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DOE OSTI2023

Challenges in Demonstrating Safety Criteria Satisfaction for External Events under a Risk-Informed Performance-Based Framework

The NRC is currently developing 10 CFR Part 53 that would establish a technology-inclusive regulatory framework for use by applicants for new commercial advanced nuclear reactors. The new draft rule language brings new explicit regulatory requirements of the quantitative health objectives (QHOs) as a constraint on plant risk and safety criteria for the plant design, including external hazards. Historically, light water reactors (LWRs) have demonstrated satisfaction of the QHOs through the use of surrogates in the form of CDF/LERF. However, these risk surrogates are not broadly applicable for all advanced reactor types, and for advanced reactors where external events are likely to be the dominant contributor to plant risk, a direct calculation of the QHOs in terms of offsite consequence is likely necessary. While the detailed analysis of low frequency events provides insights that can help justify alternative treatments to past conservatisms, the findings are dependent on the quality and confidence associated with the analyses. The assessment of external hazards presents a unique challenge, as their potential frequency of occurrence, especially of largemagnitude events, is inherently uncertain given the long return periods in question. This paper evaluates the QHOs and their potential role as safety criteria in evaluating external event risk as it applies to advanced reactors to aid in the development of consistent and appropriate analysis methodologies.

Chen, Ben↗

DOE OSTI2020

Encapsulation and retention of 225 Ac, 223 Ra, 227 Th, and decay daughters in zircon-type gadolinium vanadate nanoparticles

Abstract Unwanted targeting of healthy organs caused by the relocation of radionuclides from the target site has been one of the limiting factors in the widespread application of targeted alpha therapy in patient regimens. GdVO 4 nanoparticles (NPs) were developed as platforms to encapsulate α -emitting radionuclides 223 Ra, 225 Ac, and 227 Th, and retain their decay daughters at the target site. Polycrystalline GdVO 4 NPs with different morphologies and a zircon-type tetragonal crystal structure were obtained by precipitation of GdCl 3 and Na 3 VO 4 in aqueous media at room temperature. The ability of GdVO 4 crystals to host multivalent ions was initially assessed using La, Cs, Bi, Ba, and Pb as surrogates of the radionuclides under investigation. A decrease in Ba encapsulation was obtained after increasing the concentration of surrogate ions, whereas the encapsulation of La cations in GdVO 4 NPs was quantitative (∼100%). Retention of radionuclides was assessed in vitro by dialyzing the radioactive GdVO 4 NPs against deionized water. While 227 Th was quantitatively encapsulated (100%), a partial encapsulation of 223 Ra (∼75%) and 225 Ac (>60%) was observed in GdVO 4 NPs. The maximum leakage of 221 Fr (1st decay daughter of 225 Ac) was 55.4 ± 3.6%, whereas for 223 Ra (1st decay daughter of 227 Th) the maximum leakage was 73.0 ± 4.0%. These results show the potential of GdVO 4 NPs as platforms of α -emitting radionuclides for their application in targeted alpha therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2024

Data and scripts associated with a manuscript on residence time distribution simulation in two 10-kilometer long river sections

