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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Determining the Cause of Reduced Concurrent Flame Spread over Thin Solid Fuels in Low Pressure and Low Gravity

The spread of flames over the surface of solid combustible materials is affected by the environmental conditions. Variables such as flow velocity, oxygen concentration, ambient pressure, partial gravity or microgravity, may change the material flammability and influence the fire dynamics. This is an important fire safety issue for space exploration vehicles and space habitats which will very likely have different environments than those currently used on the International Space Station, such as reduced pressure and enriched oxygen concentration (SEA). However, testing experimentally the materials to be used and qualified for space exploration under these conditions is a difficult and expensive task. Here, numerical modeling is used to understand the dominant physico-chemical processes on the concurrent flame spread over thin fabrics under reduced ambient pressure (and in turn, buoyancy) and variable gravitational conditions. The numerical model uses the Fire Dynamics Simulator (FDS6) code with a single-step Arrhenius reaction rate for the solid phase decomposition. Different models are tested for the gas phase combustion kinetics. The model results are validated with experimental results obtained at similar reduced ambient pressure and flow conditions at 1 g. It is shown that as ambient pressure is reduced the flame spread rate over a thin fabric is also reduced, both experimentally and numerically. Numerical results are compared to an analytical approach previously developed to explain the experimental trends. Further interpretation of the model results provides information regarding the physics of the process and how they are affected by the lower pressure environments and gravity conditions. The results of this work provide guidance for potential on-earth testing for fire safety design in spacecraft and space habitats.

Fire Safety↗
NASA NTRSDate not supplied

Determining the Cause of Reduced Concurrent Flame Spread over Thin Solid Fuels in Low Pressure and Low Gravity

The spread of flames over the surface of solid combustible materials is affected by the environmental conditions. Variables such as flow velocity, oxygen concentration, ambient pressure, partial gravity or microgravity, may change the material flammability and influence the fire dynamics. This is an important fire safety issue for space exploration vehicles and space habitats which will very likely have different environments than those currently used on the International Space Station, such as reduced pressure and enriched oxygen concentration (SEA). However, testing experimentally the materials to be used and qualified for space exploration under these conditions is a difficult and expensive task. Here, numerical modeling is used to understand the dominant physico-chemical processes on the concurrent flame spread over thin fabrics under reduced ambient pressure (and in turn, buoyancy) and variable gravitational conditions. The numerical model uses the Fire Dynamics Simulator (FDS6) code with a single-step Arrhenius reaction rate for the solid phase decomposition. Different models are tested for the gas phase combustion kinetics. The model results are validated with experimental results obtained at similar reduced ambient pressure and flow conditions at 1 g. It is shown that as ambient pressure is reduced the flame spread rate over a thin fabric is also reduced, both experimentally and numerically. Numerical results are compared to an analytical approach previously developed to explain the experimental trends. Further interpretation of the model results provides information regarding the physics of the process and how they are affected by the lower pressure environments and gravity conditions. The results of this work provide guidance for potential on-earth testing for fire safety design in spacecraft and space habitats.

microgravity↗
NASA NTRS2005

Predicting Flows of Rarefied Gases

DSMC Analysis Code (DAC) is a flexible, highly automated, easy-to-use computer program for predicting flows of rarefied gases -- especially flows of upper-atmospheric, propulsion, and vented gases impinging on spacecraft surfaces. DAC implements the direct simulation Monte Carlo (DSMC) method, which is widely recognized as standard for simulating flows at densities so low that the continuum-based equations of computational fluid dynamics are invalid. DAC enables users to model complex surface shapes and boundary conditions quickly and easily. The discretization of a flow field into computational grids is automated, thereby relieving the user of a traditionally time-consuming task while ensuring (1) appropriate refinement of grids throughout the computational domain, (2) determination of optimal settings for temporal discretization and other simulation parameters, and (3) satisfaction of the fundamental constraints of the method. In so doing, DAC ensures an accurate and efficient simulation. In addition, DAC can utilize parallel processing to reduce computation time. The domain decomposition needed for parallel processing is completely automated, and the software employs a dynamic load-balancing mechanism to ensure optimal parallel efficiency throughout the simulation.

