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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A model of thermal creep and annealing in finite domains based on coupled dislocation climb and vacancy diffusion

Here, we develop a framework to investigate thermal creep and annealing in finite domains, where the climb motion of discrete dislocations is coupled to the diffusion of a continuum vacancy field. The model is first formulated in a continuum finite-deformation setting. All governing equations and boundary conditions are obtained from a unified irreversible thermodynamics principle. The resulting model couples a mechanical boundary value problem (BVP), a vacancy diffusion BVP, and the climb and glide motion of the discrete dislocation network within the crystal. The framework is then linearized for implementation in three-dimensional (3D) discrete dislocation dynamics (DDD) simulations for arbitrary anisotropic crystals. A solution scheme is developed based on the superposition principle, which is imposed weakly on the dislocation network to obtain a Galerkin solution for the nodal climb velocities. The framework includes diffusional (Nabarro–Herring) creep deformation as well as dislocation creep by climb-assisted-glide. The method is applied to simulate the annealing of vacancy loops in Al, with good agreement to experimental measurements by Silcox and Hirsch. We further consider the effects of annealing under stress, and of the proximity of the vacancy loops to loaded and free boundaries Simulations in polycrystalline materials are carried out to highlight the effects of the grain size on dislocation climb and vacancy loop annealing. The method is also applied to estimate the creep rate due to climb-assisted glide of jogged-screw dislocations in γ-TiAl, and results are compared to experiments by Viswanathan et al. Finally, we discuss the effects of uniaxial and hydrostatic stresses on the two diffusive deformation pathways of the material, namely Nabarro–Herring creep and dislocation climb.

36 MATERIALS SCIENCE↗
NASA NTRS2009

Space Communications and Navigation (SCaN) Network Simulation Tool Development and Its Use Cases

In this work, we focus on the development of a simulation tool to assist in analysis of current and future (proposed) network architectures for NASA. Specifically, the Space Communications and Navigation (SCaN) Network is being architected as an integrated set of new assets and a federation of upgraded legacy systems. The SCaN architecture for the initial missions for returning humans to the moon and beyond will include the Space Network (SN) and the Near-Earth Network (NEN). In addition to SCaN, the initial mission scenario involves a Crew Exploration Vehicle (CEV), the International Space Station (ISS) and NASA Integrated Services Network (NISN). We call the tool being developed the SCaN Network Integration and Engineering (SCaN NI&E) Simulator. The intended uses of such a simulator are: (1) to characterize performance of particular protocols and configurations in mission planning phases; (2) to optimize system configurations by testing a larger parameter space than may be feasible in either production networks or an emulated environment; (3) to test solutions in order to find issues/risks before committing more significant resources needed to produce real hardware or flight software systems. We describe two use cases of the tool: (1) standalone simulation of CEV to ISS baseline scenario to determine network performance, (2) participation in Distributed Simulation Integration Laboratory (DSIL) tests to perform function testing and verify interface and interoperability of geographically dispersed simulations/emulations.

Jennings, Esther↗
DOE OSTI2019

eeyore

The software "eeyore" solves the problem of running Markov chain Monte Carlo (MCMC) algorithms on Bayesian neural networks. There is software for MCMC algorithms and software for neural network (such as PyTorch and TensorFlow), but there are limited solutions for combining the two. The main idea is to create a subclass of the 'torch.nn.Module' for defining Bayesian neural networks with the desired functionality required by MCMC taking the form of additional methods in that sub-class. This way, neural network functionality of PyTorch is exposed to the developer and user, allowing to develop MCMC methods on top of it through a simple API. The code implements Langevin Monte Carlo algorithms and population MCMC algorithms, as well as combinations of these, for Bayesian neural networks. This kind of functionality is not available elsewhere, to the best of the authors' knowledge. Moreover, a simple API is available that allows to implement new MCMC algorithms in the future.

Papamarkou, Theodore↗
DOE OSTI2015

Nuclear criticality safety of pipes networks containing uranium - 14437

In nuclear facilities, the transfer of fissile material in solution, gas or powder forms between process stages requires many transfer lines (called 'pipes' in this paper) which may be laid out into bundles or networks. In Nuclear Criticality Safety evaluations, it is usual to assimilate such networks as a unique cylinder which area bounds the sum of the surface of pipes making up the network. However, even if this geometric simplification appears intuitively safe, it is not conservative in all cases. The aim of this paper is therefore to explore the validity and limitations associated to the assessment of pipes networks by a unique cylinder. This paper presents the impact on the k{sub eff} value of a detailed modeling of a pipes network compared to a unique cylinder. Variables of interest considered in such calculations are pipes diameter, composition, pitches and layout. Fissile media taken into account are uranium systems enriched from 5 % to 93.5 % in {sup 235}U. Then, the impact on reactivity of pipes material, such as stainless steel or plastic, is discussed and the minimal or maximal safe thickness of material has been determined. Finally, this paper provides guidance on the nuclear criticality safety assessment of pipes networks for uranium systems. (authors)

