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

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Used Nuclear Fuel Management Using the Next Generation System Analysis Model

The U.S. Department of Energy (DOE) is leading the National effort to manage the back end of the nuclear fuel cycle, encompassing the safe transportation, storage/staging, and/or eventual disposal of used nuclear fuel (UNF) and high-level radioactive waste. The Next Generation System Analysis Model (NGSAM) is DOE’s discrete-event, agent-based simulation tool designed to model the full life cycle of UNF from reactor discharge to final disposal. NGSAM supports the DOE Office of Spent Fuel and High-Level Waste Disposition by enabling a detailed, scenario-based analysis of logistics, infrastructure, and shipping strategies. NGSAM replaces legacy models with a modern, flexible platform built on Repast Simphony and enhanced by the Process Analysis Tool. NGSAM simulates the movement and interaction of individual fuel assemblies with system components such as canisters, casks, railcars, and facilities. The model integrates with the Java Transportation Operations Model to plan and execute transportation scenarios, supporting both constrained and unconstrained resource allocation. Key features include customizable allocation and acceptance algorithms, detailed facility-level operations, and a Quick Edit tool for rapid scenario adjustments. NGSAM supports multimodal transportation modeling (e.g. rail, road, barge) and provides comprehensive cost, schedule, and infrastructure data. NGSAM utilizes data from sources such as DOE’s STANDARDS UNF database and DOE’s Stakeholder Tool for Assessing Radioactive Transportation, while also allowing user-defined inputs for scenario customization. NGSAM enables stakeholders to evaluate complex UNF management strategies, assess system performance under varying assumptions, and inform decision making for future infrastructure investments. Its modular architecture and integration with other Integrated Waste Management System tools make it a critical asset for planning the safe and efficient disposition of the Nation’s growing UNF inventory.

Craig, Brian [Argonne National Laboratory (ANL)]↗
DOE OSTI2013

Light Water Reactor Sustainability Program Support and Modeling for the Boiling Water Reactor Station Black Out Case Study Using RELAP and RAVEN

The existing fleet of nuclear power plants is in the process of extending its lifetime and increasing the power generated. In order to evaluate the impact of these two factors on the safety of the plant, the Risk Informed Safety Margin Characterization (RISMC) project aims to provide insight to decision makers through a series of simulations of the plant dynamics for different initial conditions (e.g., probabilistic analysis and uncertainty quantification). This report focuses, in particular, on the impact of power uprate on the safety of a boiled water reactor system. The case study considered is a loss of off-site power followed by the loss of diesel generators, i.e., a station black out (SBO) event. Analysis is performed by using a thermo-hydraulic code, i.e. RELAP-5, and a stochastic analysis tool currently under development at INL, i.e. RAVEN. Starting from the event tree models contained in SAPHIRE, we built the input file for RELAP-5 that models in great detail system dynamics under SBO conditions. We also interfaced RAVEN with RELAP-5 so that it would be possible to run multiple RELAP-5 simulation runs by changing specific keywords of the input file. We both employed classical statistical tools, i.e. Monte-Carlo, and more advanced machine learning based algorithms to perform uncertainty quantification in order to quantify changes in system performance and limitations as a consequence of power uprate. We also employed advanced data analysis and visualization tools that helped us to correlate simulation outcome such as maximum core temperature with a set of input uncertain parameters. Results obtained gave a detailed overview of the issues associated to power uprate for a SBO accident scenario. We were able to quantify how timing of safety related events were impacted by a higher reactor core power. Such insights can provide useful material to the decision makers to perform risk-infomed safety margins management.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

SU-F-T-99: Data Visualization From a Treatment Planning Tracking System for Radiation Oncology