LeBeau, Gerald J.↗
DOE OSTI2019

Efficient Solution Methods for Large-scale Optimization Problems Constrained by Time-dependent Partial Differential Equations (Final Report)

The objectives of this project are to devise and analyze efficient solvers for several classes of PDE- constrained optimization problems. The project was initially focused on, but not restricted to time- dependent PDE-constraints such as parabolic equations, time-dependent fluid flows (Navier-Stokes equations), hyperbolic equations (Burgers equation, shallow water equations). Other constraints considered are PDEs with stochastic coefficients. Additional elements of interest are the handling of problems with control and/or state inequality constraints or non-differentiable regularization terms, and boundary controls. A direction of work not initially listed among the goals, which was triggered by conversations with DOE scientists from the Sandia National Labs, is to develop efficient solvers for optimization- based domain-decomposition PDE-solvers. Also targeted in the project are a number of technical issues necessary for facilitating the integration of the methods resulted from this project with existing high-performance packages developed at DOE national labs; this integration is expected to further enable the scientific community to use the novel methods. An example of such a task is the need to develop algebraic multigrid (AMG) versions of the algorithms. A list of accomplishments in several areas is detailed as well as a list of publications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps

Many-to-one maps are ubiquitous in machine learning, from the image recognition model that assigns a multitude of distinct images to the concept of ``cat'' to the time series forecasting model which assigns a range of distinct time-series to a single scalar regression value. While the primary use of such models is naturally to associate correct output to each input, in many problems it is also useful to be able to explore, understand, and sample from a model's fibers, which are the set of input values x such that f(x) = y, for fixed y in the output space. In this paper we show that popular generative architectures are ill-suited to such tasks. Motivated by this we introduce a novel generative architecture, a Bundle Network, based on the concept of a fiber bundle from (differential) topology. BundleNets exploit the idea of a local trivialization wherein a space can be locally decomposed into a product space that cleanly encodes the many-to-one nature of the map. By enforcing this decomposition in BundleNets and by utilizing state-of-the-art invertible components, investigating a network's fibers becomes natural.

Courts, Nicolas C.↗
DOE OSTI2015

Solving a Large Scale Thermal Radiation Problem Using an Interoperable Executive Library Framework on Petascale Supercomputers

We present a novel methodology to compute the transient thermal condi- tion of a set of objects in an open space environment. The governing energy equa- tion and the convective energy transfer are solved by the sparse iterative solvers. The average radiating energy on a set of surfaces is represented by a linear system of the radiosity equations, which is factorized by an out-of-core parallel Cholesky decomposition solver. The coupling and interplay of the direct radiosity solver us- ing GPUs and the CPU-based sparse solver are handled by a light weight software integrator called Interoperable Executive Library (IEL). IEL manages the distribu- tion of data and memory, coordinates communication among parallel processes, and also directs execution of the set of loosely coupled physics tasks as warranted by the thermal condition of the simulated object and its surrounding environment.

Wong, Kwai↗
DOE OSTI2024

Distance preserving machine learning for uncertainty aware accelerator capacitance predictions

Abstract Accurate uncertainty estimations are essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaussian process models are generally regarded as the gold standard for this task; however, they can struggle with large, high-dimensional datasets. Combining deep neural networks with Gaussian process approximation techniques has shown promising results, but dimensionality reduction through standard deep neural network layers is not guaranteed to maintain the distance information necessary for Gaussian process models. We build on previous work by comparing the use of the singular value decomposition against a spectral-normalized dense layer as a feature extractor for a deep neural Gaussian process approximation model and apply it to a capacitance prediction problem for the High Voltage Converter Modulators in the Oak Ridge Spallation Neutron Source. Our model shows improved distance preservation and predicts in-distribution capacitance values with less than 1% error.

43 PARTICLE ACCELERATORS↗
DOE OSTI2017

Partial differential equations preconditioner resilient to soft and hard faults

We present a domain-decomposition-based preconditioner for the solution of partial differential equations (PDEs) that is resilient to both soft and hard faults. The algorithm reformulates the PDE as a sampling problem, followed by a solution update through data manipulation that is resilient to both soft and hard faults. This reformulation allows us to recast the problem as a set of independent tasks, and exploit data locality to reduce global communication. We discuss two different parallel implementations: (a) a single program multiple data (SPMD) version based on a one-to-one mapping between subdomain and MPI processes responsible for both state and computation; and (b) an asynchronous server–client implementation where all state information is held by the servers and clients are designed solely as computational units. We present a scalability comparison of both implementations under nominal conditions, showing efficiency within ~80% for up to 12,000 cores. We present a resilience analysis under different fault scenarios based on the server–client implementation. This framework provides resiliency to hard faults such that if a client crashes, it stops asking for work, and the servers simply distribute the work among all of the other clients alive. Erroneous subdomain solves (e.g. due to soft faults) appear as corrupted data, which is either rejected if that causes a task to fail, or is seamlessly filtered out during the regression stage through a suitable noise model. Three different types of faults are modeled: hard faults modeling nodes (or clients) crashing; soft faults occurring during the communication of the tasks between server and clients; and soft faults occurring during task execution. We demonstrate the resiliency of the approach for a 2D elliptic PDE, and explore the effect of the faults at various failure rates.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

