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Independent Blade Pitch Controller Design for a Three-Bladed Turbine Using Disturbance Accommodating Control

Two independent pitch controllers (IPCs) based on the disturbance accommodating control (DAC) algorithm are designed for the three-bladed Controls Advanced Research Turbine to regulate rotor speed and to mitigate blade root flapwise bending loads in above-rated wind speed. One of the DAC-based IPCs is designed based on a transformed symmetrical-asymmetrical (TSA) turbine model, with wind disturbances being modeled as a collective horizontal component and an asymmetrical linear shear component. Another DAC-based IPC is designed based on a multiblade coordinate (MBC) transformed turbine model, with a horizontal component and a vertical shear component being modeled as step waveform disturbance. Both of the DAC-based IPCs are found via a regulation equation solved by Kronecker product. Actuator dynamics are considered in the design processes to compensate for actuator phase delay. The simulation study shows the effectiveness of the proposed DAC-based IPCs compared to a proportional-integral (PI) collective pitch controller (CPC). Improvement on rotor speed regulation and once-per-revolution and twice-per-revolution load reductions has been observed in the proposed IPC designs.

17 WIND ENERGY↗

UFNet: Joint U-Net and Fully Connected Neural Network to Bias Correct Precipitation Predictions from

Paper information. Shuang Yu, Indrasis Chakraborty, Gemma J. Anderson, Donald D. Lucas, Yannic Lops, and Daniel Galea. UFNet: Joint U-Net and fully connected neural network to bias correct precipitation predictions from climate models. Artificial Intelligence for the Earth Systems, 2024. Overview. This work develops the UFNet methodology to correct E3SM historical precipitation projection bias. The UFNet deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Below we provide guidance for applying UFNet to correct the Energy Exascale Earth System Model (E3SM; Golaz et al. 2019) daily precipitation projection over the contiguous United States (CONUS). Getting started 1. Obtain the historical climate simulation and observation data. The E3SM historical simulation data are available through https://aims2.llnl.gov/search/cmip6/. The CPC unified gauge-based analysis of daily precipitation can be found through https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html. The ECMWF atmospheric reanalysis of the 20th century (ERA-20C) data are available through https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-20c. The spatial resolution of E3SM and observed datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM, CPC and ERA-20C with 1° resolution can be found throught ./data/. 2. Train the fully connected network (DNN) Python train_dnn.py 3. Train the UFNet Python train_ufnet.py 4. Evaluation and compared with the baseline Python evaluation.py

Lucas, DonaldD↗

UNCERTAINTIES OF ANION AND TOC MEASUREMENTS AT THE DWPF LABORATORY

The Savannah River Remediation (SRR) Defense Waste Processing Facility (DWPF) has identified a technical issue related to the amount of antifoam added to the Chemical Process Cell (CPC). Specifically, due to the long duration of the concentration and reflux cycles for the Sludge Receipt and Adjustment Tank (SRAT), additional antifoam has been required. The additional antifoam has been found to impact the melter flammability analysis as an additional source of carbon and hydrogen. To better understand and control the carbon and hydrogen contributors to the melter flammability analysis, SRR's Waste Solidification Engineering (WSE) has requested, via a Technical Task Request (TTR), that the Savannah River National Laboratory (SRNL) conduct an error evaluation of the measurements of key Slurry Mix Evaporator (SME) anions. SRNL issued a Task Technical and Quality Assurance Plan (TTQAP) [2] in response to that request, and the work reported here was conducted under the auspices of that TTQAP. The TTR instructs SRNL to conduct an error evaluation of anion measurements generated by the DWPF Laboratory using Ion Chromatography (IC) performed on SME samples. The anions of interest include nitrate, oxalate, and formate. Recent measurements of SME samples for these anions as well as measurements of total organic carbon (TOC) were provided to SRNL by DWPF Laboratory Operations (Lab OPS) personnel for this evaluation. This work was closely coordinated with the efforts of others within SRNL that are investigating the Chemical Process Cell (CPC) contributions to the melter flammability. The objective of that investigation was to develop a more comprehensive melter flammability control strategy that when implemented in DWPF will rely on process measurements. Accounting for the uncertainty of the measurements is necessary for successful implementation. The error evaluations conducted as part of this task will facilitate the integration of appropriate uncertainties for the measurements utilized in that control strategy. The flammability control strategy presented in relies on SME measurements of TOC and nitrate while one of the uses by WSE of the oxalate and formate measurement data will be the estimation of the amount of carbon coming from antifoam additions. The estimation is to be conducted by backing out contributions to the measured TOC concentration in the SME from the oxalate and the formate concentrations that are measured in the SME. The resulting adjusted TOC value will provide a basis for WSE to estimate the amount of antifoam that was added for that SME batch. The uncertainties of the oxalate, formate, and TOC measurements provided by the evaluations conducted as part of this task will allow for the propagation of their uncertainties into the estimated quantity of carbon coming from the added antifoam. The purpose of this technical report is to present the measurements generated by the DWPF Laboratory for recent SME batches, to conduct an evaluation of their uncertainties, and to provide the approach for propagating the uncertainties associated with these measurements into DWPF's strategies for controlling melter flammability and for monitoring antifoam additions. JMP Version 7.0.2 was used to support the analyses presented in this report.

