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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Modeling Approach to Critical in the Upcoming CERBERUS Experiment

The planned Critical Experiment Reflected By coppEr to betteR Understand Scattering (CERBERUS) seeks to maximize sensitivity to elastic neutron scattering in the intermediate energy region (0.625 eV to 100 keV). It will be performed near the end of FY 2023 at the National Criticality Experiments Research Center (NCERC). Very few International Criticality Safety Benchmark Evaluation Project (ICSBEP) benchmarks are sensitive to copper in this region. Creating a benchmark sensitive in this area will make the Zeus benchmark series, an intermediate benchmark evaluation that uses a copper reflector, more useful for code and nuclear data validation. Approaching criticality in a safe manner is of utmost importance to avoid a criticality accident, which would present a safety concern and could cause damage to equipment. The two rules that are followed closely to ensure that a criticality accident does not occur are the 3/4 rule and the 1/2 rule. The 3/4 rule states that no more than 3/4 of a critical mass can be assembled by hand, and the 1/2 rule states that no more than 1/2 of the material expected to reach criticality, or 1/2 the distance needed to reach criticality, can be added before another measurement of the count rate has been taken. This work will discuss the approach to criticality as modeled in MCNP6® 1 particle transport code with the ENDF/B- VIII.0 cross-section library and the .00c data library.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

IRIS Reimagined: Advancements in Intelligent Runtime System for Task-Based Programming

Task-based programming models are gaining traction in scientific computing. IRIS is a portable runtime system that exploits multiple heterogeneous programming systems and can discover available resources and manage multiple diverse programming systems (e.g., CUDA, Hexagon, HIP, Level Zero, OpenCL, and OpenMP) simultaneously. It accounts for the constraints of task dependencies and provides customizable scheduling policies to map those tasks to heterogeneous devices. In this paper, we present new capabilities added to IRIS to improve its portability for heterogeneous programming, build-friendliness, and performance efficiency. The new additions include vendor-specific kernel support, a runtime system with a foreign function interface to eliminate writing wrapper or boilerplate code for heterogeneous kernels, an easy-to-use and configurable CMake-based build environment, automatic and efficient data transfers and orchestration, and the Hunter and DAGGER toolchains to evaluate IRIS’s task scheduling algorithms.

Miniskar, Narasinga Rao↗
DOE OSTI2022

tapir: A tool for topologies, amplitudes, partial fraction decomposition and input for reductions

The demand for precision predictions in the field of high energy physics has dramatically increased over recent years. Experiments conducted at the LHC, as well as precision measurements at the intensity frontier such as Belle II require equally precise theoretical predictions to make full use of the acquired data. To match the experimental precision, second-, third- and, for certain quantities, even higher-order calculations in perturbative quantum field theory are required. To facilitate such calculations, computer software automating as many steps as possible is required. Yet, each calculation poses different challenges and thus, a high level of configurability is required. In this context we present tapir: a tool for identification, manipulation and minimization of Feynman integral families. It is designed to integrate in toolchains based on the computer algebra system FORM, the use of which is common practice in the field. tapir can be used to reduce the complexity of multi-loop problems with cut-filters, topology mapping, partial fraction decomposition and alike. Program Title:tapir CPC Library link to program files:https://doi.org/10.17632/ptc9t46xyn.1 Developer's repository link:https://gitlab.com/tapir-devs/tapir Licensing provisions: GPLv3 Programming language:python 3, C++ Nature of problem: Multi-loop computations require the automatization of a large number of different tasks related to Feynman integral topologies. Among them are the identification and minimization of integral topologies, partial fraction decomposition of topologies in the case of linearly dependent propagators as well as mapping scalar products of loop momenta to scalar functions. Solution method: The minimization of topologies is performed by comparison of their respective Nickel indices [1], even further minimization utilizes Pak's algorithm [2]. To efficiently map scalar products of loop momenta to scalar functions FORM [3] code is generated. Additional comments including restrictions and unusual features: Minimization based on Pak's algorithm slows down for many lines and scales. A coarser minimization using the Nickel indices, however, is still possible. [1]B. Nickel, D. Meiron, G.A.J. Baker, Compilation of 2-pt and 4-pt graphs for continuous spin model, Report, University of Guelph, 1977.[2]A. Pak, J. Phys. Conf. Ser. 368 (2012) 012049, https://doi.org/10.1088/1742-6596/368/1/012049, arXiv:1111.0868.[3]B. Ruijl, T. Ueda, J. Vermaseren, FORM version 4.2, arXiv:1707.06453, 7 2017.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Wootz: a compiler-based framework for fast CNN pruning via composability

