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At least 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Driving Down HB-LED Costs. Implementation of Process Simulation Tools and Temperature Control Methods of High Yield MOCVD Growth

The overall objective of this multi-faceted program is to develop epitaxial growth systems that meet a goal of 75% (4X) cost reduction in the epitaxy phase of HB-LED manufacture. A 75% reduction in yielded epitaxy cost is necessary in order to achieve the cost goals for widespread penetration of HB-LED's into back-lighting units (BLU) for LCD panels and ultimately for solid-state lighting (SSL). To do this, the program will address significant improvements in overall equipment Cost of Ownership, or CoO. CoO is a model that includes all costs associated with the epitaxy portion of production. These aspects include cost of yield, capital cost, operational costs, and maintenance costs. We divide the program into three phases where later phases will incorporate the gains of prior phases. Phase one activities are enabling technologies. In collaboration with Sandia National Laboratories we develop a Fluent-compatible chemistry predictive model and a set of mid-infrared and near-ultraviolet pyrometer monitoring tools. Where previously the modeling of the reactor dynamics were studied within FLUENT alone, here, FLUENT and Chemkin are integrated into a comprehensive model of fluid dynamics and the most advanced transport equations developed for Chemkin. Specifically, the Chemkin model offered the key reaction terms for gas-phase nucleation, a key consideration in the optimization of the MOCVD process. This new predictive model is used to design new MOCVD reactors with optimized growth conditions and the newly developed pyrometers are used monitor and control the MOCVD process temperature to within 0.5°C run-to-run and within each wafer. This portion of the grant is in collaboration with partners at Sandia National Laboratories. Phase two activities are continuous improvement projects which extend the current reactor platform along the lines of improved operational efficiency, improved systems control for throughput, and carrier modifications for increased yield. Programmatically, improvements made in Phase I are applied to developments of Phase II when applicable. Phase three is the culmination of the individual tasks from both phases one and two applied to proposed production platforms. We selectively combine previously demonstrated tasks and other options to develop a high-volume production-worthy MOCVD system demonstrating >3x throughput, 1.3x capital efficiency, and 0.7x cost of ownership. In a parallel demonstration we validate the concept of an improved, larger deposition system which utilizes the predictive modeling of chemistry-based flow analysis and extensions of the improvements demonstrated on the current platforms. This validation includes the build and testing of a prototype version of the hardware and demonstration of 69% reduction in the cost of ownership. Also, in this phase we present a stand-alone project to develop a high-temperature system which improves source efficiency by 30% while concurrently increasing growth rate by 1.3x. The material quality is held to the same material quality specifications of our existing baseline processes. The merits of other line item tasks in phase three are discussed for inclusion on next-generation platforms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Development of Real-Time Quantitative Safety Evaluation Method for Nuclear Power Plants