08 HYDROGEN↗

PARTITIONING OF GADOLINIUM IN THE CHEMICAL PROCESSING CELL

A combination of short-term beaker tests and longer-duration Sludge Receipt and Adjustment Tank (SRAT) simulations were performed to investigate the relative partitioning behaviors of gadolinium and iron under conditions applicable to the Chemical Processing Cell (CPC). The testing was performed utilizing non-radioactive simple Fe-Gd slurries, non-radioactive Sludge Batch 6 simulant slurries, and a radioactive real-waste slurry representative of Sludge Batch 7 material. The testing focused on the following range of conditions: (a) Fe:Gd ratios of 25-100; (b) pH values of 2-6; (c) acidification via addition of nitric, formic, and glycolic acids; (d) temperatures of {approx}93 C and {approx}22 C; and (e) oxalate concentrations of <100 mg/kg and {approx}10,000 mg/kg. The purpose of the testing was to provide data for assessing the potential use of gadolinium as a supplemental neutron poison when dispositioning excess plutonium. Understanding of the partitioning behavior of gadolinium in the CPC was the first step in assessing gadolinium's potential applicability. Significant fractions of gadolinium partitioned to the liquid-phase at pH values of 4.0 and below, regardless of the Fe:Gd ratio. In SRAT simulations targeting nitric and formic acid additions of 150% acid stoichiometry, the pH dropped to a minimum of 3.5-4.0, and the maximum fractions of gadolinium and iron partitioning to solution were both {approx}20%. In contrast, in a SRAT simulation utilizing a nitric and formic acid addition under atypical conditions (due to an anomalously low insoluble solids content), the pH dropped to a minimum of 3.7, and the maximum fractions of gadolinium and iron partitioning to solution were {approx}60% and {approx}70%, respectively. When glycolic acid was used in combination with nitric and formic acids at 100% acid stoichiometry, the pH dropped to a minimum of 3.6-4.0, and the maximum fractions of gadolinium and iron partitioning to solution were 60-80% and 3-5%, respectively. Thus, the presence of glycolic acid increased dissolution of gadolinium, but decreased dissolution of iron. In beaker tests, the fractions of gadolinium partitioning to solution were all less than the minimum detection limits at pH 6, on the order of a few percent at pH 4, and ranging from 70-90% at pH 2. In contrast, the fractions of iron partitioning to solution were all less than the minimum detection limits at pH 6, {le} 0.04% at pH 4, and {le} 0.9% at pH 2. A possible explanation for the small magnitude of these fractions (as compared to the fractions observed in the SRAT simulations) was incomplete equilibrium, due to the short duration (30 minutes) of the beaker tests. As demonstrated by the SRAT simulations, the typical partitioning equilibration time was on the order of hours. The Fe:Gd ratio appeared to impact the extent of liquid-phase conditions under certain conditions, although the exact relationship was not clear. Temperature impacts on the liquid-phase gadolinium concentrations were modest, with liquid phase concentrations typically increasing about 25% as temperatures rose from {approx}22 C to {approx}93 C. The presence of high concentrations of oxalate did not appear to change the liquid-phase gadolinium concentrations - however, it did increase the liquid-phase iron concentrations (from being undetectable to being detectable but still minor). Additional gadolinium partitioning testing is recommended. Of greatest usefulness will be SRAT simulations focusing on a wider range of acid addition scenarios and alternate sludge compositions, particularly those specific to future sludge batches where addition of excess plutonium is being considered.