Convolutional Neural Networks (CNN) are widely used for Deep Learning tasks. CNN pruning is an important method to adapt a large CNN model trained on general datasets to fit a more specialized task or a smaller device. The key challenge is on deciding which filters to remove in order to maximize the quality of the pruned networks while satisfying the constraints. It is time-consuming due to the enormous configuration space and the slowness of CNN training.The problem has drawn many efforts from the machine learning field, which try to reduce the set of network configurations to explore. This work tackles the problem distinctively from a programming systems perspective, trying to speed up the evaluations of the remaining configurations through computation reuse via a compiler-based framework. We empirically uncover the existence of composability in the training of a collection of pruned CNN models, and point out the opportunities for computation reuse. We then propose composability-based CNN pruning, and design a compression-based algorithm to efficiently identify the set of CNN layers to pre-train for maximizing their reuse benefits in CNN pruning. We further develop a compiler-based framework named Wootz, which, for an arbitrary CNN, automatically generates code that builds a Teacher-Student scheme to materialize composability-based pruning. Experiments show that network pruning enabled by Wootz shortens the state-of-art pruning process by up to 186X while producing significantly better pruning results.

Guan, Hui↗
DOE OSTI2013

Seismic Soil-Structure Interaction Analyses of a Deeply Embedded Model Reactor – SASSI Analyses

This report summarizes the SASSI analyses of a deeply embedded reactor model performed by BNL and CJC and Associates, as part of the seismic soil-structure interaction (SSI) simulation capability project for the NEAMS (Nuclear Energy Advanced Modeling and Simulation) Program of the Department of Energy. The SASSI analyses included three cases: 0.2 g, 0.5 g, and 0.9g, all of which refer to nominal peak accelerations at the top of the bedrock. The analyses utilized the modified subtraction method (MSM) for performing the seismic SSI evaluations. Each case consisted of two analyses: input motion in one horizontal direction (X) and input motion in the vertical direction (Z), both of which utilized the same in-column input motion. Besides providing SASSI results for use in comparison with the time domain SSI results obtained using the DIABLO computer code, this study also leads to the recognition that the frequency-domain method should be modernized so that it can better serve its mission-critical role for analysis and design of nuclear power plants.

42 ENGINEERING↗
DOE OSTI2019

Performance Improvement of the Linear System Solver for CMFD Acceleration in PROTEUS-MOC

The PROTEUS code developed under the DOE NEAMS program is a high-fidelity capable whole core transports solver based on unstructured finite element meshes [1]. PROTEUS includes three distinct transport solvers: SN, MOC and NODAL. Among them, the PROTEUS-MOC is the 3D method of characteristics (MOC) solver for extruded geometries. In order to accelerate the convergence of 3D MOC calculation, the CMFD acceleration method was implemented in PROTEUS-MOC [2]. For solving the CMFD linear system, the unpreconditioned GMRES algorithm was used in the previous work [3, 4] for implementing the CMFD scheme because of low computation cost of coarse mesh calculations. In the performance tests, we found that the overhead of the CMFD acceleration became non-negligible as the condition number of the CMFD linear system and the size of problem increase. This indicated that the CMFD overhead should be reduced in the preconditioning technique in order to improve the computational performance. In this work, the performance of CMFD acceleration was notably improved by developing and implementing the new formulation of CMFD linear system and preconditioning technique.