This data package is associated with the publication “On the Transferability of Residence Time Distributions in Two 10-km Long River Sections with Similar Hydromorphic Units” submitted to the Journal of Hydrology (Bao et al. 2024).Quantifying hydrologic exchange fluxes (HEFs) at the stream-groundwater interface, along with their residence time distributions (RTDs) in the subsurface, is crucial for managing water quality and ecosystem health in dynamic river corridors. However, directly simulating high-spatial resolution HEFs and RTDs can be a time-consuming process, particularly for watershed-scale modeling. Efficient surrogate models that link RTDs to hydromorphic units (HUs) may serve as alternatives for simulating RTDs in large-scale models. One common concern with these surrogate models, however, is the transferability of the relationship between the RTDs and HUs from one river corridor to another. To address this, we evaluated the HEFs and the resulting RTD-HU relationships for two 10-kilometer-long river corridors along the Columbia River, using a one-way coupled three-dimensional transient surface-subsurface water transport modeling framework that we previously developed. Applying this framework to the two river corridors with similar HUs allows for quantitative comparisons of HEFs and RTDs using both statistical tests and machine learning classification models. This data package includes the model inputs files and the simulation results data. This data package contains 10 folders. The modeling simulation results data are in the folders 100H_pt_data and 300area_pt_data, for the study domain Hanford 100H and 300 area respectively. The remaining eight folders contain the scripts and data to generate the manuscript figures. The file-level metadata file (Bao_2024_Residence_Time_Distribution _flmd.csv) includes a list of all files contained in this data package and descriptions for each. The data dictionary file (Bao_2024_Residence_Time_Distribution _dd.csv) includes column header definitions and units of all tabular files.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Full-Scale Assembly 30 cm Drop Test

Can Spent Nuclear Fuel withstand the shocks and vibrations experienced during normal conditions of transport? This question was the motivation for the multi-modal transportation test (MMTT) (Summer 2017), 1/3-scale cask 30 cm drop test (December 2018), and full-scale assembly 30 cm drop tests (June 2019). The full-scale ENSA ENUN 32P cask with 3 surrogate 17x17 PWR assemblies was used in the MMTT. The 1/3-scale cask was a mockup of this cask. The 30 cm drop tests provided the accelerations on the 1/3-scale dummy assemblies. These data were used to design full-scale assembly drop tests with the goal to quantify the strain fuel rods experience inside a cask when dropped from a height of 30 cm. The drop tests were first done with the dummy and then with the surrogate assembly. This paper presents the preliminary results of the tests.

Transportation↗

DOE OSTI2021

A Bayesian Calibration Framework with Embedded Model Error for Model Diagnostics

We study the utility and performance of a Bayesian model error embedding construction in the context of molecular dynamics modeling of metallic alloys, where we embed model error terms in existing interatomic potential model parameters. To alleviate the computational burden of this approach, we propose a framework combining likelihood approximation and Gaussian process surrogates. Here we leverage sparse Gaussian process techniques to construct a hierarchy of increasingly accurate but more expensive surrogate models. This hierarchy is then exploited by multilevel Markov chain Monte Carlo methods to efficiently sample from the target posterior distribution. We illustrate the utility of this approach by calibrating an interatomic potential model for a family of gold-copper alloys. In particular, this case study highlights effective means for dealing with computational challenges with Bayesian model error embedding in large-scale physical models, and the utility of embedded model error for model diagnostics.

Bayesian inference↗

DOE OSTI2023

Optimization of a cyclone using MFIX and Nodeworks

Video depicting the optimization process of a cyclone on NETL's chemical looping reactor (CLR) using MFIX and Nodeworks. MFIX is used to model the cyclone using PIC. Nodeworks is then used to generate proposed geometry changes using a Latin hypercube. Each design is simulated, with an objective value being computed based on the cyclone efficiency and pressure drop. A Gaussian Process surrogate model is then constructed from the objective values. This surrogate model is then used by a differential evolution optimization algorithm to identify the optimal cyclone design. Details published here: Weber, J., Fullmer, W., Gel, A., and Musser, J. (February 4, 2020). "Optimization of a Cyclone Using Multiphase Flow Computational Fluid Dynamics." ASME. J. Fluids Eng. March 2020; 142(3): 031111. https://doi.org/10.1115/1.4045952 OSTI: https://www.osti.gov/pages/servlets/purl/1763893