54 ENVIRONMENTAL SCIENCES↗

SLUDGE WASHING AND DEMONSTRATION OF THE DWPF FLOWSHEET IN THE SRNL SHIELDED CELLS FOR SLUDGE BATCH 7A QUALIFICATION

Waste Solidification Engineering (WSE) has requested that characterization and a radioactive demonstration of the next batch of sludge slurry (Sludge Batch 7a*) be completed in the Shielded Cells Facility of the Savannah River National Laboratory (SRNL) via a Technical Task Request (TTR). This characterization and demonstration, or sludge batch qualification process, is required prior to transfer of the sludge from Tank 51 to the Defense Waste Processing Facility (DWPF) feed tank (Tank 40). The current WSE practice is to prepare sludge batches in Tank 51 by transferring sludge from other tanks. Discharges of nuclear materials from H Canyon are often added to Tank 51 during sludge batch preparation. The sludge is washed and transferred to Tank 40, the current DWPF feed tank. Prior to transfer of Tank 51 to Tank 40, SRNL simulates the Tank Farm and DWPF processes with a Tank 51 sample (referred to as the qualification sample). Sludge Batch 7a (SB7a) is composed of portions of Tanks 4, 7, and 12; the Sludge Batch 6 heel in Tank 51; and a plutonium stream from H Canyon. SRNL received the Tank 51 qualification sample (sample ID HTF-51-10-125) following sludge additions to Tank 51. This report documents: (1) The washing (addition of water to dilute the sludge supernate) and concentration (decanting of supernate) of the SB7a - Tank 51 qualification sample to adjust sodium content and weight percent insoluble solids to Tank Farm projections. (2) The performance of a DWPF Chemical Process Cell (CPC) simulation using the washed Tank 51 sample. The simulation included a Sludge Receipt and Adjustment Tank (SRAT) cycle, where acid was added to the sludge to destroy nitrite and reduce mercury, and a Slurry Mix Evaporator (SME) cycle, where glass frit was added to the sludge in preparation for vitrification. The SME cycle also included replication of five canister decontamination additions and concentrations. Processing parameters were based on work with a non-radioactive simulant. (3) Vitrification of a portion of the SME product and characterization and durability testing (as measured by the Product Consistency Test (PCT)) of the resulting glass. (4) Rheology measurements of the initial slurry samples and samples after each phase of CPC processing. This program was controlled by a Task Technical and Quality Assurance Plan (TTQAP), and analyses were guided by an Analytical Study Plan. This work is Technical Baseline Research and Development (R&D) for the DWPF. It should be noted that much of the data in this document has been published in interoffice memoranda. The intent of this technical report is bring all of the SB7a related data together in a single permanent record and to discuss the overall aspects of SB7a processing.

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Behavior of Mercury During DWPF Chemical Process Cell Processing

The Defense Waste Processing Facility has experienced significant issues with the stripping and recovery of mercury in the Chemical Processing Cell (CPC). The stripping rate has been inconsistent, often resulting in extended processing times to remove mercury to the required endpoint concentration. The recovery of mercury in the Mercury Water Wash Tank has never been high, and has decreased significantly since the Mercury Water Wash Tank was replaced after the seventh batch of Sludge Batch 5. Since this time, essentially no recovery of mercury has been seen. Pertinent literature was reviewed, previous lab-scale data on mercury stripping and recovery was examined, and new lab-scale CPC Sludge Receipt and Adjustment Tank (SRAT) runs were conducted. For previous lab-scale data, many of the runs with sufficient mercury recovery data were examined to determine what factors affect the stripping and recovery of mercury and to improve closure of the mercury material balance. Ten new lab-scale SRAT runs (HG runs) were performed to examine the effects of acid stoichiometry, sludge solids concentration, antifoam concentration, form of mercury added to simulant, presence of a SRAT heel, operation of the SRAT condenser at higher than prototypic temperature, varying noble metals from none to very high concentrations, and higher agitation rate. Data from simulant runs from SB6, SB7a, glycolic/formic, and the HG tests showed that a significant amount of Hg metal was found on the vessel bottom at the end of tests. Material balance closure improved from 12-71% to 48-93% when this segregated Hg was considered. The amount of Hg segregated as elemental Hg on the vessel bottom was 4-77% of the amount added. The highest recovery of mercury in the offgas system generally correlated with the highest retention of Hg in the slurry. Low retention in the slurry (high segregation on the vessel bottom) resulted in low recovery in the offgas system. High agitation rates appear to result in lower retention of mercury in the slurry. Both recovery of mercury in the offgas system and removal (segregation + recovery) from the slurry correlate with slurry consistency. Higher slurry consistency results in better retention of Hg in the slurry (less segregation) and better recovery in the offgas system, but the relationships of recovery and retention with consistency are sludge dependent. Some correlation with slurry yield stress and acid stoichiometry was also found. Better retention of mercury in the slurry results in better recovery in the offgas system because the mercury in the slurry is stripped more easily than the segregated mercury at the bottom of the vessel. Although better retention gives better recovery, the time to reach a particular slurry mercury content (wt%) is longer than if the retention is poorer because the segregation is faster. The segregation of mercury is generally a faster process than stripping. The stripping factor (mass of water evaporated per mass of mercury stripped) of mercury at the start of boiling were found to be less than 1000 compared to the assumed design basis value of 750 (the theoretical factor is 250). However, within two hours, this value increased to at least 2000 lb water per lb Hg. For runs with higher mercury recovery in the offgas system, the stripping factor remained around 2000, but runs with low recovery had stripping factors of 4000 to 40,000. DWPF data shows similar trends with the stripping factor value increasing during boiling. These high values correspond to high segregation and low retention of mercury in the sludge. The stripping factor for a pure Hg metal bead in water was found to be about 10,000 lb/lb. About 10-36% of the total Hg evaporated in a SRAT cycle was refluxed back to the SRAT during formic acid addition and boiling. Mercury is dissolved as a result of nitric acid formation from absorption of NO x . The actual solubility of dissolved mercury in the acidic condensate is about 100 times higher than the actual concentrations measured. Mercury metal present in the MWWT from previous batches could be dissolved by this acidic condensate. The test of the effect of higher SRAT condenser temperature on recovery of mercury in the MWWT and offgas system was inconclusive. The recovery at higher temperature was lower than several low temperature runs, but about the same as other runs. Factors other than temperature appear to affect the mercury recovery. The presence of chloride and iodide in simulants resulted in the formation of mercurous chloride and mercurous iodide, respectively, in the offgas system. Actual waste data shows that the chloride content is much less than the simulant concentrations. Future simulant tests should minimize the addition of chloride. Similarly, iodine addition should be eliminated unless actual waste analyses show it to be present; currently, total iodine is not measured on actual waste samples.