Jung, Yeon Sang↗
DOE OSTI2017

Advancement of Functional Expansion Tallies Capabilities in Serpent 2

The need for accident tolerant fuels (ATFs) became self-evident after the series of unfortunate nuclear events at Fukushima Daiichi in 2011. Restarting the Transient Reactor Test Facility (TREAT) at INL is one component in this concerted effort to establish the ATF development and testing program under the United States Department of Energy. Prior to the closure of TREAT in 1994, each experiment required multiple low-power calibration tests before and after each run. Since then, computing advancements have set computer-based modeling and simulation (M and S) as the preferred calibration method. The anticipated savings are both temporal and monetary. MAMMOTH, a collection of M and S tools built from the finite element-based MOOSE Framework, has been established to support the designed experiments intended for TREAT. This collection of tools includes: 1) Rattlesnake, a striking diffusion-based neutronics code, 2) BISON, an advanced materials and fuels performance code, and 3) RELAP-7, a powerful thermal-hydraulics solver. Each is capable of independently performing its function accurately and effectively; unified, they create a powerful multiphysics reactor core M and S application. One unfortunate weakness in this configuration lies in the unique geometry and design of TREAT's core. Although simple to model, this poses a challenge to the diffusion solver in Rattlesnake. One alternative is to replace Rattlesnake with Serpent, a fully-fledged Monte Carlo (MC) neutronics code. Coincidentally, Rattlesnake calculations are frequently validated against Serpent simulations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

ExaWind: Exascale Predictive Wind Plant Flow Physics Modeling

The scientific goal of the ExaWind project is to advance our fundamental understanding of the flow physics governing whole wind plant performance, including wake formation, complex terrain impacts, and turbine-turbine-interaction effects. The primary application codes in the ExaWind environment are Nalu-Wind, an unstructured-grid computational fluid dynamics (CFD) code, AMR-Wind, a structured-grid CFD code, and OpenFAST, a whole-turbine simulation code. In this poster we present our progress towards simulating the ExaWind challenge problem, which is a predictive simulation of a wind farm with tens of megawatt-scale wind turbines dispersed over an area of 50 square kilometers.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2025

Progress Towards NQA-1 for Cardinal in FY25

Cardinal is a wrapping of the GPU-oriented spectral element Computational Fluid Dynamics (CFD) code NekRS and the Monte Carlo particle transport code OpenMC within the Multiphysics Object-Oriented Simulation Environment (MOOSE). Cardinal provides high-resolution thermal-hydraulics and/or radiation transport feedback to MOOSE multiphysics simulations. Multiphysics feedback is implemented in a geometry-agnostic manner which eliminates the need for rigid one-to-one mappings. A generic data transfer implementation also allows NekRS and OpenMC to couple to any MOOSE application, enabling a broad set of multiphysics capabilities. Cardinal simulations can also leverage combinations of MPI, OpenMP, and GPU resources. Cardinal continuous development and improvement efforts have led to the software being considered as a high-fidelity design and licensing tool for key areas of nuclear reactor relevant physics, including neutron transport, fluid flow, heat transfer, and mechanical processes. The fast development and expansion of the software from a pure R&D framework towards its application in the nuclear industry and regulation require a focus on developing, enhancing,and maintaining Cardinal’s software quality through strict adherence to a Software Quality Assurance (SQA) framework and SQA program. To facilitate compliance with SQA standards, the Cardinal SQA Program was initiated during Fiscal Year 2023 (FY23). During the development of the Cardinal SQA Program, multiple gaps have been identified. These gaps are primarily related to model verification and code pedigree as they relate to the use of Cardinal as an analysis tool. These gaps were captured in a report published in 2023. A second report highlighted the progress made during Fiscal Year 2024 (FY24) and described Argonne’s effort to document and integrate software verification within Cardinal’s software development process. This report documents the progress made towards NQA-1 for Cardinal in the Fiscal Year 2025 (FY25). All cases in the expanded Continuous Integration (CI) suite of NekRS are included in this report which test the solvers and modules available in NekRS exhaustively. The NekRS tests are integrated with the Cardinal CI suite and made available in publicly accessible Github documentation. Following the CI practice permits integrating of source code changes frequently and ensuring that the integrated codebase clears the verification testing for the software. Also in this report is a brief overview of the development of the Cardinal Software Quality Assurance Plan (SQAP) that was done in FY25, though it should be noted that the rest of the documentation for the SQA program needs to be developed in a future step of this task.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Integral and Separate Effects Test Facilities To Support Water Cooled Small Modular Reactors: A Review

