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

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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Calculating Radiation Damage (DPA) from Transmutation Products

This is a poster for an INL poster session. Accurate models for radiation damage are crucial for predicting material performance in radiation environments. The uncertainty of state-of-the-art radiation damage models is large, contributing to excessive safety margins. A major source of this uncertainty is neglecting the effect that transmutation products have on radiation damage. Transmutation products are new nuclides formed by neutron activation during irradiation; they can contribute to radiation damage by additional neutron capture or decay events. Ignoring the contribution of transmutation products leads to a significant underprediction of the radiation damage (e.g., >10% error in 316 stainless steel). This underprediction is accounted for in part by adding larger safety margins to designs. Currently, the state of the art explicitly accounts for only a single transmutation product, namely nickel-59, during the radiation damage calculation. All other transmutation products are assumed to not contribute to the radiation damage, because there is currently no established method to systematically track all or a selection of radiation damage contributions of transmutation products during activation. In the case of nickel-59, the current method is to apply a precalculated correlation that cannot be used for any other nuclide and is largely dependent on all nuclear engineers being experts in this niche topic. This project proposed to methodically find other transmutation products that cause significant radiation damage, and then to develop a general framework for systematically tracking the radiation damage from these nuclides. This was accomplished by combining the radiation damage calculation into the transmutation calculation already performed for irradiated structural materials. The key idea of our framework is to introduce radiation-damage "pseudo-nuclides" to the list of nuclides used in the transmutation analysis. This allows radiation damage to be tracked alongside the creation and destruction of transmutation products. The main deliverable of this project is a general framework for computing radiation damage while the damaged material undergoes transmutation; this capability allows a significantly more accurate estimation of radiation damage, and in turn reduce required safety margins thereby reducing the cost to construct reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

IDAES Workshop Introduction

This work presents a basic overview of the IDAES Computational Modeling Framework.

Zamarripa-Perez, Miguel A.↗
DOE OSTI2022

A Framework for Error-Bounded Approximate Computing, with an Application to Dot Products

Approximate computing techniques, which trade off the computation accuracy of an algorithm for better performance and energy efficiency, have been successful in reducing computation and power costs in several domains. However, error sensitive applications in high-performance computing are unable to benefit from existing approximate computing strategies that are not developed with guaranteed error bounds. While approximate computing techniques can be developed for individual high-performance computing applications by domain specialists, this often requires additional theoretical analysis and potentially extensive software modification. Hence, the development of low-level error-bounded approximate computing strategies that can be introduced into any high-performance computing application without requiring additional analysis or significant software alterations is desirable. In this paper, we provide a contribution in this direction by proposing a general framework for designing error-bounded approximate computing strategies and apply it to the dot product kernel to develop \bf qdot---an error-bounded approximate dot product kernel. Following the introduction of qdot, here we perform a theoretical analysis that yields a deterministic bound on the relative approximation error introduced by qdot. Empirical tests are performed to illustrate the tightness of the derived error bound and to demonstrate the effectiveness of qdot on a synthetic dataset, as well as two scientific benchmarks---the conjugate gradient (CG) and power methods. In some instances, using qdot for the dot products in CG can result in many components being quantized to half precision without increasing the iteration count required for convergence to the same solution as CG using a double precision dot product.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

SU-E-I-02: A Framework to Perform Batch Simulations of Computational Voxel Phantoms to Study Organ Doses in Computed Tomography Using a Commercial Monte Carlo Software Package

