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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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DOE OSTI2019

Future Grid State Modeling for Transactive Systems

The simulation design and evaluation of a transactive energy system is contingent on the setting in which it would exist, which could vary significantly with the many drivers that impact the potential future states of the power system. This report summarizes the development of the Transactive Future Grid State (TFGS) model, which is a lightweight capacity expansion model that will support transactive energy research by allowing users to explore alternative future states. The TFGS model determines the long-term generation capacity needs of a given power system that satisfy specified constraints and support a least cost hourly dispatch. This is accomplished by solving a single optimization problem that simultaneously finds the generation fleet with capacity sufficient to meet peak load and the flexibility to meet hourly operational dispatch, all at lowest cost. The TFGS model was applied to a larger ongoing study taking place that will be simulating how distribution system operators (DSOs) could use a transactive energy system to manage distributed energy resources. The larger study, commonly referred to as the DSO+T study, involves two parallel analyses; one that represents the system today and one that represents a future system with a high degree of renewable energy penetration. The TFGS model provided the generation mix of the high renewable base case. As the TFGS model was being used to provide inputs to the DSO+T study, two issues became apparent. First, additional functionality would have been useful, and second, a larger-than-anticipated amount of user inputs and data were required to successfully use the TFGS model. These discoveries have lead to the recommendation of specific functionality that can be added in future development of the tool.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Machine Learning Surrogates of a Fuel Matrix Degradation Process Model for Performance Assessment of a Nuclear Waste Repository

Spent nuclear fuel repository simulations are currently not able to incorporate detailed fuel matrix degradation (FMD) process models due to their computational cost, especially when large numbers of waste packages breach. The current paper uses machine learning to develop artificial neural network and k-nearest neighbor regression surrogate models that approximate the detailed FMD process model while being computationally much faster to evaluate. Further, using fuel cask temperature, dose rate, and the environmental concentrations of CO 3 2- , O 2 , Fe 2+ , and H 2 as inputs, these surrogates show good agreement with the FMD process model predictions of the UO 2 degradation rate for conditions within the range of the training data. A demonstration in a full-scale shale repository reference case simulation shows that the incorporation of the surrogate models captures local and temporal environmental effects on fuel degradation rates while retaining good computational efficiency.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

A robust deep learning workflow to predict multiphase flow behavior during geological C O 2 sequestration injection and Post-Injection periods

Simulation of multiphase flow in porous media is essential to manage the geologic CO 2 sequestration (GCS) process, and physics-based simulation approaches usually take prohibitively high computational cost due to the nonlinearity of the coupled physics. This paper contributes to the development and evaluation of a deep learning workflow that accurately and efficiently predicts the temporal-spatial evolution of pressure and CO 2 plumes during injection and post-injection periods of GCS operations. Based on a Fourier Neural Operator, the deep learning workflow takes input variables or features including rock properties, well operational controls and time steps, and predicts the state variables of pressure and CO 2 saturation. To further improve the predictive fidelity, separate deep learning models are trained for CO 2 injection and post-injection periods due to the difference in primary driving force of fluid flow and transport during these two phases. We also explore different combinations of features to predict the state variables. We use a realistic example of CO 2 injection and storage in a 3D heterogeneous saline aquifer, and apply the deep learning workflow that is trained from physics-based simulation data and emulate the physics process. Through this numerical experiment, we demonstrate that using two separate deep learning models to distinguish post-injection from injection period generates the most accurate prediction of pressure, and a single deep learning model of the whole GCS process including the cumulative injection volume of CO 2 as a deep learning feature, leads to the most accurate prediction of CO 2 saturation. For the post-injection period, it is key to use cumulative CO 2 injection volume to inform the deep learning models about the total carbon storage when predicting either pressure or saturation. The deep learning workflow not only provides high predictive fidelity across temporal and spatial scales, but also offers a speedup of 250 times compared to full physics reservoir simulation, and thus will be a significant predictive tool for engineers to manage the long-term process of GCS.

