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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

LaplaceInterpolation.jl: A Julia package for fast interpolation on a grid

We implement a linear-time algorithm for interpolation on a regular multidimensional grid in the Julia language. The algorithm is an approximate Laplace interpolation (Press, 1992) when no parameters are given; and when parameters m ∈ Z and ϵ > 0 are set, the interpolant approximates a Matérn kernel, of which radial basis functions and polyharmonic splines are a special case. We implement, in addition, Neumann, Dirichlet (trivial), and average boundary conditions with potentially different aspect ratios in the different dimensions. The interpolant functions in arbitrary dimensions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

CurvilinearGrids.jl: A Julia package for curvilinear coordinate transformations

Finite-difference discretizations of partial differential equations are widespread throughout the scientific community. Oftentimes finite-differences are used to compute spatial gradients of fields on a discrete grid, which is typically a uniform or rectilinear Cartesian mesh. Arbitrary multidimensional geometry is difficult to discretize directly with finite differences, however, due to non-uniform grid spacing and non-orthogonality. Curvilinear coordinate transformations can be used as a strategy to enable arbitrary geometry. While these curvilinear transformations are straightforward, the governing PDEs require additional terms (metrics) and must adhere to strict conservation laws; these criteria complicate the application of the transformation and require careful implementation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

FAIRLinked: Data FAIRification Tools for Materials Data Science

FAIRLinked is a software package created to support the FAIRification of materials science data, ensuring proper alignment with FAIR principles: Findable, Accessible, Interoperable, and Reusable. It is built to be compatible with MDS-Onto, an ontology designed to capture the semantics of various types of materials data, enabling integration and sharing across different research workflows. The package is subdivided into three subpackages: InterfaceMDS, RDFTableConversion, and QBWorkflow. The first subpackage, InterfaceMDS allows users to search for terms using either string search or various filters, explore different domains and subdomains, and add terms to MDS-Onto. RDFTableConversion is used for serialization and deserialization of data from CSV into JSONLDs and vice versa in a way that captures the semantics of the data using MDS-Onto. Lastly, QBWorkflow is a serialization and deserialization workflow that incorporates RDF Data Cube vocabulary, useful for working with multidimensional datasets. By offering these packages, FAIRLinked lowers the barrier of creating FAIR, machine-actionable data for researchers in the materials science community.

FAIR↗
DOE OSTI2025

Towards universal unfolding of detector effects in high-energy physics using denoising diffusion probabilistic models

Correcting for detector effects in experimental data, particularly through unfolding, is critical for enabling precision measurements in high-energy physics. However, traditional unfolding methods face challenges in scalability, flexibility, and dependence on simulations. We introduce a novel approach to multidimensional object-wise unfolding using conditional Denoising Diffusion Probabilistic Models (cDDPM). Our method utilizes the cDDPM for a non-iterative, flexible posterior sampling approach, incorporating distribution moments as conditioning information, which exhibits a strong inductive bias that allows it to generalize to unseen physics processes without explicitly assuming the underlying distribution. Our results highlight the potential of this method as a step towards a "universal" unfolding tool that reduces dependence on truth-level assumptions, while enabling the unfolding of a wide range of measured distributions with improved adaptability and accuracy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Hard exclusive $\pi^{+}n$ electro production beam spin asymmetries off the proton in the GPD and TDA regimes

Hard exclusive \pi^{+} π + n electro-production can be used to gain access to the 3D nucleon structure. The QCD factorisation mechanism in the “nearly forward region” ( t/Q^{2} t / Q 2 small) allows a description by Generalized Parton Distributions (GPDs), while for the “nearly backward” kinematic region ( u/Q^{2} u / Q 2 small) a description based on nucleon to pion transitions (TDAs) is available. The paper presents a measurement of single beam spin asymmetries to extract the A^{\sin\phi}_{LU} A L U sin ϕ moments and the related cross section ratio \sigma_{LT^\prime}/\sigma_{0} σ L T ′ / σ 0 from the hard exclusive \pi^{+} π + channel off the unpolarized hydrogen target in a wide range of kinematics based on data taken with the CLAS spectrometer at Jefferson Lab. In addition, under forward angles a detailed multidimensional study will be presented based on CLAS12 data and compared to theoretical predictions.

