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DOE OSTI2021

Improvement of Front-Junction GaInP by Point-Defect Injection and Annealing: Preprint

Traditional front junction GaInP solar cells are radiation tolerant and can have good diffusion length, but have limited voltage and thus efficiency. Here, we investigate the impact of annealing on GaInP device performance. First, Zn-doped GaInP/AlGaInP double heterojunction structures are studied in order to investigate the impact of annealing in a simple structure. While standard anneals lower diffusion length and lifetime, annealing after injecting point-defects improves material quality, presumably by eliminating sources of non-radiative recombination. Atomic ordering is reduced by the anneal, which raises the device bandgap and requires consideration. These results can be used to improve front junction device performance while controlling the bandgap. Then, GaInP front-junction devices are created using anneals with and without point-defect injection. Voc and EQE trends follow the diffusion length and lifetime trends from the DH structures. Baseline devices have Woc of 0.484 V, raising to 0.504 V after a high temperature anneal. However, injecting point defects prior to the anneal results in a front junction device with Woc of 0.405 V and a diffusion length greater than 8 micrometers. Optimized front-junction GaInP devices with an ARC have ~20% efficiency without a rear reflector.

anneal↗

DOE OSTI2022

Winter 2021/2022 Solar Industry Update

Each quarter, the National Renewable Energy Laboratory (NREL) conducts the Quarterly Solar Industry Update, a presentation of technical trends within the solar industry, to the solar office staff. Each presentation focuses on global and U.S. supply and demand, module and system price, investment trends and business models, and updates on U.S. government programs supporting the solar industry.

cost trade↗

DOE OSTI2022

Spring 2022 Solar Industry Update

cost↗

DOE OSTI2022

Summer 2022 Solar Industry Update

ASP↗

DOE OSTI2022

A Scalable Method for Decarbonizing Modular Building Solutions: Preprint

The decarbonization movement emphasizes the shift in focus from energy efficiency to directly reducing global-warming impact. Blokable, LLC, a vertically integrated modular builder with an all-electric portfolio, worked with NREL on a roadmap to decarbonize its high-performance building product at a relative cost advantage by utilizing the learning curves of mass production. Previous decarbonization literature focused on either i) lifecycle assessments, or ii) efficiency measures. These decarbonization exercises were bespoke to individual building projects and did not consider positive feedback loops of builder experience or process repetition. Vertically integrated, prefab builders possess the unique ability to leverage learning and repetition to decarbonize their design-build-operate process. This collaboration between Blokable and NREL resulted in a decarbonization strategy utilizing the company's scaling and production efficiencies, on-site renewable energy and storage, and the projected evolution of building components over time based on trends and emerging legislation. The method developed here encompasses a growing business model, lifecycle carbon assessment, and projected changes in product and grid emissions over time due to existing trends and emerging legislation. This methodology incorporates learning-curve efficiencies gleaned from scaled manufacturing, as well as open-source tools integration for energy and carbon accounting. The output projects and compares cost and carbon savings per modular unit as production increases to 10,000 dwelling units annually over 15 years. The resulting roadmap illustrates a path to roughly 60% carbon savings and beyond-net-zero-energy performance at no incremental cost by 2030. The methodology can be mapped to other integrated or productized builders for methodical decarbonization.

affordable housing↗

DOE OSTI2022

A Scalable Method for Decarbonizing Modular Building Solutions

affordable housing↗

DOE OSTI2023

An Experimental Investigation of Students? Learning Effects When Using a Simplified Nuclear Simulator

This study focuses on investigating students' learning effects and performance trends over a certain period using the Rancor Microworld Simulator. Specifically, it aims to determine the training required to collect HRA data from non-experts (i.e., students) using Rancor Microworld and the differences in human performance measures between students and professional operators. A longitudinal experiment is conducted with sixteen undergraduate students, using four Rancor Microworld scenarios in each of the four experiment trials. The study considers four human performance measurements workload, situation awareness, time, and error. Finally, the trend of students' performance is compared with operator data collected from the previous experiment. Overall, this research complements previous studies by providing insights into how much training is required to collect HRA data from non-experts and the differences in human performance measures between students and professional operators.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Optimizing Rate of Penetration and Tripping Decision-Making using Real-Time Bit Wear Monitoring While Drilling Geothermal Wells