36 MATERIALS SCIENCE↗

Key Results from SB8 Simulant Flowsheet Studies

Key technically reviewed results are presented here in support of the Defense Waste Processing Facility (DWPF) acceptance of Sludge Batch 8 (SB8). This report summarizes results from simulant flowsheet studies of the DWPF Chemical Process Cell (CPC). Results include: • Hydrogen generation rate for the Sludge Receipt and Adjustment Tank (SRAT) and Slurry Mix Evaporator (SME) cycles of the CPC on a 6,000 gallon basis; • Volume percent of nitrous oxide, N 2 O, produced during the SRAT cycle; • Ammonium ion concentrations recovered from the SRAT and SME off-gas; and, • Dried weight percent solids (insoluble, soluble, and total) measurements and density.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Sludge Washing and Demonstration of the DWPF Nitric/Formic Flowsheet in the SRNL Shielded Cells for Sludge Batch 9 Qualification

Savannah River National Laboratory (SRNL) was requested by Savannah River Remediation (SRR) to qualify the next batch of sludge – Sludge Batch 9 (SB9). Current practice is to prepare sludge batches in Tank 51 by transferring sludge to Tank 51 from other tanks. The sludge is washed and transferred to Tank 40, the current Defense Waste Process Facility (DWPF) feed tank. Prior to sludge transfer from Tank 51 to Tank 40, the Tank 51 sludge must be qualified. SRNL qualifies the sludge in multiple steps. First, a Tank 51 sample is received, then characterized, washed, and again characterized. SRNL then demonstrates the DWPF Chemical Process Cell (CPC) flowsheet with the sludge. The final step of qualification involves chemical durability measurements of glass fabricated in the DWPF CPC demonstrations. In past sludge batches, SRNL had completed the DWPF demonstration with Tank 51 sludge. For SB9, SRNL has been requested to process a blend of Tank 51 and Tank 40 at a targeted ratio of 44% Tank 51 and 56% Tank 40 on an insoluble solids basis.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Impact of Salt Waste Processing Facility Streams on the Nitric-Glycolic Flowsheet in the Chemical Processing Cell

An evaluation of the previous Chemical Processing Cell (CPC) testing was performed to determine whether the planned concurrent operation, or “coupled” operations, of the Defense Waste Processing Facility (DWPF) with the Salt Waste Processing Facility (SWPF) has been adequately covered. Tests with the nitricglycolic acid flowsheet, which were both coupled and uncoupled with salt waste streams, included several tests that required extended boiling times. This report provides the evaluation of previous testing and the testing recommendation requested by Savannah River Remediation. The focus of the evaluation was impact on flammability in CPC vessels (i.e., hydrogen generation rate, SWPF solvent components, antifoam degradation products) and processing impacts (i.e., acid window, melter feed target, rheological properties, antifoam requirements, and chemical composition).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

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.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

SRNL Sludge Batch 10 Qualification SRAT and SME Off-Gas Results

Savannah River National Laboratory (SRNL) completed a small-scale demonstration of the Defense Waste Processing Facility (DWPF) Chemical Process Cell (CPC) utilizing the nitric-glycolic acid (NGA) flowsheet to support Sludge Batch 10 (SB10) qualification. The demonstration utilized a Tank 51 slurry sample washed by SRNL (with added H-canyon material). The purpose of this document is to report the observed off-gas results from the demonstration. With the NGA flowsheet, DWPF has a CPC hydrogen generation limit of 2.4×10 -2 lb/h. The peak observed rate was nearly 90 times less than that limit during SRNL testing.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Modeling of Chemical Slurry Rheology in DWPF Sludge Batch (SB) 10 Simulants

