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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Machine learning bandgaps of double perovskites

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. As a result, the developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Scalable, two–stage, autoinduction of recombinant protein expression in E. coli utilizing phosphate depletion

We report the scalable production of recombinant proteins in Escherichia coli, reliant on tightly controlled autoinduction, triggered by phosphate depletion in the stationary phase. The method, reliant on engineered strains and plasmids, enables improved protein expression across scales. Expression levels using this approach have reached as high as 55% of the total cellular protein. The initial use of the method in instrumented fed–batch fermentations enables cell densities of ~30 gCDW/L and protein titers up to 8.1 ± 0.7 g/L (~270 mg/gCDW). The process has also been adapted to an optimized autoinduction media, enabling routine batch production at culture volumes of 20 μl (384–well plates), 100 μl (96–well plates), 20 ml, and 100 ml. In batch cultures, cell densities routinely reach ~5–7 gCDW/L, offering protein titers above 2 g/L. Here, the methodology has been validated with a set of diverse heterologous proteins and is of general use for the facile optimization of routine protein expression from high throughput screens to fed–batch fermentation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

A knowledge brokerage approach for assessing the impacts of the setting up young farmers policy measure in Greece

This study explores Knowledge Brokerage (KB) aspects of an ex-post Impact Assessment (IA) for the Rural Development Programme (RDP) measure of setting up young farmers, under the Common Agricultural Policy (CAP), at the regional level in Northern Greece. The measure supports the entry of young farmers in agriculture by moving land from older to younger farmers. The aim of the study was to test a set of KB tools for improving the interaction between researchers and policy makers. Our analysis mainly focused on a suite of IA Support Modules to guide practitioners, and on a technical tool kit, a web-based contextualisation platform, to support the IA of the specific test case. Offering a structured approach towards IA, both the Support Modules and LIAISE-KIT allow framing the context, organisation, scheduling and method selection in the light of KB objectives. The evaluation of how IA Support Modules influence the Science Policy Interface (SPI), in the case of the ex-post assessment, demonstrated the high relevance of KB activities for facilitating the interaction between researchers and regional policy makers. The assessment bridges the gap between knowledge producers developing scientific output to be applied in a specific context, and knowledge users, who want clear messages regarding the policy challenges they face. Other conclusions include the need for specific guidelines and training for knowledge users, especially with regard to the use of tools. According to our findings, a consequent application of KB activities is a crucial pre-condition for successfully implementing IAs in future RDP measures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Decentralised demand response market model based on reinforcement learning

A new decentralised demand response (DR) model relying on bi‐directional communications is developed in this study. In this model, each user is considered as an agent that submits its bids according to the consumption urgency and a set of parameters defined by a reinforcement learning algorithm called Q‐learning. The bids are sent to a local DR market, which is responsible for communicating all bids to the wholesale market and the system operator (SO), reporting to the customers after determining the local DR market clearing price. From local markets’ viewpoint, the goal is to maximise social welfare. Four DR levels are considered to evaluate the effect of different DR portions in the cost of the electricity purchase. The outcomes are compared with the ones achieved from a centralised approach (aggregation‐based model) as well as an uncontrolled method. Numerical studies prove that the proposed decentralised model remarkably drops the electricity cost compare to the uncontrolled method, being nearly as optimal as a centralised approach.

Shafie‐Khah, Miadreza↗
DOE OSTI2018

The Michigan New Technology Training and Research (MNTR) Translational Program in Nuclear Medicine (Final Report)

