SEEK - Scoping neuromorphic architecture impact enabling advanced sensing capabilities.
Abstract not provided.
Vineyard, Craig Michael↗
SEARCH · Engineering Papers
Results for “scoping”
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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On the Interpretation and Scope of the V&V 20 Standard for Verification and Validation in Computational Fluid Dynamics and Heat Transfer.
Abstract not provided.
Source record↗
Broadening the scope of Education, Career and Open Science in HEP
High Energy Particle Physics (HEP) faces challenges over the coming decades with a need to attract young people to the field and STEM careers, as well as a need to recognize, promote and sustain those in the field who are making important contributions to the research effort across the many specialties needed to deliver the science. Such skills can also serve as attractors for students who may not want to pursue a PhD in HEP but use them as a springboard to other STEM careers. This paper reviews the challenges and develops strategies to correct the disparities to help transform the particle physics field into a stronger and more diverse ecosystem of talent and expertise, with the expectation of long-lasting scientific and societal benefits.
Malik, Sudhir↗
ANS Winter 2022: ATF-2C Physics Safety and Scoping Analysis
ATF-2C comprises four tiers with six rodlets each. The experiment test train (TT) is situated in Loop 2A in the center flux trap (CFT) of the ATR, in which typical pressurized water reactor (PWR) conditions shall be approximated. Tier 1 (bottom) of the TT tests Framatome silicon carbide (SiC) cladding, containing molybdenum heater rods. Low-enriched uranium (LEU) Framatome rodlets with chrome-coated M5 cladding have been modeled in their place for the physics safety analysis to allow the substitution of this tier with fuel if needed. Tier 2 tests LEU Framatome rodlets with M5 cladding, which had irradiated in ATR in prior ATF experiments. Tier 3 tests General Atomics SiC composite cladding, also containing molybdenum heater rods. Tier 4/5/6 (top) tests the Japan Atomic Energy Agency (JAEA)/Mitsubishi Heavy Industries (MHI) fueled rodlets. This upper tier tests LEU rodlets with chrome-coated MDA cladding—several with temperature or pressure instrumentation. To flatten the axial power profile, lower portion of Tier 4/5/6 is surrounded by a hafnium (Hf) shroud, and the bottom two UO2 pellets are of natural enrichment. The ATF-2C TT is to be irradiated in the CFT of ATR (Fig. 1) for three or four 60-day cycles.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
LANSCE Modernization Project (LAMP) Scope Discussion for the Independent Technical Review Committee (ITRC) Kickoff Meeting
Explore the source record for details and available documents.
Dale, Gregory E.↗
Scoping Study of Effects on the Advanced Test Reactor’s Center Flux Trap Due to Various Loadings in the H and Inner A Test Positions
This study determined the effects experienced by the In-Pile Tube, of the Advanced Test Reactor’s center flux trap, due to various loading configurations of the H and Inner A positions. The effects of concern included: the axial profile of the linear heat generation rate of each fuel rodlet, the axial flux shape, and spectrum, in the fuel, and cladding, of each rodlet, and the total fission heating rate of the entire In-Pile Tube. Additionally, the reactivity worth of each configuration, relative to an ATR configuration with water filling all concerning test positions, was determined. The test positions were filled with commonly used test specimens, backup test specimens, and homogeneous substances. The study was performed using the modeling software MCNP6.2.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
A Machine Learning Approach to Improve Air Traffic Management Initiatives
Collaborating closely with commercial air carriers and related organizations, the Federal Aviation Administration(FAA) regulates air traffic and ensures the safety and efficiency of air operations. Air traffic controllers make strategic decisions, such as delaying, rerouting, or canceling flights, partly based on guidance provided by the FAA’s Air TrafficControl System Command Center (ATCSCC). The guidance includes, among other things, control measures known asTraffic Management Initiatives (TMIs) designed to enhance safety and improve operational efficiency. TMIs play a crucial role in managing the demand and capacity within the U.S. National Airspace System (NAS). Two major TMIs that are routinely used (primarily to mitigate the adverse effects of bad weather) are Ground Delay Programs (GDPs) andGround Stops (GSs). In a GDP, flights destined for airports facing thunderstorm activity experience delays at their origin airports. This proactive approach minimizes the risk of routing aircraft through hazardous weather conditions and also replaces (fuel burning) airborne delays with ground delays. In a GS, a temporary restriction is imposed on the