Purpose: ImpactMC (CT Imaging, Erlangen, Germany) is a Monte Carlo (MC) software package that offers a GPU enabled, user definable and validated method for 3D dose distribution calculations for radiography and Computed Tomography (CT). ImpactMC, in and of itself, offers limited capabilities to perform batch simulations. The aim of this work was to develop a framework for the batch simulation of absorbed organ dose distributions from CT scans of computational voxel phantoms. Methods: The ICRP 110 adult Reference Male and Reference Female computational voxel phantoms were formatted into compatible input volumes for MC simulations. A Matlab (The MathWorks Inc., Natick, MA) script was written to loop through a user defined set of simulation parameters and 1) generate input files required for the simulation, 2) start the MC simulation, 3) segment the absorbed dose for organs in the simulated dose volume and 4) transfer the organ doses to a database. A demonstration of the framework is made where the glandular breast dose to the adult Reference Female phantom, for a typical Chest CT examination, is investigated. Results: A batch of 48 contiguous simulations was performed with variations in the total collimation and spiral pitch. The demonstration of the framework showed that the glandular dose to the right and left breast will vary depending on the start angle of rotation, total collimation and spiral pitch. Conclusion: The developed framework provides a robust and efficient approach to performing a large number of user defined MC simulations with computational voxel phantoms in CT (minimal user interaction). The resulting organ doses from each simulation can be accessed through a database which greatly increases the ease of analyzing the resulting organ doses. The framework developed in this work provides a valuable resource when investigating different dose optimization strategies in CT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Building an Integrated Ecosystem of Computational and Observational Facilities to Accelerate Scientific Discovery

Future scientific discoveries will rely on flexible ecosystems that incorporate modern scientific instruments, high performance computing resources, parallel distributed data storage, and performant networks across multiple, independent facilities. In addition to connecting physical resources, such an ecosystem presents many challenges in logistics and accessibility, especially in orchestrating computations and experiments that span across leadership computing systems and experimental instruments. Past efforts have typically been application-specific or limited to interfaces for computing resources. This paper proposes a general framework for integrating computation resources and instrument operations, addressing challenges in code development/execution, data staging and collection, software stack, control mechanisms, resource authorization and governance, and hardware integration. We also describe a demonstration use case wherein a Bayesian optimization algorithm running on an edge computing resource guides a scanning probe microscope to autonomously and intelligently characterize a material sample. This science edge ecosystem framework will provide a blueprint for federating multi-institutional, disparate resources and orchestrating scientific workflows across them to enable next-generation discoveries.

Somnath, Suhas↗
DOE OSTI2012

NetSim Project contributions to ns-3

ns-3 is an external (non-LLNL) open-source framework for modeling computer networks. The LLNL NetSim project uses the ns-3 framework to address specific questions in computer network design, operation, and security. As part of the NetSim work, we develop bug fixes, deature enhancements, and new capabilities for the ns-3 framework. The virtual package referenced here, ns-3-contrib, consists of those developments we have (or will) contribute back to the ns-3 project in source code form, for inclusion in future releases of ns-3.

Source record↗
DOE OSTI2014

Computational Design of Metal-Organic Frameworks Based on Stable Zirconium Building Units for Storage and Delivery of Methane

A metal organic framework (MOF) with high volumetric deliverable capacity for methane was synthesized after being identified by computational screening of 204 hypothetical MOF structures featuring (Zr6O4)(OH)(4)(CO2)(n) inorganic building blocks. The predicted MOF (NU-800) has an fcu topology in which zirconium nodes are connected via ditopic 1,4-benzenedipropynoic acid linkers. Based on our computer simulations, alkyne groups adjacent to the inorganic zirconium nodes provide more efficient methane packing around the nodes at high pressures. The high predicted gas uptake properties of this new MOF were confirmed by high-pressure isotherm measurements over a large temperature and pressure range. The measured methane deliverable capacity of NU-800 between 65 and 5.8 bar is 167 cc(STP)/cc (0.215 g/g), the highest among zirconium-based MOFs. High-pressure uptake values of H-2 and CO2 are also among the highest reported. These high gas uptake characteristics, along with the expected highly stable structure of NU-800, make it a promising material for gas storage applications.