Diehl, Stefan↗
DOE OSTI2016

Device Scale Modeling of Solvent Absorption using MFIX-TFM

Recent climate change is largely attributed to greenhouse gases (e.g., carbon dioxide, methane) and fossil fuels account for a large majority of global CO 2 emissions. That said, fossil fuels will continue to play a significant role in the generation of power for the foreseeable future. The extent to which CO 2 is emitted needs to be reduced, however, carbon capture and sequestration are also necessary actions to tackle climate change. Different approaches exist for CO 2 capture including both post-combustion and pre-combustion technologies, oxy-fuel combustion and/or chemical looping combustion. The focus of this effort is on post-combustion solvent-absorption technology. To apply CO 2 technologies at commercial scale, the availability and maturity and the potential for scalability of that technology need to be considered. Solvent absorption is a proven technology but not at the scale needed by typical power plant. The scale up and down and design of laboratory and commercial packed bed reactors depends heavily on the specific knowledge of two-phase pressure drop, liquid holdup, the wetting efficiency and mass transfer efficiency as a function of operating conditions. Simple scaling rules often fail to provide proper design. Conventional reactor design modeling approaches will generally characterize complex non-ideal flow and mixing patterns using simplified and/or mechanistic flow assumptions. While there are varying levels of complexity used within these approaches, none of these models resolve the local velocity fields. Consequently, they are unable to account for important design factors such as flow maldistribution and channeling from a fundamental perspective. Ideally design would be aided by development of predictive models based on truer representation of the physical and chemical processes that occur at different scales. Computational fluid dynamic (CFD) models are based on multidimensional flow equations with first principle foundations. CFD models can include a more accurate physical description of flow processes and be modified to include more complex behavior. Wetting performance and spatial liquid distribution inside the absorber are recognized as weak areas of knowledge requiring further investigation. CFD tools offer a possible method to investigating such topics and gaining a better understanding of their influence on reactor performance. This report focuses first on describing a hydrodynamic model for countercurrent gas-liquid flow through a packed column and then on the chemistry, heat and mass transfer specific to CO 2 absorption using monoethanolamine (MEA). The indicated model is implemented in MFIX, a CFD open source software package. The user defined functions needed to build this model are described in detail along with the keywords for the corresponding input file. A test case is outlined along with a few results. The example serves to briefly illustrate the developed CFD tool and its potential capability to investigate solvent absorption.

09 BIOMASS FUELS↗
DOE OSTI2017

Inverse and Predictive Modeling

The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.

58 GEOSCIENCES↗
DOE OSTI2017

ATDM LANL FleCSI: Topology and Execution Framework

FleCSI is a compile-time configurable C++ framework designed to support multi-physics application development. As such, FleCSI attempts to provide a very general set of infrastructure design patterns that can be specialized and extended to suit the needs of a broad variety of solver and data requirements. This means that FleCSI is potentially useful to many different ECP projects. Current support includes multidimensional mesh topology, mesh geometry, and mesh adjacency information, n-dimensional hashed-tree data structures, graph partitioning interfaces, and dependency closures (to identify data dependencies between distributed-memory address spaces). FleCSI introduces a functional programming model with control, execution, and data abstractions that are consistent with state-of-the-art task-based runtimes such as Legion and Charm++. The model also provides support for fine-grained, data-parallel execution with backend support for runtimes such as OpenMP and C++17. The FleCSI abstraction layer provides the developer with insulation from the underlying runtimes, while allowing support for multiple runtime systems, including conventional models like asynchronous MPI. The intent is to give developers a concrete set of user-friendly programming tools that can be used now, while allowing flexibility in choosing runtime implementations and optimizations that can be applied to architectures and runtimes that arise in the future. This project is essential to the ECP Ristra Next-Generation Code project, part of ASC ATDM, because it provides a hierarchically parallel programming model that is consistent with the design of modern system architectures, but which allows for the straightforward expression of algorithmic parallelism in a portably performant manner.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Improved Safety Margin Characterization of Risk from Loss of Offsite Power

