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At least 379 records · Page 21

Desal.jl

SAND2026-22942O Desal.jl is a Julia software package for lightweight reverse-osmosis desalination simulation. It supports static and dynamic operation of power-limited desalination systems without requiring optimization dependencies. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Michelen Strofer, Carlos [Sandia National Lab. (SN↗

Preliminary Results on Process Modeling Tools for Determining Variability in Additively Manufactured Stainless Steel 316 Parts

The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the distinct characteristics of additive manufacturing (AM) materials, stemming from their unique processing history, microstructure, and properties, pose significant challenges for the qualification and certification of nuclear components. These challenges primarily arise from component-scale variations in microstructure and properties influenced by local process conditions and geometry, which affect thermal history, melt pool dynamics, and microstructure evolution. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents preliminary results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Initial results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing the challenges inherent in process modeling of AM components and outlines a plan for future development needs.

36 MATERIALS SCIENCE↗

FY24 Advancements in PFLOTRAN Development for GDSA Framework

The Spent Fuel & Waste Science and Technology (SFWST) Campaign of the Office of Spent Fuel & Waste Disposition of U.S. Department of Energy Office of Nuclear Energy (DOE-NE) is conducting research and development on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). This report describes fiscal year 2024 accomplishments in the Geologic Disposal Safety Assessment (GDSA) PFLOTRAN Development work package, which is charged with developing subsurface simulation software for postclosure performance assessment of deep geologic disposal of SNF and HLW.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Assessment of Microstructure Prediction Capabilities for Powder Bed Fusion Stainless Steel 316

The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the characteristic process-structure-property relationships of additive manufacturing (AM) materials pose challenges for the qualification and certification of AM nuclear components. In particular, component-scale variations in microstructure and properties can be driven by localized changes in melt pool dynamics due to how process parameters interact with different part geometries. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents final results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing identified challenges and future opportunities for connecting the developed simulation workflow with mechanics simulations for prediction of part performance.

36 MATERIALS SCIENCE↗

Predicting Instability and the Effect of Wind Loading on Single-Axis Trackers

As PV modules continue to trend toward larger, thinner, and more flexible forms they grow more susceptible to damage from dynamic wind loading. As a result, understanding the impact of wind on PV systems, particularly when mounted on solar-tracking hardware, and identifying robust, stable array layouts and stow strategies is becoming increasingly important for the PV community. In our ongoing DuraMAT project, we are developing an open-source software package, PVade (PV aerodynamic design engineering), to simulate the cascading fluid-structure interaction that occurs within solar-tracking arrays to enable researchers to test hardware, layout, and tracker control changes, leading to enhanced stability and a reduction in wind-driven damage. We will give an overview of the PVade software, highlighting recent user-interface and algorithm developments, before presenting the latest outcomes from our ongoing validation campaign in which we analyze and compare with experimental data obtained from a DuraMAT 1 project. From there, we will present simulated results from a larger, multi-row array and highlight relationships between varying tracker angles and stability as measured by different experimentally validated metrics.

fluid structure interaction↗

Evaluating Climate Models with the CLIVAR 2020 ENSO Metrics Package

El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual climate variability on the planet, with far-reaching global impacts. It is therefore key to evaluate ENSO simulations in state-of-the-art numerical models used to study past, present, and future climate. Recently, the Pacific Region Panel of the International Climate and Ocean: Variability, Predictability and Change (CLIVAR) Project, as a part of the World Climate Research Programme (WCRP), led a community-wide effort to evaluate the simulation of ENSO variability, teleconnections, and processes in climate models. The new CLIVAR 2020 ENSO metrics package enables model diagnosis, comparison, and evaluation to 1) highlight aspects that need improvement; 2) monitor progress across model generations; 3) help in selecting models that are well suited for particular analyses; 4) reveal links between various model biases, illuminating the impacts of those biases on ENSO and its sensitivity to climate change; and to 5) advance ENSO literacy. By interfacing with existing model evaluation tools, the ENSO metrics package enables rapid analysis of multipetabyte databases of simulations, such as those generated by the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6). The CMIP6 models are found to significantly outperform those from CMIP5 for 8 out of 24 ENSO-relevant metrics, with most CMIP6 models showing improved tropical Pacific seasonality and ENSO teleconnections. Only one ENSO metric is significantly degraded in CMIP6, namely, the coupling between the ocean surface and subsurface temperature anomalies, while the majority of metrics remain unchanged.