The Defense Waste Processing Facility (DWPF) treats high-activity radionuclides from sludge through a process called vitrification. This process converts radioactive liquid waste currently stored in tank farms into a solid glass form that is suitable for long-term storage and disposal. Due to the complexities involved in vitrifying this waste within each operation of the Chemical Processing Cell (CPC), waste rheology is studied to characterize the fluid-mechanical properties as it passes through the CPC and into the Melter. To better understand the waste and validate flow behavior, slurry rheology of simulants that represents the waste was studied at various acid stoichiometry percentages and solids concentrations to determine the simulant’s yield stress and viscosity. This research work has been supported by the DOE-FIU Science & Technology Workforce Development Initiative, an innovative program developed by the U.S. Department of Energy’s Office of Environmental Management (DOE-EM) and Florida International University’s Applied Research Center (FIU-ARC). During the spring of 2022, a DOE Fellow intern, Brendon Cintas, spent 10 weeks doing a summer internship at Savannah River National Laboratory (SRS) under the supervision and guidance of Dan Lambert, Chemical Flowsheet Development. The intern’s project was initiated on June 6, 2022, and continued through August 11, 2022 with the objective of assisting scientists at SRNL’s Rheology and Grout Laboratory at Aiken Country Technology Lab (ACTL) better understand the sludge composition on the rheology of a simulant slurry using a HAAKE RheoStress 6000 rheometer and extrapolate the results to the real-waste data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Texas A&M University Mobile Facility Measurements during TRACER (Field Campaign Report)

One of the main goals of the U.S Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Tracking Aerosol Convection Interactions Experiment (TRACER) near Houston, Texas is to improve understanding of how meteorology and aerosols impact storm dynamical and microphysical processes in deep convection to better constrain and improve their model representation. The Houston area is strongly influenced by sea- and bay-breeze circulations that generate convergence and help to initiate and organize deep convection. To properly isolate and understand the roles of varied meteorological conditions and cloud condensation nuclei (CCN) and ice nucleation particles (INP) distributions in different air masses, co-located thermodynamic, kinematic, and aerosol vertical profile observations are needed. The focus of this campaign was to provide key measurements in air masses both in front of and behind sea/bay breeze fronts moving through the greater Houston area to sample the airmass heterogeneity. The overarching scientific goal of this campaign is to understand how the vertical distributions of both CCN and INP correspond to the inflow layer of deep convection in maritime, background continental, and polluted continental air masses, and how these variations influence deep convection. To fully sample the heterogeneity in both meteorological conditions and aerosols across the sea-breeze front (SBF), Texas A&M University (TAMU) deployed a InterMet 3050A 403 MHz mobile unit launching iMet-4 radiosondes and the new Rapid Onsite Atmospheric Measurement Van (ROAM-V) for aerosol sampling during the TRACER intensive operational period (IOP) from June to September 2022. The suite of instruments deployed on ROAM-V included a condensation particle counter (CPC; GRIMM Model 5.403 CPC), scanning mobility particle sizer (SMPS; TSI 3750 detector, TSI 3082 classifier, TSI 3088 neutralizer, TSI 3081A differential mobility analyzer), cloud condensation nuclei counter (Droplet Measurement Technologies CCN counter), micropulse lidar (Droplet Measurement Technologies micropulse lidar [miniMPL]), and a Davis Rotating Uniform size-cut Monitor (DRUM; DRUMAir 4-DRUM). Before sampling at each location, the latitude and longitude were recorded using the Global Positioning System (GPS) on the phone application “My Altitude”. The DRUM data were collected as part of a closely related ARM field campaign and also supported by DOE Atmospheric System Research grant DE-SC0021047. The TAMU team sampled these airmass heterogeneities by strategically choosing deployment sites in a different airmass than the ARM fixed sites in La Porte and Guy, Texas. On days when the sea/bay breeze boundary was pushing inland, the TAMU team would usually sample the airmass on the maritime side of the SBF at a coastal site in Galveston, Texas in the early afternoon (1730-1900 UTC) and then move inland ahead of the SBF to sample the airmass on the continental side during late afternoon (2030-2230). Figure 1 shows the Galveston maritime site and the array of sites for the late afternoon continental measurements.