Gomez-Gualdron, DA↗
DOE OSTI2013

RAVEN: a GUI and an Artificial Intelligence Engine in a Dynamic PRA Framework

Increases in computational power and pressure for more accurate simulations and estimations of accident scenario consequences are driving the need for Dynamic Probabilistic Risk Assessment (PRA) [1] of very complex models. While more sophisticated algorithms and computational power address the back end of this challenge, the front end is still handled by engineers that need to extract meaningful information from the large amount of data and build these complex models. Compounding this problem is the difficulty in knowledge transfer and retention, and the increasing speed of software development. The above-described issues would have negatively impacted deployment of the new high fidelity plant simulator RELAP-7 (Reactor Excursion and Leak Analysis Program) at Idaho National Laboratory. Therefore, RAVEN that was initially focused to be the plant controller for RELAP-7 will help mitigate future RELAP-7 software engineering risks. In order to accomplish this task, Reactor Analysis and Virtual Control Environment (RAVEN) has been designed to provide an easy to use Graphical User Interface (GUI) for building plant models and to leverage artificial intelligence algorithms in order to reduce computational time, improve results, and help the user to identify the behavioral pattern of the Nuclear Power Plants (NPPs). In this paper we will present the GUI implementation and its current capability status. We will also introduce the support vector machine algorithms and show our evaluation of their potentiality in increasing the accuracy and reducing the computational costs of PRA analysis. In this evaluation we will refer to preliminary studies performed under the Risk Informed Safety Margins Characterization (RISMC) project of the Light Water Reactors Sustainability (LWRS) campaign [3]. RISMC simulation needs and algorithm testing are currently used as a guidance to prioritize RAVEN developments relevant to PRA.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

A new line-of-sight approach to the non-linear Cosmic Microwave Background

We develop the transport operator formalism, a new line-of-sight integration framework to calculate the anisotropies of the Cosmic Microwave Background (CMB) at the linear and non-linear level. This formalism utilises a transformation operator that removes all inhomogeneous propagation effects acting on the photon distribution function, thus achieving a split between perturbative collisional effects at recombination and non-perturbative line-of-sight effects at later times. The former can be computed in the framework of standard cosmological perturbation theory with a second-order Boltzmann code such as SONG, while the latter can be treated within a separate perturbative scheme allowing the use of non-linear Newtonian potentials. We thus provide a consistent framework to compute all physical effects contained in the Boltzmann equation and to combine the standard remapping approach with Boltzmann codes at any order in perturbation theory, without assuming that all sources are localised at recombination.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

IRIS: A Performance-Portable Framework for Cross-Platform Heterogeneous Computing

From edge to exascale, computer architectures are becoming more heterogeneous and complex. The systems typically have fat nodes, with multicore CPUs and multiple hardware accelerators such as GPUs, FPGAs, and DSPs. This complexity is causing a crisis in programming systems and performance portability. Several programming systems are working to address these challenges, but the increasing architectural diversity is forcing software stacks and applications to be specialized for each architecture. As we show, all of these approaches critically depend on their software framework for discovery, execution, scheduling, and data orchestration. To address this challenge, we believe that a more agile and proactive software framework is essential to increase performance portability and improve user productivity. To this end, we have designed and implemented IRIS: a performance-portable framework for cross-platform heterogeneous computing. IRIS can discover available resources, manage multiple diverse programming platforms (e.g., CUDA, Hexagon, HIP, Level Zero, OpenCL, OpenMP) simultaneously in the same execution, respect data dependencies, orchestrate data movement proactively, and provide for user-configurable scheduling. To simplify data movement, IRIS introduces a shared virtual device memory with relaxed consistency among different heterogeneous devices. IRIS also adds an automatic kernel workload partitioning technique using the polyhedral model so that it can resize kernels for a wide range of devices. Our evaluation on three architectures, ranging from Qualcomm Snapdragon to a Summit supercomputer node, shows that IRIS improves portability across a wide range of diverse heterogeneous architectures with negligible overhead.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Towards Z-Next: The Integration of Theory, Experiments, and Computational Simulation in a Bayesian Data Assimilation Framework