54 ENVIRONMENTAL SCIENCES↗

Benchmark Calculation for the Quad Cities Unit 1 Cycles 1-3 Using the SCALE 6.3/Polaris–PARCS v3.4.2 Code Package

In this study, benchmark calculations were performed for the Quad Cities Unit 1 cycles 1–3 to validate the SCALE 6.3/Polaris–PARCS v3.4.2 code package with the ENDF/B-VII.1 AMPX 56-group library by comparing the simulated results with the measured data. The benchmark results will be used in evaluating uncertainties of the SCALE/Polaris–PARCS code package for boiling water reactor physics analysis for key nuclear parameters such as reactivity and assembly power peaking factors. This report details plant and fuel design specifications and input data for SCALE/Polaris, GenPMAXS, and PARCS; additionally, detailed information is provided for all the input and output files produced for the benchmark calculations. The benchmark results are summarized herein so that they can be used to evaluate uncertainties with other benchmark results for key nuclear parameters.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Land Model Testbed: Accelerating Development, Benchmarking and Analysis of Land Surface Models

A Land Model Testbed (LMT), designed to provide a computational framework for systematically assessing model fidelity and supporting rapid development of complex multiscale models, offers a general-purpose workflow for conducting large ensemble simulations of multiple land surface models, post-processing large volumes of model output, and evaluating model results. It leverages existing tools for launching model simulations and the International Land Model Benchmarking (ILAMB) package for assessing model fidelity through comparison with best-available observational datasets. Increased complexity and proliferation of uncertain parameters in process representations in land surface models has driven the need for frequent and intensive testing and evaluating of models to quantify uncertainties and optimize parameters such that results are consistent with observations. The LMT described here meets these needs by providing tools to run thousands of ensemble simulations simultaneously and post-process their output files, by automating execution of an enhanced version of ILAMB with site-specific benchmarks and multivariate functional relationships, and by offering ensemble diagnostics and a customizable dashboard for displaying model performance metrics and associated graphics. We envision the LMT capabilities will serve as a foundational computational resource for a proposed user facility focused on terrestrial multiscale model--data integration.

Sreepathi, Sarat↗

Robust Combined Heat and Hybrid Power (CHHP) for High Electrical Efficiency Cogeneration

Georgia Tech (Prime Recipient), the University of Texas at El Paso (UTEP) and the National Energy Technology Laboratory (NETL) investigated a hybrid fuel cell/ gas turbine system concept as a combined heat and hybrid power (CHHP) system for both robust and high power-to-process heat ratio cogeneration. The novelty of the proposed system entailed the distinct, elevated electrical efficiencies it maintains while simultaneously supporting a broad span of heating needs (e.g., supply temperatures) demanded across variable heat loads. The scope included: 1) leveraging a pioneering national lab facility configured for dynamic system operability development of hybrid fuel cell/gas turbine cycles; 2) enabling technology development to adjust and modulate the quality and quantity of thermal supply to bottoming heat loads via novel extreme temperature gas bypass valves. Hybrid fuel cell/gas turbine systems have primarily been reduced-to-practice in a constrained (e.g., initial proof-of-concept) manner and have still demonstrated considerable electrical efficiencies. However, these pre-pilot systems have focused upon electrical efficiencies and electrical power generation as the exclusive energy demand. Such hybrid systems had not been extensively researched or developed for flexible and variable operation consisting of both power and heat demands; however, these variable combined power and heat demands are characteristic of many types of manufacturers such as animal/poultry processing, bakeries and milk/flour/pastry manufacturing, textile mills, and electrochemical processing. Commercially, developing the system into a working combined heat and power system benefits these types of manufacturers by allowing them to meet their power and heat demands at a lower cost, higher efficiency, and/or through onsite generation. Therefore, the technical scope of this project was largely to study and facilitate these hybrid systems as combined heat and hybrid power (CHHP) systems that include dynamic operability for variable heat and power loads and/or grid dynamics for various types of manufacturers. Simulation results were used to predict the performance of the CHHP system and conceptually develop it to achieve desired dynamic operability. Experimentally, the primary goal was to design, manufacture, and experiment upon a high-temperature bypass valve. Experimental data included air mass flow rates through the valve orifice when the valve was changed to variable extent between fully closed and fully open. The experimental data was then used to create a semi-empirical computational model of the bypass valve. Concluded simulation goals for the research included developing computational heat exchanger models for the hybrid system inclusive of the bottoming heat exchanger and the recuperative heat exchanger, and then combining the computational recuperator model with the computational valve model. Afterwards, the computational models were then integrated to predict the dynamic operation of hybrid fuel cell/gas turbine cycles throughout a design space and reporting such. The scope stated in the preceding paragraph was packaged into five specific goals: 1) enabling the simulation of dynamic combined heat and power through the creation of computational, modular heat exchanger models; 2) simulation and exploration of the CHHP system’s performance by integrating the heat exchanger models with the national lab’s pre-existing hybrid system (computational) simulation, but without the recuperator bypass valve concept in order to initially determine how the (baseline) system behaves and can be controlled in order to meet variable heat and power demands; 3) development and initial deployment of the high-temperature recuperator bypass valve technology in order to confirm and characterize the approach; 4) usage of the experimental data for the valve to create a semi-empirical computational model for the bypass valve which could then be combined with the heat exchanger computational models; 5) repeat of the second task of simulating and exploring the system’s performance, but this time including the bypass valve to resolve its efficacy. Tasks were successfully completed, and the general notion of flexibly operating, high electrical efficiency CHHP was further corroborated. Supportive details are provided in the report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Concurrent Relaxation through Accelerated Deep Learning

