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At least 145 records · Page 8

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DOE OSTI2023

Supplemental Structural Analyses Used in Support of Certification of the Defense Programs Package 3

The Defense Programs Package (DPP)-3 is to be certified by the National Nuclear Security Administration (NNSA) Packaging and Transportation Division (PTD). Certification is based on successful physical testing of the package, and six certification test units (CTUs) have been subjected to the normal conditions of transport (NCT) and hypothetical accident conditions (HAC) outlined in Title 10 of the Code of Federal Regulations Part 71 (10 CFR 71). During regulatory testing of one of the CTUs, the drum lid closure bolt closest to the impact surface failed during the HAC 30-ft side drop. One of the testing goals for this CTU was to minimize the thermal pathway from the drum exterior shell to the containment vessel (CV) flange and O-rings, leading to the maximum damage scenario in preparation for thermal testing at 1475 °F. Therefore, the puncture bar impact proceeded as originally planned by striking the side wall of the package exterior closest to the location of the CV flange. This puncture bar impact did not attempt to exploit the region of the failed drum lid closure bolt to potentially cause more damage to the package. After the conclusion of regulatory testing, the DPP-3 package finite element model was used to numerically evaluate the effects of puncture bar impacts to the drum exterior in the vicinity of the failed drum lid closure bolt to further demonstrate the robustness of the DPP-3 design. Three alternate puncture bar impact locations and orientations were evaluated to demonstrate that these puncture bar impacts would not substantially reduce the effectiveness of the DPP-3 packaging. This paper describes the supplemental finite element models performed and structural analysis results that demonstrate the alternate HAC 40-in. puncture bar impact tests would have no deleterious effect on the performance of the DPP-3.

Sakalaukus Jr., Peter J.↗

DOE OSTI2020

Horizontal Gene Transfer to a Defensive Symbiont with a Reduced Genome in a Multipartite Beetle Microbiome

Symbiotic mutualisms of bacteria and animals are ubiquitous in nature, running a continuum from facultative to obligate from the perspectives of both partners. The loss of functions required for living independently but not within a host gives rise to reduced genomes in many symbionts. Although the phenomenon of genome reduction can be explained by existing evolutionary models, the initiation of the process is not well understood. Here, we describe the microbiome associated with the eggs of the beetle Lagria villosa, consisting of multiple bacterial symbionts related to Burkholderia gladioli, including a reduced-genome symbiont thought to be the exclusive producer of the defensive compound lagriamide. We show that the putative lagriamide-producing symbiont is the only member of the microbiome undergoing genome reduction and that it has already lost the majority of its primary metabolism and DNA repair pathways. The key step preceding genome reduction in the symbiont was likely the horizontal acquisition of the putative lagriamide lga biosynthetic gene cluster. Unexpectedly, we uncovered evidence of additional horizontal transfers to the symbiont’s genome while genome reduction was occurring and despite a current lack of genes needed for homologous recombination. These gene gains may have given the genome-reduced symbiont a selective advantage in the microbiome, especially given the maintenance of the large lga gene cluster despite ongoing genome reduction.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Artificial Diversity and Defense Security (ADDSec)

Artificial Diversity and Defense Security (ADDSec) machine learning algorithms are used to classify and cluster threats so that an appropriate response can be initiated as a mitigation strategy. The package includes an ensemble of machine learning algorithms such as Support Vector Machines, naïve bayes, logistic regression, and random forest that evolve with the data to recognize anomalous behavior at the host and network levels. Inputs into the machine learning algorithms include end host system calls, system utilization, packet captures, and syslog messages. The machine learning algorithms can be retrained based on user defined intervals or on the number of packets received. ADDSEC's threat responses include Internet Protocol (IP) Address randomization, application port number randomization, and application library randomization. The IP randomization implementation is built on top of a Software Defined Networking (SDN) framework. The SDN controller installs flows on each of the SDN switches with randomized source and destination IP addresses. The application port numbers are randomized using iptables. The application library randomization is created with a LLVM compiler. All randomization schemes are transparent to the endpoints on the network. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-3379 O

Cox, RebeccaE.↗

DOE OSTI2024

Double Visual Defense

This is the official code for the paper "Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness". This code can be used to produce vision language models (VLMs), like LLaVA, with enhanced robustness to adversarial attacks (e.g. jailbreaks).