Human operators involved with large process control systems such as nuclear power plants (NPPs) perform significant roles in the operation of systems. In this reason, deficiencies in human factors such as poor HSI (Human System Interface) design, procedure, and training, are significant contributing factors to NPPs incidents and accidents. In this regard, many kinds of studies including human reliability analysis (HRA), operation support system, automation, have been conducted in order to reduce human errors. Among these fields of studies, automation has its own specialty since it mainly focuses on eliminating the human error-inducing tasks to reduce human error, while other topics are primarily focusing on reducing human error by aiding human operators during operation. It is commonly believed that replacing human operators to the automated system would guarantee greater efficiency, lower workloads, and fewer human errors. However, these beliefs are not always true in large and complex systems such as NPP, since excessive automation can generate new roles for human operators, change human operators' activities in unexpected ways, and inducing 'out-of-the-loop' (OOTL) problem. Especially, although human error probability drastically increases during abnormal situations in NPP due to overload of information, high workload, and short time available for diagnosis, automation is not actively adopted since conventional machine learning techniques are neither capable nor reliable to handle overwhelmingly complex abnormal situations in NPP. Recently, newly developed 'deep learning' techniques have been actively applied to many fields, and some of these studies proved that the artificial intelligences (AI) based on deep learning techniques show better performance than that of human being. In 2015, deep Q-network (DQN) which is one of the deep learning techniques was developed and applied to train AI that automatically plays various Atari 2800 games, and this AI surpassed the human-level playing in many kind of games. Also in 2016, 'AlphaGo', which was developed by 'Google Deepmind' based on deep learning technique to play the game of Go, was defeated Se-dol Lee who is the World Go champion with score of 4:1. In the point of view that the game of Go is considered as one of the challenging area in machine learning field, it is verified that these new machine learning techniques can perform well, even better than the human being in high-complexity tasks. Previously, most of AI applications in nuclear field were focusing on supporting human operators' tasks such as diagnosis and decision making by rapid classification and prediction, based on fixed training data sets. However, it is expected that AI can be applied to solve more complicated problems (i.e. automation during abnormal states in NPP) by adopting advanced machine learning methodologies. In order to apply the automation system in NPP, firstly it is needed to evaluate the status of the plant quantitatively in real-time, since the information about which state is better (i.e. safe) or worse (i.e. unsafe) is essential for training properly working automation AI. Currently, there are several methodologies for NPP safety evaluation such as probabilistic safety assessment (PSA), safety performance indicator (SPI). However, existing methodologies are not feasible for development of automated system since they are not capable for the cases which are not included in the underlying model, not able to consider delicate changes in the NPP, and hard to evaluate in real-time. Therefore, development of new NPP safety evaluation methodology which can cover previously mentioned requirements is needed to conduct the study on NPP automation further. In this study, the concept of early warning score (EWS) in medical field was adopted to develop the methodology which can evaluate the safety level of the NPP quantitatively and in real-time. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

DECOVALEX2019 Task C: Results of Step 2 modeling

The work for Step 1 performed at Sandia National Laboratories and reported in Section 7 has been updated to incorporate new data and to conduct new simulations using a new larger base case domain. The new simulations also include statistical analysis for different fracture realizations. A sensitivity analysis was also conducted to the study of the effect of domain size. A much larger mesh was selected to minimize boundary effects. The DFN model was upscaled to the new base case domain and the much larger domain to generate relevant permeability and porosity fields for each case. The calculations updated for Step 2 are described in Section 12.1. New calculations have also been conducted to model the flooding of the CTD and the resulting pressure recovery. The modeling includes matching of pressure and chloride experimental data at the six observation locations in Well 12M133. The modeling was done for the 10 fracture realizations. The Step 2 recovery simulations are described in Section 12.2. The Step 2 work is summarized in Section 12.3.

42 ENGINEERING↗
DOE OSTI2018

Task 2 Report - A GIS-Based Technical Potential Assessment of Domestic Energy Resources for Electricity Generation

The purpose of this report is to support the Lao Ministry of Energy and Mines (MEM) in assessing the technical potential of domestic energy resources for utility scale electricity generation in the Lao PDR. Specifically, this work provides assessments of technical potential, and associated maps of developable areas, for energy technologies of interest. This report details the methodology, assumptions, and datasets employed in this analysis to provide a transparent, replicable process for future analyses. The methodology and results presented are intended to be a fundamental input to subsequent decision making and energy planning-related analyses. This report is the second output of the Energy Alternatives Study for the Lao PDR (Energy Alternatives Study), a collaboration led by MEM and the United States Agency for International Development under the auspices of the Smart Infrastructure for the Mekong program.1 The Energy Alternatives Study is composed of five successive tasks that collectively support the project’s goals as shown in FIGURE ES-1. This work is focused on Task 2 – Assess technical potential of domestic energy resources for electricity generation . The work was carried out by a team from the U.S. Department of Energy’s National Renewable Energy Laboratory (NREL) in collaboration with MEM and other Lao power sector stakeholders. This work concentrates on domestic energy resources for utility-scale electricity generation and considers solar photovoltaic (PV), wind, biomass, and coal resources. This work does not consider potentially imported energy resources (e.g., natural gas) or domestic energy resources that are not present in sufficient quantity for utility-scale generation (e.g., geothermal resources). A technical potential assessment of hydropower resources is currently not feasible due to the absence of required data including site-level assessments of multiple characteristics (e.g., geology environment and access) as well as spatial data on estimated non-exploited hydropower resources (Lee et al. 2018). Previous studies estimated total hydropower resource potential to be between 23,000 and 26,000 MW, of which approximately 3,894 MW has been developed (IES and MKE 2016; MEM 2015b, 2011). MEM and Électricité du Laos have gained significant experience in the evaluation and development of hydropower resources and expect to develop close to 10,034 MW of additional capacity by 2025 (MEM 2017).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi- center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), im- age preprocessing (97%), data curation (79%), and post- processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