cyclone↗

DOE OSTI2024

IDAES-PSE 2.7.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.7.0 Release Highlights New features: AutoScaler and CustomScalerBase classes: Such tools are the core of the new scaling framework being implemented in IDAES. Wider adoption of scaling tools among users will result in quicker and more robust model solutions. Scaler for equilibrium reactor and saponification properties: These scaler models are examples to follow for how to use the new scaling tools. ONNX Surrogate support from Optimization & Machine Learning Toolkit (OMLT): ONNX is an open standard format to save and load ML/AI models that is widely supported by all major frameworks. This capability makes it easier for IDAES users to create surrogate models and use them without having to support each framework individually. 1D Membrane Model for CO2 Capture and Utilization: Supports ongoing efforts for modeling and optimizing polymer membrane processes for CO2 capture and conversion into formic acid. StreamScaler unit model: Unrelated to the CustomScalerBase, this unit model allows a stream’s extensive variables to be scaled by a fixed factor. This allows streams being processed by multiple units in parallel to be scaled down to unit scale and scaled back up to process scale. Bug fixes or improvements: Scaling, EoS, Diagnostics tool, Modular Properties, tests & documentation Deprecations: Old Cubic EoS

AS↗

DOE OSTI2025

APOLLO: a facility-scale differentiable virtual accelerator at Fermilab FAST/IOTA

As the design complexity of modern accelerators grows, there is more interest in using advanced simulations that have fast execution time or yield additional insights like gradients. The FAST/IOTA facility has been working on implementing and experimentally validating an end-to-end digital twin that is both fast and gradient-aware, allowing for rapid prototyping of new software and experiments with minimal beam time costs. Our framework integrates physics and ML codes for linac and ring simulation through a set of generic interfaces between surrogate and physics-based sections. To reproduce device inputs and outputs, system state is exposed as a deterministic event loop in a specialized discrete event simulator architecture. Because Fermilab is undergoing control system transition, several APIs were implemented as final user interfaces - a fully asynchronous EPICS soft IOC, a gRPC-based Data Pool Manager (DPM), and legacy ACNET protocols. We discuss implementation details as well as challenges handling live data assimilation and future plans to extend modelling to main complex proton accelerators like PIPII and Booster.

Kuklev, Nikita [Fermilab]↗

DOE OSTI2025

Digital twin framework for PIP-II linac: AI-driven multi-scale modeling from ion source to 800 MeV

The PIP-II superconducting linac at Fermilab is designed to deliver multi-megawatt proton beams for neutrino physics and other high-intensity applications. To expedite commissioning and enhance operational reliability, we have developed an EPICS-based data flow framework that seamlessly integrates digital twins (DT) with physical twins (PT). These digital twins comprise high-fidelity beam dynamics models or data-driven surrogate models connected to their physical counterparts through real-time diagnostics and advanced machine-learning algorithms.Central to this framework is Linac_Gen, an accelerated simulation tool that incorporates convolutional neural networks, random forests, and genetic algorithms to provide up to a tenfold speedup in optimizing the accelerator geometry model. An EPICS translator layer ensures interoperability by efficiently mapping lattice parameters across diverse simulation platforms.Our EPICS-based framework supports multiple operational modes—monitoring, passive learning, closed-loop control, and online learning—covering the entire machine lifecycle. By leveraging HPC resources and multi-objective optimization techniques, the digital twin enables adaptive trajectory correction, real-time fault detection, and predictive modeling of beam stability. This comprehensive approach paves the way for robust, high-intensity operation and data-driven accelerator R&D at Fermilab.

Pathak, Abhishek [Fermilab]↗

DOE OSTI2023

Mitigating the noise of DESI mocks using analytic control variates

In order to address fundamental questions related to the expansion history of the Universe and its primordial nature with the next generation of galaxy experiments, we need to model reliably large-scale structure observables such as the correlation function and the power spectrum. Cosmological N-body simulations provide a reference through which we can test our models, but their output suffers from sample variance on large scales. Fortunately, this is the regime where accurate analytic approximations exist. To reduce the variance, which is key to making optimal use of these simulations, we can leverage the accuracy and precision of such analytic descriptions using Control Variates (CV). The power of control variates stems from utilizing inexpensive but highly correlated surrogates of the statistics one wishes to measure. The stronger the correlation between the surrogate and the statistic of interest, the larger the variance reduction delivered by the method. We apply two control variate formulations to mock catalogs generated in anticipation of upcoming data from the Dark Energy Spectroscopic Instrument (DESI) to test the robustness of its analysis pipeline. Our CV-reduced measurements offer a factor of 5-10 improvement in the measurement error compared with the raw measurements. We explore the relevant properties of the galaxy samples that dictate this reduction and comment on the improvements we find on some of the derived quantities relevant to Baryon Acoustic Oscillation (BAO) analysis.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership

The 2005 Intergovernmental Panel on Climate Change (IPCC) Special Report on CCS raised the profile of CO2 capture and storage (CCS) as an important technology for reducing greenhouse gas (GHG) emissions. CCS is now recognized as a key component of most climate change mitigation scenarios. Since publication of that report the international research, development, and deployment (RD&D) community has advanced key technical aspects, clarified regulatory requirements, explored value chain and infrastructure solutions, and developed incentive paradigms to enable and promote large-scale deployment of CCS. These efforts have included research to better characterize geologic storage resources, to improve injection performance and storage efficiency, to assess and manage subsurface environmental risks, and to advance monitoring technologies to assure system conformance. These efforts have helped to build confidence in the viability of geologic carbon storage (GCS), but stakeholder concerns about long-term risks and liability associated with GCS remain a hurdle to broad acceptance and large-scale deployment of CCS. Since 2010, the U.S. DOE’s National Risk Assessment Partnership (NRAP) – a research collaboration between five contributing national laboratories – has worked to establish and demonstrate methods and tools to quantify and manage the subsurface environmental risks associated with GCS, amidst uncertainty. This work supports the Office of Fossil Energy and Carbon Management Carbon Transport and Storage Program’s goal of advancing safe and secure commercial-scale GCS deployment. To address the technical challenge of simulating the physical response of the GCS site to large-scale CO2 injection, NRAP has adopted an approach that relies on coupling computationally efficient reduced-order and/or data-driven proxy models of important system components (i.e., storage reservoir, sealing caprock, leakage pathways, intermediate formations, overlying groundwater aquifers, and the atmosphere) in integrated assessment framework. That integrated model of the physical system is complemented with fit-for purpose functionality to support site characterization and risk-related decisions. The recently released NRAP Phase II toolset includes the Open-Source Integrated Assessment Model (NRAP-Open-IAM) for evaluation of trends in leakage risk and potential impact, tools to support monitoring design optimization (Designs for Risk Evaluation and Management – DREAM v3.0 and Passive Seismic Monitoring Tool - PSMT), and tools for state of stress evaluation (State-of-Stress Analysis Tool - SOSAT) and forecasting induced seismicity risk. The NRAP team has also released a pair of reports describing conceptual workflows to incorporate physics-based, quantitative risk assessment into many of the design, planning, operation, and closure decisions for GCS projects. An online catalogue highlights published studies where these tools and methods are demonstrated. In this presentation, the utility of these products to assess risks and address key stakeholder questions will be highlighted through examples, and related insights about the safety and security of geologic carbon storage in qualified storage sites will be discussed. The prospect of rapid, large-scale deployment of GCS technology to aggressively reduce anthropogenic CO2 emissions requires careful consideration of interference between multiple commercial-scale storage projects within a basin. Going forward, NRAP is expanding and adapting site-scale risk quantification tools and methods to enable assessment of risks and inform management decisions for basin-scale deployment. Increasingly, this work will leverage next-generation approaches for surrogate modelling, fast prediction, and advanced visualization enabled by machine learning and artificial intelligence to promote virtual learning, scenario evaluation, and augment risk-based decision making.

quantitative risk assessment, geologic carbon stor↗

DOE OSTI2011

DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis.