Bartoldson, Brian [Lawrence Livermore National Lab↗

DOE OSTI2020

Conserved defense responses between maize and sorghum to Exserohilum turcicum

Abstract Background Exserohilum turcicum is an important pathogen of both sorghum and maize, causing sorghum leaf blight and northern corn leaf blight. Because the same pathogen can infect and cause major losses for two of the most important grain crops, it is an ideal pathosystem to study plant-pathogen evolution and investigate shared resistance mechanisms between the two plant species . To identify sorghum genes involved in the E. turcicum response, we conducted a genome-wide association study (GWAS). Results Using the sorghum conversion panel evaluated across three environments, we identified a total of 216 significant markers. Based on physical linkage with the significant markers, we detected a total of 113 unique candidate genes, some with known roles in plant defense. Also, we compared maize genes known to play a role in resistance to E. turcicum with the association mapping results and found evidence of genes conferring resistance in both crops, providing evidence of shared resistance between maize and sorghum . Conclusions Using a genetics approach, we identified shared genetic regions conferring resistance to E. turcicum in both maize and sorghum. We identified several promising candidate genes for resistance to leaf blight in sorghum, including genes related to R-gene mediated resistance. We present significant advancements in the understanding of host resistance to E. turcicum , which is crucial to reduce losses due to this important pathogen.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Permanganate Oxidation of Actual Defense Waste Processing Facility (DWPF) Slurry Mix Evaporator Condensate Tank (SMECT) and Offgas Condensate Tank (OGCT) Samples to Remediate Glycolate

Savannah River Remediation (SRR) tasked Savannah River National Laboratory (SRNL) to examine glycolate destruction in radioactive samples emulating the proposed process. Experiments with radioactive Defense Waste Processing Facility (DWPF) condensates were performed to demonstrate the oxidation of glycolate using sodium permanganate. One test each was performed for Off-Gas Condensate Tank (OGCT) and Slurry Mix Evaporator Condensate Tank (SMECT) samples received. Condensates were blended with Recycle Collection Tank (RCT) heel simulant and Slurry Mix Evaporator (SME) product containing glycolate to represent typical RCT compositions with estimated nominal entrainment of glycolate. A permanganate to glycolate (P/G) molar ratio targeting 5.7 decreased sample glycolate concentration below Ion Chromatography (IC), glycolate method reporting limits in less than 2 hours for the OGCT sample and in less than 8 hours for the SMECT sample.

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DOE OSTI2020

Antifoam Development for Eliminating Flammability Hazards and Decreasing Cycle Time in the Defense Waste Processing Facility