CRADL captures performance metrics of machine learning algorithms operating on mesh data from multiphysics codes This proxy application is a tool to explore scalability of inference on HPC platforms, and also gather performance metrics for inference on new machine learning specific hardware. CRADL is designed to give users as fine a control as possible over an inference simulation. Users may select the number of cycles, amount of data, and batch size to pass to the accelerator of choice. Additionally the user may select a number of performance optimization libraries and flags. CRADL comes packaged with a repository of anonymized multi-physics simulation data, as well as a pretrained model for inference. The code allows a user to load their own pre-trained model and data if they wish. The code can operate in multiple parallelization schemes, with performance enhancing options such as half-precision libraries, PyTorch benchmarking, and pinned memory with non-blocking data transfers.

Zieb, KristoferJ.↗

Medium and Heavy Duty Vehicle Powertrain Electrification and Fleet Demonstration

The Contract Award Grant # EE0007513 dated 2 June, 2016 is for the design, development and demonstration of medium duty package delivery vehicles that will result in a 100% increase in fuel economy as measured in miles per gallon. The project was proposed in 3 phases over two budget periods. The Phases were: 1. Modeling and Simulation – This phase was completed and formed the basis of the specifications for the design and build of the physical systems. The modeling and simulation was conducted by AVL and used real world package delivery routes that AVL mapped. The motors, battery pack, range extender and balance of plant support systems were modeled and iterated until a system with the desired fuel economy improvement was realized. Early design of the major subsystems using the actual vehicle space constraints was also completed. This phase was successfully completed in May of 2017. 2. 1st Build and FE Confirmation - This phase was completed and resulted in the final design, release, procurement and build of all major vehicle systems for commissioning into the first vehicle. The work was a partnered effort between McLaren and AVL. McLaren built the eAxle system while AVL built the range extender and integrated the range extender, battery and balance of plant sub-systems into the first vehicle with a control system to make it operate. Prior to the end of this phase a real world HIL system test gave evidence that the fuel economy improvement would be achieved in the real world. This phase was exited with a 7 month delay due mostly to procurement and supply base issues and ended in May, 2018. 3. Final Build and Demonstration - This phase included the final build and commissioning of the four program vehicles and the operation of a demonstration on a set of routes near San Diego California. During this phase, a multitude of issues arose in different systems in the vehicle. These issue resolution exercises were quite complex and caused repeated delays but were all successfully completed. It should also be noted that an early commercialization analysis resulted in the conclusion that the complexity of the system would not make it cost effective, leading to the start of a follow-on design that used newly available component technologies to specify a more cost effective system. This design will be repurposed and put into demonstration on a separately (non-DOE) funded program. It was decided and agreed with DOE that this phase should be considered completed. The knowledge and know-how gained on this program has been incorporated into and transferred to the new program. This phase and the entire program was considered closed on June 1, 2021.

33 ADVANCED PROPULSION SYSTEMS↗

SNEWPY: A Data Pipeline from Supernova Simulations to Neutrino Signals

Current neutrino detectors will observe hundreds to thousands of neutrinos from a Galactic supernovae, and future detectors will increase this yield by an order of magnitude or more. With such a data set comes the potential for a huge increase in our understanding of the explosions of massive stars, nuclear physics under extreme conditions, and the properties of the neutrino. However, there is currently a large gap between supernova simulations and the corresponding signals in neutrino detectors, which will make any comparison between theory and observation very difficult. SNEWPY is an open-source software package which bridges this gap. The SNEWPY code can interface with supernova simulation data to generate from the model either a time series of neutrino spectral fluences at Earth, or the total time-integrated spectral fluence. Data from several hundred simulations of core-collapse, thermonuclear, and pair-instability supernovae is included in the package. This output may then be used by an event generator such as sntools or an event rate calculator such as SNOwGLoBES. Additional routines in the SNEWPY package automate the processing of the generated data through the SNOwGLoBES software and collate its output into the observable channels of each detector. In this paper we describe the contents of the package, the physics behind SNEWPY, the organization of the code, and provide examples of how to make use of its capabilities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Character Memory- Umbra Package