The Michigan New Technology Training and Research (MNTR) Translational Program in Nuclear Medicine and Molecular Imaging This project established the Michigan New Technology Training and Research (MNTR) translational program, focused on the acceleration of new clinical molecular imaging methods. Molecular imaging approaches to imaging are a very flexible and powerful development, with many recent examples advancing the diagnosis and guiding the management of human disease. These methods depend on the design, synthesis and testing of new radiotracers (molecules designed to target specific cells or tissues in the body), labeled with short-lived radioactive atoms (positron emitters). The new radiotracers are imaged in the human body using positron emission tomography (PET) imaging. The University of Michigan PET Center has considerable experience in the discovery, development, refinement and initial human applications of new PET radiotracers. An emerging impediment to efficient availability of new PET radiotracers, however, involves the regulatory environment at multiple levels. Many potentially successful ideas are delayed or even prevented from entering human use by these barriers. In the current grant, we provided hands-on experience for the next generation of young scientists working in molecular imaging so that they are equipped to effectively move new ideas and radiotracers from the research laboratory to the nuclear medicine clinic (bench-to-bedside). For new radiotracer development, oversight by the Food and Drug Administration (FDA), the Nuclear Regulatory Commission (NRC), and local institutional committees and reviews are required. These aspects of translation from laboratory to clinic are often omitted from the formal training of new scientists, but were provided to three trainees via direct mentorship in the MNTR program. The MNTR training program built on the translational successes of the University of Michigan Division of Nuclear Medicine and capitalized on our strengths (facilities, faculty, a wide-ranging selection of projects, and the experience and commitment to bring new radiotracers into human studies) to systematically train three fellows in radiochemistry and nuclear medicine. Integrated training in development, validation and clinical translation of new PET radiotracers occurred through practical involvement in multiple research projects, each at a different stage along the pathway from bench-to-bedside. Each trainee obtained experience of the entire process over about 2 years. Projects involved development of new radiotracers for neurodegenerative disorders (e.g. Alzheimer disease), movement disorders (e.g. Parkinson disease, progressive supranuclear palsy) and neurodevelopmental syndromes (e.g. Tourette syndrome) as well as new radiochemistry methods for synthesizing radiotracers. Each project was pursued in a close collaboration between the fellow trainee and both the clinical and basic science faculty at the University of Michigan PET Center, and provided an unprecedented opportunity for the highest-level training in academic radiochemistry and nuclear medicine research. The project has resulted in 17 peer-reviewed publications to date, and at least 4 more are in preparation. Additionally, 1 book chapter and 26 conference presentations have resulted from this grant. Upon completion of the MNTR Program, the trainees have emerged as translational scientists wielding the necessary skill-set (practical research experience, multidisciplinary training, regulatory know-how) to embark on successful careers in translational molecular imaging, bringing continued advances in the field to bear on important human disorders. As a metric of the Program’s success, one fellow has already secured employment in the field, while the other two are finishing writing papers; they wish to also stay in the radiochemistry and nuclear medicine community, and are currently applying for jobs.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2015

BCL::SAXS: GPU accelerated Debye method for computation of small angle X-ray scattering profiles

Small angle X-ray scattering (SAXS) is an experimental technique used for structural characterization of macromolecules in solution. In this work, we present BCL::SAXS--an algorithm designed to replicate SAXS profiles from rigid protein models at different levels of detail. We first show our derivation of BCL::SAXS and compare our results with the experimental scattering profile of hen egg white lysozyme. Using this protein we show how to generate SAXS profiles representing: (1) complete models, (2) models with approximated side chain coordinates, and (3) models with approximated side chain and loop region coordinates. We evaluated the ability of SAXS profiles to identify a correct protein topology from a non-redundant benchmark set of proteins. We find that complete SAXS profiles can be used to identify the correct protein by receiver operating characteristic (ROC) analysis with an area under the curve (AUC)>99%. We demonstrate how our approximation of loop coordinates between secondary structure elements improves protein recognition by SAχS for protein models without loop regions and side chains. Agreement with SAXS data is a necessary but not sufficient condition for structure determination. We conclude that experimental SAXS data can be used as a filter to exclude protein models with large structural differences from the native.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Uncertainty propagation of p-boxes using sparse polynomial chaos expansions