departure or arrival of aircraft at a specific airport or within a designated airspace. Although other TMIs (e.g., miles-in-trail) are also implemented as part of (air) traffic flow management in the NAS, the focus of this work is on GDPs and GSs. Since TMIs, by design, lead to flight delays or cancellations, it is crucial to put in place the right set of parameters(e.g., scope and duration of the GDP). For example, when the end time of a GDP extends beyond what is necessary, it imposes unnecessary delays on departing flights. This situation could occur as a result of inaccurate prediction of the(required) duration of the GDP based on the weather forecast. On the other hand, if a GDP ends prematurely before the underlying capacity constraints are resolved at the destination airport, it may result in airborne holding. The delicate balance lies in matching the termination of the GDP precisely with the resolution of capacity constraints, avoiding both the imposition of unnecessary ground delays and the need for airborne holding due to premature program termination.Failing to specify the right parameters for TMIs also leads to flight delays, creating a significant obstacle in managing the increasing traffic volumes causing increased work load for the controllers. To address this issue, we propose the integration of Machine Learning (ML) models in the traffic flow management(TFM) pipeline. In current operations, decisions are made by human experts based on extensive training, historical patterns, available traffic and weather data. Since we have an abundance of data from past events that tell us the likely impact of various TMIs, by ingesting historical data, properly trained ML models can offer valuable insights and aid human decision-making. With the FAA increasingly exploring advanced analytics, ML emerges as a focal point for enhancing TFM within the National Airspace System (NAS). As a first step, this study aims to provide traffic controllers with decision-making support for the issuance and adjustment of TMIs. Data analytics and machine learning have been previously employed to address some of the challenges associated with TMIs. Numerous studies have concentrated on various facets of TMI issuance, exploring factors influencing TMI parameters, including arrival rate, airport capacity, and delay prediction. For example, using weather forecasts, several statistical methods were used to produce probabilistic capacity profiles which in conjunction with deterministic models provided insights into the GDP planning process [1–4]. The downside of using deterministic models is that they rely on fixed inputs and predetermined rules, which lack the ability to account for the inherent uncertainty and variability present in real-world scenarios. In a separate series of studies, researchers aimed to predict the occurrences of GDPs and GSs. The majority of these studies utilized various supervised learning methods, including Decision Trees, Naive Bayes, Support VectorMachines, and Random Forests to analyze the influence of weather conditions and arrival demand on TMI incidents[5–8]. However, these studies primarily focused on predicting the incidence of TMIs without explicitly addressing the scope of TMIs, including their duration and their geographical coverage. Furthermore, the emphasis of these studies was largely on GDPs, given their higher frequency and longer duration when compared to GSs. A limited number of studies focused on predicting the parameters of TMIs, specifically addressing their duration and extent. In one such study focusing on optimizing the TMI parameters at San Francisco International Airport (SFO),the authors utilized a probabilistic forecast of fog [9]. They simulated various capacity scenarios based on the (fog)burn-off forecasts, selecting GDP parameters that minimized airborne and overall ground delays. However, this approach exclusively emphasizes stratus (fog) burn-off as the primary determinant of GDP and GS, neglecting other influential factors like severe weather events, runway closures, lower capacity than traffic demand, and other important variables. Given the complexity of predicting the TMI and determining its scope, we seek a more holistic approach. We aim to consider all significant factors that could impact TMIs and their parameters. What sets this research apart is the fusion of all data sources relevant to the issuance and adjustment of TMIs and it represents the first comprehensive attempt to optimize TMIs in this manner. Since this comprehensive solution involves various aspects, we break down the problem into smaller components and input all parameters into a unified model called the “TMI Adjuster”. Figure 1 shows the overall framework and the list of datasets used in each model. The objective of the TMI Adjuster module is to deliver reliable, consistent and expedited recommendations for the progression, adjustment, and termination of TMIs. The ML solution entails developing a pipeline capable of predicting the