Character Memory provides visual and acoustic sensing capabilities for characters within the Umbra simulation framework. Character Memory tracks where the character has seen and heard things, and then classifies them based on what they were and what their affiliation might be. The package provides outputs that can drive behaviors in response to those detections. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-3053 O

Hart, BrianE↗

Magnetohydrodynamics with physics informed neural operators

Abstract The modeling of multi-scale and multi-physics complex systems typically involves the use of scientific software that can optimally leverage extreme scale computing. Despite major developments in recent years, these simulations continue to be computationally intensive and time consuming. Here we explore the use of AI to accelerate the modeling of complex systems at a fraction of the computational cost of classical methods, and present the first application of physics informed neural operators (NOs) (PINOs) to model 2D incompressible magnetohydrodynamics (MHD) simulations. Our AI models incorporate tensor Fourier NOs as their backbone, which we implemented with the TensorLY package. Our results indicate that PINOs can accurately capture the physics of MHD simulations that describe laminar flows with Reynolds numbers Re ⩽ 250 . We also explore the applicability of our AI surrogates for turbulent flows, and discuss a variety of methodologies that may be incorporated in future work to create AI models that provide a computationally efficient and high fidelity description of MHD simulations for a broad range of Reynolds numbers. The scientific software developed in this project is released with this manuscript.

97 MATHEMATICS AND COMPUTING↗

Scoping Thermal Response Calculations of RNS Waste During Transport to and Disposal at the WIPP

Sandia National Laboratories (SNL) was contracted by the United States Department of Energy Environmental Management (DOE-EM), Los Alamos Field Office to perform mechanical and thermal scoping calculations as part of a study seeking to understand the ignitability risk of the Remediated Nitrate Salts (RNS) waste drums during transportation from the Waste Control Specialists (WCS) facility to Waste Isolation Pilot Plant (WIPP) and permanent disposal of the waste at WIPP. The scoping thermal simulations described in this report pertain to thermal calculations performed with a packaging system consisting of one Standard Waste Box (SWB) loaded with drums placed inside a Standard Large Box 2 (SLB2). During transportation, the SLB2 is inside Transuranic Package Transporter Model III (TRUPACT-III), which provides the third layer of the packaging. Once at the WIPP, it is assumed the SLB2 is extracted from the TRUPACT-III and maintained above ground, and then subsequently placed underground for permanent disposal. In these proposed configurations, the space between the SLB2 and the SWB is always filled by a layer of insulation consisting of air-filled glass microbubbles except for the bottom which rests directly on the SLB2. The thermal scoping calculations described in this report specifically address whether the introduction of external heat inputs, combined with the contributions from the internally generated radiolytic decay heat and chemical reactions, lead to an unstable thermal state during the time of its movement and placement in the permanent disposal location. The external heat inputs are of two forms: 1) ambient thermal irradiation (e.g., solar and ambient storage/disposal temperatures) and 2) accident-induced fire. Three scoping calculation scenarios were derived as representative, conservative scenarios: 1A) TRUPACT-III transient transportation, 1B) SLB2 48-hour outdoor storage with solar radiation, and 2) fully-engulfing fire during SLB2 handling or emplacement following a steady-state analysis in a 38 °C environment. All the simulated scenarios are conservative relative to the operational conditions expected for handling the waste package during transportation and placement in the WIPP underground disposal unit. The predictions obtained from simulating the three exposure scenarios revealed that adding the SLB2 and the air-filled glass microbubbles to the transport and storage/disposal configurations provides additional thermal protection of the drums beyond what the SWB provides alone, both during long-term above ground insolation and underground during a fire accident. Under the current transportation/storage/disposal concepts, the degree of protection provided by the packaging concept is sufficient to prevent the waste from being ignitable. The simulation results demonstrate that there is adequate margin to safely transport and place the RNS waste from WCS to the WIPP under the current operational concept.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Generic Post-Closure Safety Assessment for Deep Borehole Disposal of Heat-Generating Waste - 20451

