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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Characterization of the SRNL-Washed Tank 51 Sludge Batch 10 Qualification Sample

Savannah River National Laboratory (SRNL) personnel have been requested to qualify the next sludge batch (Sludge Batch 10 – SB10) for processing at the Defense Waste Processing Facility (DWPF). To accomplish this task, Savannah River Remediation (SRR) sent SRNL two 3-L samples of Tank 51H slurry to be characterized, washed, and then used in a lab-scale demonstration of the DWPF flowsheet. Sample HTF-51-19-114 was received on January 28, 2020, and HTF-51-20-15 was received on February 4, 2020. SRNL washed the Tank 51H sample per the Tank Farm washing strategy. During washing, material from H Canyon Tanks 16.3 and 16.4 was also added to the Tank 51 samples to simulate canyon discharges subsequent to sampling. A part of the qualification process is extensive radionuclide and chemical characterization of the SRNL-washed Tank 51H slurry. This report documents the chemical characterization of the washed slurry; radiological characterization will be documented in a separate report. The major supernatant components, elements on a weight percent calcined basis, and the weight percent solids of the SRNL-washed sample were comparable to the Tank Farm projections, with the exception of free hydroxide and carbonate. Therefore, this SRNL-washed sample is suitable for further SB10 qualification activities and SRR planning for SB10.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Deep Packet/Flow Analysis using GPUs

Deep packet inspection (DPI) faces severe performance challenges in high-speed networks (40/100 GE) as it requires a large amount of raw computing power and high I/O throughputs. Recently, researchers have tentatively used GPUs to address the above issues and boost the performance of DPI. Typically, DPI applications involve highly complex operations in both per-packet and per-flow data level, often in real-time. The parallel architecture of GPUs fits exceptionally well for per-packet network traffic processing. However, for stateful network protocols such as TCP, their data stream need to be reconstructed in a per-flow level to deliver a consistent content analysis. Since the flow-centric operations are naturally antiparallel and often require large memory space for buffering out-of-sequence packets, they can be problematic for GPUs, whose memory is normally limited to several gigabytes. In this work, we present a highly efficient GPU-based deep packet/flow analysis framework. The proposed design includes a purely GPU-implemented flow tracking and TCP stream reassembly. Instead of buffering and waiting for TCP packets to become in sequence, our framework process the packets in batch and uses a deterministic finite automaton (DFA) with prefix-/suffix- tree method to detect patterns across out-of-sequence packets that happen to be located in different batches. In conclusion, evaluation shows that our code can reassemble and forward tens of millions of packets per second and conduct a stateful signature-based deep packet inspection at 55 Gbit/s using an NVIDIA K40 GPU.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Key-value store with internal key-value storage interface

A key-value store is provided having one or more key-value storage interfaces. A key-value store on at least one compute node comprises a memory for storing a plurality of key-value pairs; and an abstract storage interface comprising a software interface module that communicates with at least one persistent storage device providing a key-value interface for persistent storage of one or more of the plurality of key-value pairs, wherein the software interface module provides the one or more key-value pairs to the at least one persistent storage device in a key-value format. The abstract storage interface optionally processes one or more batch operations on the plurality of key-value pairs. A distributed embodiment for a partitioned key-value store is also provided.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Deep Packet/Flow Analysis using GPUs

aDeep packet inspection (DPI) faces severe performance challenges in high-speed networks (40/100 GE) as it requires a large amount of raw computing power and high I/O throughputs. Recently, researchers have tentatively used GPUs to address the above issues and boost the performance of DPI. Typically, DPI applications involve highly complex operations in both per-packet and per-flow data level, often in real-time. The parallel architecture of GPUs fits exceptionally well for per-packet network traffic processing. However, for stateful network protocols such as TCP, their data stream need to be reconstructed in a per-flow level to deliver a consistent content analysis. Since the flow-centric operations are naturally anti-parallel and often require large memory space for buffering out-of-sequence packets, they can be problematic for GPUs, whose memory is normally limited to several gigabytes. In this work, we present a highly efficient GPU-based deep packet/flow analysi s framework. The proposed design includes a purely GPU-implemented flow tracking and TCP stream reassembly. Instead of buffering and waiting for TCP packets to become in sequence, our framework process the packets in batch and uses a deterministic finite automaton (DFA) with prefix-/suffix- tree method to detect patterns across out-of-sequence packets that happen to be located in different batches. Evaluation shows that our code can reassemble and forward tens of millions of packets per second and conduct a stateful signature-based deep packet inspection at 55 Gbit/s using an NVIDIA K40 GPU.