This study reviews previous experimental facilities and test programs relevant to water-cooled reactor system design and analysis to meet regulatory compliances. This study aims to find the best solution for designing the required experiments, obtaining necessary test data, and verifying the developed computer code/models to support the new reactor design and development while minimizing cost and time while leveraging experiences from previous facilities to minimize. Nuclear reactor licensing requires supportive design, analysis, and experimental results to ensure the safety of the full-scale prototype reactor in regular operation, as well as during postulated accident scenarios. These reactor design analyses are generally performed using system codes and other associated simulation tools that require assessment, verification and validation using an appropriate experimental dataset. Experimental facilities used for reactor system safety analysis and system code assessments are categorized into integral effects test (IET) and separate effects test (SET) facilities. Further, these IET and SET experiments and studies use geometrically scaled systems to reproduce the prototype system behavior at a reasonable cost, albeit with some scaling-related distortions. The design challenge of these model facilities is to identify and minimize scaling distortions while reproducing the most important operational phenomena in steady-state operation and in postulated accident scenarios. Lessons learned from previous experimental facilities, models, and correlations can support the development of new multipurpose, scaled, hybrid, integrated, and modular experimental facilities for advanced light water-cooled small modular reactors (SMRs). Successful operation of these facilities can significantly reduce upfront reactor development and demonstration costs and time to deployment.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Machine Learning-enabled Scalable Performance Prediction of Scientific Codes

Hardware architectures become increasingly complex as the compute capabilities grow to exascale. Here, we present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input and predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP transforms the code to an (architecture-independent) intermediate representation, then (i) analyzes the basic block structure of the code, (ii) processes architecture-independent virtual memory access patterns that it uses to build memory reuse distance distribution models for each basic block, and (iii) runs detailed basic-block level simulations to determine hardware pipeline usage. PPT-AMMP uses machine learning and regression techniques to build the prediction models based on small instances of the input code, then integrates into a higher-order discrete-event simulation model of PPT running on Simian PDES engine. We validate PPT-AMMP on four standard computational physics benchmarks and present a use case of hardware parameter sensitivity analysis to identify bottleneck hardware resources on different code inputs. We further extend PPT-AMMP to predict the performance of a scientific application code, namely, the radiation transport mini-app SNAP. To this end, we analyze multi-variate regression models that accurately predict the reuse profiles and the basic block counts. We validate predicted SNAP runtimes against actual measured times.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Improving Mathematical Exposition of an Industrial-Scale Linear Program

Industrial-scale models require considerable setup time; hence, once built, they are used in myriad ways to consider closely related cases. In practice, the code for these models frequently evolves without appropriate notational choices, largely as a result of the lengthy development time of, and the number of individuals contributing to, their formulation. This leads to inefficiencies and obfuscates model structures that might be leveraged to expedite solutions. In this paper, we advocate for an emerging literature on model formulation “best practices” and present the reformulation of a widely used industrial-scale linear program. The efficient mathematical expression of this linear program, used to plan capacity expansion in the energy sector, allows for greater transparency of model structures and enhanced ability to identify computational performance improvements, as well as a lucid interpretation of its solutions. This type of formulation is employed in several mathematical programming courses at our university as an example of the advantages of best practices; the model more broadly is used widely to inform policy in the U.S. energy sector.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Summary Report for ASC L2 Milestone #4782: Assess Newly Emerging Programming and Memory Models for Advanced Architectures on Integrated Codes