In modern engineering, physical processes are modelled and analysed using advanced computer simulations, such as finite element models. Furthermore, concepts of reliability analysis and robust design are becoming popular, hence, making efficient quantification and propagation of uncertainties an important aspect. In this context, a typical workflow includes the characterization of the uncertainty in the input variables. In this paper, input variables are modelled by probability-boxes (p-boxes), accounting for both aleatory and epistemic uncertainty. The propagation of p-boxes leads to p-boxes of the output of the computational model. A two-level meta-modelling approach is proposed using non-intrusive sparse polynomial chaos expansions to surrogate the exact computational model and, hence, to facilitate the uncertainty quantification analysis. The capabilities of the proposed approach are illustrated through applications using a benchmark analytical function and two realistic engineering problem settings. They show that the proposed two-level approach allows for an accurate estimation of the statistics of the response quantity of interest using a small number of evaluations of the exact computational model. This is crucial in cases where the computational costs are dominated by the runs of high-fidelity computational models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

The MFiX Particle-in-Cell Method (MFiX-PIC) Theory Guide

MFiX (Multiphase Flow with Interphase eXchanges) is an open-source multiphase flow solver developed at the National Energy Technology Laboratory. Within the code, users have access to a single phase or interpenetrating continua-based multiphase two-fluid model (TFM), a discrete element model (DEM), and a particle-in-cell model (PIC). TFM, DEM, and PIC can all be used to create multiphase simulations that include hydrodynamics, chemical reactions, and heat transfer.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2015

Influence of radiation dose and reconstruction algorithm in MDCT assessment of airway wall thickness: A phantom study

Purpose: Wall thickness (WT) is an airway feature of great interest for the assessment of morphological changes in the lung parenchyma. Multidetector computed tomography (MDCT) has recently been used to evaluate airway WT, but the potential risk of radiation-induced carcinogenesis—particularly in younger patients—might limit a wider use of this imaging method in clinical practice. The recent commercial implementation of the statistical model-based iterative reconstruction (MBIR) algorithm, instead of the conventional filtered back projection (FBP) algorithm, has enabled considerable radiation dose reduction in many other clinical applications of MDCT. The purpose of this work was to study the impact of radiation dose and MBIR in the MDCT assessment of airway WT. Methods: An airway phantom was scanned using a clinical MDCT system (Discovery CT750 HD, GE Healthcare) at 4 kV levels and 5 mAs levels. Both FBP and a commercial implementation of MBIR (Veo{sup TM}, GE Healthcare) were used to reconstruct CT images of the airways. For each kV–mAs combination and each reconstruction algorithm, the contrast-to-noise ratio (CNR) of the airways was measured, and the WT of each airway was measured and compared with the nominal value; the relative bias and the angular standard deviation in the measured WT were calculated. For each airway and reconstruction algorithm, the overall performance of WT quantification across all of the 20 kV–mAs combinations was quantified by the sum of squares (SSQs) of the difference between the measured and nominal WT values. Finally, the particular kV–mAs combination and reconstruction algorithm that minimized radiation dose while still achieving a reference WT quantification accuracy level was chosen as the optimal acquisition and reconstruction settings. Results: The wall thicknesses of seven airways of different sizes were analyzed in the study. Compared with FBP, MBIR improved the CNR of the airways, particularly at low radiation dose levels. For FBP, the relative bias and the angular standard deviation of the measured WT increased steeply with decreasing radiation dose. Except for the smallest airway, MBIR enabled significant reduction in both the relative bias and angular standard deviation of the WT, particularly at low radiation dose levels; the SSQ was reduced by 50%–96% by using MBIR. The optimal reconstruction algorithm was found to be MBIR for the seven airways being assessed, and the combined use of MBIR and optimal kV–mAs selection resulted in a radiation dose reduction of 37%–83% compared with a reference scan protocol with a dose level of 1 mGy. Conclusions: The quantification accuracy of airway WT is strongly influenced by radiation dose and reconstruction algorithm. The MBIR algorithm potentially allows the desired WT quantification accuracy to be achieved with reduced radiation dose, which may enable a wider clinical use of MDCT for the assessment of airway WT, particularly for younger patients who may be more sensitive to exposures with ionizing radiation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Constraining the local variance of H {sub 0} from directional analyses