Purpose: A treatment planning process tracker database with input forms and a TV-viewable display webpage was developed and implemented in our clinic to collect time data points throughout the process. Tracking plan times is important because it directly affects the patient quality of care. Simply, the longer a patient waits after their initial simulation CT for treatment to begin, the more time the cancer has to progress. The tracker helps to drive workflow through the clinic, while the data collected can be used to understand and manage the process to find and eliminate inefficiencies. Methods: The overall process steps tracked are CT-simulation, mark patient, draw normal contours, draw target volumes, create plan, and review/approve plan. Time stamps for task completion were extracted and used to generate a set of clinic metrics, among which include average time for each step in the process split apart by type of treatment, average time to completion for plans started in a given week, and individual overall completion time per plan. Results: Trends have been tracked for fourteen weeks of clinical data (196 plans). On average, drawing normal contours and target volumes is taking 2–5 times as long as creating the plan itself. This is potentially an issue because it could mean the process is taking too long initially, and it could be forcing the planning step to be done in a short amount of time. We also saw from our graphs that there appears to be no clear trend on the average amount of time per plan week-to-week. Conclusion: A tracker of this type has the potential to provide insight into how time is utilized in our clinic. By equipping our dosimetrists, radiation oncologists, and physicists with individualized metric sets, the tracker can help provide visibility and drive workflow. Funded in part by CPRIT (RP140105).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

State-Space Model to Estimate Salmon Escapement Using Multiple Data Sources

Abstract Accurate estimates of salmonids passing Lower Granite Dam on the Snake River, by species and origin, are a critical input to assessing the status and trends of various populations as well as successful management of fisheries in the Snake River basin. Here, we describe a state-space model that estimates such escapement past a dam by using window counts, PIT tag observations, and data from an adult fish trap, accounting for issues such as nighttime passage, fallback and reascension, potential observation error at the window, and uncertainty in the adult trap rate. We tested the approach using a simulation framework that mimicked several levels of observation error, differences between nighttime passage and reascension rates, and the possibility of the adult trap being closed for some period of time. Our results demonstrate that the model produced unbiased estimates across all tested scenarios. We also applied this model to empirical data from Lower Granite Dam to produce estimates of wild, clipped hatchery, and unclipped hatchery spring/summer-run Chinook Salmon Oncorhynchus tshawytscha and steelhead O. mykiss from spawn years 2010–2019.

See, Kevin E. (ORCID:0000000297626442)↗
DOE OSTI2026

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Comparison of Deterministic and Statistical Models for Water Quality Compliance Forecasting in the San Joaquin River Basin, California

Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood of agricultural communities. In the agriculture-dominated San Joaquin River Basin of California, real-time salinity management (RTSM) is a state-sanctioned program that helps to maximize allowable salt export while protecting existing basin beneficial uses of water supply. The RTSM strategy supplants the federal total maximum daily load (TMDL) approach that could impose fines associated with exceedances of monthly and annual salt load allocations of up to $1 million per year based on average year hydrology and salt load export limits. The essential components of the current program include the establishment of telemetered sensor networks, a web-based information system for sharing data, a basin-scale salt load assimilative capacity forecasting model and institutional entities tasked with performing weekly forecasts of river salt assimilative capacity and scheduling west-side drainage export of salt loads. Web-based information portals have been developed to share model input data and salt assimilative capacity forecasts together with increasing stakeholder awareness and involvement in water quality resource management activities in the river basin. Two modeling approaches have been developed simultaneously. The first relies on a statistical analysis of the relationship between flow and salt concentration at three compliance monitoring sites and the use of these regression relationships for forecasting. The second salt load forecasting approach is a customized application of the Watershed Analysis Risk Management Framework (WARMF), a watershed water quality simulation model that has been configured to estimate daily river salt assimilative capacity and to provide decision support for real-time salinity management at the watershed level. Analysis of the results from both model-based forecasting approaches over a period of five years shows that the regression-based forecasting model, run daily Monday to Friday each week, provided marginally better performance. However, the regression-based forecasting model assumes the same general relationship between flow and salinity which breaks down during extreme weather events such as droughts when water allocation cutbacks among stakeholders are not evenly distributed across the basin. A recent test case shows the utility of both models in dealing with an exceedance event at one compliance monitoring site recently introduced in 2020.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs: Preprint

Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulations have been conducted in TETRAD-G and CMG STARS to explore different injection and production fluid flow rates and allocations and to develop a training data set for ML. This process included simulating the historical injection and production since 1979 and prediction of future performance through 2040. ML networks were created and trained using TensorFlow based on multilayer perceptron (MLP), long short-term memory (LSTM), and convolutional neural network (CNN) architectures. These networks took as input selected flow rates, injection temperatures, and historical field operation data and produced estimates of future production temperatures. This approach was first successfully tested on a simplified single fracture doublet system, followed by the application to the BHS reservoir. Using an initial BHS dataset with 37 simulated scenarios, the trained and validated network predicted the production temperature for 6 production wells with the mean absolute percentage error of less than 8%. In a complementary analysis effort, the principal component analysis applied to 13 BHS geological parameters revealed that vertical fracture permeability shows the strongest correlation with fault density and fault intersection density. A new BHS reservoir model was developed considering the fault intersection density as proxy for permeability. This new reservoir model helps to explore under-exploited zones in the reservoir. A data gathering plan to obtain additional subsurface data was developed; it includes temperature surveying for three idle injection wells, at which the reservoir simulations indicate high bottom-hole temperatures. The collected data assist with calibrating the reservoir model and may lead to converting these wells to producers to access under-exploited zones in the reservoir. Data gathering activities are planned for the first quarter of 2021.

40 EE - Geothermal Technologies Office (EE-4G)↗
DOE OSTI2012

Analysis of energy recovery potential using innovative technologies of waste gasification

Highlights: Black-Right-Pointing-Pointer Energy recovery from waste by gasification was simulated. Black-Right-Pointing-Pointer Two processes: high temperature gasification and gasification associated to plasma. Black-Right-Pointing-Pointer Two types of feeding waste: Refuse Derived Fuel (RDF) and pulper residues. Black-Right-Pointing-Pointer Different configurations for the energy cycles were considered. Black-Right-Pointing-Pointer Comparison with performances from conventional Waste-to-Energy process. - Abstract: In this paper, two alternative thermo-chemical processes for waste treatment were analysed: high temperature gasification and gasification associated to plasma process. The two processes were analysed from the thermodynamic point of view, trying to reconstruct two simplified models, using appropriate simulation tools and some support data from existing/planned plants, able to predict the energy recovery performances by process application. In order to carry out a comparative analysis, the same waste stream input was considered as input to the two models and the generated results were compared. The performances were compared with those that can be obtained from conventional combustion with energy recovery process by means of steam turbine cycle. Results are reported in terms of energy recovery performance indicators as overall energy efficiency, specific energy production per unit of mass of entering waste, primary energy source savings, specific carbon dioxide production.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Validation of the Community Land Model Version 5 over the Contiguous United States (CONUS) using in-situ and remote sensing datasets

The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM Version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the Contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125? during 1979-2018 over the CONUS using different configurations of CLM, namely CLM5-biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5-satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote-sensed and in-situ datasets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC-simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM-simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation datasets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially-distributed plant phenology and physiology parameters and regional-specific agricultural management practices (e.g., planting, harvest).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Control Valve Trajectories for SOFC Hybrid System Startup

Control and management of cathode airflow in a solid oxide fuel cell gas turbine hybrid power system was analyzed using the Hybrid Performance (HyPer) hardware simulation at the National Energy Technology (NETL), U.S. Department of Energy. This work delves into previously unexplored operating practices for HyPer, via simultaneous manipulation of bypass valves and the electric load on the generator. The work is preparatory to the development of a Multi-Input, Multi-Output (MIMO) controller for HyPer. A factorial design of experiments was conducted to acquire data for 81 different combinations of the manipulated variables, which consisted of three air flow control valves and the electric load on the turbine generator. From this data the response surface for the cathode airflow with respect to bypass valve positions was analyzed. Of particular interest is the control of airflow through the cathode during system startup and during large load swings. This paper presents an algorithm for controlling air mass flow through the cathode based on a modification of the steepest ascent method.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

River Dissolved Oxygen Prediction Using Machine Learning Models and Wireless Sensor Measurements