Understanding bit wear while drilling is critical to minimizing non-productive time (NPT) and optimizing rate of penetration (ROP). Lengthening drilling runs with damaged bits does not only lower the ROP, but also elevates the risk of inducing severe bit damage, which could potentially lead to time-consuming fishing operations. When drillers believe the bit has worn off substantially, the bit is tripped out to be replaced. On geothermal wells, tripping can take up to 20% of the overall well construction time, and this is generally acknowledged as an opportunity for improvement. Ideally, a bit run should be terminated before the bit is damaged beyond repair. At the same time, premature bit pulls are to be avoided as well. This study aims to leverage bit and tooth wear metrics that can be obtained in real time to characterize bit condition in order to optimize ROP and determine the optimal time to pull the bit. Two metrics were explored in this study: a bit wear metric that incorporated depth-of-cut, and a tooth wear metric developed by Bourgoyne & Young characterizing the state of bit teeth dull. Both metrics were computed using recorded data from 12¼ inches roller cone insert bit runs in five geothermal wells targeting a granodiorite formation in the western United States. Together with the actual dull grades, determined after the bits were pulled to surface, the metric trends were interpreted to characterize the downhole bit condition and identify the point at which the bit should have optimally been tripped out. The insights from studying the actual dull grades and how they relate to the two metrics were used to establish a reliable bit pull criterion. The bit wear metric trend correctly showed a noticeable departure from baseline for bits experiencing major dulling behavior. Additionally, the tooth wear model predicted the cutter dull within two dull grades for most runs, with better performance in predicting the inner teeth dull. Moreover, the combination of the bit wear and tooth wear metrics was effective in revealing the cause of the bit performance impairment. Proactive tracking of these two metrics in real-time can facilitate geothermal drilling ROP optimization and better-informed tripping decision-making, thereby avoiding wasted time and cost.

Ashari, Rahmat↗

DOE OSTI2023

An Experimental Investigation of Students’ Learning Effects When Using a Simplified Nuclear Simulator

Securing enough data has been a main challenge in human reliability analysis (HRA). Many researchers and institutes have made a lot of efforts for collecting HRA data to produce reasonable human error probabilities (HEPs) as well as reduce the uncertainty of HRA quantification. Representatively, U.S. Nuclear Regulatory Commission (U.S. NRC), Korea Atomic Energy Research Institute (KAERI) and Idaho National Laboratory (INL) have led lots of empirical research regarding the HRA data collection. The U.S. NRC and KAERI have mainly carried out full-scope simulator research collecting HRA data through experiments using full-scope simulators with actual operators. In contrast, INL has experimentally collected the data using simplified simulators and student operators. INL has proposed the Simplified Human Error Experimental Program (SHEEP) framework to complement full-scope data collection efforts by suggesting a way to infer full-scope data based on experimental data collected from students operating simplified simulators, specifically the Rancor Microworld Simulator (Rancor) and the Compact Nuclear Simulator (CNS). The aim of the SHEEP framework is to lower the entry point for collecting useful HRA data by securing large sample sizes at a reasonable amount of cost and labor while also guaranteeing a high degree of freedom when designing experiments. The authors’ previous research investigated whether data collected from the SHEEP framework could support a representative full-scope study. Besides, human performance differences between professional and student operators when using Rancor and CNS have been analyzed to understand the lack of fidelity of the simplified simulators and student operators within the SHEEP study. As a follow up research, this study experimentally investigates students’ learning effects and the performance trends over a certain period when using Rancor. This study aims to find out 1) how much training or education is required to collect HRA data from non-experts (i.e., students) when using Rancor and 2) how much differences there are in human performance measures between students and professional operators. In this study, a longitudinal experiment is developed. The four experiment trials with two weeks interval are carried out for sixteen undergraduate students majoring nuclear engineering at Chosun University. Totally four scenarios randomly selected from ten Rancor scenarios are used in each experiment trial. Four human performance measurements (i.e., workload, situation awareness, time and error) are considered in the experiment. Lastly, the trend of students’ performance is compared with operator data having been collected from the previous experiment.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Nuclear Trade Atlas 2016-2020