Eisenmann, Matthias↗
DOE OSTI2016

Comprehensive Metabolite Identification Strategy Using Multiple Two-Dimensional NMR Spectra of a Complex Mixture Implemented in the COLMARm Web Server

Identification of metabolites in complex mixtures represents a key step in metabolomics. A new strategy is introduced, which is implemented in a new public web server, COLMARm, that permits the co-analysis of up to three 2D NMR spectra, namely 13C-1H HSQC, 1H-1H TOCSY, and 13C-1H HSQC-TOCSY for the comprehensive, accurate, and efficient performance of this task. The highly versatile and interactive nature of COLMARm permits its application to a wide range of metabolomics samples independent of the magnetic field. Database query is performed using the HSQC spectrum and the top metabolite hits are then validated against the TOCSY-type experiment(s) by superimposing the expected cross-peaks on the mixture spectrum. In this way the user can directly accept or reject candidate metabolites by taking advantage of the complementary spectral information offered by these experiments and their different sensitivities. The power of COLMARm is demonstrated for a human serum sample uncovering the existence of 14 metabolites that hitherto were not identified by NMR.

Bingol, Kerem↗
DOE OSTI2024

Accelerating science: The usage of commercial clouds in ATLAS Distributed Computing

The ATLAS experiment at CERN is one of the largest scientific machines built to date and will have ever growing computing needs as the Large Hadron Collider collects an increasingly larger volume of data over the next 20 years. ATLAS is conducting R&D projects on Amazon Web Services and Google Cloud as complementary resources for distributed computing, focusing on some of the key features of commercial clouds: lightweight operation, elasticity and availability of multiple chip architectures. The proof of concept phases have concluded with the cloud-native, vendoragnostic integration with the experiment’s data and workload management frameworks. Google Cloud has been used to evaluate elastic batch computing, ramping up ephemeral clusters of up to O(100k) cores to process tasks requiring quick turnaround. Amazon Web Services has been exploited for the successful physics validation of the Athena simulation software on ARM processors. We have also set up an interactive facility for physics analysis allowing endusers to spin up private, on-demand clusters for parallel computing with up to 4 000 cores, or run GPU enabled notebooks and jobs for machine learning applications. The success of the proof of concept phases has led to the extension of the Google Cloud project, where ATLAS will study the total cost of ownership of a production cloud site during 15 months with 10k cores on average, fully integrated with distributed grid computing resources and continue the R&D projects.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Maximizing efficiency of dataset compression for machine learning potentials with information theory

Machine learning interatomic potentials (MLIPs) balance high accuracy and lower costs compared to density functional theory calculations, but their performance often depends on the size and diversity of training datasets. Large datasets improve model accuracy and generalization but are computationally expensive to produce and train on, while smaller datasets risk discarding rare but important atomic environments and compromising MLIP accuracy/reliability. Here, we develop an information-theoretical framework to quantify the efficiency of dataset compression methods and propose an algorithm that maximizes this efficiency. By framing atomistic dataset compression as an instance of the minimum set cover (MSC) problem over atom-centered environments, our method identifies the smallest subset of structures that contains as much information as possible from the original dataset while pruning redundant information. The approach is extensively demonstrated on the GAP-20 and TM23 datasets and validated on 64 varied datasets from the ColabFit repository. Across all cases, MSC consistently retains outliers, preserves dataset diversity, and reproduces the long-tail distributions of forces even at high compression rates, outperforming other subsampling methods. Furthermore, MLIPs trained on MSC-compressed datasets exhibit reduced error for out-of-distribution data even in low-data regimes. We explain these results using an outlier analysis and show that such quantitative conclusions could not be achieved with conventional dimensionality reduction methods. The algorithm is implemented in the open-source QUESTS package and can be used for several tasks in atomistic modeling, from data subsampling, outlier detection, and training improved MLIPs at a lower cost.