Original intent: The original intent of this task was “support of the Risk-Informed Safety Margin Characteristic (RISMC) methodology in order” “to address … efficiency of computation so that more accurate and cost-effective techniques can be used to address safety margin characterizations” (S. M. Hess et al., “Risk-Informed Safety Margin Characterization,” Procs. ICONE17, Brussels, July 2009, CD format). It was intended that “in Task 1 itself this improvement will be directed toward upon the very important issue of Loss of Offsite Power (LOOP) events,” more specifically toward the challenge of efficient computation of the multidimensional nonrecovery integral that has been discussed by many previous contributors to the theory of nuclear safety. It was further envisioned that “three different computational approaches will be explored,” corresponding to the three subtasks listed below; deliverables were tied to the individual subtasks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Materials Dynamics

The Materials Dynamics area of leadership focuses on understanding structure-properties-performance relationships for the extreme conditions of dynamic loading. This research encompasses controlled synthesis of materials to meet dynamic performance requirements and requires computational coupling across length and time scales for three-dimensional microstructure modeling. For this leadership area, we define dynamic loading as strain rates greater than 103/s. A key grand challenge of this area is to predict and measure the evolution of microstructural phases, defect structures, and electronic structure under dynamic conditions while also dynamically measuring local temperature to understand transition states. Solving this challenge will require agile, multidimensional data analysis and interpretation capability.

36 MATERIALS SCIENCE↗
DOE OSTI2018

R&D Roadmap for Enhanced Resilient Plant Systems, Metrics, Scenarios, Risk Analysis, and Modeling and Simulation

The nuclear industry and the U.S. Department of Energy are jointly developing accident tolerant fuel (ATF) which has the potential to greatly improve the safety and economic performance of the current fleet of NPPs. The combination of ATF with the optimal use of FLEX equipment (deployed as an industry response to the Fukushima Daiichi Nuclear Power Plant (NPP) accident in 2012), enhancements on plant components and systems, incorporation of augmented cooling systems or new passive cooling systems, and improvements in fuel cycle efficiency, have the potential to render the NPPs more efficient and resilient to events (both internally and externally initiated) that have the potential to challenge the integrity of the fuel or plant containment. In this report, combinations of these plant improvements are called Enhanced Resilient Plant Systems. In order to accelerate the development and deployment of the components that comprise Enhanced Resilient Plant Systems, a well-integrated toolkit that is fuel/clad-, fuel cycle-, systems-, and scenario-centric is required to provide a ready platform for the analysis of the advanced ATF fuel and cladding materials that are being developed, as well as plant systems modifications to achieve enhanced resiliency. Since the deployment of these enhancements will comprise a risk/benefit tradeoff of their impacts on plant safety and economics, the envisioned toolkit will need to be capable to characterizing the multidimensional aspects associated with these evaluations and provide decision-makers with information that can optimize the technological, economic, and safety benefits that are to be obtained. The Risk-Informed Systems Analysis (RISA) Pathway of the DOE Light Water Reactor Sustainability (LWRS) Program initiated several research tasks to develop analytical capabilities to support the industry in the transition to the ATF and Enhanced Resilient Plant Systems. The general idea behind the initiative is the development of an integrated evaluation approach which combines the plant probabilistic risk assessment (PRA) models with Multi-Physics Best Estimate Plus Uncertainty (MP-BEPU) analyses in a seamless fashion. The integrated evaluation framework aims at enabling plant system configuration variations to be studied with speed and precision, including detailed risk and benefit assessments of introducing ATF and Enhanced Resilient Plant Systems into current LWR plants to achieve both safety and operational performance enhancements. The focus of this report is to present an integrated research and development (R&D) roadmap to identify and perform high value evaluations of proposed ATF and Enhanced Resilient Plant Systems concepts to identify both the technical (e.g. benefits to risk, safety, and operational margins) and the economic (i.e. business and cost) elements associated with industry adoption of the technology. The integrated evaluation approach will be developed to support the development, testing, qualification, licensing, and deployment of ATF and Enhanced Resilient Plant Systems technology that is capable of achieving substantial safety and economic improvements as well as timely widespread adoption by the US nuclear industry.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Summary of Expansions and Updates in GREET® 2018