This document serves as a detailed companion to the powerpoint slides presented as part of the ASC L2 milestone review for Integrated Codes milestone #4782 titled “Assess Newly Emerging Programming and Memory Models for Advanced Architectures on Integrated Codes”, due on 9/30/2014, and presented for formal program review on 9/12/2014. The program review committee is represented by Mike Zika (A Program Project Lead for Kull), Brian Pudliner (B Program Project Lead for Ares), Scott Futral (DEG Group Lead in LC), and Mike Glass (Sierra Project Lead at Sandia). This document, along with the presentation materials, and a letter of completion signed by the review committee will act as proof of completion for this milestone.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Multi-Physics Simulations for Molten Salt Reactor Evaluation: Chemistry Modeling and Database Development

To aid in design and licensing of molten salt reactors, a framework integrating the complex interaction of reactor neutronics, thermal hydraulics, and chemistry is being developed within the Department of Energy Advanced Reactor Technology Program’s Molten Salt Reactor (MSR) campaign. The challenges of integrating thermochemical and thermophysical behavior into a multi-physics reactor simulations include the following: (1) population of data needed for refinement of current models and development of nonexistent models through experimental measurements, first principles calculations, and development of a machine learning approach (2) thermochemical and thermophysical model development, (3) further development of Thermochimica, an open-source efficient equilibrium solver used to link thermochemical models to the multi-physics code, (4) a framework for integrating kinetic phenomena: nucleation, precipitation, mass/heat transport, and corrosion models, and (5) a computational environment to efficiently utilize the data and models within a multi-physics modeling tool. These challenges are being addressed through a collaboration among Oak Ridge National Laboratory, Argonne National Laboratory, the University of South Carolina, and the University of Ontario Institute of Technology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Fabrication and Evaluation of Weldments for Alloy 709 Commercial Heat Plates (FY 2019)

Alloy 709 is an attractive candidate construction material for the Sodium Fast Reactor (SFR). As part of the Alloy 709 Intermediate Term Test Program, this work covers the development of the technical basis for weld fabrication and weld qualification of Alloy 709. The phase of research reported herein focused on the issue of weld solidification cracking recently experienced in some experimental heats of Alloy 709 with relatively high phosphorous levels. The computational simulation results revealed that that phosphorous has the most important impact on solidification behavior. Phosphorous is expected to significantly increase the susceptibility of weld solidification cracking of Alloy 709. A strategy to successfully weld Alloy 709 having wide range of chemistries without weld solidification cracking is developed to support ASME code qualification. The strategy includes use of low phosphorous level weld wire to weld Alloy 709 base metal having relatively high phosphorous. The welding experiment and subsequent weld qualification testing per ASME Section IX, conducted this reporting period, confirmed that a commercial heat Alloy 709 with 140wppm phosphorous can be successfully welded using a low phosphorous (20wppm) weld wire using the gas tungsten arc welding process and welding input levels typically used by industry. On the other hand, micro cracks are formed when the phosphorous level in the weld wire increases to 140wppm, which requires additional R&D to develop appropriate welding techniques to produce code qualified welds.

36 MATERIALS SCIENCE↗
DOE OSTI2015

SU-E-T-235: Data Mining for Evaluating Treatment Performances Over a Large Quantity of Data to Monitor and Improve SBRT Workflow