54 ENVIRONMENTAL SCIENCES↗

Sulfuric Acid New Particle Formation Field Campaign Report

This campaign aimed to measure gaseous sulfuric acid concentrations at the surface level using a newly developed sulfuric acid dimethylamine-reactive condensation particle counter (SAD-RCPC). Sulfuric acid is a key nucleation precursor and is the likely driving compound for new particle formation at the Atmospheric Radiation Measurement (ARM) user facility’s Southern Great Plains (SGP) observatory. The SAD-RCPC will be deployed in the future at ARM SGP (AFC010010: "Vertically Resolved Nucleation Precursors at SGP") scheduled for 2024. The campaign was in conjunction with Eleanor Browne's campaign (AFC010075: Boundary-layer gradients in new particle formation) and took place from 05/7/23 to 5/21/23. The measured sulfuric acid concentrations by the SAD-RCPC will be compared to concentrations measured by Browne's chemical ionization mass spectrometer (CIMS). The SAD-RCPC consisted of a glass flow reactor coupled to a 1-nm diethylene glycol condensation particle counter (DEG CPC). Sampled air was mixed with a high concentration of dimethylamine vapor (~1 ppbv). Sulfuric acid within the sampled gas nucleated with dimethylamine to form detectable 1-nm particles. The measured particle concentration was converted to sulfuric acid concentration using nucleation reaction kinetics. The pulse height distribution of particles detected by the DEG CPC was observed using a multi-channel analyzer. Large pre-existing particles were separated from the freshly formed particles in the SAD-RCPC from the pulse height distribution (PHD). During the short campaign, seven new particle formation events were observed. The SAD-RCPC detected an increase in sulfuric acid concentration, but this also coincided with an increase in pre-existing <3 nm particle concentrations. In addition, Browne's CIMS detect sulfuric acid concentrations in the range of 10 5 -10 6 cm -3 .

54 ENVIRONMENTAL SCIENCES↗

SLUDGE WASHING AND DEMONSTRATION OF THE DWPF FLOWSHEET IN THE SRNL SHIELDED CELLS FOR SLUDGE BATCH 6 QUALIFICATION

Prior to initiating a new sludge batch in the Defense Waste Processing Facility (DWPF), Savannah River National Laboratory (SRNL) is required to simulate this processing, including Chemical Process Cell (CPC) simulation, waste glass fabrication, and chemical durability testing. This report documents this simulation for the next sludge batch, Sludge Batch 6 (SB6). SB6 consists of Tank 12 material that has been transferred to Tank 51 and subjected to Low Temperature Aluminum Dissolution (LTAD), Tank 4 sludge, and H-Canyon Pu solutions. Following LTAD and the Tank 4 addition, Liquid Waste Operations (LWO) provided SRNL a 3 L sample of Tank 51 sludge for SB6 qualification. Pu solution from H Canyon was also received. SB6 qualification included washing the sample per LWO plans/projections (including the addition of Pu from H Canyon), DWPF CPC simulations, waste glass fabrication (vitrification), and waste glass characterization and chemical durability evaluation. The following are significant observations from this demonstration. Sludge settling improved slightly as the sludge was washed. SRNL recommended (and the Tank Farm implemented) one less wash based on evaluations of Tank 40 heel projections and projections of the glass composition following transfer of Tank 51 to Tank 40. Thorium was detected in significant quantities (>0.1 wt % of total solids) in the sludge. In past sludge batches, thorium has been determined by Inductively Coupled Plasma-Mass Spectroscopy (ICP-MS), seen in small quantities, and reported with the radionuclides. As a result of the high thorium, SRNL-AD has added thorium to their suite of Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES) elements. The acid stoichiometry for the DWPF Sludge Receipt and Adjustment Tank (SRAT) processing of 115%, or 1.3 mol acid per liter of SRAT receipt slurry, was adequate to accomplish some of the goals of SRAT processing: nitrite was destroyed to below 1,000 mg/kg and mercury was removed to below the DWPF target with 750 g of steam per g of mercury. However, rheological properties did not improve and were above the design basis. Hydrogen generation rates did not exceed DWPF limits during the SRAT and Slurry Mix Evaporator (SME) cycles. However, hydrogen generation during the SRAT cycle approached the DWPF limit. The glass fabricated with the Tank 51 SB6 SME product and Frit 418 was acceptable with respect to chemical durability as measured by the Product Consistency Test (PCT). The PCT response was also predictable by the current durability models of the DWPF Product Composition Control System (PCCS). It should be noted, however, that in the first attempt to make glass from the SME product, the contents of the fabrication crucible foamed over. This may be a result of the SME product's REDOX (Reduction/Oxidation - Fe{sup 2+}/{Sigma}Fe) of 0.08 (calculated from SME product analytical results). The following are recommendations drawn from this demonstration. In this demonstration, at the request of DWPF, SRNL caustic boiled the SRAT contents prior to acid addition to remove water (to increase solids concentration). During the nearly five hours of caustic boiling, 700 ppm of antifoam was required to control foaming. SRNL recommends that DWPF not caustic boil/concentrate SRAT receipt prior to acid addition until further studies can be performed to provide a better foaming control strategy or a new antifoam is developed for caustic boiling. Based on this set of runs and a recently completed demonstration with the SB6 Waste Acceptance Product Specifications (WAPS) sample, it is recommended that DWPF not add formic acid at the design addition rate of two gallons per minute for this sludge batch. A longer acid addition time appears to be helpful in allowing slower reaction of formic acid with the sludge and possibly decreases the chance of a foam over during acid addition.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Identifying a regional aerosol baseline in the eastern North Atlantic using collocated measurements and a mathematical algorithm to mask high-submicron-number-concentration aerosol events