Building on prior work developing a Strategic Trade Atlas promoting understanding of global trade flows of strategic goods, a Nuclear Trade Atlas was developed based on publicly available trade data. The Nuclear Trade Atlas examines trade patterns and trends for seventeen commodities based on a two-step assessment of the trade’s nuclear relevance: first estimating the probability that trade classified under a given Harmonized System (HS) code represents a specific commodity included in the export control lists of the Nuclear Suppliers Group, and second estimating the probability that the listed, often dual-use, commodity would be indicative of nuclear use. These estimates combine to provide a measure of the likelihood trade classified under a given HS code may be nuclear relevant. By exclusively focusing on the HS codes most highly ranked by this measure, the resulting data visualizations comprising the Nuclear Trade Atlas provide those responsible for regulating or monitoring nuclear trade greater understanding of relevant global trading patterns and trends. The Atlas includes both commodity- and country-based views of nuclear trade. It is published as a book and as an interactive tool. Besides trade data, the interactive Atlas also includes data about countries’ non-proliferation commitments, allowing users to take this information into consideration while analyzing specific trade flows.

strategic trade, world customs organization, trade↗

DOE OSTI2021

Resilient Information Architecture Platform for Smart Grid (RIAPS)

A number of emerging trends will substantially alter the operation and control of the electric grid over the next several decades. These trends include ensuring resiliency under severe weather events, increasing integration of renewable electricity generation, supporting changing electricity demand patterns, and the improving cost effectiveness of distributed energy resources. To address these challenges, the future “Smart Grid” management will need to transition from centralized to coordinated distributed control paradigm. Reliable operation of the Smart Grid depends on distributed intelligence realized through software applications that run on distributed computing devices attached to the power system to collect data and collaboratively manage resources. However, much of the existing software for Smart Grid-enabled devices is either proprietary or developed with custom solutions, which limits interoperability among the heterogeneous devices and hinders the ability to manage system-level reliability, security, and resiliency requirements. Additionally, this approach makes Smart Grid applications hard to maintain, evolve, verify, and replace; resulting in high development and deployment costs. Further development of the Smart Grid requires a reusable software base-layer to move from hard-coded functionality to a plug-and-play architecture capable of managing system-level objectives and constraints in addition to providing consistent common services across heterogeneous devices and applications. Vanderbilt University, in collaboration with North Carolina State University and Washington State University has developed a foundation ‘software platform’ for developing and deploying robust, reliable, effective and secure software applications for the Smart Grid. The Resilient Information Architecture Platform for the Smart Grid (RIAPS) provides core services for building effective and powerful smart grid applications. It offers unique services for real-time data dissemination, fault tolerance, and coordination across apps distributed over the network.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Machine-Learning Assisted Identification of Battery Life Models

Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.

battery↗

DOE OSTI2020

Applications of Hyporheic Zone Sampling for Assessing VOC Plume Engineered Remediation and Natural Attenuation Effectiveness - 20414

