selection tool on a wide range of algae species, thus helping establish species-agnostic (but composition-driven) biorefineries.

biofuels↗
DOE OSTI2022

Improved Moisture Management Technology for Eco-N-Control Fertilizer

Three fertilizer technologies to manage nitrogen (N) release rates in soil dominate the market today: 1) bulk controlled-release technologies, which use the intrinsic solubility properties of a N-organic co-polymer to control N release; 2) barrier-based controlled-release technologies, which use porous polymer or sulfur coatings as a physical barrier to control N release from encapsulated N nutrients; and 3) slow-release technologies, which use enzyme inhibitors to chemically slow down the transformation of N nutrients into ammonia (NH 3 ) or to temporarily reduce populations of bacteria in the soil that are responsible for converting ammonium (NH 4 + ) to highly soluble nitrates. These technologies are expensive and provide only limited control of N-release rates. Also, from another environmental perspective, synthetic polymer coatings do not decompose easily and will accumulate over time with continuous application of fertilizer. For most of the past two decades we have been developing the Eco-N-Control solid-nitride fertilizer technology, a type of bulk controlled-release technology. With Eco-N-Control, solid-nitride compounds weather like a mineral, slowly releasing N at a rate determined by their composition. Testing by an industrial collaborator, however, showed that earlier versions of Eco-N-Control released at 25% of the desired rate when deployed in unsaturated soils. The overall goal of the current project, therefore, was to strengthen our IP position and the potential for licensing of Eco-N-Control solid-nitride fertilizer technology by developing new ways to manage the level of moisture in contact with the fertilizer when applied to soil thereby ensuring the desired N-release rate. We achieved this goal by developing a biodegradable polymer cross-linking approach that substantially improved performance of our patented solid-nitride fertilizer. We found that cross-linking an environmentally friendly and biodegradable polymer with a mixture of solid nitrides, other N-nutrient compounds and other additives offers several advantages: 1) mechanical robustness of fertilizer beads, 2) chemical stability during storage, 3) maintenance of sufficient water content in the vicinity of the nitride, and 4) control of N-release rates. We also updated our N-release testing protocol to more realistically simulate the conditions found in soil. Finally, we filed an invention disclosure record to protect these new aspects of Eco-N-Control technology, which offers a potential way to lower both agricultural and industrial pollution by providing an efficient N-delivery mechanism to plants.

42 ENGINEERING↗
DOE OSTI2020

Calibration of the Falcon Solid-state Energetic Electron Detector (SEED)

The Falcon Solid-state Energetic Electron Detector (FalconSEED) is an energetic charged particle sensor that has been developed and tested at the United States Air Force Academy in an effort to monitor electron flux across the energy range of 14 to 145 keV in geosynchronous orbit. This sensor has been developed to complement ongoing efforts by the Air Force Research Laboratory to advance a comprehensive space environment sensor suite, Compact Environmental Anomaly Sensor (CEASE3), for anomaly resolution. In addition, FalconSEED is intended to demonstrate the ability to operate a radiation sensor based on predominantly commercial off the shelf components in the geosynchronous environment. This paper describes the design, development, and calibration of FalconSEED. The electron energy spectrum of interest for the FalconSEED sensor is 14 to 145 keV, and is intended to supplement data acquired from the CEASE3 instrument for anomaly resolution. The calibrations at the Space Atmospheric Research Center at the United States Air Force Academy and Kirtland Air Force Base were used to first establish the conversion between histogram bin number and deposited energy in the sensor, and second have been used to determine the geometric factor as a function of energy. The FalconSEED will be integrated as a science payload on the Space Test Program Satellite-6 which also includes the first flight of the CEASE3 instrument and is scheduled to launch in 2021.

47 OTHER INSTRUMENTATION↗