36 MATERIALS SCIENCE↗
DOE OSTI2021

An end-to-end trainable hybrid classical-quantum classifier

Abstract We introduce a hybrid model combining a quantum-inspired tensor network and a variational quantum circuit to perform supervised learning tasks. This architecture allows for the classical and quantum parts of the model to be trained simultaneously, providing an end-to-end training framework. We show that compared to the principal component analysis, a tensor network based on the matrix product state with low bond dimensions performs better as a feature extractor for the input data of the variational quantum circuit in the binary and ternary classification of MNIST and Fashion-MNIST datasets. The architecture is highly adaptable and the classical-quantum boundary can be adjusted according to the availability of the quantum resource by exploiting the correspondence between tensor networks and quantum circuits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

PIPES (Pipeline for Integrated Projects in Energy Systems) [SWR-24-89]

The Pipeline for Integrated Projects in Energy Systems (PIPES) is a comprehensive project, data, and workflow management tool designed for integrated modeling teams. PIPES facilitates the management of data requirements, tasks, and progress tracking, serving as a higher-level integration layer that works across various data and modeling software. This tool integrates models, data, and tools to perform large-scale, integrated analysis work at scale. PIPES is designed to streamline integrated modeling projects, enhance collaboration, and ensure the quality and efficiency of data management and workflow processes. https://github.com/nrel-pipes/pipes-api https://github.com/nrel-pipes/pipes-web https://github.com/nrel-pipes/nrel-pipes

Gu, Jianli↗
DOE OSTI2014

Learning from the Past, Looking to the Future: Modeling Social Unrest in Karachi, Pakistan

Social unrest represents a major challenge for policy makers around the globe, as it can quickly escalate from small scale disturbances to highly public protests, riots and even civil war. This research was motivated by a need to understand social instability and to unpack the comments made during a spring 2013 conference hosted by Pacific Northwest National Laboratory’s Center for Global Security and the U.S. Institute for Peace, where policymakers noted that models considering social instability are often not suitable for decision-making. This analysis shows that existing state level models of instability could be improved in spatial scale to the city level, even without significantly improved data access. Better data would make this analysis more complete and likely improve the quality of the model. Another challenge with incorporating modeling into decision-making is the need to understand uncertainty in a model. Policy makers are frequently tasked with making decisions without a clear outcome, so characterization of uncertainty is critical. This report describes the work and findings of the project. It took place in three phases: a literature review of social stability research, a “hindsight scan” that looked at historical data, and a “foresight scan” looking at future scenarios.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Rendering the Topological Spines

Many tools to analyze and represent high dimensional data already exits yet most of them are not flexible, informative and intuitive enough to help the scientists make the corresponding analysis and predictions, understand the structure and complexity of scientific data, get a complete picture of it and explore a greater number of hypotheses. With this in mind, N-Dimensional Data Analysis and Visualization (ND²AV) is being developed to serve as an interactive visual analysis platform with the purpose of coupling together a number of these existing tools that range from statistics, machine learning, and data mining, with new techniques, in particular with new visualization approaches. My task is to create the rendering and implementation of a new concept called topological spines in order to extend ND²AV's scope. Other existing visualization tools create a representation preserving either the topological properties or the structural (geometric) ones because it is challenging to preserve them both simultaneously. Overcoming such challenge by creating a balance in between them, the topological spines are introduced as a new approach that aims to preserve them both. Its render using OpenGL and C++ and is currently being tested to further on be implemented on ND²AV. In this paper I will present what are the Topological Spines and how they are rendered.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Task 1. Monitoring real time materials degradation. NRC extended In-situ and real-time Monitoring