necessity of a TMI (e.g., GS or GDP) along with its various parameters. For example, in the case of a GS, this includes the scope of the GS either in terms of distance from the destination airport or based on pre-defined airspace sectors. Here, scope refers to those regions and departing airports that are subject to the GS. In this paper, we concentrate on the issuance of GSs in the three major airports in the New York area — LaGuardia(LGA), John F. Kennedy International (JFK), and Newark Liberty International (EWR). We fuse traffic, weather and other relevant aviation data from years 2017 to 2019 to train and validate the ML models. In particular, we use the following datasets: •Terminal Aerodrome Forecast (TAF): meteorological forecasts specific to each airport, issued four times a day, covering predefined time periods. •TMI data: includes all GSs and GDPs along with their respective parameters. •Aviation System Performance Metrics (ASPM): includes traffic related data such as aircraft delays, arrival, and departure rates. •Notices to Airmen (NOTAMs): utilized to extract runway closure data and manage interdependencies between terminals in close proximity. •Flight cancellation data •Airspace Flow Programs (AFP): includes information on flight airborne holdings caused by TMIs. The data preprocessing entails transforming ASPM, TMI, AFP, NOTAMs, and weather data into an hourly format and consolidating all datasets by merging them based on date and time as the primary key. The TMI Adjuster framework comprises two parallel models: one dedicated to GS and a second model focused on GDP. As previously mentioned, our specific focus is on the GS model as a multi-classification problem. In this framework, each data point of the GS model input summarizes ten hours of data. Specifically, the data loader for the GS model generates the input and output of the model as follows: at a given time step, the input includes the actual traffic, weather, and TMI data from the two-hour window before the time step, alongside the weather forecast and scheduled traffic for the next 8 hours starting from the time step. Based on this information, the output of the GS model for each time interval consists of three dimensions. The first dimension represents a binary decision on whether there should be a GS in place for the next hour or not. The second dimension is related to the scope of the GS in the United States, and the third dimension is related to the scope of the GS in Canada (i.e., to determine if the GS impacts airports in Canada).One of the challenges with TMI modeling is the sparsity of TMI events, particularly regarding its scope. To address this challenge in the scope of the GS model output, we implement grouping. The GS scope for the US region is defined based on a list of centers that should be included when the GS is in place. With 20 centers in the US, we utilized historical data to group them into 4 categories. In particular, we summarized our historical data in a graph format where nodes represent centers, and link weights are defined based on the co-occurrence of centers in the scope parameter ofTMIs. By identified strongly connected components in this graph, we were able to partition the centers into four groups. We consider two model structures for the GS Model. Firstly, a hierarchical classification model [10], where the human decision-making for a GS is of hierarchical nature. The decision-maker first decides whether there is a need fora GS, and if the answer is yes, determines the scope. A hierarchical classification model organizes the problem into a class hierarchy, typically a tree or a Directed Acyclic Graph (DAG) structure, and considers the dependency of the decision in the previous step to the next component [10]. Here, we employ the local classifier per level approach, which involves training one multi-class classifier for each level of the class hierarchy. The second structure is the independent structure. In this setting, as the name suggests, we do not consider the dependency of the decisions in the different dimensions of the output of the model. Instead, for each dimension, we train a multi-class classifier independently. Table 1 summarizes GS model statistics for training, validation and testing. The table documents the effect of limiting data to the time steps when there was actually a TMI in place or when a TMI had just terminated. This resulted in a more balanced distribution of the GS class(GS positive class)versus “No GS”(GS negative class), which might help the training process. While JFK and LGA follow very similar distributions, with 40% and 42% GS positive class respectively, EWR has proportionally fewer GS incidents at 28%. Our subsequent phase involves evaluating the performance of both hierarchical structure and independent structure using different state-of-the-art multi-class classifier models such as Random Forest, Decision Trees, K-nearest Neighbors, and Logistic Regression and forecast the duration and scope of the GSs.