We evaluate the local variance of the Hubble Constant H {sub 0} with low-z Type Ia Supernovae (SNe). Our analyses are performed using a hemispherical comparison method in order to test whether taking the bulk flow motion into account can reconcile the measurement of the Hubble Constant H {sub 0} from standard candles ( H {sub 0} = 73.8±2.4 km s{sup -1} Mpc {sup -1}) with that of the Planck's Cosmic Microwave Background data ( H {sub 0} = 67.8 ± 0.9km s{sup -1} Mpc{sup -1}). We obtain that H {sub 0} ranges from 68.9±0.5 km s{sup -1} Mpc{sup -1} to 71.2±0.7 km s{sup -1} Mpc{sup -1} through the celestial sphere (1 σ uncertainty), implying a Hubble Constant maximal variance of δ H {sub 0} = (2.30±0.86) km s{sup -1} Mpc{sup -1} towards the ( l,b ) = (315°,27°) direction. Interestingly, this result agrees with the bulk flow direction estimates found in the literature, as well as previous evaluations of the H {sub 0} variance due to the presence of nearby inhomogeneities. We assess the statistical significance of this result with different prescriptions of Monte Carlo simulations, obtaining moderate statistical significance, i.e., 68.7% confidence level (CL) for such variance. Furthermore, we test the hypothesis of a higher H {sub 0} value in the presence of a bulk flow velocity dipole, finding some evidence for this result which, however, cannot be claimed to be significant due to the current large uncertainty in the SNe distance modulus. Then, we conclude that the tension between different H {sub 0} determinations can plausibly be caused to the bulk flow motion of the local Universe, even though the current incompleteness of the SNe data set, both in terms of celestial coverage and distance uncertainties, does not allow a high statistical significance for these results or a definitive conclusion about this issue.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Flowing fluid electrical conductivity logging of a deep borehole during and following drilling: estimation of transmissivity, water salinity and hydraulic head of conductive zones

Flowing fluid electrical conductivity (FFEC) logging is a hydrogeologic testing method that is usually conducted in an existing borehole. However, for the 2,500-m deep COSC-1 borehole, drilled at Åre, central Sweden, it was done within the drilling period during a scheduled 1-day break, thus having a negligible impact on the drilling schedule, yet providing important information on depths of hydraulically conductive zones and their transmissivities and salinities. In this paper, we present a reanalysis of this set of data together with a new FFEC logging data set obtained soon after drilling was completed, also over a period of 1 day, but with a different pumping rate and water-level drawdown. Their joint analysis not only results in better estimates of transmissivity and salinity in the conducting fractures intercepted by the borehole, but also yields the hydraulic head values of these fractures, an important piece of information for the understanding of hydraulic structure of the subsurface. Two additional FFEC logging tests were done about 1 year later, and are used to confirm and refine this analysis. Results show that from 250 to 2,000 m depths, there are seven distinct hydraulically conductive zones with different hydraulic heads and low transmissivity values. For the final test, conducted with a much smaller water-level drawdown, inflow ceased from some of the conductive zones, confirming that their hydraulic heads are below the hydraulic head measured in the wellbore under non-pumped conditions. Lastly, the challenges accompanying 1-day FFEC logging are summarized, along with lessons learned in addressing them.

58 GEOSCIENCES↗
DOE OSTI2024

Contributions From Cloud Morphological Changes to the Interannual Shortwave Cloud Feedback Based on MODIS and ISCCP Satellite Observations

The surface temperature-mediated change in cloud properties, referred to as the cloud feedback, continues to dominate the uncertainty in climate projections. A larger number of contemporary global climate models (GCMs) project a higher degree of warming than the previous generation of GCMs. This greater projected warming has been attributed to a less negative cloud feedback in the Southern Ocean. Here, we apply a novel “double decomposition method” that employs the “cloud radiative kernel” and “cloud regime” concepts, to two data sets of satellite observations to decompose the interannual cloud feedback into contributions arising from changes within and shifts between cloud morphologies. Our results show that contributions from the latter to the cloud feedback are large for certain regimes. We then focus on interpreting how both changes within and between cloud morphologies impact the shortwave cloud optical depth feedback over the Southern Ocean in light of additional observations. Results from the former cloud morphological changes reveal the importance of the wind response to warming increases low- and mid-level cloud optical thickness in the same region. Results from the latter cloud morphological changes reveal that a general shift from thick storm-track clouds to thinner oceanic low-level clouds contributes to a positive feedback over the Southern Ocean that is offset by shifts from thinner broken clouds to thicker mid- and low-level clouds. Our novel analysis can be applied to evaluate GCMs and potentially diagnose shortcomings pertaining to their physical parameterizations of particular cloud morphologies.