Simultaneous flooding&heat and droughts&heat events can potentially destabilize hydro-meteorological conditions to deteriorate the water quality of Neches River. Machine learning (ML) models utilizing wireless sensor measurements have been applied to predict water quality and optimize various water management strategies. This study aims to develop ML models to predict dissolved oxygen (DO) prediction under various hydro-meteorological conditions and enhance water management decision-making. Wireless sensor measurements of DO, water temperature, sample depth, conductivity, turbidity, and pH, along with discharge from the United States Geological Survey stations, are collected for model inputs at the Pine Island Bayou C749 station (PIB-C749) and Neches River Saltwater Barrier (SWB). Multilayer perceptron neural networks, recurrent neural networks, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) with and without attention mechanism (AT) are tested to determine the best model, which is applied the rolling forecast method to predict 14-day DO. Traditional and recurrent transfer learning (TL and RTL) methods are adopted to overcome insufficient data at the SWB. The input feature importance analysis using the integrated gradients (IG) algorithm is applied to determine dominant inputs. The results show LSTM-based models are capable handling long sequential data. AT-BiLSTM and RTL-LSTM demonstrate the best performance at the PIB-C749 (RMSE=0.054) and the SWB (RMSE=0.028), respectively. TL and RTL methods significantly improve model performance at the SWB. DO, temperature, and pH show higher importance, consistent with hydrodynamics and water chemistry. Both best models are applied to predict 14-day DO and demonstrate reasonable performance for decision-making. Hydro-meteorological conditions of 2017 flood and 2012 drought events are simulated and reveal that possible hypoxia occurs after flooding due to increasing temperature and turbidity, and DO concentration decreases significantly under heat and drought conditions. In conclusion, LSTM-based models utilizing wireless sensor data can be a timely and effective approach to make appropriate decisions on water resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Large scale centrifuge test of a geomembrane-lined landfill subject to waste settlement and seismic loading

Highlights: • A large centrifuge model of a geomembrane-lined landfill was seismically loaded. • The scaled peak acceleration of the strongest input motion was 0.6 g. • Laboratory testing was conducted to obtain properties of the model materials. • Collected data included liner strains due to downdrag and seismic loading. • Results show that settlement and seismic loading can potentially damage geomembranes. • Results can be used to validate numerical models for geomembrane performance. •The laboratory and centrifuge testing data is publicly available. - Abstract: A large scale centrifuge test of a geomembrane-lined landfill subject to waste settlement and seismic loading was conducted to help validate a numerical model for performance based design of geomembrane liner systems. The test was conducted using the 240 g-ton centrifuge at the University of California at Davis under the U.S. National Science Foundation Network for Earthquake Engineering Simulation Research (NEESR) program. A 0.05 mm thin film membrane was used to model the liner. The waste was modeled using a peat-sand mixture. The side slope membrane was underlain by lubricated low density polyethylene to maximize the difference between the interface shear strength on the top and bottom of the geomembrane and the induced tension in it. Instrumentation included thin film strain gages to monitor geomembrane strains and accelerometers to monitor seismic excitation. The model was subjected to an input design motion intended to simulate strong ground motion from the 1994 Hyogo-ken Nanbu earthquake. Results indicate that downdrag waste settlement and seismic loading together, and possibly each phenomenon individually, can induce potentially damaging tensile strains in geomembrane liners. The data collected from this test is publically available and can be used to validate numerical models for the performance of geomembrane liner systems.