ExaTN

A challenging scientific problem that will benefit from exascale computing is predictive simulation of two- and three-dimensional quantum many-body Hamiltonians in condensed matter physics, together with the related strong entanglement problems in quantum chemistry and quantum computing. Efficient numerical simulations will require new insights into wavefunction compression algorithms that scale on extremely heterogeneous HPC architectures. To address this need, we propose to develop a scalable math library of parallel multi-linear algebraic numerical primitives for hierarchical tensor compression. Our library will subsequently be used for building an efficient, performance-portable HPC framework for simulating strongly correlated quantum systems. In particular, our will will be the first to deliver a massively parallel implementation of the multiscale entanglement renormalization ansatz (MERA), a hierarchical tensor decomposition quantum scheme capable of expressing local expectation values of strongly entangled higher-dimensional quantum systems efficiently. To address the extreme complexity and high computational cost of such schemes, we will automate their parallel implementation by delivering an integrated software framework, ExaTN (Exascale Tensor Networks), that will process hierarchical tensor representations on exascale HPC systems via an asymchronous task-based programming model. As a specific application, our framework will provide scientists with a capability of simulating strongly entangled quantum systems, thus positioning ORNL as a strategic leader in modeling of strongly correlated materials.

McCaskey, AlexanderJ↗
DOE OSTI2017

FleCSPH notes

FleCSI is a compile-time con gurable framework designed to support multi-physics application development. As such, FleCSI provides 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. FleCSI currently supports multi-dimensional mesh topology, geometry, and adjacency information, as well as n-dimensional hashed-tree data structures, graph partitioning interfaces, and dependency closures. FleCSI introduces a functional programming model with control, execution, and data abstractions that are consistent both with MPI and with state-of-the-art, task-based runtimes such as Legion and Charm++. The abstraction layer insulates developers from the underlying runtime, while allowing support for multiple runtime systems including conventional models like asynchronous MPI. The intent is to provide developers with a concrete set of user-friendly programming tools that can be used now, while allowing exibility in choosing runtime implementations and optimization that can be applied to future architectures and runtimes. FleCSI's control and execution models provide formal nomenclature for describing poorly understood concepts such as kernels and tasks. FleCSI's data model provides a low-buy-in approach that makes it an attractive option for many application projects, as developers are not locked into particular layouts or data structure representations. FleCSI currently provides a parallel but not distributed implementation of Binary, Quad and Oct-tree topology. This implementation is base on space lling curves domain decomposition, the Morton order. The current FleCSI version requires the implementation of a driver and a specialization driver. The role of the specialization driver is to provide the data distribution. This feature is not complete in FleCSI code and we provide it. The next step will be to incorporate it directly from FleCSPH to FleCSI as we reach a good level of performance. Then the driver represent the general execution of the resolution without worrying of the data locality and communications. As FleCSI is an On-Development code the structure may change in the future and we keep track of these changes in FleCSPH.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Radioisotope identification method for poorly resolved gamma-ray spectrum of nuclear security concern

Gamma-ray signal can be used as a fingerprint for radioisotope identification. In the context of radioactive and nuclear materials security at the border control point, the detection task can present a significant challenge due to various constraints such as the limited measurement time, the shielding conditions, and the noise interference. This study proposes a novel method to identify the signal of one or several radioisotopes from a poorly resolved gamma-ray spectrum. In this method, the noise component in the raw spectrum is reduced by the wavelet decomposition approach, and the removal of the continuum background is performed using the baseline determination algorithm. Finally, the identification of radioisotope is completed using the matrix linear regression method. The proposed method has been verified by experiments using the poorly resolved gamma-ray signals from various scenarios including single source, mixing of natural uranium with five of the most common industrial radioactive sources (57Co, 60Co, 133Ba, 137Cs, and 241Am). The preliminary results show that the proposed algorithm is comparable with the commercial method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2010