58 GEOSCIENCES↗
DOE OSTI2018

Dissection of the multichannel reaction of acetylene with atomic oxygen: from the global potential energy surface to rate coefficients and branching dynamics

The O( 3 P) + C 2 H 2 reaction is the first step in acetylene oxidation. The accurate kinetic data and the understanding of the reaction dynamics is of great importance. To this end, in this study a full-dimensional global potential energy surface (PES) for the ground triplet state of the O( 3 P) + C 2 H 2 reaction is constructed based on approximately 85,000 ab initio points calculated at the level of explicitly correlated unrestricted coupled cluster single, double, and perturbative triple excitations with the explicitly correlated polarized valence triple zeta basis set (UCCSD(T)-F12b/VTZ-F12). The PES is fit using the permutation invariant polynomial-neural network (PIP-NN) approach with a total root mean square error of 0.21 kcal mol –1 . The key topographic features of the PES, including multiple potential wells and saddle points along different reaction pathways, are well represented by this fit PES. The kinetics and dynamics of the O( 3 P) + C 2 H 2 reaction are investigated using the quasi-classical trajectory (QCT) method. The calculated rate coefficients are in good agreement with experimental data over a wide temperature range, especially when the temperature is lower than 1500 K. The product branch ratio has also been determined, which indicates the H + HCCO channel as the dominant reaction pathway at 298–3000 K, accounting for 80–90% of the overall rate coefficient, in agreement with experimental observations. The dynamics of the reaction is analyzed in detail.

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

Results of the first implementation of RF phase signature matching at LANSCE

The LINAC at the Los Alamos Neutron Science Center (LANSCE) has been utilizing the Delta-t method to match the RF cavities to the design acceleration parameters since its commissioning in 1972. The differences in time-of-flight between two subsequent Beam Position and Phase Monitors (BPPMs) are measured with both accelerated and drifting beams, depending on the whether the module is set to on or off. The algorithm optimizes the module amplitude and phase via iterative measurements if the initial phase is in the vicinity of the design value. With an upgrade to a faster readout system, a scan over the whole RF cavity phase range requires relatively less time than the classical optimization procedure. The Phase Scan Signature Matching (PSSM) method provides a time-efficient method that ensures the phase selection lands on the bunching side and empowers future analyses to build module-specific models. The PSSM also utilizes a direct model to determine the correct amplitude to sub-percent level instead of using linearized matrices. Furthermore, lacking a reliable energy measurement method in the LINAC, we measure the beam phases at two downstream locations to increase the precision of energy measurements. In this letter, we also discuss the sensitivities of PSSM, error propagation, and the implementation results for the 2019 and 2020 beam cycles.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

SU-F-P-45: Clinical Experience with Radiation Dose Reduction of CT Examinations Using Iterative Reconstruction Algorithms