Determining the efficacy of engineered remedial actions and/or natural attenuation as an acceptable EPA Record of Decision (ROD) must address long-term temporal and spatial variability. Since final plume downslope destinations are aquifers and related surface streams and rivers, hyporheic zone sampling is essential. Comprehensive hyporheic sampling documents plume groundwater remediation prior to upwelling into stream benthic strata and natural attenuation within this dynamic zone itself. Our intensive sampling of the Savannah River Site (SRS) Chemical, Metals, and Pesticides (CMP) Pits VOC plume-fringe from 2005 to 2018 documented PCE natural attenuation and effectiveness of In Situ Thermal Treatment (ISTT). Most plume hyporheic upwelling was delimited to a 20 m inflow reach along Pen Branch. Our study utilized corroborative sampling methodologies: ambient hole-water grab samples: passive diffusion bags (PDB); and hole-core sediment samples to confirm station to station spatial and temporal trends. VOC hot spot detections were confirmed from both groundwater and soil core samples. VOC pathways through the Pen Branch hyporheic zone were assessed at hole-installation using sequential soil samples from augered cores and grab-samples of hole-bottom water. Longer term, composite water samples were collected from PDBs deployed for a minimum two-week equilibration period. Soil samples were collected from the 15 and 65 cm core levels in December 2017 and June 2018 at six stations. Additional core depths of 40 and 85 cm were sampled in June 2018 at the highest VOC station (5DB80). All samples were analyzed in an EPA certified lab by purge and trap GC-MS. Effectiveness of previous ISTT and soil vapor extraction (SVE) for this CMP Pits PCE plume, was clearly documented by significant downward trends in VOC at hyporheic zone stations prior to December 2015. Achieving final stage conversions of PCE daughter products to ethene and CO{sub 2} may be limited depending upon suitable interactions between hyporheic zone sediment morphology, hydrology, and redox conditions. Because declines in VOC daughter products at our hyporheic stations may also be caused by physical actions of dilution and dispersion rather than true biochemical dechlorination, full quantification of natural attenuation requires the use of compound-specific isotope ratio analysis (CSIA) and microbial 16S rRNA with qPCR. To assist these next-phase efforts, a natural attenuation index (NAI) was used to help produce quicker, more cost-effective decision making for locating CSIA and microbial sampling efforts. The NAI used natural logarithm transformed GC-MS results for station VOC degradation compounds versus VOC parent compounds. Wide differences were observed between stations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Advances in Resin Management Using 3R-Scan - 20154

The most important factor underlying optimal waste management is developing a clear picture of the radioactivity content of the waste and its impact on waste disposal cost. For over 35 years since the publishing of 10CFR61, waste characterization has relied on sampling the final waste product after formation. In the days following 10CFR61, the cost of final disposal was marginal with only a small impact on the overall costs. Constraints were added by provisions of the Low Level Waste Policy Act of 1985 leading to increasingly limited access to those disposal sites that remained available. In addition, Nuclear Regulatory Commission (NRC) pressure promoting waste volume reduction led to disposal costs inevitably rising. Despite this, characterization practices in monitoring of waste generation for activity content still center on the same dated processes. This results in a disposal classification on the basis of endpoint sampling without consideration of the homogeneity of the waste mixture. It can also disregard consideration of the representativeness of the single or small sample base. As a minimum effort, a formalized sampling program of a fixed grouping of waste streams can be implemented that could account for more than 95% of all of the activity carried in solid waste products. The sample results for each radionuclide could then be trended as time passes to develop reasonable scaling factors for difficult to measure radionuclides. This process, identified in NRC guidance, has been rigorously followed by a relatively small number of facilities. The trended scaling factors serve to improve accuracy by identifying anomalous results that could otherwise go undetected. Direct monitoring of the accumulation of activity in process streams generating solid radwaste, including demineralizers and filter streams, is a more precise approach. Nearly all of these streams are monitored by plant chemistry on a regular schedule to maintain water quality. This paper discusses viable options for developing the basis for characterization through process monitoring of the accumulation of activity at the point of generation. Special focus is on resin bed tracking and how process knowledge of these streams can be brought together to form a consistent and precise solid waste radioactivity inventory. Some of the specific points covered in this paper include the merger of the fission product release computer program, 3R-STAT, with the radwaste sample analysis computer program, SCAN4 to create 3R-SCAN, the importance of individual waste stream influences on the overall source term, and the use of historic sample data to develop scaling factors using an automated process. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

The Industry's Long-term, Yet Near-term, Issues: The Need for Radiation Protection Professionals - 20266