Abstract. High-time-resolution measurements of in situ aerosol and cloud properties provide the ability to study regional atmospheric processes that occur on timescales of minutes to hours. However, one limitation to this approach is that continuous measurements often include periods when the data collected are not representative of the regional aerosol. Even at remote locations, submicron aerosols are pervasive in the ambient atmosphere with many sources. Therefore, periods dominated by local aerosol should be identified before conducting subsequent analyses to understand aerosol regional processes and aerosol–cloud interactions. Here, we present a novel method to validate the identification of regional baseline aerosol data by applying a mathematical algorithm to the data collected at the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility in the eastern North Atlantic (ENA). The ENA central facility (C1) includes an aerosol observing system (AOS) for the measurement of aerosol physical, optical, and chemical properties at time resolutions from seconds to minutes. A second temporary supplementary facility (S1), located ∼0.75 km from C1, was deployed for ∼1 year during the Aerosol and Cloud Experiments (ACE-ENA) campaign in 2017. First, we investigate the local aerosol at both locations. We associate periods of high submicron number concentration (Ntot) in the fine-mode condensation particle counter (CPC) and size distributions from the Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) as a function of wind direction using a meteorology sensor with local sources. Elevated concentrations of Aitken-mode (< 100 nm diameter) particles were observed in correspondence with the wind directions associated with airport operations. At ENA, the Graciosa Airport and its associated activities were found to be the main sources of high-concentration aerosol events at ENA, causing peaks in 1 min Ntot that exceeded 8000 and 10 000 cm−3 at C1, in summer and winter, respectively, and 5000 cm−3 at S1 in summer. Periods with high Ntot not associated with these wind directions were also observed. As a result, the diverse local sources at ENA yielded a poor relationship between Ntot measurements collected at C1 and S1 (R2=0.03 with a slope =0.05±0.001). As a first approach to mask these events, the time periods when the wind direction was associated with the airport operations (west to northwest and southeast to south at C1 and east to south at S1) were applied. The meteorological masks removed 38.9 % of the data at C1 and 43.4 % at S1, and they did not significantly improve the relationship between the two sites (R2=0.18 with a slope =0.06±0.001). Due to the complexity of high-Ntot events observed at ENA, we develop and validate a mathematical ENA Aerosol Mask (ENA-AM) to identify high-Ntot events using 1 min resolution data from the AOS CPC at C1 and S1. After its parameterization and application, ENA-AM generated a high correlation between Ntot in the summer at C1 and S1 (R2=0.87 with a slope =0.84±0.001). We identified the regional baseline at ENA to be 428±228 cm−3 in the summer and 346±223 cm−3 in the winter. Lastly, we compared masked measurements from the AOS with the ARM Aerial Facility (AAF) during flights over C1 in the summer to understand submicron aerosol vertical mixing over C1. The high correlation (R2=0.71 with a slope of 1.04±0.01) observed between C1 and the AAF Ntot collected within an area of 10 km surrounding ENA and at altitudes < 500 m indicated that the submicron aerosol at ENA was well mixed within the first 500 m of the marine boundary layer during the month of July during ACE-ENA. Our novel method for determining a regional aerosol baseline at ENA can be applied to other time periods and at other locations with validation by a secondary site or additional collocated measurements.

54 ENVIRONMENTAL SCIENCES↗

The PERSIANN family of global satellite precipitation data: a review and evaluation of products

Abstract. Over the past 2 decades, a wide range of studies have incorporated Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products. Currently, PERSIANN offers several precipitation products based on different algorithms available at various spatial and temporal scales, namely PERSIANN, PERSIANN-CCS, and PERSIANN-CDR. The goal of this article is to first provide an overview of the available PERSIANN precipitation retrieval algorithms and their differences. Secondly, we offer an evaluation of the available operational products over the contiguous US (CONUS) at different spatial and temporal scales using Climate Prediction Center (CPC) unified gauge-based analysis as a benchmark. Due to limitations of the baseline dataset (CPC), daily scale is the finest temporal scale used for the evaluation over CONUS. Additionally, we provide a comparison of the available products at a quasi-global scale. Finally, we highlight the strengths and limitations of the PERSIANN products and briefly discuss expected future developments.