The Savannah River National Laboratory (SRNL) was requested to develop a new antifoam control method for the Defense Waste Processing Facility’s (DWPF) Chemical Process Cell (CPC). SRNL completed testing of both chemical and nonchemical foam controls. The nonchemical foam controls were either ineffective (or worse, created more foam) or impractical (a water spray can control foam, but excessive water is needed). As a result, the focus of this study was on finding a superwetter or commercial antifoam for controlling foam. Thirty potential antifoams were tested as part of this study. A series of tests were developed to help screen out ineffective alternatives including: 1. Spreading testing of superspreaders, 2. Foam column testing with physical simulants, 3. Boiling testing with physical and chemical simulants, 4. Days-only Sludge Receipt and Adjustment Tank (SRAT) process simulations with sludge (containing noble metals and mercury), Precipitate Reactor Feed Tank (PRFT), and Slurry Mix Evaporator Feed Tank (SEFT) simulants in the RC1 Reaction Calorimeter (purchased for antifoam testing), and 5. Around-the-clock SRAT and Slurry Mix Evaporator (SME) process simulations with sludge(containing noble metals and mercury), PRFT, and SEFT simulants in the RC1 Reaction Calorimeter. Evonik Surfynol® MD20, a commercially available defoamer, was relatively effective in controlling foam, while remaining chemically stable in SRAT and SME processing across the pH range of 4 to 13. No degradation products were detected in the offgas, in the condensate or in the SRAT and SME products. In nitric-glycolic acid flowsheet testing, 250 mg/kg Evonik Surfynol® MD20 was needed for foam control compared to 1,625 mg/kg for Antifoam 747, DWPF’s current antifoam. In nitric-formic acid flowsheet testing, 1,125 mg/kg of Evonik Surfynol® MD20 was needed to control foam throughout the SRAT and SME cycles. The commercially available superspreader Momentive™ Y-17112 was even more effective than Evonik Surfynol® MD20 as both a defoamer and an antifoam. Not only was the foam destroyed upon addition but also was less persistent between additions. It was the most effective antifoam in testing using both the nitric-glycolic acid flowsheet and the nitric-formic acid flowsheet. In nitric-glycolic acid flowsheet testing, only 100 mg/kg Momentive™ Y-17112 was needed to control foam throughout the SRAT and SME cycles. In nitric-formic acid flowsheet testing, 300 mg/kg Momentive™ Y-17112 was needed to control foam throughout the SRAT and SME cycles. Momentive™ Y-17112 is also resistant to hydrolysis as demonstrated by its chemical stability in SRAT and SME processing across the pH range of 4 to 13 and lack of degradation products in offgas or condensate. Both candidates were effective as potential replacements for Antifoam 747, with Y-17112 demonstrating superior foam control. During nitric-glycolic flowsheet testing 50% less antifoam was needed when using Momentive™ Y-17112 compared to MD20. During nitric-formic flowsheet testing 75% less antifoam was needed when using Momentive™ Y-17112 compared to MD20. Foam remediated with Momentive™ Y-17112 was less persistent throughout testing. In addition, no degradation products were detected in the offgas, in the condensate or in the SRAT and SME products. Based on this testing, Momentive™ Y-17112 is clearly superior to Evonik Surfynol® MD20 and Antifoam 747, especially for the nitric-formic acid flowsheet processing; it is recommended that Momentive™ Y-17112 replace Antifoam 747 in DWPF. An antifoam addition strategy is recommended for both the nitric-glycolic acid flowsheet and the nitric-formic acid flowsheet. Implementation of Momentive™ Y-17112 is expected to decrease SRAT and SME boiling times by up to 50%, eliminate the issues resulting from Antifoam 747 degradation products, and minimize foamovers. To validate the effectiveness of these defoaming agents, SRNL recommends irradiation of a SRAT or SME product simulant containing fresh antifoam. The goal of this testing is to determine whether the irradiation causes decomposition of the antifoam that would make it less effective or produce new species in the offgas or slurry. This testing began in April 2020. An evaluation should be completed to determine the thermolytic hydrogen and methane generation rate in downstream equipment, including the High-Level Waste evaporators.

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DOE OSTI2020

Modeling the Destruction of Glycolate in the Defense Waste Processing Facility (DWPF) Recycle Stream and Concentration Factors for Glycolate in the 2H Evaporator

Two models were developed to predict maximum glycolate concentrations in the Savannah River Site (SRS) Concentration, Storage, and Transfer Facility (CSTF) from implementation of the Nitric-Glycolic flowsheet at the Defense Waste Processing Facility (DWPF). One model describes the kinetics of glycolate destruction via chemical oxidation with sodium permanganate. This model conservatively predicts glycolate concentration delivered to the CSTF with a high probability the actual glycolate concentration is lower than predicted. The second model describes the potential concentration of said residual glycolate within the 242-16H (i.e., “2H”) Evaporator system.

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DOE OSTI2020

Antifoam Development for Eliminating Flammability Hazards and Decreasing Cycle Time in the Defense Waste Processing Facility

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DOE OSTI2021

Antifoam Development for Eliminating Flammability Hazards and Decreasing Cycle Time in the Defense Waste Processing Facility

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DOE OSTI2020

White Paper to Justify the Use of Strip Effluent in the Defense Waste Processing Facility During Slurry Mix Evaporator Processing

At the request of Savannah River Remediation (SRR), a white paper was written to assess whether it would be acceptable to process Strip Effluent (SE) in both the Sludge Receipt and Adjustment Tank (SRAT) and Slurry Mix Evaporator (SME) in the Defense Waste Processing Facility (DWPF) for the nitric-glycolic acid flowsheet. Savannah River National Laboratory (SRNL) experimental data and DWPF Sludge Batch 9 (SB9) process data was reviewed, looking at batches which included Precipitate Reactor Feed Tank (PRFT) and/or Strip Effluent Feed Tank (SEFT) feeds that led to long processing times. These batches were reviewed looking for processing problems such as melter feed trips, foamovers, heating rod or steam coil fouling, missed Reduction/Oxidation (REDOX) targets, and other process anomalies.