Gong, Qian↗
DOE OSTI2017

Savannah River National Laboratory Collaborative Innovation Pilot Team - 17560

The Collaborative Innovation (COIN) program was established by the Savannah River National Laboratory (SRNL) in March 2016. The goal of the SRNL COIN program is to identify innovative options to significantly reduce program and project risks (improve safety, reduce schedule and cost) of high priority Department of Energy Environmental Management, National Nuclear Security Administration, and Nuclear Energy missions. The COIN Advisory Board selected as the target outcome of the first COIN effort a 50% improvement to the Savannah River Site (SRS) H-Canyon spent nuclear fuel (SNF) processing 'baseline,' or current plan, as defined in the SRS Nuclear Materials Planning Roadmap. A small team of SRNL and Savannah River Nuclear Solutions personnel with pertinent experience and expertise was co-located and largely sequestered for eight weeks to identify improvements to H-Canyon processing of SNF. Following the process defined in the SRNL COIN Charter, the COIN Pilot Team analyzed the baseline for processing SNF through H Canyon; generated alternative ideas; tested seemingly viable ideas by reaching out to other subject-matter experts; and identified options for consideration for implementation by the COIN Advisory Board. The COIN Pilot produced a number of important outcomes, including important lessons learned for subsequent COIN efforts. The two principal outcomes are these: 1. The COIN Pilot Team conducted a comprehensive examination of the individual unit operations, the integrated system, and external interfaces to produce a timeline of H-Canyon processing and sampling times that had not previously existed. The general timeline served as the baseline from which the Pilot Team identified processing improvements. In addition, the timeline has already been employed in other planning efforts. 2. The COIN Pilot Team determined that innovations to the H-Canyon Dissolving unit operation alone could increase the number of SNF bundles processed through H Canyon by the target of 50%, but with little margin for error. To ensure that the gains in throughput in the Dissolving unit operation would be maintained throughout the facility, the Pilot Team identified four additional improvements to create greater efficiency and flexibility in downstream H-Canyon unit operations. The COIN Pilot Team's innovations reduce the processing time of a single batch of SNF through H Canyon from about 70 days to about 50 days. This talk will review the COIN process and the results of the pilot of the process. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Collated experience of the use of multi-group analysis (MGA) in non-destructive waste assay systems - 15257

The quantification of plutonium is an important aspect of non-destructive assay of Special Nuclear Material (SNM). The calculation of {sup 240}Pu{sub eff} can be done through prior knowledge of the composition of the waste but more often is done through the analysis of a measured plutonium gamma-ray spectrum performed using an isotopic analysis code. In this work, the {sup 240}Pu{sub eff} results from a variety of non-destructive waste assay systems in the UK are presented and compared using MGA v9.63C and v10. The body of the results include real measurement results of a variety of waste materials containing plutonium, the results of reanalysis of these initial waste measurements with changes to certain setup parameters and the results of reanalysis with a later version of MGA (v10). Additionally, measurements of test matrices with known source isotopics are measured and the results analysed and presented here. The use of the 'waste' flag parameter introduced to allow measurement of unusual spectra in v10 is also evaluated. When using MGA as part of the waste assay characterisation process in automatic or batch mode, results have proven to be very dependent on the quality of the spectra measured and on the setup parameters that the code is reliant upon. With a modern version of the code, the likelihood of under-reporting the total plutonium content of a 200 litre/55 gallon drum using PNCC and MGA isotopics in combination is low. Additionally, the latest 'hardened' code will also reduce the number of cases where MGA fails to run and the use of default isotopics results in an over-pessimistic evaluation of the total plutonium mass. (authors)

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

Summary of Results from Batch 1 Qualification Samples for Tank Closure Cesium Removal 1A (TCCR 1A)