Since the discovery of X-rays and radioactivity, the civil use of nuclear technology has thrived for more than a half-century and has matured to be safer and more efficient. A balance between radiological and non-radiological knowledge and experience has been gained and retained through the years. Nevertheless, a slowly developing gap has emerged during the past few decades. National and international data show a declining trend in the existence of health physics programs and associated enrollment and training, which directly impact the pipeline of radiation professionals (i.e., educators, technicians, researchers, and regulators). This decline can be attributed to many factors including the lack of scientific inquisitiveness, declining funding, and the perception that existing protocols are sufficiently protective. Decommissioning and dismantling nuclear facilities is a complex and multi-phased process that generates large quantities of various levels of radioactive waste. With multiple facilities reaching their end of operational life, and others being shut down for economic reasons, the current pipeline for radiation protection experts cannot meet the future's need. Consequently, international organizations have proposed a variety of programs to transfer knowledge of radiation protection principles and practice. While many change-oriented efforts to counteract this trend aim to promote mobility, this is just a partial solution to the actual issue: long-term availability. This paper suggests multidisciplinary collaborations with modern technologies as a path forward to educate young professionals on the diversity of radiation-related applications to retain expert knowledge and foster innovation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Latest Developments in PM Systems and Methods and their Application to WM Projects - 20364

How do you improve on an industry that is well established yet often misses the mark on key metrics demonstrating effective planning and execution of project scope, schedule and budget? With the increased complexity of DOE projects relative to either poorly kept records of what wastes are stored or the lack of specific knowledge relative to the waste concentrations/quantities, this is a question continually asked and investigated by many. In fact, in 2017, the GAO's report to Congress indicated that although the DOE has made some progress on its monitoring effectiveness and demonstrating progress criteria, these goals were not met and there needs to be more improvement and monitoring. This paper will offer some insights into the latest developments in project management methodologies and the possible application of these to waste management projects. Traditional data generated via a project schedule that utilizes critical path methodology (CPM) and additional earned value management (EVM) data needs to be accurate and enable the project manager to better understand the probability of project success. Although this data has readily been available on most large-scale projects, success as measured by various stakeholder groups including the client, project team, regulators, etc. is less than optimal. Therefore, an urgent need exists to explore how traditional project data (CPM and EVM) coupled with new methodologies allows for better planning and execution by the project team. Many new approaches/methodologies can even be used to predict the success of options prior to full implementation on the actual project. Three approaches/methodologies will be discussed: - Artificial Intelligence; - Change Management; - Blend of Traditional Management (Gantt chart) and Agile Methodologies. As market forces change, so will the direction of project management. The days of 'one size fits all' are no longer valid. A combination of predictive and adaptive approaches in the management of projects is essential to enable execution of project deliverables on time, under budget, and within the contractual scope. This paper is intended to provide the reader with insight to the close relationship of project management trends and business management trends, and to provoke critical thinking about what it means to align project management strategy with an environment of non-stop innovation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Quantifying the impact of climate change on erosion - 20397