The GREET® (Greenhouse gases, Regulated Emissions, and Energy use in Transportation) model has been developed by Argonne National Laboratory (Argonne) with the support of the U.S. Department of Energy(DOE). GREET is a life-cycle analysis (LCA) tool, structured to systematically examine energy and environmental effects of a wide variety of transportation fuels and vehicle technologies in major transportation sectors (i.e., road, air, marine, and rail). There are two GREET modeling platforms; GREET Excel is a multidimensional spreadsheet model that provides a comprehensive LCA tool, and GREET.Net provides an interactive graphical toolbox to perform LCA. The GREET 2018 release includes expansions and updates for both platforms, and this report provides a summary of the release.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Solar-Driven Supercritical CO2 Brayton Cycle Testing (ANC)

Sandia National Laboratories (SNL) and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) collaborated to conduct an investigation of the dynamic performance of a solar-driven supercritical CO2 (SCO2) Brayton cycle. This was performed on the SNL recompression closed Brayton cycle (RCBC) at the Nuclear Energy Systems Laboratory / Brayton Lab. Historical data were provided to CSIRO since the test assembly remained inoperable throughout the project. Also, multidimensional trade studies were generated using the SNL Recompression closed Brayton cycle Engineering and Trade Studies (RETS©) tool.

42 ENGINEERING↗
DOE OSTI2019

Optical Two-Dimensional Spectroscopy of Semiconductor Quantum Wells and Quantum Dots (Final Report)

The goal of this program was to implement optical 2-dimensional coherent spectroscopy and apply it to electronic excitations, including excitons, in semiconductors. Specif ically of interest were epitaxially grown quantum wells that exhibit disorder due to well width fluctuations and quantum dots. In both cases, 2-D spectroscopy provided information regarding coupling among excitonic localization sites. In the first year of the project, a significant fraction of our progress was on quantum wells, rather than quantum dots, which have been more our focus in recent years. The two main results were 1) providing an understanding of the polarization dependence of the homogeneous linewidth in quantum wells based on using an anharmonic boson model, and 2) using multidimensional spectroscopy to observe spectral diffusion due to exciton migration amongst within localized states due to disorder.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

A simple json format for storing and exchanging code calculation and experimental results

Calculation codes and code systems give their results in a plethora of different formats and/or files. For instance, MCNP gives a user the MCNP specific output file, the mctal file, the ptrac file, etc. while PARTISN has a PARTISN specific output file and a number of binary files with various kinds of information about the calculation. However, the results we are interested in are always the same. These can be single values such as the effective multiplication factor k eff or the effective delayed neutron fraction β eff . It can be histogram data (such as particle spectra, reaction rates, sensitivity profiles, etc.) or even pointwise data (for example the nuclide composition as a function of time, etc.). It is also worthwhile to note that this would also apply to experimental data that we may wish to compare with as well. Calculation and experimental results can basically be split into two components: attributes (or metadata) and the actual result itself. Attributes represent information about the result that are not necessarily required for its’ interpretation but that are still good to have. Often, these attributes are what we will want to filter results on. This means that these attributes are often the answer to specific questions we may have about the result. The following is a non exhaustive list of such basic questions: what type of result is it: e.g. the effective multiplication factor k eff , the effective delayed neutron fraction β eff , etc.; which case does it relate to: e.g. a specific ICSBEP case such as PU-MET-FAST-001-001; which code and version produced this result: e.g. MCNP 6.2, etc.; which nuclear data library was used to obtain the result: e.g. ENDF/B-VIII.0 or JEFF 3.3; which nuclide and reaction does it apply to (for reaction rates and sensitivity profiles); which volume in the geometry was it calculated in? The second component of the calculation or experimental result is the actual numerical data of the result. It is this data that we want to compare, exchange, plot, interpret, etc. It consists of values with optional uncertainties and units. It also includes the underlying structure (if any structure is required to interpret the result). Depending on the type of result, it may be a single value (e.g. the effective multiplication factor), an array of values (e.g. a reaction rate or particle spectrum) or even a multidimensional array. In order to exchange results between different organisations, for instance for benchmarking, people often resort to spreadsheets or custom text files that need to be filled out. While this approach works well if little information is to be exchanged, it can become cumbersome when dealing with large amounts of data (like for instance particle spectra for thousands of cases). In addition, such an approach does not always capture the metadata we may be interested in to allow for automated processing of these files. The purpose of this report is to propose a standardised json structure to store any type of result and present an associated python interface to interact with these results directly. For such a structure to be useful, it must fulfill a specific set of functional requirements that we try to meet with this proposal. The format must allow for the calculation result to stand by itself. This means that we should not have to look in multiple places to be able to understand it. For example, if the result relates to a particle spectrum, the group structure in which the result is given should be stored with it. The format should be code agnostic. This means that we do not need to know where it came from to understand it. The format should be result type agnostic. This means that the format must be generic enough so that we can even use it for results we have not thought of yet. The format should be relatively easy to interact with through different scripting or programming languages (i.e. the machine interface) while still providing a clean structure that can be used directly by any human.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