Purpose: To quality assure a large quantity of retrospective treatment cases for treatment performances by randomly sampling is inefficient. Here we provide a method to efficiently monitor and investigate the QA of SBRT workflow over Mosaiq. Methods: The code developed with Microsoft SQL Server Management Studio 2008R2 and VBA was used for retrieving and sorting data from Mosaiq (version 2.3–2.6 during 2012–2015). SBRT patients were filtered by fractional dose over 350cGy and total fraction number less than 6, which SBRT prescriptions were defined. The quality assurance on the SBRT workflow was focused on the treatment deliveries such as patient positioning setup, CBCT indicated offsets and couch shifted corrections. The treatment delivery were done by Varian Truebeam systems and the record/verify by Mosaiq. Results: Total 82 SBRT patients corresponding to 103 courses and 854 CBCT images were found by the retrieval query. Most centers record daily pre-treatment (Pre-Tx: before treatment shift) image-guided shifts along treatment course for inter-fraction motion record, and it is useful to also verify it with post-treatment imaging (Post-Tx: after treatment CBCT verification) to verify intra-fraction motion. Analyzing the details of daily recorded shifts can reveals the information of patient-setup and staff’s record/verify behaviors. 3 examples were provided as solid evidences and on-going rectification for preventing future mistakes. Conclusions: The report gave feasible examples for inspector to verify a large amount of data during site investigation. This program can also be extended to a scheduled data mining with software to periodical analyze the timely records in Mosaiq, for example, a various control charts for different QA purposes. As the current trend of automation in radiation therapy field, the data mining would be a necessary tool in the future, just as the automatic plan quality evaluation has been under development in Eclipse.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Stochastic optimization of GeantV code by use of genetic algorithms

GeantV is a complex system based on the interaction of different modules needed for detector simulation, which include transport of particles in fields, physics models simulating their interactions with matter and a geometrical modeler library for describing the detector and locating the particles and computing the path length to the current volume boundary. The GeantV project is recasting the classical simulation approach to get maximum benefit from SIMD/MIMD computational architectures and highly massive parallel systems. This involves finding the appropriate balance between several aspects influencing computational performance (floating-point performance, usage of off-chip memory bandwidth, specification of cache hierarchy, etc.) and handling a large number of program parameters that have to be optimized to achieve the best simulation throughput. This optimization task can be treated as a black-box optimization problem, which requires searching the optimum set of parameters using only point-wise function evaluations. Here, the goal of this study is to provide a mechanism for optimizing complex systems (high energy physics particle transport simulations) with the help of genetic algorithms and evolution strategies as tuning procedures for massive parallel simulations. One of the described approaches is based on introducing a specific multivariate analysis operator that could be used in case of resource expensive or time consuming evaluations of fitness functions, in order to speed-up the convergence of the black-box optimization problem.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

FLASH: FPGA-Accelerated Smart Switches with GCN Case Study

Some communication switches, e.g., the Mellanox SHArP and those in the IBM BlueGene clusters, are augmented to process packets at the application level with fixed-function collectives. This approach, however, lacks flexibility, which limits their applicability in diverse and dynamic workloads. Recently, a new type of programmable packet processor, which uses high-level languages, e.g., P4, has emerged as possible candidates. P4-based switches, however, fall short in certain applications, including machine learning, where capabilities not currently supported by P4 are needed. These include more complex calculation, such as sparse computation and fused multiply-accumulate, data-intensive floating point operations, data reuse, and significant memory. The problem addressed here is that such a switch augmentation needs to support: a large amount of state, significant flexible compute capability, and ease of programming, all while maintaining full functionality, including ensuring high throughput, and demonstrating utility. In this work, we propose a programmable look-aside-type accelerator that can be embedded into, or attached to, existing communication switch pipelines and that is capable of processing packets at line-rate. The proposed in-switch accelerator is based on mixing an ISA (subset of RISC-V instructions) with dataflow graphs (found in CGRAs). To augment performance, vector instructions are also supported. To facilitate usability, we have developed a complete toolchain to compile user-provided C/C++ codes to appropriate back-end instructions for configuring the accelerator. While this approach is flexible enough to support various workloads, in this paper, we consider Graph Convolutional Networks (GCNs) as a case study. Experimental results show that this approach considerably improves the performance of distributed GCN applications.

Haghi, Pouya↗