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DOE OSTI2021

Antifoam Development for Eliminating Flammability Hazards and Decreasing Cycle Time in the Defense Waste Processing Facility

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DOE OSTI2021

An Evaluation of the Impact of Glycolate and Glycolate Mitigation on the Defense Waste Processing Facility Recycle Diversion Project Flowsheet

The Savannah River Site (SRS) Defense Waste Processing Facility (DWPF) processes radioactive High Level Waste (HLW) sludge solids from the Concentration, Storage, and Transfer Facilities (CSTF); which includes the SRS Tank Farm and Evaporator facilities, and a concentrated Cs-137 laden stream and a Monosodium Titanate (MST) and sludge solids stream from the SRS Salt Waste Processing Facility (SWPF). The waste is chemically adjusted with acids and reductant (currently with 50 wt.% nitric acid and ~90 wt.% formic acid, but eventually formic acid will be substituted with ~70 wt.% glycolic acid), and frit is added so that a durable, borosilicate glass waste form can be produced when the material is vitrified in the melter. As a result of the evaporation of water during both the melter feed preparation and the melter feed vitrification steps in DWPF, a recycle waste stream is generated, neutralized, and sent back to the CSTF. The recycle waste is a dilute aqueous stream originating from the collection of condensate liquids containing some minor sludge, MST, and frit solids and other waste components resulting from melter feed entrainment during foamover events and transfer of volatile species into the condensate. The recycle stream volume is significant and is expected to approach 3 million gallons per year once SWPF reaches full operation, requiring the use of multiple large CSTF tanks for storage. The recycle waste is currently collected in the SRS Tank Farm and periodically evaporated in the 242-16H (2H) Evaporator to conserve storage space.

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DOE OSTI2021

Demonstration of Defense Waste Processing Facility (DWPF) Higher Fissile Content Glass

The Accelerated Basin De-inventory (ABD) Program has been proposed as an alternative for future spent nuclear fuel (SNF) and nuclear material processing at the Savannah River Site (SRS). This approach would change the baseline H-Canyon (HCAN), Concentrate, Storage, and Transfer Facility (CSTF), and Defense Waste Processing Facility (DWPF) operations. The ABD Program would require that all domestic and foreign research reactor SNF currently at SRS be dissolved, stored, and then transferred to CSTF without the recovery of uranium. Preliminary assessments in the ABD Program plan have shown that ~5000 extra SRS high-level waste (HLW) canisters would be produced if the fissile mass loading remains at the current 897 g/m 3 limit; however, increasing the limit to 2500 g/m 3 would result in ~520 extra canisters. Thus, the ABD Program plan requires an increase of the DWPF fissile mass loading limit to 2500 g/m 3 to minimize canister production. DWPF considers the following isotopes in the calculation of fissile mass loading: U-233, U-235, Pu-239 and Pu-241.

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DOE OSTI2021

Defense Waste Processing Facility Nitric-Glycolic Flowsheet Chemical Process Cell Chemistry: Part 2