58 GEOSCIENCES↗
DOE OSTI2026

Interface and Thermophysical Properties of R 32 Refrigerant

Driven by the urgent demand for efficient cooling in microelectronics and advanced thermal management systems, difluoromethane (R32/CH 2 F 2 ) has emerged as a promising candidate owing to its favorable thermophysical properties, including high heat transfer efficiency and low viscosity. While bulk properties such as density, viscosity, and thermal conductivity have been widely studied, interfacial properties, including surface tension and interfacial thickness, remain comparatively underexplored, despite their importance in phase-transition dynamics. Here, we perform molecular dynamics (MD) simulations from 180 to 300 K using an optimized transferable force field for fluoropropenes with enhanced electrostatics to assess both bulk and interfacial behavior of R32. Simulations reproduced density within ±2.1%, viscosity within 3.05%, and thermal conductivity within 7.41% of NIST reference data. Heat capacities (C p and C v ) were predicted within 5%. For interfacial properties, surface tension trends were reproduced within 13.58% deviation, and the vapor–liquid coexistence curve closely matched reference data, yielding a critical temperature of 345.7 K (1.6% deviation) and a critical density of 0.397 g/cm 3 (6.4% deviation). Importantly, the vapor–liquid interface exhibited pronounced temperature-dependent broadening across the 180–290 K range. This behavior correlates with increasing molecular kinetic energy, reduction in intermolecular cohesive interactions, and a progressive loss of preferential dipole alignment, which collectively enhance thermal fluctuation amplitudes at elevated temperatures. These validated results provide predictive molecular-level insights, particularly for interfacial properties that remain less characterized. By reducing property prediction errors in key parameters such as critical temperature, this work provides reliable inputs for heat-exchanger and system models. Such correlations can support optimized component sizing, improved performance, and reduced refrigerant charge. Beyond R32, the methodology offers a transferable framework for blended and next-generation low-GWP refrigerants, contributing to sustainable thermal management aligned with the 2027 EU F-Gas regulation and 2030 Kigali Amendment.

Fluids↗
DOE OSTI2022

A comprehensive and fair comparison of two neural operators (with practical extensions) based on $\mathrm{FAIR}$ data

Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering. Herein, we investigate the performance of two neural operators, which have shown promising results so far, and we develop new practical extensions that will make them more accurate and robust and importantly more suitable for industrial-complexity applications. The first neural operator, DeepONet, was published in 2019 (Lu et al., 2019), and its original architecture was based on the universal approximation theorem of Chen & Chen (1995). The second one, named Fourier Neural Operator or FNO, was published in 2020, and it is based on parameterizing the integral kernel in the Fourier space. DeepONet is represented by a summation of products of neural networks (NNs), corresponding to the branch NN for the input function and the trunk NN for the output function; both NNs are general architectures, e.g., the branch NN can be replaced with a CNN or a ResNet. According to Kovachki et al. (2021), FNO in its continuous form can be viewed conceptually as a DeepONet with a specific architecture of the branch NN and a trunk NN represented by a trigonometric basis. In order to compare FNO with DeepONet computationally for realistic setups, we develop several extensions of FNO that can deal with complex geometric domains as well as mappings where the input and output function spaces are of different dimensions. We also develop an extended DeepONet with special features that provide inductive bias and accelerate training, and we present a faster implementation of DeepONet with cost comparable to the computational cost of FNO, which is based on the Fast Fourier Transform. Here we consider 16 different benchmarks to demonstrate the relative performance of the two neural operators, including instability wave analysis in hypersonic boundary layers, prediction of the vorticity field of a flapping airfoil, porous media simulations in complex-geometry domains, etc. We follow the guiding principles of FAIR (Findability, Accessibility, Interoperability, and Reusability) for scientific data management and stewardship. The performance of DeepONet and FNO is comparable for relatively simple settings, but for complex geometries the performance of FNO deteriorates greatly. We also compare theoretically the two neural operators and obtain similar error estimates for DeepONet and FNO under the same regularity assumptions.

42 ENGINEERING↗
DOE OSTI2012

Prioritization Risk Integration Simulation Model (PRISM) For Environmental Remediation and Waste Management - 12097