Farzan Masrour Shalmani↗
Cleaner Chips: Decarbonization in Semiconductor Manufacturing
The growth of the information and communication technology sector has vastly accelerated in recent decades because of advancements in digitalization and Artificial Intelligence (AI). Scope 1, 2, and 3 greenhouse gas emissions data of the top six semiconductor manufacturing companies (Samsung Electronics, Taiwan Semiconductor Manufacturing Corporation, Micron, SK Hynix, Kioxia, and Intel) were gathered from the publicly accessible Carbon Disclosure Project’s (CDP) website for 2020. Scope 3 emissions had the largest share in total annual emissions with an average share of 52%, followed by Scope 2 (32%) and Scope 1 (16%). Because of the absence of a standardized methodology for Scope 3 emissions estimation, each company used different methodologies that resulted in differences in emissions values. An analysis of the CDP reporting data did not reveal information on strategies implemented by companies to reduce Scope 3 emissions. The use of renewable energy certificates had the largest effect on decarbonization centered on reducing Scope 2 emissions, followed by the deployment of perfluorocarbon reduction technologies to help reduce Scope 1 fugitive emissions. Technology-specific marginal abatement costs of CO2 were also estimated and varied between −416 and 12,215 USD/t CO2 eq., which primarily varied depending on the technology deployed.
42 ENGINEERING↗
Safety-Related Instrumentation & Control Pilot Upgrade Initiation Phase Implementation Report
This research report (1) describes the process followed and products developed during the SR I&C Pilot Project Initial Scoping Phase, and (2) captures lessons learned. Exelon Generation and LWRS collaborated to develop a Digital Transformation Strategy as part of a larger Advanced Concept of Operations. The proposed SR I&C Pilot Upgrade provides a foundation stone for this Digital Transformation that will improve plant safety, reliability, and operational performance while lowering plant Total Cost of Ownership (TCO). Initial Scoping Phase activities for this Pilot Project have been performed in accordance with industry processes that have been adapted to better support digital upgrades. These processes include IP-ENG-001, Standard Design Process (SDP) [Reference 2], NISP-EN-04, Standard Digital Engineering Process (SDEP) [Reference 3], and Electric Power Research Institute (EPRI) Report 3002011816, Digital Engineering Guide (DEG). Completing Initial Scoping Phase Engineering and Operations, Licensing, and Project Management Activities was necessary to sufficiently bound the scope, schedule, and estimated cost of the Project to enable utility management to authorize moving into the Conceptual Design Phase. A significant finding of the Business Case Analysis (BCA) methodology developed and applied as part of this effort was that the growth rate of material costs for sustaining the operation of obsolete SR I&C equipment is accelerating. This directly contributed to the Project Economic Analysis created to justify continuing the Project. Project Initial Scoping Phase lessons learned have also been captured to assist the larger industry in understanding the Digital Transformation Strategy and SR I&C Pilot Project Initial Scoping Phase efforts. This is in keeping with the public/private partnership that has been established between the Department of Energy (DOE) and Exelon for this effort with engagement from the NRC. By addressing first-of-a-kind (FOAK) risks and capturing lessons learned, the SR I&C Pilot Upgrade Project addresses technical, regulatory, and business risks to enable subsequent implementers of similar upgrades.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Data for Impact of Vertical and Seasonal Variation in Leaf Traits on Simulating Soybean Canopy Photosynthesis via 1D and 3D Modeling
Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax25), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax25 profile achieved comparable performance to the scheme with the measured Vcmax25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.
Photosynthesis↗
Analysis and effects of the advanced solid rocket motor on the Space Shuttle elements
This interim final report summarizes the activities and major accomplishments under NASA/MSFC Contract NAS8-39235 for the initial two year contractual effort beginning October 1, 1991, and ending September 30, 1993. It is not intended as an exhaustive treatment or review of any particular technical issue which was addressed during this two-year period, but does provide an overview of the many induced environment studies and test support activities undertaken by REMTECH and its subcontractors during this two-year period. The initial scope of work has been pursued vigorously through this two-year period by separating the effort into five functional areas. The five functional areas are ascent plume radiation, ascent plume convection, ascent aerodynamic heating, launch stand environments, and reentry heating. A work breakdown structure (WBS) for each functional area was devised to permit nearly autonomous activities within each functional responsibility while maintaining commonality in reporting procedures and cost accounting. The contract was modified 11 times to provide incremental funding and to expand the scope of work. Additional work breakdown structure was incorporated to meet the expanded scope. A contract chronology is provided in Section 2. The initial scope of work, expanded scope, and corresponding WBS designation are described in Section 3. A summary review of major accomplishments is also provided in Section 4 to show the breadth of activities and extent of reporting. Reports, meetings, test activities, and other accomplishments within each functional area are also summarized by WBS heading in the appendices.