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the DAKOTA software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of DAKOTA-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of DAKOTA's iterative analysis capabilities.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2013

Unreviewed Disposal Question Evaluation: Waste Disposal In Engineered Trench #3

Because Engineered Trench #3 (ET#3) will be placed in the location previously designated for Slit Trench #12 (ST#12), Solid Waste Management (SWM) requested that the Savannah River National Laboratory (SRNL) determine if the ST#12 limits could be employed as surrogate disposal limits for ET#3 operations. SRNL documented in this Unreviewed Disposal Question Evaluation (UDQE) that the use of ST#12 limits as surrogates for the new ET#3 disposal unit will provide reasonable assurance that Department of Energy (DOE) 435.1 performance objectives and measures (USDOE, 1999) will be protected. Therefore new ET#3 inventory limits as determined by a Special Analysis (SA) are not required.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2013

Fuel Assembly Shaker Test Simulation

This report describes the modeling of a PWR fuel assembly under dynamic shock loading in support of the Sandia National Laboratories (SNL) shaker test campaign. The focus of the test campaign is on evaluating the response of used fuel to shock and vibration loads that a can occur during highway transport. Modeling began in 2012 using an LS-DYNA fuel assembly model that was first created for modeling impact scenarios. SNL’s proposed test scenario was simulated through analysis and the calculated results helped guide the instrumentation and other aspects of the testing. During FY 2013, the fuel assembly model was refined to better represent the test surrogate. Analysis of the proposed loads suggested the frequency band needed to be lowered to attempt to excite the lower natural frequencies of the fuel assembly. Despite SNL’s expansion of lower frequency components in their five shock realizations, pretest predictions suggested a very mild dynamic response to the test loading. After testing was completed, one specific shock case was modeled, using recorded accelerometer data to excite the model. Direct comparison of predicted strain in the cladding was made to the recorded strain gauge data. The magnitude of both sets of strain (calculated and recorded) are very low, compared to the expected yield strength of the Zircaloy-4 material. The model was accurate enough to predict that no yielding of the cladding was expected, but its precision at predicting micro strains is questionable. The SNL test data offers some opportunity for validation of the finite element model, but the specific loading conditions of the testing only excite the fuel assembly to respond in a limited manner. For example, the test accelerations were not strong enough to substantially drive the fuel assembly out of contact with the basket. Under this test scenario, the fuel assembly model does a reasonable job of approximating actual fuel assembly response, a claim that can be verified through direct comparison of model results to recorded test results. This does not offer validation for the fuel assembly model in all conceivable cases, such as high kinetic energy shock cases where the fuel assembly might lift off the basket floor to strike to basket ceiling. This type of nonlinear behavior was not witnessed in testing, so the model does not have test data to be validated against.a basis for validation in cases that substantially alter the fuel assembly response range. This leads to a gap in knowledge that is identified through this modeling study. The SNL shaker testing loaded a surrogate fuel assembly with a certain set of artificially-generated time histories. One thing all the shock cases had in common was an elimination of low frequency components, which reduces the rigid body dynamic response of the system. It is not known if the SNL test cases effectively bound all highway transportation scenarios, or if significantly greater rigid body motion than was tested is credible. This knowledge gap could be filled through modeling the vehicle dynamics of a used fuel conveyance, or by collecting acceleration time history data from an actual conveyance under highway conditions.

Klymyshyn, Nicholas A.↗

DOE OSTI2013

Alternative Methods for Tritium Recovery from the Life Lithium Blanket: FY13 Final Report

One of the critical components for the Laser Inertial Fusion Energy (LIFE) power plant is the recovery of tritium from the reactor lithium cooling blanket. In the process proposed by Maroni, high temperature halide salts extract tritium from the lithium using a high-speed centrifugal contactor. Because of high-temperature fluid mixture is very corrosive, this system is not practical. The LIFE team has proposed an alternative process that utilizes a halide-free salt (e.g., LiOH) with a non-centrifuge separation process to extract tritium from the lithium cooling blanket, thus eliminating the need for high-temperature, halide corrosion-resistant materials. To facilitate the design of the contactor and separator, Colorado State University (CSU) was subcontracted by LLNL to conduct the following initial steps in the design process during FY13: • Develop preliminary designs for the separator and Plexiglas models for flow visualization and computational model development, • Select surrogate fluids and flow conditions for the separator, and • Select an appropriate flow visualization fluorescent dye for the surrogate tritium solute.