The conversions of nitrite to nitrate, the destruction of glycolate, and the conversion of glycolate to formate and oxalate were modeled for the Nitric-Glycolic flowsheet using data from Chemical Process Cell (CPC) simulant runs conducted by Savannah River National Laboratory (SRNL) from 2011 to 2016. The goal of this work was to develop empirical correlation models to predict these values from measurable variables from the chemical process so that these quantities could be predicted a-priori from the sludge or simulant composition and measurable processing variables. The need for these predictions arises from the need to predict the REDuction/OXidation (REDOX) state of the glass from the Defense Waste Processing Facility (DWPF) melter. This report summarizes the work on these correlations based on the aforementioned data. Previous work on these correlations was documented in a technical report covering data from 2011-2015. This current report supersedes this previous report. Further refinement of the models as additional data are collected is recommended. The glass REDOX depends on the concentrations of nitrate and manganese (oxidants), and of glycolate, formate, oxalate, carbon, and antifoam (reductants) in the melter feed. The waste sludge contains nitrite, nitrate, manganese (Mn), and oxalate. Virtually all of the nitrite is converted to nitrate or NO+NO 2 +N 2 O gases in the CPC. The portion of the nitrite converted to nitrate increases the amount of nitrate in the sludge. The amount of glycolate in the final melter feed depends on the amount of the glycolic acid feed that is destroyed. Similarly, the amounts of formate and oxalate formed during the decomposition of glycolic acid are required. The material balance on carbon was found to not close in most cases. Generally, there was less carbon at the end of testing compared to the inputs. The most uncertain product variable was glycolate, so material balances were performed where the glycolate concentration was adjusted, usually upward, to close the balance. Correlation versus the original, as-measured, data was generally poor, but correlation against the material balance adjusted values was greatly improved. It was also shown that the correlation of the measured REDOX versus the predicted REDOX was much better when the material balance adjusted glycolate values were used. Three data series were primarily used during the regressions of the data; these series were 1) Sludge Batch 9 NG flowsheet simulant runs NG51-62 (SB9-NG); 2) Scaled Runs + Bounding Hydrogen Runs (SR+BH); and 3) Runs GN43-50 and 57 (43-50,57). The glycolate destruction was found to correlate with acid stoichiometry (AS), percent reducing acid (PRA), and for some data series, headspace to simulant volume ratio (HSV), mercury (Hg), and nitrate. Although glycolate destruction for pairs of data series (e.g., [SB9-NG] and [SR+BH]) were found to depend on HSV, the combination of all three data series was not found to have significant dependence on this variable. The best model for glycolate destruction depended on AS, nitrate, and Hg. This model predicted the product glycolate compositions of the data to within 92-106%. The conversion of glycolate to formate was high when noble metals and Hg were not present, with values up to 100%. When noble metals and Hg were present, this conversion ranged from zero to 7%, and was dependent on AS. Lower AS gave higher conversions to formate. The conversion to oxalate was found to depend on the AS and the initial concentration of nitrite. An alternative fit versus AS and the form of ruthenium (Ru) used is a possible alternative. This fit was somewhat less statistically significant. This second model predicts that more oxalate is formed when Ru-nitrosyl nitrate is used rather than Ru chloride. The conversion of glycolate to oxalate ranged from zero to 6%. The conversion of nitrite to nitrate depended primarily on AS and PRA, with HSV and Hg being significant when these variables were varied. For multiple series of data, nitrite was also needed to SRNL-STI-2017-00172 5HYLVLRQ viL distinguish between data series, and the effect of HSV became insignificant. The best model for nitrite to nitrate conversion depended on AS, PRA, nitrite, and Hg. The 95% confidence intervals on the predicted values of glycolate destruction, glycolate to oxalate conversion, and nitrite to nitrate conversion were used to determine the uncertainty in the predicted REDOX when starting with only the composition of the sludge, AS, and PRA. Using the 95% confidences on an individual value (that is the confidence in getting a particular value for one single test as opposed to what the mean would be for multiple tests), the uncertainty in the predicted REDOX was calculated. The uncertainty in the actual product composition glycolate, oxalate, formate, and nitrate concentrations translated to an uncertainty in the REDOX value of ±0.1,which is approximately the uncertainty claimed in the REDOX model itself.

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DOE OSTI2021

Analysis of Defense Waste Processing Facility Sample: Recycle Collection Tank Sample Batch 4945

The Savannah River National Laboratory (SRNL) was requested by Savannah River Remediation (now Savannah River Mission Completion (SRMC)), through a Technical Task Request, to characterization the “as-received” Recycle Collection Tank (RCT) Sample identified as sample batch 4945 [Sludge Receipt and Adjustment Tank (SRAT) batch 796)], which was delivered to SRNL Shielded Cells on January 28, 2021. The RCT characterization data will be used as input to the Defense Waste Processing Facility (DWPF) Recycle Diversion Project. This RCT report is the first of three sample characterization reports that will be used for this DWPF Project. The other DWPF reports will involve the characterization of the Off-Gas Condensate Tank (OGCT) and the Slurry Mix Evaporator Condensate Tank (SMECT) samples.

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DOE OSTI2022

Considerations for Department of Defense Implementation of Zero-Emission Vehicles and Charging Infrastructure

This guide provides a roadmap to comply with Executive Order (EO) 14057 requirements and transition to a zero-emission vehicle (ZEV) fleet efficiently and quickly. EO 14057 on Catalyzing Clean Energy Industries and Jobs Through Federal Sustainability requires the Department of Defense (DoD) to transition its non-tactical vehicles to a 100% ZEV fleet, including 100% of light-duty acquisitions by 2027 and 100% of medium- and heavy-duty acquisitions by 2035. The report covers planning for ZEVs and electric vehicle supply equipment (EVSE), roles and responsibilities of key stakeholders in designing EVSE, and execution issues including acquisition, installation, and ongoing fleet management.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Analysis of Defense Waste Processing Facility Sample: Recycle Collection Tank Sample Batch 4945

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