Source record↗
The Solar Connections Observatory for Planetary Environments
The NASA Sun-Earth Connection theme roadmap calls for comparative study of how the planets, comets, and local interstellar medium (LISM) interact with the Sun and respond to solar variability. Through such a study we advance our understanding of basic physical plasma and gas dynamic processes, thus increasing our predictive capabilities for the terrestrial, planetary, and interplanetary environments where future remote and human exploration will occur. Because the other planets have lacked study initiatives comparable to the terrestrial ITM, LWS, and EOS programs, our understanding of the upper atmospheres and near space environments on these worlds is far less detailed than our knowledge of the Earth. To close this gap we propose a mission to study {\it all) of the solar interacting bodies in our planetary system out to the heliopause with a single remote sensing space observatory, the Solar Connections Observatory for Planetary Environments (SCOPE). SCOPE consists of a binocular EUV/FUV telescope operating from a remote, driftaway orbit that provides sub-arcsecond imaging and broadband medium resolution spectro-imaging over the 55-290 nm bandpass, and high (R>10$^{5}$ resolution H Ly-$\alpha$ emission line profile measurements of small scale planetary and wide field diffuse solar system structures. A key to the SCOPE approach is to include Earth as a primary science target. From its remote vantage point SCOPE will be able to observe auroral emission to and beyond the rotational pole. The other planets and comets will be monitored in long duration campaigns centered when possible on solar opposition when interleaved terrestrial-planet observations can be used to directly compare the response of both worlds to the same solar wind stream and UV radiation field. Using a combination of observations and MHD models, SCOPE will isolate the different controlling parameters in each planet system and gain insight into the underlying physical processes that define the solar connection.
Oliversen, Ronald J.↗
An Experimental Investigation of Students’ Learning Effects When Using a Simplified Nuclear Simulator
Securing enough data has been a main challenge in human reliability analysis (HRA). Many researchers and institutes have made a lot of efforts for collecting HRA data to produce reasonable human error probabilities (HEPs) as well as reduce the uncertainty of HRA quantification. Representatively, U.S. Nuclear Regulatory Commission (U.S. NRC), Korea Atomic Energy Research Institute (KAERI) and Idaho National Laboratory (INL) have led lots of empirical research regarding the HRA data collection. The U.S. NRC and KAERI have mainly carried out full-scope simulator research collecting HRA data through experiments using full-scope simulators with actual operators. In contrast, INL has experimentally collected the data using simplified simulators and student operators. INL has proposed the Simplified Human Error Experimental Program (SHEEP) framework to complement full-scope data collection efforts by suggesting a way to infer full-scope data based on experimental data collected from students operating simplified simulators, specifically the Rancor Microworld Simulator (Rancor) and the Compact Nuclear Simulator (CNS). The aim of the SHEEP framework is to lower the entry point for collecting useful HRA data by securing large sample sizes at a reasonable amount of cost and labor while also guaranteeing a high degree of freedom when designing experiments. The authors’ previous research investigated whether data collected from the SHEEP framework could support a representative full-scope study. Besides, human performance differences between professional and student operators when using Rancor and CNS have been analyzed to understand the lack of fidelity of the simplified simulators and student operators within the SHEEP study. As a follow up research, this study experimentally investigates students’ learning effects and the performance trends over a certain period when using Rancor. This study aims to find out 1) how much training or education is required to collect HRA data from non-experts (i.e., students) when using Rancor and 2) how much differences there are in human performance measures between students and professional operators. In this study, a longitudinal experiment is developed. The four experiment trials with two weeks interval are carried out for sixteen undergraduate students majoring nuclear engineering at Chosun University. Totally four scenarios randomly selected from ten Rancor scenarios are used in each experiment trial. Four human performance measurements (i.e., workload, situation awareness, time and error) are considered in the experiment. Lastly, the trend of students’ performance is compared with operator data having been collected from the previous experiment.