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

Uncertainty Quantification of Calculated Temperatures for the AGR 3/4 Experiment

A series of Advanced Gas Reactor (AGR) irradiation experiments are being conducted within the Advanced Reactor Technology (ART) Fuel Development and Qualification Program. The main objectives of the fuel experimental campaign are to provide the necessary data on fuel performance to support fuel process development, qualify a fuel design and fabrication process for normal operation and accident conditions, and support development and validation of fuel performance and fission product transport models and codes (PLN 3636, “Technical Program Plan for INL Advanced Reactor Technologies Technology Development Office/Advanced Gas Reactor Fuel Development and Qualification Program”). The AGR 3/4 test was inserted in the Northeast Flux Trap position in the Advanced Test Reactor (ATR) core at Idaho National Laboratory (INL) in December 2011 and successfully completed irradiation in mid-April 2014, resulting in irradiation of the tristructural isotropic (TRISO) fuel for 369.1 effective full-power days (EFPDs) during approximately 2.4 calendar years. The AGR 3/4 data, including the irradiation data and calculated results, were qualified and stored in the Nuclear Data Management and Analysis System (NDMAS). To support the U.S. TRISO fuel performance assessment and to provide data for validation of fuel performance and fission product transport models and codes, the daily as run thermal analysis has been performed separately on each of twelve AGR 3/4 capsules for the entire irradiation as discussed in ECAR-2807, “AGR 3/4 Daily As Run Thermal Analyses”. The ABAQUS code’s finite element-based thermal model predicts the daily average volume average (VA) fuel temperature (FT), peak FT, and graphite matrix, sleeve, and sink temperature in each capsule. The JMOCUP simulation codes were also created to perform depletion calculations for the AGR 3/4 experiment (ECAR-2753, “JMOCUP As-Run Daily Physics Depletion Calculation for the AGR 3/4 TRISO Particle Experiment in ATR Northeast Flux Trap”). This depletion analysis provides fast fluence and fission heat rate data for all components (fuel compacts, graphite rings, stainless steel retainer, etc.), which are used as inputs for the thermal analysis codes. The graphite temperatures from thermocouples (TCs) in each capsule were used to calibrate these thermal analysis codes. However, given the high rate of TC failure under the harsh irradiation and thermal conditions in the AGR capsules, the thermal analysis results are very useful in aiding TC data qualification, increasing the confidence in delineating failures of the measuring instruments (TCs) from physical mechanisms that may have shifted the system thermal response.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Evaluating the effectiveness of program data features for guiding memory management

Recent trends have led to the adoption of larger and more complex memory systems, often with multiple tiers of memory performance within the same platform. To utilize complex memory systems efficiently, current data management strategies must be altered to map usage demands to the underlying hardware. Applications, as the generators of memory accesses, are well-suited to guide this process, but building and maintaining separate source code versions for different memory systems is not feasible in most cases. One potential solution is to employ automated program profiling and analysis to facilitate the production of application-based guidance. By attaching memory usage information to static or lightweight program features, compilers and runtime systems can generate fine-grained guidance without additional efforts from users or developers. Recent works have employed this approach with some success, but it is not clear which program features are most useful for guiding data management.This work evaluates the effectiveness of using different program data features to predict memory usage and guide memory management. It employs a custom set of simulation tools, based in the Intel® Pin framework, to collect and analyze the usage characteristics of application data associated with common program data features, such as allocation sites, types, and context. The results show that even relatively simple features, such as object size, are useful for selecting data with similar usage properties, but finer-grained features, such as the instructions that access a particular object, are often much more effective. Additionally, this work evaluates the performance of using different data features and different program inputs to guide data placement on a heterogeneous memory platform with a limited amount of high performance memory.

Effler, T. Chad↗
DOE OSTI2016

Active Interrogation using Photofission Technique for Nuclear Materials Control and Accountability

Innovative systems with increased sensitivity and resolution are in great demand to detect diversion and to prevent misuse in support of nuclear materials management for the U.S. fuel cycle. Nuclear fission is the most important multiplicative process involved in non-destructive active interrogation. This process produces the most easily recognizable signature for nuclear materials. In addition to thermal or high-energy neutrons, high-energy gamma rays can also excite a nucleus and cause fission through a process known as photofission. Electron linear accelerators (linacs) are widely used as the interrogating photon sources for inspection methods involving photofission technique. After photofission reactions, prompt signals are much stronger than the delayed signals, but it is difficult to quantify them in practical measurements. Delayed signals are easily distinguishable from the interrogating radiation. Linac-based, advanced inspection techniques utilizing the delayed signals after photofission have been extensively studied for homeland security applications. Previous research also showed that a unique delayed gamma ray energy spectrum exists for each fissionable isotope. In this work, high-energy delayed γ-rays were demonstrated to be signatures for detection, identification, and quantification of special nuclear materials. Such γ-rays were measured in between linac pulses using independent data acquisition systems. A list-mode system was developed to measure low-energy delayed γ-rays after irradiation. Photofission product yields of 238 U and 239 Pu were determined based on the measured delayed γ-ray spectra. The differential yields of delayed γ-rays were also proven to be able to discriminate nuclear from non-nuclear materials. The measurement outcomes were compared with Monte Carlo simulation results. It was demonstrated that the current available codes have capabilities and limitations in the simulation of photofission process. A two-fold approach was used to address the high-rate challenge in used nuclear fuel assay based on photofission technique. First, a standard HPGe preamplifier was modified to improve its capabilities in high-rate pulsed photofission environment. Second, advanced pulse processing algorithms were shown to greatly improve throughput rate without large sacrifice in energy resolution at ultra-high input count rate. Two customized gamma spectroscopy systems were also developed in real-time on FPGAs. They were shown to have promising performance matching available commercial units.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