The PRISM (Prioritization Risk Integration Simulation Model), a computer model was developed to support the Department of Energy's Office of Environmental Management (DOE-EM) in its mission to clean up the environmental legacy from the Nation's nuclear weapons materials production complex. PRISM provides a comprehensive, fully integrated planning tool that can tie together DOE-EM's projects. It is designed to help DOE managers develop sound, risk-informed business practices and defend program decisions. It provides a better ability to understand and manage programmatic risks. The underlying concept for PRISM is that DOE-EM 'owns' a portfolio of environmental legacy obligations (ELOs), and that its mission is to transform the ELOs from their current conditions to acceptable conditions, in the most effective way possible. There are many types of ELOs - - contaminated soils and groundwater plumes, disused facilities awaiting D and D, and various types of wastes waiting for processing or disposal. For a given suite of planned activities, PRISM simulates the outcomes as they play out over time, allowing for all key identified uncertainties and risk factors. Each contaminated building, land area and waste stream is tracked from cradle to grave, and all of the linkages affecting different waste streams are captured. The progression of the activities is fully dynamic, reflecting DOE-EM's prioritization approaches, precedence requirements, available funding, and the consequences of risks and uncertainties. The top level of PRISM is the end-user interface that allows rapid evaluation of alternative scenarios and viewing the results in a variety of useful ways. PRISM is a fully probabilistic model, allowing the user to specify uncertainties in input data (such as the magnitude of an existing groundwater plume, or the total cost to complete a planned activity) as well as specific risk events that might occur. PRISM is based on the GoldSim software that is widely used for risk and performance assessment calculations. PRISM can be run in a deterministic mode, which quickly provides an estimate of the most likely results of a given plan. Alternatively, the model can be run probabilistically in a Monte Carlo mode, exploring the risks and uncertainties in the system and producing probability distributions for the different performance measures. The PRISM model demonstrates how EM can evaluate a portfolio of ELOs, and transform the ELOs from their current conditions to acceptable conditions, utilizing different strategic approaches. There are many types of ELOs - contaminated soils and groundwater plumes, disused facilities awaiting D and D, and various types of wastes waiting for processing or disposal. This scope of work for the PRISM process and the development of a dynamic simulation model are a logical extension of the GoldSim simulation software used by the OCRWM to assess the long-term performance for the Yucca Mountain Project and by NNSA to assess project risk at its sites. Systems integration modeling will promote better understanding of all project risks, technical and nontechnical, and more defensible decision-making for complex projects with significant uncertainties. It can provide effective visual communication and rapid adaptation during interactions with stakeholders (Administration, Congress, State, Local, and NGO). It will also allow rapid assessment of alternative management approaches. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

PHASM: A Toolkit for Creating AI Surrogate Models within Legacy Codebases

PHASM (“Parallel Hardware viA Surrogate Models”) is a software toolkit for creating AI-based surrogate models of scientific code. AI-based surrogate models are widely used for creating fast and inverse simulations. PHASM anticipates an additional future use case: adapting legacy code to modern hardware. While data centers are investing in heterogeneous hardware such as GPUs and FPGAs, many established scientific codebases remain unable to take advantage of the hardware’s higher parallelism without undergoing a costly rewrite. An alternative is to train a AI-based surrogate model to mimic computationally intensive functions in the code, and run the surrogate instead. PHASM formalizes a development lifecycle for such surrogate models, including discovering functions amenable to replacement with a surrogate model, predicting the resulting performance, identifying the function’s space of inputs and outputs, binding the model to the code, and managing model versions. A suite of software tools for facilitating these steps was written and validated against a set of model problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Technology Development: From Idea to Implementation - 12131

There are good ideas and new technologies proposed every day to solve problems within the DOE complex. A process to transition a new technology from inception to the decision to launch a project with baselines is described. Examples from active technology development projects within Savannah River Remediation (SRR) will be used to illustrate the points. The process includes decision points at key junctures leading to preliminary design. At that point, normal project management tools can be employed. The technology development steps include proof-of-principle testing, scaled testing and analysis, and conceptual design. Tools are used that define the scope necessary for each step of technology development. The tools include use of the DOE technology readiness guide, Consolidated Hazards Analysis (CHA) and internal checklists developed by Savannah River Remediation. Integration with operating or planned facilities is also included. The result is a roadmap and spreadsheet that identifies each open question and how it may be answered. Performance criteria are developed that enable simple decisions to be made after the completion of each step. Conceptual design tasks should begin as the technology development continues. The most important conceptual design tasks at this point in the process include process flow diagrams (PFDs), high level Process and Instrumentation Drawings (P and IDs), and general layout drawings. These should influence the design of the scaled simulant testing. Mechanical and electrical drawings that support cost and schedule development should also be developed. An early safety control strategy developed from the CHA will also influence the cost. The combination of test results, calculations and early design output with rough order of magnitude cost and schedule information provide input into the decisions to proceed with a project and data to establish the baseline. This process can be used to mature any new technology, especially those that must be integrated into complicated flow sheets. Then fully informed decisions can be made to usher technology development ideas through project implementation. Examples from active technology development projects, Tank 48 Treatment and Enhanced Chemical Cleaning, are presented. This model for technology development integrates several tools and activities to provide a logical method for maturing new ideas and technologies. It provides a framework to develop a good idea into a full up design/build project. This model defines a set of integrated tasks to mature the technology and define scope, cost and schedule necessary to make business decisions for implementing technology development. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