Making reliable predictions in the presence of uncertainty is critical to high-consequence modeling and simulation activities, such as those encountered at Sandia National Laboratories. Surrogate or reduced-order models are often used to mitigate the expense of performing quality uncertainty analyses with high-fidelity, physics-based codes. However, phenomenological surrogate models do not always adhere to important physics and system properties. This project develops surrogate models that integrate physical theory with experimental data through a maximally-informative framework that accounts for the many uncertainties present in computational modeling problems. Correlations between relevant outputs are preserved through the use of multi-output or co-predictive surrogate models; known physical properties (specifically monotoncity) are also preserved; and unknown physics and phenomena are detected using a causal analysis. By endowing surrogate models with key properties of the physical system being studied, their predictive power is arguably enhanced, allowing for reliable simulations and analyses at a reduced computational cost.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Average Rényi entanglement entropy in Gaussian boson sampling

Recently, many experiments have been conducted with the goal of demonstrating a quantum advantage over classical computation. One popular framework for these experiments is Gaussian boson sampling, where quadratic photonic input states are interfered via a linear optical unitary and subsequently measured in the Fock basis. In this paper, we study the modal entanglement of the output states in this framework just before the measurement stage. Specifically, we compute Page curves as measured by various Rényi- α entropies, where the Page curve describes the entanglement between two partitioned groups of output modes averaged over all linear optical unitaries. We derive these formulas for α = 1 (i.e., the von Neumann entropy) and, more generally, for all positive integer α , in the asymptotic limit of infinite number of modes and for input states that are composed of single-mode-squeezed-vacuum state with equal squeezing strength. We then analyze the limiting behaviors when the squeezing is small and large. Having determined the averages, we then explicitly calculate the Rényi- α variance for integers α > 1 and are able to show that these entropies are weakly typical. Published by the American Physical Society 2025

Youm, Jason (ORCID:0009000057597782)↗
DOE OSTI2022

Sequential Decision Making (SDM) for Mesh Refinement and Model Selection in Multiscale, Multi-Physics Applications

Intelligent automation and decision support are needed to enhance computational efficiency and robustness in multiscale and multi-physics problems, including materials science, manufacturing, and climate and weather modeling. Current scientific computing approaches for enabling decisions by scientists fail to explore the role of learning, reasoning, and probabilistic planning. Often these decisions are not performed in real-time during the computation but are made prior to the start of the computation, which must be interrupted in order to make changes to the prior choices. Such interruptions at different stages of the computation increase the total computing time and the need for a human expert to frequently monitor the results. State of art scientific computing methods consist of rule-based algorithms that cannot automatically adapt to a dynamically changing computing environment. The development of a Sequential Decision Making (SDM) framework will automate scientific computing by optimizing the policies for mesh refinement, time-stepping, model and algorithm selection, resource allocation, and pre and post-processing. Our agent SDM framework for scientific computing will consist of data-driven learning (Classifier), automated reasoning (contextual knowledge), and probabilistic planning (Reinforcement Learning). In this project, we focused on three problems to demonstrate our SDM framework on a set of ordinary and partial differential equations. Classification of Lorenz system regions using Feed-Forward Neural Networks examined learning in the SDM framework. On the other hand, reasoning and planning in the SDM framework were used in two problems: adaptive time-stepping for nonlinear ODEs using on-policy RL algorithms, and adaptive mesh refinement for 2-D PDEs using off-policy RL algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Realizing the data-driven, computational discovery of metal-organic framework catalysts

Metal-organic frameworks (MOFs) have been widely investigated for challenging catalytic transformations due to their well-defined structures and high degree of synthetic tunability. These features, at least in principle, make MOFs ideally suited for a computational approach towards catalyst design and discovery. Nonetheless, the widespread use of data science and machine learning to accelerate the discovery of MOF catalysts has yet to be substantially realized. In this review, we provide an overview of recent work that sets the stage for future high-throughput computational screening and machine learning studies involving MOF catalysts. This is followed by a discussion of several challenges currently facing the broad adoption of data-centric approaches in MOF computational catalysis, and we share possible solutions that can help propel the field forward.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Field-Driven Simulations to Probe the Impact of Ionic Correlations on Solution Transport Coefficients in Binary, Ternary, and Reciprocal Quaternary Aqueous Electrolytes