99 GENERAL AND MISCELLANEOUS↗
A framework to collect human reliability analysis data for nuclear power plants using a simplified simulator and student operators
Data scarcity in human reliability analysis (HRA) has been a major challenge in the quantification process. Many institutes have collected HRA data through experiments using full-scope simulators with actual operators. Nevertheless, there are still some limitations to relying solely on full-scope studies. This paper aims to propose how full-scope data collection studies can be supported through the Simplified Human Error Experimental Program (SHEEP). The SHEEP framework was developed by Idaho National Laboratory (INL) to collect HRA data through a simplified simulator and student operators. This paper introduces the major tasks in the SHEEP framework, with a particular focus on differences that arise due to participant type (i.e., student vs. actual operator), based on experiments using a simplified simulator (i.e., the Rancor Microworld). This paper also describes whether the data collected via this approach could support a representative full-scope data collection study (i.e., the HuREX study) based on the experimental data.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DeepLearnMOR: a deep-learning framework for fluorescence image-based classification of organelle morphology
Abstract The proper biogenesis, morphogenesis, and dynamics of subcellular organelles are essential to their metabolic functions. Conventional techniques for identifying, classifying, and quantifying abnormalities in organelle morphology are largely manual and time-consuming, and require specific expertise. Deep learning has the potential to revolutionize image-based screens by greatly improving their scope, speed, and efficiency. Here, we used transfer learning and a convolutional neural network (CNN) to analyze over 47,000 confocal microscopy images from Arabidopsis wild-type and mutant plants with abnormal division of one of three essential energy organelles: chloroplasts, mitochondria, or peroxisomes. We have built a deep-learning framework, DeepLearnMOR (Deep Learning of the Morphology of Organelles), which can rapidly classify image categories and identify abnormalities in organelle morphology with over 97% accuracy. Feature visualization analysis identified important features used by the CNN to predict morphological abnormalities, and visual clues helped to better understand the decision-making process, thereby validating the reliability and interpretability of the neural network. This framework establishes a foundation for future larger-scale research with broader scopes and greater data set diversity and heterogeneity.
Plant Sciences↗
Time Dependence of Methane Generation Rates from Saltstone
This report documents the tests performed to address the requirements listed in the Technical Request, which resulted in a Task Technical & Quality Assurance Plan (TTQAP) and a Run Plan to obtain the required information. The research expands previous scoping work to better understand methane generation in the Saltstone Disposal Facility (SDF). That scoping work demonstrated that methane is produced when sulfide-containing material is added to methylmercury (MeHg), which is found in the SDF Tank 50 feed waste. When Tank 50 waste is combined with grout premix solids dry feed ingredients of cement, slag cement, and fly ash, sulfides are released from the slag cement component of the premix. The prior testing results showed that while the concentration of total mercury was not affected in the pH 14 waste, the MeHg likely reacted with the sulfide contained in the slag. The scoping tests were performed in sealed reactors and thus, methane generation was not measured continuously; rather, it was measured after one hour and after 24 hours. After 24 hours, a large percentage of the MeHg was converted to methane, but after 1 hour only a small percentage of methane was released. It is noteworthy that the methane generation rate (MGR) was greater at the one-hour measurement. The data compiled from the scoping work needed to be expanded to better understand the generation of methane released from grout mixed with Tank 50 waste. This expanded study developed a more extensive knowledge of how and when methane is generated to inform the Documented Safety Analysis (DSA) of the SDF. As such, employing 8 sealed reactors, the methane generation time periods were extended to 2, 4, 6, 18, 30, 48, 72, and 96 hours (4 days). Results are documented herein to determine the capacity for MeHg, contained within Tank 50 salt solution (T50SS) simulant, to produce methane when mixed with saltstone dry feed ingredients, i.e., individual components (cement, slag, and fly ash) or a combination of those components.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