A Thermal Model for the Performance Analysis of Large Scale Spent Fuel Cooling Pond - 19173

This paper presents a thermal model for the performance analysis of a large scale cooling pond for the long term storage of spent nuclear fuel. The model has been developed using the knowledge gained from fundamental investigation using CFD and has been validated by comparing against reliable experimental and/or numerical data. It was then applied in the context of operation of a large scale pond to ascertain the sensitivity of various operational and environmental conditions. To develop the model, the water body along with the heating elements was considered as one zone and humid air inside the building as another zone, both of which are considered as well-mixed zones. Various input conditions namely, total heat load from the heat source, environmental temperature and relative humidity, ventilation rate, efficiency of the cooling tower, recirculation and make-up water flow rates have been programmed in the model. The statistical method AVONA has been used to assess the influence of the above variables. The results reveal that the efficiency of the cooling tower is the most influential parameter on the cooling performance of cooling pond within the considered values of heat load. It has also been found that the majority of the heat is taken by the recirculating water for very high heat load while the other modes of heat transfer are small but not insignificant. The model is capable of simulating the scenario of loss of cooling water and suggests that it can be used for managing a large pond efficiently as well as recovery from an accident situation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Comprehensive, Quantitative Risk Assessment of CO 2 Geologic Sequestration (Final Technical Report)

A Quantitative Failure Modes and Effects Analysis (QFMEA) was developed to conduct comprehensive, quantitative risk assessments on CO 2 capture, transportation, and sequestration or use in deep saline aquifers, enhanced oil recovery operations, or enhanced coal bed methane operations. The model identifies and characterizes potential risks; identifies the likely failure modes, causes, effects and methods of detection; lists possible risk prevention and risk mitigation steps; estimates potential damage recovery costs, mitigation costs and costs savings resulting from mitigation; and ranks (prioritizes) risks according to the probability of failure, the severity of failure, the difficulty of early failure detection and the potential for fatalities. The QFMEA model generates the necessary information needed for effective project risk management. Diverse project information can be integrated into a concise, common format that allows comprehensive, quantitative analysis, by a cross-functional team of experts, to determine: What can possibly go wrong? How much will damage recovery cost? How can it be prevented or mitigated? What is the cost savings or benefit of prevention or mitigation? Which risks should be given highest priority for resolution? The QFMEA model can be tailored to specific projects and is applicable to new projects as well as mature projects. The model can be revised and updated as new information comes available. It accepts input from multiple sources, such as literature searches, site characterization, field data, computer simulations, analogues, process influence diagrams, probability density functions, financial analysis models, cost factors, and heuristic best practices manuals, and converts the information into a standardized format in an Excel spreadsheet. Process influence diagrams, geologic models, financial models, cost factors and an insurance schedule were developed to support the QFMEA model. Comprehensive, quantitative risk assessments were conducted on three (3) sites using the QFMEA model: (1) SACROC Northern Platform CO 2 -EOR Site in the Permian Basin, Scurry County, TX, (2) Pump Canyon CO 2 -ECBM Site in the San Juan Basin, San Juan County, NM, and (3) Farnsworth Unit CO 2 -EOR Site in the Anadarko Basin, Ochiltree County, TX. The sites were sufficiently different from each other to test the robustness of the QFMEA model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Model Package Report: Central Plateau Vadose Zone Models

This model package report describes concisely the modeling objectives, conceptualization, implementation, uncertainty and sensitivity, configuration control, limitations, data needs, and recommendations for improvement of the Central Plateau Vadose Zone Models. This collection of models is developed to meet the vadose zone simulation needs of the Hanford Site Composite Analysis (CA), the Hanford Site Cumulative Impact Evaluation (CIE), and are expected to find other applications in Hanford Site remedial cleanup decision-making processes. This model package report describes and documents the development of the models themselves to fulfill technical approaches defined for the CA and the CIE. This report does not document any specific calculation using these models: instead, applications of individual vadose models to perform specific calculations will be documented in environmental calculation files, including inputs and results, as appropriate. Configuration management for the Central Plateau Vadose Zone Models is managed through assignment of unique sequential numerical version numbers and archival of the models in the Environmental Modeling Management Archive.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Inception Based Deep Convolutional Neural Network for Remaining Useful Life Estimation of Turbofan Engines