Savannah River Remediation (SRR) is currently operating the Tank Closure Cesium Removal 1A (TCCR 1A) process to remove 137 Cs from tank waste supernate using an ion exchange process. As part of that process, Savannah River National Laboratory (SRNL) receives and analyzes samples in support of the qualification of each waste supernate batch to be processed. SRNL recently received supernate samples retrieved from Tank 10H as well as in-tank batch contact samples for characterization in support of qualifying Batch 1 for processing through the TCCR 1A unit.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Demand for the development of an automated and integrated refining process for biofuels has increased in recent years due to the lack of generalized process inspection tools. In bio-oil upgrading processes, all process variables are maintained based on the offline specification of intermediates and products. A lack of real-time product specifications in batch-wise monitoring can cause process failure and wasted resources. Therefore, there is a need for a fast and accurate intermediates/product specification tool that can be used for real-time specification to reduce waste and mitigate the risk of process failure. Here, to address this gap, we developed a machine learning (ML) model for predicting speciated bio-oil composition, including paraffin, iso-paraffins, olefins, naphthene, and aromatics. The model is trained using the mass spectra from upgraded products collected in the vapor phase before condensation and predicts the composition of the condensed product. Training ML models using raw mass spectra is challenging due to numerous overlapped peaks originating from different parent compounds. With this in mind, we propose a protocol that (i) transforms raw mass spectra to chemistry-inspired predefined features and (ii) trains decision tree-based models using these features. Our results show that the random forest model was robust against overfitting and had the highest accuracy compared to other models. Moreover, a stochastic ablation method determined the eight most significant features while maximizing the accuracy. Our protocol facilitates real-time compositional analysis of upgraded bio-oils and thus real-time process monitoring. Additionally, this protocol enables the rational design of efficient catalysts and the determination of optimal process conditions.

09 BIOMASS FUELS↗
DOE OSTI2015

Impact Of Sodium Oxalate, Sodium Aluminosilicate, and Gibbsite/Boehmite on ARP Filter Performance

The Savannah River Site (SRS) is currently treating radioactive liquid waste with the Actinide Removal Process (ARP) and the Modular Caustic Side Solvent Extraction Unit (MCU). Recently, the low filter flux through the ARP of approximately 5 gallons per minute has limited the rate at which radioactive liquid waste can be treated. Salt Batch 6 had a lower processing rate and required frequent filter cleaning. Savannah River Remediation (SRR) has a desire to understand the causes of the low filter flux and to increase ARP/MCU throughput. SRR requested SRNL to conduct bench-scale filter tests to evaluate whether sodium oxalate, sodium aluminosilicate, or aluminum solids (i.e., gibbsite and boehmite) could be the cause of excessive fouling of the crossflow or secondary filter at ARP. The authors conducted the tests by preparing slurries containing 6.6 M sodium Salt Batch 6 supernate, 2.5 g MST/L slurry, and varying concentrations of sodium oxalate, sodium aluminosilicate, and aluminum solids, processing the slurry through a bench-scale filter unit that contains a crossflow primary filter and a dead-end secondary filter, and measuring filter flux and transmembrane pressure as a function of time. Among the conclusions drwn from this work are the following: (1) All of the tests showed some evidence of fouling the secondary filter. This fouling could be from fine particles passing through the crossflow filter. (2) The sodium oxalate-containing feeds behaved differently from the sodium aluminosilicate- and gibbsite/boehmite-containing feeds.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Chemical-Free Lithium Separation from High-Salinity Brines Using Model-Informed and Machine Learning-Optimized Multi-Column Zwitterionic Chromatography

Direct Lithium Extraction (DLE) technologies often struggle to produce high-purity lithium salts from high-salinity brines, as current approaches require chemical-based elution, regeneration, and precipitation steps, resulting in significant environmental footprints. A novel salt fractionation approach using carboxybetaine resin, known as zwitterionic chromatography (ZIC), has demonstrated that lithium ions can be separated from divalent cations under high-salinity conditions using only water as eluent, with no regeneration required. To enable continuous and scalable deployment of this approach, we developed a chemical-free Multi-column Zwitterionic Chromatography (MZC) process and its theoretical and process models. To predict and optimize this nontraditional separation system, we introduced a novel anti-Langmuir isotherm, and the isotherm parameters were estimated through a machine learning-driven optimization based on artificial neural network ensembles with numerical feasibility assessment. Using machine learning-driven optimization, the MZC process achieved 98.0% lithium recovery, 99.5 % Li/(Li + Mg + Ca) purity, a 31.3% productivity increase, and a 33% reduction in water use compared to batch operation. The proposed MZC process enables lithium separation at $0.6-1.2 kg-1 Li, with costs dominated by resin manufacturing, while offering lower separation costs and carbon footprint compared with conventional carbonation. Overall, these findings position the MZC process as an effective polishing step within scalable and sustainable lithium production pipelines.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Characterization of Tank 9H Salt Dissolution Batch 2A in Support of Tank Closure Cesium Removal (TCCR) 1A Batch 2 Preparations