42 ENGINEERING↗

DOE OSTI2013

Annual Report: Carbon Capture Simulation Initiative (CCSI) (30 September 2013)

The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and academic institutions that is developing and deploying state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. The CCSI Toolset will provide end users in industry with a comprehensive, integrated suite of scientifically validated models, with uncertainty quantification (UQ), optimization, risk analysis and decision making capabilities. The CCSI Toolset incorporates commercial and open-source software currently in use by industry and is also developing new software tools as necessary to fill technology gaps identified during execution of the project. Ultimately, the CCSI Toolset will (1) enable promising concepts to be more quickly identified through rapid computational screening of devices and processes; (2) reduce the time to design and troubleshoot new devices and processes; (3) quantify the technical risk in taking technology from laboratory-scale to commercial-scale; and (4) stabilize deployment costs more quickly by replacing some of the physical operational tests with virtual power plant simulations. CCSI is led by the National Energy Technology Laboratory (NETL) and leverages the Department of Energy (DOE) national laboratories’ core strengths in modeling and simulation, bringing together the best capabilities at NETL, Los Alamos National Laboratory (LANL), Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), and Pacific Northwest National Laboratory (PNNL). The CCSI’s industrial partners provide representation from the power generation industry, equipment manufacturers, technology providers and engineering and construction firms. The CCSI’s academic participants (Carnegie Mellon University, Princeton University, West Virginia University, Boston University and the University of Texas at Austin) bring unparalleled expertise in multiphase flow reactors, combustion, process synthesis and optimization, planning and scheduling, and process control techniques for energy processes. During Fiscal Year (FY) 13, CCSI announced the initial release of its first set of computational tools and models during the October 2012 meeting of its Industry Advisory Board. This initial release led to five companies licensing the CCSI Toolset under a Test and Evaluation Agreement this year. By the end of FY13, the CCSI Technical Team had completed development of an updated suite of computational tools and models. The list below summarizes the new and enhanced toolset components that were released following comprehensive testing during October 2013. 1. FOQUS. Framework for Optimization and Quantification of Uncertainty and Sensitivity. Package includes: FOQUS Graphic User Interface (GUI), simulation-based optimization engine, Turbine Client, and heat integration capabilities. There is also an updated simulation interface and new configuration GUI for connecting Aspen Plus or Aspen Custom Modeler (ACM) simulations to FOQUS and the Turbine Science Gateway. 2. A new MFIX-based Computational Fluid Dynamics (CFD) model to predict particle attrition. 3. A new dynamic reduced model (RM) builder, which generates computationally efficient RMs of the behavior of a dynamic system. 4. A completely re-written version of the algebraic surrogate model builder for optimization (ALAMO). The new version is several orders of magnitude faster than the initial release and eliminates the MATLAB dependency. 5. A new suite of high resolution filtered models for the hydrodynamics associated with horizontal cylindrical objects in a flow path. 6. The new Turbine Science Gateway (Cluster), which supports FOQUS for running multiple simulations for optimization or UQ using a local computer or cluster. 7. A new statistical tool (BSS-ANOVA-UQ) for calibration and validation of CFD models. 8. A new basic data submodel in Aspen Plus format for a representative high viscosity capture solvent, 2-MPZ system. 9. An updated RM tool for CFD (REVEAL) that can create a RM from MFIX. A new lightweight, stand-alone version will be available in late 2013. 10. An updated RM integration tool to convert the RM from REVEAL into a CAPE-OPEN or ACM model for use in a process simulator. 11. An updated suite of unified steady-state and dynamic process models for solid sorbent carbon capture included bubbling fluidized bed and moving bed reactors. 12. An updated and unified set of compressor models including steady-state design point model and dynamic model with surge detection. 13. A new framework for the synthesis and optimization of coal oxycombustion power plants using advanced optimization algorithms. This release focuses on modeling and optimization of a cryogenic air separation unit (ASU). 14. A new technical risk model in spreadsheet format. 15. An updated version of the sorbent kinetic/equilibrium model for parameter estimation for the 1st generation sorbent model. 16. An updated process synthesis superstructure model to determine optimal process configurations utilizing surrogate models from ALAMO for adsorption and regeneration in a solid sorbent process. 17. Validation models for NETL Carbon Capture Unit utilizing sorbent AX. Additional validation models will be available for sorbent 32D in 2014. 18. An updated hollow fiber membrane model and system example for carbon capture. 19. An updated reference power plant model in Thermoflex that includes additional steam extraction and reinjection points to enable heat integration module. 20. An updated financial risk model in spreadsheet format.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2014