Accurate estimation of the remaining useful life (RUL) is a key component of condition based maintenance (CBM) and prognosis and health management (PHM). Data-based models for the estimation of RUL are of particular interest because expert knowledge of systems is not always available and physical modeling is often not feasible. In this paper, a deep convolutional neural network (CNN) architecture is investigated for its ability to estimate the RUL of turbofan engines. The input to the model is a window of time series data collected from the engine under test. Inputting raw sensor data allows features to be learned instead of manually determined. To incorporate the ability to detect features of differing lengths, inception modules are used in the neural network architecture. The model is trained and tested using the new Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) data set and high prognosis accuracy was achieved. The developed model was used in the 2021 PHM Society Data Challenge and received second place, further validating its ability to accurately estimate RUL.

DeVol, Nathaniel↗
DOE OSTI2016

Developing a New Model to Estimate the Costs to Decommission DOE's Active Facilities - 16368

The Active Facilities Data Collection System (AFDCS) is a major means the U.S. Department of Energy (DOE or Department) uses to estimate its out-year environmental liability for the active and surplus facilities across the Department. The previous AFDCS cost model was based on cost assumptions that date back to the mid-1990s. The model is being replaced with a model that reflects more recent facility disposition experience and costs. DOE's Office of Environmental Management's (DOE-EM) historical cost collection database provides the basis for the new AFDCS cost model. This paper describes the process that was used to update the AFDCS facility data inputs, analyze this data, and formulate historical costs and parameters. This process created a robust and reliable cost model that will provide DOE with a valid estimate of the facilities' disposition costs. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Assessment of tank designs for hydrogen storage on heavy duty vehicles using metal hydrides

The objective of this project was to evaluate material-based hydrogen storage solutions as a replacement for high-pressure hydrogen gas or liquid hydrogen on Class 7 or 8 tractor fuel cell electric vehicles. The project focused on low-density main-group hydrides, a well-known class of materials for hydrogen storage. Prior research has considered metal amides as storage materials for light-duty vehicles but not for heavy-duty applications. The project established the basis for further development of storage systems of this type for heavy duty vehicles (HDV). Systems analysis of an HDV storage system comprised of a tank and associated balance of plant (piping, coolant tubes, burner) was performed to determine the usable hydrogen capacity. A composite storage material comprised of a metal hydride mixed with a high thermal-conductivity carbon is predicted to have a usable hydrogen volumetric capacity comparable to or exceeding that of 700 bar pressurized hydrogen gas. The gravimetric capacity of this material is also predicted to be competitive with pressurized gas, particularly if costly carbon fiber composite Type III or Type IV tanks are excluded. The storage system design parameters and material properties served as inputs to a second model that simulates fuel cell operation in conjunction with the storage system during an HDV drive cycle. The results show that sufficient hydrogen pressure can be produced to operate a Class 8 HDV, yielding a range of ~480 miles. These results are particularly relevant for high-impact regions, such as the South Coast Air Quality Management District, for which an economical vehicular hydrogen storage system with minimal impact on cargo capacity could accelerate adoption of heavy-duty fuel cell electric vehicles. An additional benefit is that knowledge generated by this project can assist in development of material-based storage for stationary applications such as microgrids and backup power for data centers.

08 HYDROGEN↗
DOE OSTI2015

Characterization of the Uranium-Bearing Products of the Ammonia Injection Remediation Method - 15707