ZND Verification Tests for Reactive Burn Models in FLAG

The ZND theory, named after Zeldovich, von Neumann, and Doering, provides a simple model for one-dimensional ideal steady-state detonation. It assumes that the detonation wave front starts with a shock that is a discontinuous jump and is followed by a finite-length reaction zone. Reactive burn (also called reactive flow) models are based on ZND theory, as they model the shock initiation and detonation process with a finite reaction rate. The ZND wave propagation test is essentially the only available test case where an analytic solution exists for verification of reactive burn models in numerical codes. However, there are extensions and variants of the ZND test that have been devised for verification of multidimensional flows. The objective of this work is to provide verification of the reactive burn models currently implemented in the Lagrangian hydrocode FLAG and investigate the influence of mesh resolution, artificial viscosity models, and the Arbitrary Lagrangian-Eulerian (ALE) Euler relaxer on the simulation results. The burn models of interest are the Wescott-Stewart-Davis (WSD) model, the Scaled Uniform Reactive Flow (SURF) model (specifically with the SURFplus model extension), and the Arrhenius shock temperature state dependent WSD (AWSD) model. Previously, Ralph Menikoff has used ZND tests for verification of the SURF and SURFplus models in the Eulerian hydrocode xRAGE. The ZND tests here are somewhat different than the approach by Menikoff. In particular, we use a piston-driven ZND detonation wave (via a prescribed constant velocity boundary condition) in a Lagrangian framework whereas Menikoff had a ZND wave followed by a invariant rarefaction wave. The xRAGE simulations were carried out on uniform grids and adaptive mesh refinement (AMR) grids. Although AMR was recently implemented in FLAG and now fully functional for 2D simulations, it will not be evaluated with ZND tests at this time. Some work has been done previously for validating the reactive burn models in FLAG. For example, the validation studies of SURF with shock-to-detonation (SDT) tests, cylinder tests, and gap-stick tests. Further validation of the AWSD, WSD, and SURF models is described in for SDT, multi-shock, cylinder, and corner-turning tests. Recently, a large parameter study with approximately twenty SDT tests was performed to validate the AWSD, WSD, SURF, and SURFplus models while also investigating mesh resolution and artificial viscosity settings. To the best of our knowledge, the current work represents the first documented verification of these burn models in FLAG. The reader should note that many of the tables and figures in this report use units of cm/g/µs, which are the default units for FLAG. However, some lengths and velocities are expressed in µm and mm/µs, respectively, which are typical units for detonation analyses.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Metaheuristic Optimization Tool

The metaheuristic optimization tool (MOT) is a generic python module for solving a real value, multiobjective, multidimensional optimization problem. Users design their optimization problem by defining search variables, simulations, evaluation metric functions, and constraints. Search variables and constraints are straightforwardly added via python object methods, while simulations and metric functions used for search variable set evaluation must be implemented as a python class. Final optimum result(s) are provided in a tabular text file, as well as search history if needed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

FLOP for FLAG Output Plotting

The Los Alamos hydrodynamics code, FLAG, is capable of dumping many types of output for many different variables in an array of formats. While some outputs are best viewed in a multidimensional engineering analysis visualization software, others are best viewed as 1-D “this versus that” curves. During development of a FLAG model, it is frequently required to quickly assess a model’s performance by reviewing such curves, and doing so may involve writing scripts repeatedly, adapting them each time to a specific model’s parameters. This report describes an application developed specifically to improve user efficiency in reviewing 1-D curve dumps from FLAG.

42 ENGINEERING↗