An approach to determine a defensible spent fuel ratio.

Sabotage of spent nuclear fuel casks remains a concern nearly forty years after attacks against shipment casks were first analyzed and has a renewed relevance in the post-9/11 environment. A limited number of full-scale tests and supporting efforts using surrogate materials, typically depleted uranium dioxide (DUO2), have been conducted in the interim to more definitively determine the source term from these postulated events. In all the previous studies, the postulated attack of greatest interest was by a conical shape charge (CSC) that focuses the explosive energy much more efficiently than bulk explosives. However, the validity of these large-scale results remain in question due to the lack of a defensible Spent Fuel Ratio (SFR), defined as the amount of respirable aerosol generated by an attack on a mass of spent fuel compared to that of an otherwise identical DUO2 surrogate. Previous attempts to define the SFR have resulted in estimates ranging from 0.42 to 12 and include suboptimal experimental techniques and data comparisons. Different researchers have suggested using SFR values of 3 to 5.6. Sound technical arguments exist that the SFR does not exceed a value of unity. A defensible determination of the SFR in this lower range would greatly reduce the calculated risk associated with the transport and dry storage of spent nuclear fuel. Currently, Oak Ridge National Laboratory (ORNL) is in possession of several samples of spent nuclear fuel (SNF) that were used in the original SFR studies in the 1980 s and were intended for use in a modern effort at Sandia National Laboratories (SNL) in the 2000 s. A portion of these samples are being used for a variety of research efforts. However, the entirety of SNF samples at ORNL is scheduled for disposition at the Waste Isolation Pilot Plant (WIPP) by approximately the end of 2015. If a defensible SFR is to be determined for use in storage and transportation security analyses, the need to begin this effort is urgent in order to secure the only known available SNF samples with a clearly defined path to disposal.

Durbin, Samuel G.↗

DOE OSTI2014

Continuing the Validation of CCIM Processability for Glass Ceramic HLLW Forms: Plan for Test AFY14CCIM-GC1

This test plan covers test AFY14CCIM-GC1which is the first of two scheduled FY-2014 test runs involving glass ceramic waste forms in the Idaho National Laboratory’s Cold Crucible Induction Melter Pilot Plant. The test plan is based on the successes and challenges of previous tests performed in FY-2012 and FY-2013. The purpose of this test is to continue to collect data for validating the glass ceramic High Level Liquid Waste form processability advantages using Cold Crucible Induction Melter technology. The major objective of AFYCCIM-GC1 is to complete additional proposed crucible pouring and post tapping controlled cooling experiments not completed during previous tests due to crucible drain failure. This is necessary to qualify that no heat treatments in standard waste disposal canisters are necessary for the operational scale production of glass ceramic waste forms. Other objectives include the production and post-test analysis of surrogate waste forms made from separate pours into the same graphite mold canister, testing the robustness of an upgraded crucible bottom drain and drain heater assembly, testing the effectiveness of inductive melt initiation using a resistive starter ring with a square wave configuration, and observing the tapped molten flow behavior in pans with areas identical to standard High Level Waste disposal canisters. Testing conditions, the surrogate waste composition, key testing steps, testing parameters, and sampling and analysis requirements are defined.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