Commissioned in 1943, the Hanford Site is the home of the world's first full-scale nuclear production facility. After more than 40 years of processing, improper discharge and failed storage tanks have resulted in the contamination of the Hanford vadose zone with radiologically contaminated waste. Remediation methods considered for the area include sequestration of the mobile uranium by injection of ammonia (NH{sub 3}) gas. Injection of this reactive gas increases the pore water pH, promoting the dissolution of soil minerals; a subsequent re-establishment of natural conditions is believed to result in the recrystallization of those soil minerals and the co-precipitation of uranium phases. Characterization of the precipitates formed when the ammonia (NH{sub 3}) injection method, proposed for the Hanford vadose zone, is applied to synthetic pore water on a laboratory scale. This involves: Identification of the uranium-bearing phases; A study of the effect of pore water constitution on the phases produced. Synthetic pore water solutions were prepared to mimic selected constituents of the pore water from the Hanford 200 Area: Varying constituent concentrations; Observations led to modifications to increase relative uranium content: 200 ppm → 500 ppm U; Substituted sodium silicate for silicic acid. Dried samples were analyzed by SEM-EDS and powder X-ray diffraction (XRD). A suspension in ethanol was prepared and analyzed by Transmission Electron Microscope (TEM) with Selective Area Electron Diffraction (SAED). Studied and applied geochemical modeling software (Visual Minteq and Geo-chemist's Workbench) for speciation and prediction of potential species formed. Discussion/Conclusions: - SEM-EDS Analysis: Scanning electron microscopy revealed areas of high average atomic weight which would be confirmed to be uranium-rich by energy dispersive spectroscopy analysis. The samples prepared with a more than two-fold increase in uranium concentration did not show the crystal-like forms spotted in the lower [U] samples. The samples prepared with silicic acid substituted in showed little to no uranium precipitation [not shown]. - Diffraction Analysis: Powder XRD analysis of the low [U] samples confirmed the presence of repeating crystalline patterns in the precipitate samples. Nitratine (NaNO{sub 3}) showed a massive presence, potentially obscuring significant peaks in the low [U] samples. Though it was not predicted, there was a tentative match for cejkaite (Na{sub 4}(UO{sub 2})(CO{sub 3}){sub 3}). Elevated uranium samples showed none of the needle-like crystalline shapes [diffraction data pending]. TEM - SAED confirmed the presence of a polycrystalline phase. Data analysis is complex but a comparison of average d-spacings with those of reference minerals could further support a match with cejkaite. - Geochemical Modeling: The system conditions were input into geochemical modeling software to predict the forms present throughout the reaction process. The model was used to demonstrate that the pH of the system could be increased using [NH{sub 4}{sup +}] in solution to better represent the system. Using the [NH{sub 4}{sup +}] increase, the changes in significant species with increasing pH were observed. Future Work: Repeat samples will be prepared and analyzed to bolster any potential identification. Promising samples will be submitted to the PNNL's EMSL facility, which is better equipped to perform the required analyses. An expanded study on the impact of carbonate (CO{sub 3}{sup 2-}) on the phases produced.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

ORIGEN-based Nuclear Fuel Inventory Module for Fuel Cycle Assessment: Final Project Report

The goal of this project, “ORIGEN-based Nuclear Fuel Depletion Module for Fuel Cycle Assessment" is to create a physics-based reactor depletion and decay module for the Cyclus nuclear fuel cycle simulator in order to assess nuclear fuel inventories over a broad space of reactor operating conditions. The overall goal of this approach is to facilitate evaluations of nuclear fuel inventories for a broad space of scenarios, including extended used nuclear fuel storage and cascading impacts on fuel cycle options such as actinide recovery in used nuclear fuel, particularly for multiple recycle scenarios. The advantages of a physics-based approach (compared to a recipe-based approach which has been typically employed for fuel cycle simulators) is in its inherent flexibility; such an approach can more readily accommodate the broad space of potential isotopic vectors that may be encountered under advanced fuel cycle options. In order to develop this flexible reactor analysis capability, we are leveraging the Origen nuclear fuel depletion and decay module from SCALE to produce a standalone “depletion engine” which will serve as the kernel of a Cyclus-based reactor analysis module. The ORIGEN depletion module is a rigorously benchmarked and extensively validated tool for nuclear fuel analysis and thus its incorporation into the Cyclus framework can bring these capabilities to bear on the problem of evaluating long-term impacts of fuel cycle option choices on relevant metrics of interest, including materials inventories and availability (for multiple recycle scenarios), long-term waste management and repository impacts, etc. Developing this Origen-based analysis capability for Cyclus requires the refinement of the Origen analysis sequence to the point where it can reasonably be compiled as a standalone sequence outside of SCALE; i.e., wherein all of the computational aspects of Origen (including reactor cross-section library processing and interpolation, input and output processing, and depletion/decay solvers) can be self-contained into a single executable sequence. Further, to embed this capability into other software environments (such as the Cyclus fuel cycle simulator) requires that Origen’s capabilities be encapsulated into a portable, self-contained library which other codes can then call directly through function calls, thereby directly accessing the solver and data processing capabilities of Origen. Additional components relevant to this work include modernization of the reactor data libraries used by Origen for conducting nuclear fuel depletion calculations. This work has included the development of new fuel assembly lattices not previously available (such as for CANDU heavy-water reactor assemblies) as well as validation of updated lattices for light-water reactors updated to employ modern nuclear data evaluations. The CyBORG reactor analysis module as-developed under this workscope is fully capable of dynamic calculation of depleted fuel compositions from all commercial U.S. reactor assembly types as well as a number of international fuel types, including MOX, VVER, MAGNOX, and PHWR CANDU fuel assemblies. In addition, the Origen-based depletion engine allows for CyBORG to evaluate novel fuel assembly and reactor design types via creation of Origen reactor data libraries via SCALE. The establishment of this new modeling capability affords fuel cycle modelers a substantially improved ability to model dynamically-changing fuel cycle and reactor conditions, including recycled fuel compositions from fuel cycle scenarios involving material recycle into thermal-spectrum systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2012