Ionic correlations play a critical role in governing transport properties of mixed-salt aqueous electrolyte solutions, yet their quantitative characterization remains challenging, particularly for multicomponent aqueous systems. Here, we develop a general nonequilibrium molecular dynamics framework to efficiently compute Onsager transport coefficients and ionic correlations in mixed-salt aqueous solutions. Using field-driven simulations, we obtain accurate Onsager matrices for LiCl/ KCl, KCl/KBr, and LiBr/KCl electrolyte solutions with significantly reduced computational cost relative to equilibrium Green−Kubo methods. The framework enables direct assessment of how attractive cation−anion and repulsive like-ion interactions contribute to conductivity and salt diffusivity across compositions. While static ion pairing exhibits strong composition dependence, dynamic ion correlations remain nearly invariant, leading to constant deviations from Nernst−Einstein predictions. These results highlight the disconnect between static ion association and dynamic transport correlations, and they establish a transferable approach for analyzing ion transport in complex electrolyte environments relevant to separation processes and electrochemical systems.

36 MATERIALS SCIENCE↗
DOE OSTI2017

High-Throughput Computational Screening of Multivariate Metal–Organic Frameworks (MTV-MOFs) for CO 2 Capture

Multivariate metal–organic frameworks (MTV-MOFs) contain multiple linker types within a single structure. Arrangements of linkers containing different functional groups confer structural diversity and surface heterogeneity and result in a combinatorial explosion in the number of possible structures. Here, we carried out high-throughput computational screening of a large number of computer-generated MTV-MOFs to assess their CO 2 capture properties using grand canonical Monte Carlo simulations. The results demonstrate that functionalization enhances CO 2 capture performance of MTV-MOFs when compared to their parent (unfunctionalized) counterparts, and the pore size plays a dominant role in determining the CO 2 adsorption capabilities of MTV-MOFs irrespective of the combinations of the three functional groups (-F, -NH 2 , and -OCH 3 ) that we explored. We also found that the functionalization of parent MOFs with small pores led to larger enhancements in CO 2 uptake and CO 2 /N 2 selectivity than functionalization in larger-pore MOFs. Free energy contour maps are presented to visually compare the influence of linker functionalization between frameworks with large and small pores.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

OpenACC to FPGA: A Framework for Directive-based High-Performance Reconfigurable Computing

This paper presents a directive-based, high-level programming framework for high-performance reconfigurable computing. It takes a standard, portable OpenACC C program as input and generates a hardware configuration file for execution on FPGAs. We implemented this prototype system using our open-source OpenARC compiler; it performs source-to-source translation and optimization of the input OpenACC program into an OpenCL code, which is further compiled into a FPGA program by the backend Altera Offline OpenCL compiler. Internally, the design of OpenARC uses a high- level intermediate representation that separates concerns of program representation from underlying architectures, which facilitates portability of OpenARC. In fact, this design allowed us to create the OpenACC-to-FPGA translation framework with minimal extensions to our existing system. In addition, we show that our proposed FPGA-specific compiler optimizations and novel OpenACC pragma extensions assist the compiler in generating more efficient FPGA hardware configuration files. Our empirical evaluation on an Altera Stratix V FPGA with eight OpenACC benchmarks demonstrate the benefits of our strategy. To demonstrate the portability of OpenARC, we show results for the same benchmarks executing on other heterogeneous platforms, including NVIDIA GPUs, AMD GPUs, and Intel Xeon Phis. This initial evidence helps support the goal of using a directive-based, high-level programming strategy for performance portability across heterogeneous HPC architectures.

Lee, Seyong↗