54 ENVIRONMENTAL SCIENCES↗

Ice nucleation measurements from DRUM impactors during TRACER campaign in the Houston TX region from July to September 2022

During TRACER, three Davis Rotating Uniform size-cut Monitors (DRUM; DRUMAir 4-DRUM) were used to collect aerosols for ice nucleation measurements in the Brooks laboratory at Texas A&M University. The three instruments were located at AMF1 in La Porte, Ancillary site in Guy (ANC), and onboard the Texas A&M Rapid Onsite Atmospheric Measurements Van (ROAM-V). ROAM-V was deployed to capture airmasses behind (maritime) and ahead (continental) of the passage of the sea-breeze front through Houston. On select sampling days, ROAM-V sampled in the morning/mid-day on the coast and then transited to a second inland site for the afternoon/evening. The suite of instruments deployed on ROAM-V included a Condensation Particle Counter (CPC; GRIMM Model 5.403 CPC), Scanning Mobility Particle Sizer (SMPS; TSI 3750 detector, TSI 3082 classifier, TSI 3088 neutralizer, TSI 3081A Differential Mobility Analyzer), Cloud Condensation Nuclei counter (Droplet Measurement Technologies CCN Counter), micro pulse lidar (Droplet Measurement Technologies Micro Pulse LiDAR (miniMPL)), and one of the Davis Rotating Uniform size-cut Monitors (DRUM; DRUMAir 4-DRUM). Before sampling at each location, the latitude and longitude were recorded using the GPS on the phone application “My Altitude”. Each DRUM sampler was operated at a flow rate of 23 LPM. Each DRUM has four stages where the aerodynamic diameter size cuts are as follows: stage 1 larger than 3 μm, stage 2 from 3 to 1.2 μm, stage 3 from 1.2 to 0.34 μm, and stage 4 from 0.34 to 0.15 μm. Pretreated aluminum foil was used as a substrate on all stages. At AMF1 and ANC sites, the DRUMs were operated on the shared aerosol inlet, with generous support of the DOE ARM site staff. These instruments rotated 24 mm per day and only contained aluminum foil substrates. Substrates were changed weekly and transported to Texas A&M for storage in -80C freezer until analysis. The DRUM onboard ROAM-V was operated at a faster rotation rate of 150 mm per day to clearly separate the multiple deployment locations for ROAM-V. For the ROAM-V DRUM, substrates were changed every deployment and transported to Texas A&M for storage in -80C freezer until analysis. At Texas A&M, ice nucleation experiments were conducted to measure the ice nucleation temperature of the ambient aerosol samples collected from the three DRUMs using our previously established procedures (Alsante et al., 2023; Fornea et al., 2009; Matthews et al., 2023). For ice nucleation, only samples collected on stage 3 were analyzed, given that these are the most relevant size (1.2 to 0.34 μm diameter) for potential ice nucleating particles. For the AMF1 and ANC sites, we cut and analyzed 2 mm (2-hour) samples. We analyzed the time periods of the AMF1 site instrument when the ROAM-V was deployed. A 72-hour period from July 11th at 23:49 through July 15th at 1:49 was analyzed from the ANC site instrument. For the ROAM-V instrument, we separated the daily samples by site location. Between 1- and 6-hour independent samples were analyzed at each location. We also cut the ROAM-V samples in half to allow for compositional analysis of the aerosol on the other half of the substrate. All viable samples from the ROAM-V were analyzed. Analysis was done using a custom-built ice nucleation apparatus, recently updated to include an array of 16 individual samples (Matthews et al., 2023). On our experimental setup, we used 100 μL of Ultra-High-Performance Liquid Chromatography (UHPLC) water (Sigma Aldrich, >99.9% purity) to wash off the aerosol from the DRUM substrate. Then, we micropipetted 2 μL droplet samples into each of the 16 wells of the array, using hydrophobically coated microscope slides. Experiments would cycle 28 times from 10 C to -40C over a 20-hour period. During analysis of the TRACER samples, nine experiments were conducted with UHPLC water process blanks that followed an identical preparation procedure. The average freezing temperature and standard deviation for these process blanks is -27.7±2.4 C. This data was collected for ARM Field Campaign AFC07023 and supported by DOE ASR grant DE-SC0021047. For any further questions, please feel free to contact the instrument PI, Sarah D. Brooks, sbrooks@tamu.edu. Alsante, A. N., Thornton, D. C., & Brooks, S. D. (2023). Ice nucleation catalyzed by the photosynthesis enzyme RuBisCO and other abundant biomolecules. Communications Earth & Environment, 4(1), 51. Fornea, A. P., Brooks, S. D., Dooley, J. B., & Saha, A. (2009). Heterogeneous freezing of ice on atmospheric aerosols containing ash, soot, and soil. Journal of Geophysical Research: Atmospheres, 114(D13). DOI:10.1029/2009JD011958 Matthews, B. H., Alsante, A. N., & Brooks, S. D. (2023). Pollen Emissions of Subpollen Particles and Ice Nucleating Particles. ACS Earth and Space Chemistry.

54 ENVIRONMENTAL SCIENCES↗