DABAM: An open-source database of X-ray mirrors metrology

An open-source database containing metrology data for X-ray mirrors is presented. It makes available metrology data (mirror heights and slopes profiles) that can be used with simulation tools for calculating the effects of optical surface errors in the performances of an optical instrument, such as a synchrotron beamline. A typical case is the degradation of the intensity profile at the focal position in a beamline due to mirror surface errors. This database for metrology (DABAM) aims to provide to the users of simulation tools the data of real mirrors. The data included in the database are described in this paper, with details of how the mirror parameters are stored. An accompanying software is provided to allow simple access and processing of these data, calculate the most usual statistical parameters, and also include the option of creating input files for most used simulation codes. In conclusion, some optics simulations are presented and discussed to illustrate the real use of the profiles from the database.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Defining Modeling and Analysis Complexity Needs for Remediation - 18425

Many subsurface remedy decisions rely on model predictions or other types of data analyses to support site characterization and remedy selection, design, implementation, performance assessment, and closure. Thus, determining the appropriate level of complexity for modeling and data analysis is important to facilitate subsurface remedy decisions. Multiple factors are important in selecting the appropriate level of complexity in modeling and data analysis. For instance, the type and number of inputs required typically increases with increased complexity of analysis, and the ability to obtain or verify those inputs needs to be considered. The approach needs to establish the relationship between parameters or processes and contaminant transport and the detail of the description needed to meet the analysis objectives. Assessing the modeling complexity associated with the appropriate level of detail can be facilitated by considering complexity of the 1) modeling approach, 2) description of process, and 3) description of heterogeneity. As demonstrated by case studies, using a framework to guide determination of modeling and analysis complexity facilitates selection of the appropriate approach for the site situation. (authors)

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

Advancing stream temperature prediction with a generalizable large-sample framework across CONUS river reaches

Accurately predicting stream temperature in ungauged basins remains a critical challenge for water resource management, thermoelectric power plant cooling, and ecosystem conservation. Large-sample machine learning models trained on hundreds of well-monitored river basins have shown remarkable performance; however, such models have yet to be developed solely using forcing data that can be readily extracted to simulate stream temperatures anywhere in the contiguous United States (CONUS). In this study, we present a scalable, large-sample deep learning framework using Long Short-Term Memory (LSTM) networks to simulate daily stream temperatures in ungauged basins across the CONUS. The framework leverages both modeled reanalysis of meteorological and streamflow inputs as well as static attributes available for all 2.7 million CONUS river reaches in the National Hydrography Dataset Plus (NHDPlusV2). By generating dynamical inputs from predefined thermally relevant upstream contributing areas, rather than the entire upstream basin, the model also offers improvements in very large basins where full-basin averaging can dilute the most important influences on stream temperature. Evaluated across 300 basins, the model achieves a median Mean Absolute Error (MAE) of 1.1 °C and a Nash-Sutcliffe Efficiency (NSE) of 0.95 on temporally and spatially distinct test folds—comparable to models trained exclusively using meteorological and streamflow observational data. The flexible, high-performing framework generalizes to any unmonitored river reach without significant regulation or unnatural thermal input immediately upstream, substantially expanding predictive capabilities in data-scarce regions.

Hydrology↗