The New York State Energy Research and Development Authority (NYSERDA) is the owner of the Western New York Nuclear Service Center (WNYNSC), a 1,351-ha (3,338-ac) site located approximately 48 km (30 mi) south of Buffalo, New York. In 1962, Nuclear Fuel Services, Inc. (NFS) entered into Agreements with the Atomic Energy Commission and New York State to construct the first commercial reprocessing plant of nuclear fuel in the United States. NFS, a private company, built and operated the spent fuel reprocessing plant and waste disposal facilities, processing 640 Mg (metric tons, or 705 short tons) of spent nuclear fuel from 1966 to 1972 under an Atomic Energy Commission license. Nuclear fuel reprocessing operations ended in 1972 and never reopened, leaving behind radioactive and chemical wastes. Erosion can play an important role in the fate and transport of waste at sites where disposal of long-lived waste is anticipated. The statistical characterization of key processes related to erosion is essential to the understanding of site stability through time. One of the key processes governing erosion is extreme precipitation and it is critical that trends in the distribution of extreme precipitation events through time be represented. The evidence of climate change is increasingly well documented and projected impacts on extreme precipitation events should be incorporated in performance assessment studies when relevant. Not evaluating future climate states in a performance assessment is contradictory to good modeling practice. Specifically, excluding climate change limits development of modeling information that could aid in effective decision making. The current climate literature provides both observational evidence and climate model projections of climate trends and/or climate change in the late 20. and early 21. centuries for North America and the northeast United States. In this work, this information was used to assess the impacts of potential changes in climate on erosion processes. The goal was to understand how projections of future climate relate to the performance of the WNYNSC through time. In the first stage of this work multi-temporal historical aerial images were analyzed in conjunction with orthophotography and Lidar data to develop probability distributions for variables representing important erosion processes. The information from these analyses was then used in conjunction with simulated data from the West Valley Erosion Working Group (EWG). Analysis of EWG simulations provides estimates for the change in erosion rates through time that is driven by changes in climate. The time-varying rates of erosion change were applied to the historical aerial imagery data in order to inform time-varying rates of erosion that are driven by changes in climate. Ultimately, this process resulted in the identification of locations for features like gully heads using both the Lidar dataset and projection of the estimated location from the historical aerial photo under consideration back to the Lidar dataset. The distance between the estimate of the location from the historical image and that of the Lidar dataset was the estimated distance the feature has moved. This distance was then divided by the number of years between the Lidar dataset and the year of the historical aerial image of interest to get a rate of movement through time. This was done for several dozen points on each historical aerial image. The analyses from the EWG were used to characterize the relative impact of climate change on the erosion rates. This relative impact was quantified by comparing the LEM based estimates of erosion that were derived using historical climate data with LEM based estimates of erosion that were derived using projections of future climate. The relative increase in the erosion rates was then applied to the historical estimates of erosion derived from the analysis of the historical aerial imagery. This approach was used since the LEM-based estimated of the historical erosion rates from the past century have a bias towards underestimating erosion. This underestimation is hypothesized to be a consequence of an inability of the LEMs to account for the impacts of land-use change (i.e. deforestation) which had been shown to significantly increase erosion in similar Northern hardwood forest ecosystems. In summary, gully head retreat rate and gully widening rate were characterized using statistical probability distributions from the historical aerial image analysis. Simulated data from the EWG was used to estimate the climatically-driven changes in erosion rates through time. The changes through time from the EWG were applied to the gully head retreat rate and gully widening rate characterized using the historical aerial image analysis. This information can be used to inform PPA models to project future risks from a given site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

The Challenge of Achieving Site Closure of a Complex Groundwater Plume - 20531

Over the last three decades, DOE has made substantial progress in nearly every area of nuclear waste cleanup and has closed and transitioned 91 of the 107 sites in the EM complex. Several of the remaining groundwater plumes at Hanford, Los Alamos, Paducah, Oak Ridge, and Savannah River are complex and tend to have unique site or contaminant characteristics which provide an almost impossible challenge to remediate to regulatory levels. As a result, these complex sites will require a large investment to remediate. In order for DOE to achieve regulatory closure, a technically sound case must be developed and presented to regulators and stakeholders that clearly demonstrates that remedial goals have been or will be irreversibly achieved or that there is an alternate acceptable end-state. Five lines of evidence are required: - A conceptual site model that is as complete as possible, with the knowledge that it may continue to evolve with time; - Demonstration that trends in contaminant concentrations, or flux, versus time indicate that remedial goals will be achieved on schedule or within a reasonable time thereafter and that trends that deviate from this path are explainable by the conceptual model; - Demonstration that remobilization of attenuated contaminants is unlikely to occur in the future; - Explicit consideration of remedial contingencies, should deviations in the path toward remedial goals occur; - Development of an effective long-term monitoring plan to detect any conditions that might alter the path toward remedial goals. SRS has made significant progress in the remediation of contamination associated with the F-area and H-Area Seepage Basins, and it is currently developing a strategic closure path that addresses each of these lines of evidence and identifies both basic and applied scientific issues that will need to be addressed in order to obtain regulatory closure. (authors)

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