Establishing Consensus Turbulence Statistics for Hot Subsonic Jets

Many tasks in fluids engineering require knowledge of the turbulence in jets. There is a strong, although fragmented, literature base for low order statistics, such as jet spread and other meanvelocity field characteristics. Some sources, particularly for low speed cold jets, also provide turbulence intensities that are required for validating Reynolds-averaged Navier-Stokes (RANS) Computational Fluid Dynamics (CFD) codes. There are far fewer sources for jet spectra and for space-time correlations of turbulent velocity required for aeroacoustics applications, although there have been many singular publications with various unique statistics, such as Proper Orthogonal Decomposition, designed to uncover an underlying low-order dynamical description of turbulent jet flow. As the complexity of the statistic increases, the number of flows for which the data has been categorized and assembled decreases, making it difficult to systematically validate prediction codes that require high-level statistics over a broad range of jet flow conditions. For several years, researchers at NASA have worked on developing and validating jet noise prediction codes. One such class of codes, loosely called CFD-based or statistical methods, uses RANS CFD to predict jet mean and turbulent intensities in velocity and temperature. These flow quantities serve as the input to the acoustic source models and flow-sound interaction calculations that yield predictions of far-field jet noise. To develop this capability, a catalog of turbulent jet flows has been created with statistics ranging from mean velocity to space-time correlations of Reynolds stresses. The present document aims to document this catalog and to assess the accuracies of the data, e.g. establish uncertainties for the data. This paper covers the following five tasks: Document acquisition and processing procedures used to create the particle image velocimetry (PIV) datasets. Compare PIV data with hotwire and laser Doppler velocimetry (LDV) data published in the open literature. Compare different datasets acquired at roughly the same flow conditions to establish uncertainties. Create a consensus dataset for a range of hot jet flows, including uncertainty bands. Analyze this consensus dataset for self-consistency and compare jet characteristics to those of the open literature. One final objective fulfilled by this work was the demonstration of a universal scaling for the jet flow fields, at least within the region of interest to aeroacoustics. The potential core length and the spread rate of the half-velocity radius were used to collapse of the mean and turbulent velocity fields over the first 20 jet diameters in a highly satisfying manner.

Bridges, James↗
DOE OSTI2015

Simplified Predictive Models for CO 2 Sequestration Performance Assessment: Research Topical Report on Task #4 - Reduced-Order Method (ROM) Based Models

Reduced-order models provide a means for greatly accelerating the detailed simulations that will be required to manage CO 2 storage operations. In this work, we investigate the use of one such method, POD-TPWL, which has previously been shown to be effective in oil reservoir simulation problems. This method combines trajectory piecewise linearization (TPWL), in which the solution to a new (test) problem is represented through a linearization around the solution to a previously-simulated (training) problem, with proper orthogonal decomposition (POD), which enables solution states to be expressed in terms of a relatively small number of parameters. We describe the application of POD-TPWL for CO 2 -water systems simulated using a compositional procedure. Stanford’s Automatic Differentiation-based General Purpose Research Simulator (AD-GPRS) performs the full-order training simulations and provides the output (derivative matrices and system states) required by the POD-TPWL method. A new POD-TPWL capability introduced in this work is the use of horizontal injection wells that operate under rate (rather than bottom-hole pressure) control. Simulation results are presented for CO 2 injection into a synthetic aquifer and into a simplified model of the Mount Simon formation. Test cases involve the use of time-varying well controls that differ from those used in training runs. Results of reasonable accuracy are consistently achieved for relevant well quantities. Runtime speedups of around a factor of 370 relative to full- order AD-GPRS simulations are achieved, though the preprocessing needed for POD-TPWL model construction corresponds to the computational requirements for about 2.3 full-order simulation runs. A preliminary treatment for POD-TPWL modeling in which test cases differ from training runs in terms of geological parameters (rather than well controls) is also presented. Results in this case involve only small differences between training and test runs, though they do demonstrate that the approach is able to capture basic solution trends. The impact of some of the detailed numerical treatments within the POD-TPWL formulation is considered in an Appendix.