Savannah River Remediation (SRR) is currently preparing the second batch of material to be processed through the Tank Closure Cesium Removal (TCCR) 1A system. The feed for TCCR 1A consists of dissolved saltcake from Tank 9H. The first batch of salt to make up Batch 2 (Batch 2A) has been dissolved in Tank 9H and will later be transferred to Tank 10H. Savannah River National Laboratory (SRNL) received samples from the batch of dissolved salt for characterization.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Batch contact testing with variable Na AN-107 and AP-105

The Hanford Site stores an estimated 56 million gallons of mixed radioactive and chemically hazardous waste in large underground tanks. In support of the Direct Feed Low-Activity Waste (DFLAW) program for expediting Hanford tank waste supernate treatment, laboratory-scale ion exchange processing using prototypic unit operations was conducted on AW-105 tank waste at the Pacific Northwest National Laboratory Radiochemical Processing Laboratory. This report describes the batch contact testing for tanks AN-107 and AP-105 to help evaluate CST performance on tanks waste supernate after processing it in the ion exchange columns. Batch columns were performed with the wastes at six Na concentrations.

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

Evaluation of the Potential for Precipitation of Solids during Storage of Non-Aluminum SNF Solutions

Non-aluminum clad spent nuclear fuels (NASNF) stored in the L-Area basin will be dissolved in H-Canyon using the 6.3D electrolytic dissolver. The solutions will be stored in either the hot or warm canyon until the preparation of a sludge batch for the Defense Waste Processing Facility. Spent nuclear fuel solutions could be stored for 1-2 years before transfer to the H-Area Tank Farm depending on the interval between sludge batches. The solution level in the storage tanks will be maintained; therefore, precipitation of solids due to evaporation is not an issue. However, the precipitation of solids from completely dissolved SNF due to solution instabilities has been observed during intermediate storage of solutions generating hydrated oxides.The presence of fissile material in these solids is generally associated with zirconium molybdate, which is known to act as a host lattice for Pu and can carry the actinides upon precipitation. The formation of zirconium molybdate solids which carry fissile material is a potential concern for the storage of NASNF solutions. To address this concern, the Savannah River National Laboratory performed a literature review to identify knowledge gaps which may require experimental work to determine if the formation of solids is a concern during storage of these solutions. Based on the literature review, the precipitation of zirconium molybdate solids from the Campaign 1 NASNF solutions during intermediatestorage is expected. This conclusion is supported by the identification of zirconium molybdate solids found on the H-Canyon 6.1D Dissolver MK-12 insert spacer. The formation of the zirconium molybdate solids is attributed to hydrolysis and radiolytic processes in the nitric acid solution. As the molybdate solids form, U and Pu can substitute for Zr in the crystal lattice resulting in co-precipitation. Generally, the Pu substitutes directly into the crystal lattice during precipitation while the U associated with the molybdate solids more likely absorbs from the solution. The U in the NASNF solutions is present as uranyl nitrate, a 2+ cation which will not substitute as easily into the molybdate crystal lattice for the Zr 4+ ion.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Recycle of enriched Mo targets for economic production of 99 Mo/ 99m Tc medical isotope without use of enriched uranium

A new recycle process for recovery of enriched 98 Mo or 100 Mo used for production of 99 Mo/ 99m Tc medical isotope was developed. In this process, Mo is precipitated from spent NorthStar Mo/Tc generator solution containing ~200 g/L Mo as K 2 MoO 4 in 5 M KOH using acetic acid and then washed with nitric acid. High purification factors from potassium were achieved, and typical Mo recovery yields were ~95 %. In conclusion, the recycle process was performed with up to 260 g of Mo per batch and can be easily implemented for processing of up to 400 g of Mo.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Biochemical process of low level radioactive liquid simulation waste containing detergent