Application of 'Six Sigma{sup TM}' and 'Design of Experiment' for Cementation - Recipe Development for Evaporator Concentrate for NPP Ling AO, Phase II (China) - 12555

Cementation of radioactive waste is a common technology. The waste is mixed with cement and water and forms a stable, solid block. The physical properties like compression strength or low leach ability depends strongly on the cement recipe. Due to the fact that this waste cement mixture has to fulfill special requirements, a recipe development is necessary. The Six Sigma{sup TM}' DMAIC methodology, together with the Design of experiment (DoE) approach, was employed to optimize the process of a recipe development for cementation at the Ling Ao nuclear power plant (NPP) in China. The DMAIC offers a structured, systematical and traceable process to derive test parameters. The DoE test plans and statistical analysis is efficient regarding the amount of test runs and the benefit gain by getting a transfer function. A transfer function enables simulation which is useful to optimize the later process and being responsive to changes. The DoE method was successfully applied for developing a cementation recipe for both evaporator concentrate and resin waste in the plant. The key input parameters were determined, evaluated and the control of these parameters were included into the design. The applied Six Sigma{sup TM} tools can help to organize the thinking during the engineering process. Data are organized and clearly presented. Various variables can be limited to the most important ones. The Six Sigma{sup TM} tools help to make the thinking and decision process trace able. The tools can help to make data driven decisions (e.g. C and E Matrix). But the tools are not the only golden way. Results from scoring tools like the C and E Matrix need close review before using them. The DoE is an effective tool for generating test plans. DoE can be used with a small number of tests runs, but gives a valuable result from an engineering perspective in terms of a transfer function. The DoE prediction results, however, are only valid in the tested area. So a careful selection of input parameter and their limits for setting up a DoE is very important. An extrapolation of results is not recommended because the results are not reliable out of the tested area. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

The Dark Energy Survey Data Processing and Calibration System

The Dark Energy Survey (DES) is a 5000 deg2 grizY survey reaching characteristic photometric depths of 24th magnitude (10 sigma) and enabling accurate photometry and morphology of objects ten times fainter than in SDSS. Preparations for DES have included building a dedicated 3 deg2 CCD camera (DECam), upgrading the existing CTIO Blanco 4m telescope and developing a new high performance computing (HPC) enabled data management system (DESDM). The DESDM system will be used for processing, calibrating and serving the DES data. The total data volumes are high (~2PB), and so considerable effort has gone into designing an automated processing and quality control system. Special purpose image detrending and photometric calibration codes have been developed to meet the data quality requirements, while survey astrometric calibration, coaddition and cataloging rely on new extensions of the AstrOmatic codes which now include tools for PSF modeling, PSF homogenization, PSF corrected model fitting cataloging and joint model fitting across multiple input images. The DESDM system has been deployed on dedicated development clusters and HPC systems in the US and Germany. An extensive program of testing with small rapid turn-around and larger campaign simulated datasets has been carried out. The system has also been tested on large real datasets, including Blanco Cosmology Survey data from the Mosaic2 camera. In Fall 2012 the DESDM system will be used for DECam commissioning, and, thereafter, the system will go into full science operations.

79 ASTRONOMY AND ASTROPHYSICS↗