Mishra, Srikanta↗
DOE OSTI2023

Build Optimization Software Tools (BOOST) v2.0.0

Build Optimization Software Tools (BOOST) accelerate the design of DNA, RNA and protein sequences for their synthesis and assembly in an automated and scalable fashion. The BOOST Juggler automates the following two design tasks: - Reverse-Translation of protein sequences into DNA sequences - Codon Juggling of DNA sequences The BOOST Polisher provides the following design tasks: - The Verification of DNA sequences against DNA synthesis constraints - The Modification of DNA sequences in case they violate any DNA synthesis constraint In addition, the BOOST Polisher can be instructed to verify and modify DNA sequences against sequence patterns, such as restriction sites. The BOOST Partitioner supports both Gibson/chewback and Yeast assembly (also known as Transformation-Associated Recombination, or TAR) methods when performing the decomposition of large sequences that exceed the maximum length of DNA synthesis into synthesizable building blocks. The Partitioner can be instructed to find building blocks with appropriate overlap sequences, making their assembly into the complete construct more efficient. BOOST Workflows enable, in a customized fashion, the execution of the Juggler, Polisher, and Partitioner tools on a batch of sequences.

Sharkey, Michael↗
NASA NTRS1986

Catalysts for the decomposition of hydrazine and its derivatives and a method for its production

Catalysts of various types are used to decompose hydrazine and its derivatives. One type of catalyst is made as follows: the aluminum is dissolved out of an alloy of cobalt or nickel/aluminum so that a structure is produced that is chemically active for the monergol and that has a large active surface. The objective was to avoid difficulties and to create a catalyst that not only has a short start time but that can also be manufactured easily and relatively inexpensively. The solution to this task is to coat the base structure of the catalyst with oxides of copper, cobalt and cerium or oxides of copper, cobalt and cerite earth.

Sasse, R.↗
DOE OSTI2023

Identifying human failure events (HFEs) for external hazard probabilistic risk assessment

In recent years, several advancements in nuclear power plant (NPP) probabilistic risk assessment (PRA) have been driven by increased understanding of external hazards, plant response, and uncertainties. However, major sources of uncertainty associated with external hazard PRA remain. One important source is how risk-significant human actions that are carried out to enable plant response and recovery from natural hazards cause the close coupling of physical impacts on plants and overall plant risk during these hazard events. This makes human reliability and human-plant interactions important elements to consider in resolving PRA gaps in external hazards. One of the challenges in considering human response in external hazard probabilistic risk assessment (XHPRA) is that most existing human reliability analysis (HRA) models were not developed for assessing actions outside the control room (termed ex-control room actions) and hazard response. To support this new scope, HRA models will need to be developed or modified to support identification of human activities, causal factors, and uncertainties inherent in external hazard response, thereby providing insights regarding event timing and physical event conditions as they relate to human performance. In this study, there are two main objectives: (1) evaluate the applicability of an existing cognitive-based HRA method, Phoenix, to ex-control room actions, and (2) identify sources of uncertainty to be characterized or reduced in order to make this method suitable for XHPRA. The first step of such work is performed by assessing the suitability of existing HRA methods to support identifying human failure events (HFEs) for human response to flooding hazards. These HFEs are human actions or inactions that are involved in human responses to flooding hazards and could contribute to the loss of a critical function for the plant in the scenario being examined. Here, in this work, decomposition analyses using the cognitive-based Phoenix HRA model are used to identify HFEs. The Phoenix method was found to be suitable for analyzing ex-control room actions as well as identifying specific HFEs and underlying crew failure modes (CFMs). However, the method's suitability for use in ex-control room actions would benefit from expanding the available CFMs to accommodate a larger variety of physical and communication tasks.

42 ENGINEERING↗
DOE OSTI2024

Nanoscopic Imaging of Self-Propelled Ultrasmall Catalytic Nanomotors

Ultrasmall nanomotors (<100 nm) are highly desirable nanomachines for their size-specific advantages over their larger counterparts in applications spanning nanomedicine, directed assembly, active sensing, and environmental remediation. While there are extensive studies on motors larger than 100 nm, the design and understanding of ultrasmall nanomotors have been scant due to the lack of high-resolution imaging of their propelled motions with orientation and shape details resolved. Here, we report the imaging of the propelled motions of catalytically powered ultrasmall nanomotors─hundreds of them─at the nanometer resolution using liquid-phase transmission electron microscopy. These nanomotors are Pt nanoparticles of asymmetric shapes (“tadpoles” and “boomerangs”), which are colloidally synthesized and observed to be fueled by the catalyzed decomposition of NaBH4 in solution. Statistical analysis of the orientation and position trajectories of fueled and unfueled motors, coupled with finite element simulation, reveals that the shape asymmetry alone is sufficient to induce local chemical concentration gradient and self-diffusiophoresis to act against random Brownian motion. Our work elucidates the colloidal design and fundamental forces involved in the motions of ultrasmall nanomotors, which hold promise as active nanomachines to perform tasks in confined environments such as drug delivery and chemical sensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