The deep borehole disposal (DBD) concept for high-activity waste consists of drilling a large-diameter borehole into crystalline basement rock to a depth of about 5,000 m, placing waste packages in the lower portion of the borehole - the waste emplacement zone - and sealing and plugging the upper portion of the borehole with a combination of bentonite, cement plugs, and sand or crushed rock ballast. The waste emplacement zone is several times deeper than typical mined repositories and is well below the typical maximum depth of fresh groundwater resources. Recent work describes a generic reference case for deep borehole disposal of Cs and Sr capsules stored at the Hanford Site. Two scenarios are described: an undisturbed (nominal) scenario, and disturbed scenario, in which a waste package is stuck adjacent to a transmissive fracture zone in crystalline basement. Simulations of the processes affecting radionuclide release and transport in the two scenarios provide the basis for quantitative post-closure performance assessment (PA) of the reference case. This paper presents a probabilistic PA of the stuck waste package scenario, i.e. uncertainties affecting radionuclide mobilization and transport are propagated through 200 realizations of a predictive model to quantify uncertainties in predicted radionuclide concentrations within the borehole and the fracture zone. Simulations predict no radionuclide transport in the borehole to distances greater than 100 m above the stuck waste package. An assumed lateral pressure gradient results in predictions of limited radionuclide transport in the fracture zone at a lateral distance of 200 m from the stuck waste package. These results complement previous results for the nominal scenario and contribute to the development of a generic safety case for DBD. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Deep learning for NLTE spectral opacities

Computer simulations of high energy density science experiments are computationally challenging, consisting of multiple physics calculations including radiation transport, hydrodynamics, atomic physics, nuclear reactions, laser–plasma interactions, and more. To simulate inertial confinement fusion (ICF) experiments at high fidelity, each of these physics calculations should be as detailed as possible. However, this quickly becomes too computationally expensive even for modern supercomputers, and thus many simplifying assumptions are made to reduce the required computational time. Much of the research has focused on acceleration techniques for the various packages in multiphysics codes. In this work, we explore a novel method for accelerating physics packages via machine learning. The non-local thermodynamic equilibrium (NLTE) package is one of the most expensive calculations in the simulations of indirect drive inertial confinement fusion, taking several tens of percent of the total wall clock time. We explore the use of machine learning to accelerate this package, by essentially replacing the physics calculation with a deep neural network that has been trained to emulate the physics code. Overall, we demonstrate the feasibility of this approach on a simple problem and perform a side-by-side comparison of the physics calculation and the neural network inline in an ICF Hohlraum simulation. We show that the neural network achieves a 10× speed up in NLTE computational time while achieving good agreement with the physics code for several quantities of interest.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Large-scale parametric modeling of spent nuclear fuel dynamics in the 30 cm package drop scenario

Packages used to transport spent nuclear fuel (SNF) are required by the U.S. Code of Federal Regulations 10 CFR 71.71 to demonstrate satisfactory performance during a drop scenario. While the CFR is meant to ensure safe package function, it does not evaluate survival of the SNF within. The U.S. Department of Energy Spent Fuel and Waste Science and Technology program is working on closing the knowledge gap related to the response of SNF to external mechanical loads, including the hypothetical 30 cm package drop scenario in the CFR. In support of this effort, LS-DYNA finite element simulations were developed by Pacific Northwest National Laboratory (PNNL) to model generic drop scenarios at both the package and fuel assembly level. The models were validated against one-third scale package and full scale fuel assembly drop test data and were exercised to predict fuel cladding strains in a narrow range of model configurations. This work describes a large-scale parametric study conducted by PNNL using the previously developed and validated PWR finite element model, with the addition of a new generic BWR assembly model. The motivation for the parametric study was to characterize the broad range of SNF responses in the 30 cm package drop scenario. This was accomplished by varying the drop orientation, fuel assembly type (17x17 PWR and 10x10 BWR), burnup, cladding temperature, spacer grid buckling load, package mass, impact limiter stiffness, and mechanical gap conditions within the basket. A MATLAB framework was developed to automate LS-DYNA model generation and execution on PNNL institutional computing resources. In total, over 2000 simulations were performed. For each simulation, the SNF response was quantified in terms of permanent grid deformation, fuel rod contact pressure, and strains within the fuel rods, guide tubes, and water rods. The results provide valuable insight into the range of responses that could be reasonably expected from SNF in the hypothetical drop scenario, as well as the sensitivity to each input parameter. The results of this parametric study are a key component of the testing and modeling strategy the Spent Fuel and Waste Science and Technology program is using to close the external loads knowledge gap.

Kadooka, Kevin↗