Research of biochemical process of low level radioactive liquid waste containing detergent has been done. Thse organic liquid wastes are generated in nuclear facilities such as from laundry. The wastes that are cotegorized as hazard and poison materials are also radioactive. It must be treated properly by detoxification of the hazard and decontamination of the radionuclides to ensure that the disposal of the waste meets the requirement of standard quality of water. This research was intended to determine decontamination factor and separation efficiensies, its kinetics law, and to produce a supernatant that ensured the environmental quality standard. The radioactive element in the waste was thorium with activity of 5.10{sup −5} Ci/m{sup 3}. The radioactive liquid waste which were generated in simulation plant contains detergents that was further processed by aerobic biochemical process using SGB 103 bacteria in a batch reactor equipped with aerators. Two different concentration of samples were processed and analyzed for 212 hours and 183 hours respectively at a room temperature. The product of this process is a liquid phase called as supernatant and solid phase material called sludge. The chemical oxygen demand (COD), biological oxygen demand (BOD), suspended solid (SS), and its alpha activity were analyzed. The results show that the decontamination factor and the separation efficiency of the lower concentration samples are higher compared to the samples with high concentration. Regarding the decontamination factor, the result for 212 hours processing of waste with detergent concentration of 1.496 g/L was 3.496 times, whereas at the detergent concentration of 0.748 g/L was 15.305 times for 183 hours processing. In case of the separation efficiency, the results for both samples were 71.396% and 93.465% respectively. The Bacterial growth kinetics equation follow Monod’s model and the decreasing of COD and BOD were first order with the rate constant of 0.01 hour{sup −1}.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Algae-based sorbents for removal of gallium from semiconductor manufacturing wastewater

Gallium is increasingly used in industry, and particularly as a component of new materials that have entered semiconductor manufacturing. The release of gallium in factory’s wastewater is potentially an environmental issue and a human health concern. Utilization of algae-based sorbents in the treatment of wastewater polluted by heavy metals has been regarded as a potentially efficient and environmentally-friendly technology. Removal of gallium from simulated wastewater using dead freshwater green algae, ( Chlorella sorokiniana ), as a sorbent was studied. Both batch and continuous flow operations were investigated. Mass balance-based process models were developed and validated for both batch and flow systems. These models were found to be valuable for both understanding the process steps as well as for analyzing the experimental data and determining the fundamental kinetics and thermodynamic parameters. Adsorption capacity was found to be 14.1 mg/g at pH of 2.3, increasing to 38.5 mg/g when the pH is raised to 2.8. The effect of pH is attributed to the surface charge interactions between biomass and gallium and indicates that gallium can be acid-washed and desorbed from the biomass. This property allows for capture followed by recovery of gallium from wastewater using algae based biosorbents.

09 BIOMASS FUELS↗
DOE OSTI2021

Modelling a compressible packed bed flow-through washing and deacetylation reactor for corn stover pretreatment

The deacetylation process is a key step in the lignocellulosic biomass pretreatment process that currently is done in batch reactors. A flow-through diffuser type reactor is a promising approach to make the process more efficient and economically favorable. A washing step before deacetylation can further reduce chemical usage and increase the process efficiency. However, the highly compacted biomass particles can cause significant pressure-drop for fluid flow through the bed, which in the worst case can cause self-enhancing pressure-drop leading to reactor blocking. Detailed understanding of the fundamentals of flow through a compressible packed bed is necessary to scale up the washing and deacetylation process. This work closely examines the classical Kozeny Carman equation and proposes modifications to accurately predict the pressure-drop at different compaction states. Here, a compressible packed bed model is further developed based on the interaction between solid mechanics and fluid dynamics. Experimental measurements of water flow through a packed bed of corn stover show good agreement with the developed model. Corn stover washing process scale up simulations show that smaller reactor diameter, higher friction lining material, lower process throughput, and higher process temperature is favorable in stabilizing the bed from extensive compression when the bed height is fixed.

09 BIOMASS FUELS↗
DOE OSTI2015

Results of Hg Speciation Testing on DWPF Batch 735 RCT and OGCT Samples

Eurofins Frontier Global Sciences, Inc. in Seattle, WA on behalf of the Savannah River Remediation (SRR) Mercury Task Team tasked the Savannah River National Laboratory (SRNL) with preparing and shipping samples for Hg speciation. The fifth shipment of samples was designated to include Defense Waste Processing Facility (DWPF) samples from Batch 735 including two Recycle Collection Tank (RCT) samples and one Off Gas Condensate Tank (OGCT) sample; both are described fully in Table 1. This work was requested by DWPF in a Technical Assistance Request (TAR).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