The overall objective of this project was to perform a scoping study to identify, in concert with the nuclear industry, those sensors and techniques that have the most promising commercial viability and fill a critical inspection or monitoring need. Candidates to be considered include sensors to monitor real-time material degradation, characterize residual stress, monitor and inspect component fabrication, assess radionuclide and associated chemical species concentrations in ground water and soil, characterize fuel properties, and monitor severe accident conditions. Under Task 1—Monitoring Real-Time Materials Degradation—scoping studies were conducted to assess the feasibility of potential inspection and monitoring technologies (i.e., a combination of sensors, advanced signal processing techniques, and data analysis methods) that could be utilized in LWR and/or advanced reactor applications for continuous monitoring of degradation in-situ. The goal was to identify those techniques that appear to be the most promising, i.e., those that are closest to being both technically and commercially viable and that the nuclear industry is most likely to pursue. Current limitations and associated issues that must be overcome before commercial application of certain techniques have also been addressed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Results of Second Set of Analyses of the Salt Waste Processing Facility (SWPF) Batch 2 Tank 21H Qualification Samples

Savannah River National Laboratory (SRNL) analyzed samples from Tank 21H in support of qualification of Salt Waste Processing Facility (SWPF) Batch 2 for processing. This document reports the second set of results of the analyses of the Tank 21H qualification sample. Analysis of the Tank 21H SWPF Batch 2 sample indicates that the material contains measured analytes in typical concentrations. This memo along with the previous results memo satisfies Deliverable 3 of the Technical Task Request (TTR).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Saltstone third quarter calendar year 2019 (3QCY19) toxicity characteristic leaching procedure (TCLP) results

The aqueous waste from Tank 50 (salt solution) is sampled quarterly for transfers to the Saltstone Production Facility (SPF). Salt solution is treated at SPF and disposed of in the Saltstone Disposal Facility (SDF). Per request of customer, X-TAR-Z-00008, Revision 01, two SDF waste form (saltstone) samples were prepared in the Savannah River National Laboratory (SRNL) from the Tank 50 Waste Acceptance Criteria (WAC) sample and Z-Area premix material for the third quarter of calendar year 2019 (3QCY19). One sample contained a Full Premix which included 10:45:45 (by weight) of cement, slag and fly ash. The second sample contained 60:40 (by weight) of slag and fly ash only referred to as the “Cement-Free grout sample”. Results from this technical report support Task 2: ‘Grout Leaching Analyses’ of the Task Technical Request (TTR) prepared by Savannah River Remediation (SRR). After a 28 day cure, a sample of each of the SDF waste forms was collected and shipped to a certified laboratory for analysis using the Toxicity Characteristic Leaching Procedure (TCLP). The 3QCY19 saltstone (Full Premix) and the Cement-Free grout samples met the South Carolina (SC) Code of Regulations for Hazardous Waste Management Regulations (HWMR) 61-79.261.24 and 61-79-268.48 requirements for a non-hazardous waste form with respect to Resource Conservation and Recovery Act (RCRA) metals and Underlying Hazardous Constituents (UHCs), and also met the SPF WAC.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

GraphAlign: Graph-Enabled Machine Learning for Seismic Event Filtering

This report summarizes results from a 2 year effort to improve the current automated seismic event processing system by leveraging machine learning models that can operated over the inherent graph data structure of a seismic sensor network. Specifically, the GraphAlign project seeks to utilize prior information on which stations are more likely to detect signals originating from particular geographic regions to inform event filtering. To date, the GraphAlign team has developed a Graphical Neural Network (GNN) model to filter out false events generated by the Global Associator (GA) algorithm. The algorithm operates directly on waveform data that has been associated to an event by building a variable sized graph of station waveforms nodes with edge relations to an event location node. This builds off of previous work where random forest models were used to do the same task using hand crafted features. The GNN model performance was analyzed using an 8 week IMS/IDC dataset, and it was demonstrated that the GNN outperforms the random forest baseline. We provide additional error analysis of which events the GNN model performs well and poorly against concluded by future directions for improvements.