Purpose: Iterative reconstruction (IR) algorithms have been adopted by medical centers in the past several years. IR has a potential to substantially reduce patient dose while maintaining or improving image quality. This study characterizes dose reductions in clinical settings for CT examinations using IR. Methods: We retrospectively analyzed dose information from patients who underwent abdomen/pelvis CT examinations with and without contrast media in multiple locations of our Healthcare system. A total of 743 patients scanned with ASIR on 64 slice GE lightspeed VCTs at three sites, and 30 patients scanned with SAFIRE on a Siemens 128 slice Definition Flash in one site was retrieved. For comparison, patient data (n=291) from a GE scanner and patient data (n=61) from two Siemens scanners where filtered back-projection (FBP) was used was collected retrospectively. 30% and 10% ASIR, and SAFIRE Level 2 was used. CTDIvol, Dose-length-product (DLP), weight and height from all patients was recorded. Body mass index (BMI) was calculated accordingly. To convert CTDIvol to SSDE, AP and lateral dimensions at the mid-liver level was measured for each patient. Results: Compared with FBP, 30% ASIR reduces dose by 44.1% (SSDE: 12.19mGy vs. 21.83mGy), while 10% ASIR reduced dose by 20.6% (SSDE 17.32mGy vs. 21.83). Use of SAFIRE reduced dose by 61.4% (SSDE: 8.77mGy vs. 22.7mGy). The geometric mean for patients scanned with ASIR was larger than for patients scanned with FBP (geometric mean is 297.48 mmm vs. 284.76 mm). The same trend was observed for the Siemens scanner where SAFIRE was used (geometric mean: 316 mm with SAFIRE vs. 239 mm with FBP). Patient size differences suggest that further dose reduction is possible. Conclusion: Our data confirmed that in clinical practice IR can significantly reduce dose to patients who undergo CT examinations, while meeting diagnostic requirements for image quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-527: A Novel Dynamic Multileaf Collimator Leaf-Sequencing Algorithm in Radiation Therapy

Purpose: A novel leaf-sequencing algorithm is developed for generating arbitrary beam intensity profiles in discrete levels using dynamic multileaf collimator (MLC). The efficiency of this dynamic MLC leaf-sequencing method was evaluated using external beam treatment plans delivered by intensity modulated radiation therapy technique. Methods: To qualify and validate this algorithm, integral test for the beam segment of MLC generated by the CORVUS treatment planning system was performed with clinical intensity map experiments. The treatment plans were optimized and the fluence maps for all photon beams were determined. This algorithm started with the algebraic expression for the area under the beam profile. The coefficients in the expression can be transformed into the specifications for the leaf-setting sequence. The leaf optimization procedure was then applied and analyzed for clinical relevant intensity profiles in cancer treatment. Results: The macrophysical effect of this method can be described by volumetric plan evaluation tools such as dose-volume histograms (DVHs). The DVH results are in good agreement compared to those from the CORVUS treatment planning system. Conclusion: We developed a dynamic MLC method to examine the stability of leaf speed including effects of acceleration and deceleration of leaf motion in order to make sure the stability of leaf speed did not affect the intensity profile generated. It was found that the mechanical requirements were better satisfied using this method. The Project is sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Special Analysis: Impact of Updated GSA Flow Model on E-Area Low-Level Waste Facility Groundwater Performance

In March 2018, the Savannah River National Laboratory (SRNL) notified Solid Waste Management (SWM) of new information that could potentially impact groundwater (GW) disposal limits and possibly require temporary protective measures (Crowley 2018). GW flow directions in the E-Area Low-Level Waste Facility (LLWF) Slit Trenches (STs), Engineered Trenches (ETs), and the Low Activity Waste Vault (LAWV) have notably changed in the new 2018 General Separations Area (GSA) flow model (Flach 2018a). The 2018 GSA flow model had been updated using 20 years of new hydrologic field data and model calibration methods that employed mathematical optimization software. It was later determined that the primary reason for the change in GW flow directions was due to the influence of low permeability caps that had been placed over the Old Burial Grounds and the Low Level Radioactive Waste Disposal Facility (LLRWDF). Updated flow directions in the model produce a higher degree of plume overlap for disposal units (DU’s) in the southeastern portion of E-Area than had been predicted in the 2008 E-Area LLWF Performance Assessment (PA). SWM and SRNL outlined the scope for this Special Analysis (SA) and proposed a set of interim measures (IM’s) to protect trench operations from exceeding Performance Objectives (PO’s) during the preparation of this SA, both of which were approved by the SWM Performance Assessment Review Committee (PARC) (Mooneyhan 2018).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