58 GEOSCIENCES↗
DOE OSTI2024

Williston Basin CORE-CM Initiative Final Report

The University of North Dakota Energy & Environmental Research Center (EERC) is leading the Williston Basin Carbon Ore, Rare Earth, and Critical Minerals (CORE-CM) Initiative to drive the expansion and transformation of coal and coal-based resource usage within the Williston Basin to produce rare-earth elements (REEs), CMs, and nonfuel carbon-based products (CBPs). This project is the first phase in a long-term program and set the stage for future work by assessing resource, market, technology, and infrastructure knowledge; identifying knowledge gaps; developing a series of plans to be carried out in future work; and initiating stakeholder engagement. Composed of several tasks, the project sought to identify, characterize, and assess several necessary aspects vital to make this future work a reality. The project’s fundamental task was to characterize the Williston Basin CORE-CM resources. Over 2500 samples from multiple sources were utilized to begin the assessment. Several locations were identified in western North Dakota where sample analysis identified the total REE (TREE) concentration as being over 500 parts per million (ppm), which is at a concentration level that would be suitable to consider for mining and extraction. Current operating coal mines have sufficient concentrations of TREEs for consideration. However, the current data across the basin are still not adequate to fully characterize REE and CM content nor give reliable estimates of the total resource potential. Waste stream reuse was also considered, and several streams were identified which ranged from potential energy sources to chemicals to material wastes. This includes streams that result from oil and gas production. These streams are not fully characterized, and further data are needed before they can be accurately assessed. Infrastructure within the Williston Basin is suitable for expansion of a new industry to mine, extract, and concentrate REEs and CMs. The development of this industry will not only preserve many existing jobs in the coal-mining industry but produce many new jobs. The supply chain for REEs and CMs is currently controlled outside of the United States in nations such as China, but the potential to develop the supply chain within the basin is considered possible. Processing of the mined materials for REEs and CMs needs further research. The technology and knowhow exist outside of the United States, and within the country much of the knowledge has been lost and must be regained. To develop the supply chain and regain lost processing technology, the creation of technology innovation centers (TICs) is crucial. The Williston Basin contains several similar centers and entrepreneurial assistance for other industries that can be applied in the development of REE and CM innovation centers. Education to develop the new skill sets required is also needed. Outreach is important for the development of the REE and CM industry within the basin. Understanding throughout federal and state governments, state agencies, industry, and resource end users is vital for the industry to form and grow. Through this project these groups have been contacted through bulletins, presentations, webinars, and annual symposiums. The report is a summary of the work conducted and throughout refers to a series of appendixes which contain more thorough and specific information about each section.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

April 2024 Semiannual Composite Salt Waste Processing Facility (SWPF) Decontaminated Salt Solution (DSS) Toxicity Characteristic Leaching Procedure (TCLP) Results

The aqueous waste from the Salt Waste Processing Facility (SWPF) is sampled semiannually for transfers to the Saltstone Production Facility (SPF). Salt solution is treated at SPF and disposed of in the Saltstone Disposal Facility (SDF). Per request of customer, X-TTR-Z-00027, Revision 0, one SDF waste form (saltstone) sample was prepared in the Savannah River National Laboratory (SRNL) from the SWPF Decontaminated Salt Solution (DSS) Waste Acceptance Criteria (WAC) sample and Z-area premix material for the April 2024 semiannual Toxicity Characteristic Leaching Procedure (TCLP) sample. The sample contained 60:40 (by weight) of slag and fly ash (referred to as the “Cement-Free grout sample”). Results from the technical report support Task 2: ‘Grout Leaching Analyses’ of the Task Technical Request (TTR) prepared by Savannah River Mission Completion (SRMC). After at least 28 days cured, a sample of the SDF waste form was collected and shipped to a certified laboratory for analysis using the Toxicity Characteristic Leaching Procedure (TCLP). The April 2024 semiannual Cement-Free grout sample met the South Carolina (SC) Code of Regulations for Hazardous Waste Management Regulations (HWMR) 61-79.261.24 and 61-79.268.48 requirements for a non-hazardous waste form with respect to the Resource Conservation and Recovery Act (RCRA) metals and Underlying Hazardous Constituents (UHCs), and met the SPF WAC that was in effect at the time of the tank sampling.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