42 ENGINEERING↗
NASA NTRSDate not supplied

NASA’s Progress Toward Commercial Space Communications—SATCOM Demonstrations and Wideband Multilingual Terminal Development

The National Aeronautics and Space Administration (NASA) Space Communications and Navigation (SCaN) Program is continuing to forge a path toward programmatic and operational transformation enabling commercialization of communications and navigation services to the greatest extent possible for near Earth users. National Space Policy is driving government agencies to incorporate commercial offerings and services to the maximum extent possible. As such, NASA has no plans to build or deploy Tracking and Data Relay Satellites, but will seek to fill NASA mission space-relay capability needs using commercial satellite communications (SATCOM) providers and services. The goal is to support an approach that is both flexible to commercial service constructs and provides continuity of support with current assets as long as required. Progress toward commercialization is advancing in two key areas: (1) awards have been made for demonstrations of SATCOM services, and (2) development of wideband and multilingual user terminals has advanced to the flight demonstration phase. The Communications Services Project (CSP) at Glenn Research Center (GRC), has awarded Funded Space Act Agreements (FSAA) to six industry vendors. Inmarsat Government Inc., Kuiper Government Solutions (KGS) LLC, SES Government Solutions, Space Exploration Technologies, Telesat U.S. Services LLC, and Viasat Incorporated received a combined $278.5 million to complete technology development and in-space demonstrations over the next several years. The end-to-end service capabilities being targeted are based on existing NASA mission operational needs. Accordingly, each company has proposed a technical approach to lower costs, increase flexibility, and improve performance for a broad range of missions. In the current market, industry vendors operate at a range of different frequencies and use variable, sometime proprietary, coding and modulations schemes. As NASA strives for a network comprised of services provided by multiple distinct suppliers, interoperability is a fitting solution to unify the network. Wideband and multi-lingual user terminals are being developed to bridge differences in industry services. Building on ground demonstrations completed in 2021, the Johns Hopkins Applied Physics Lab (APL) will be flight testing a multi-lingual wideband terminal (payload) and demonstrating connectivity to both government and commercial relay services. This paper provides a discussion of the progress toward NASA’s commercial space communications goals and consideration of key challenges and next steps.

space communications↗
DOE OSTI2012

Molecular ecological network analyses

Background: Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data. Results: Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA). Conclusions: The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1974

Geometrical geodesy techniques in Goddard earth models

The method for combining geometrical data with satellite dynamical and gravimetry data for the solution of geopotential and station location parameters is discussed. Geometrical tracking data (simultaneous events) from the global network of BC-4 stations are currently being processed in a solution that will greatly enhance of geodetic world system of stations. Previously the stations in Goddard earth models have been derived only from dynamical tracking data. A linear regression model is formulated from combining the data, based upon the statistical technique of weighted least squares. Reduced normal equations, independent of satellite and instrumental parameters, are derived for the solution of the geodetic parameters. Exterior standards for the evaluation of the solution and for the scale of the earth's figure are discussed.

Lerch, F. J.↗
NASA NTRSDate not supplied

NASA's Progress Toward Commercial Space Communications — SATCOM Demonstrations and Wideband Multilingual Terminal Development

The National Aeronautics and Space Administration (NASA) Space Communications and Navigation (SCaN) Program is continuing to forge a path toward programmatic and operational transformation enabling commercialization of communications and navigation services to the greatest extent possible for near Earth users. U.S. National Space Policy is driving government agencies to incorporate commercial offerings and services to the maximum extent possible. As such, NASA has no plans to build or deploy additional Tracking and Data Relay Satellites (TDRS), but will seek to fill NASA mission space-relay capability needs using commercial satellite communications (SATCOM) providers and services. The goal is to support an approach that is both flexible to commercial service constructs and provides continuity of support with current assets as long as required. Progress toward commercial services is advancing in two key areas: (1) awards have been made for demonstrations of SATCOM services, and (2) development of wideband and multilingual user terminals has advanced to the flight demonstration phase. The Communications Services Project (CSP) at Glenn Research Center (GRC), has awarded Funded Space Act Agreements (FSAA) to six industry vendors. Inmarsat Government Inc., Kuiper Government Solutions (KGS) LLC, SES Government Solutions, Space Exploration Technologies, Telesat U.S. Services LLC, and Viasat Incorporated will receive a combined $278.5 million to complete technology development and in-space demonstrations over the next several years. The end-to-end service capabilities being targeted are based on existing NASA mission operational needs. Accordingly, each company has proposed a technical approach to lower costs, increase flexibility, and improve performance for a broad range of missions. In the current market, industry vendors operate at a range of different frequencies and use variable, sometimes proprietary, coding and modulations schemes. As NASA strives for a network comprised of services provided by multiple distinct suppliers, interoperability is a fitting solution to unify the network. Wideband and multi-lingual user terminals are being developed to bridge differences in industry services. Building on ground demonstrations completed in 2021, the Johns Hopkins Applied Physics Lab (APL) will be flight testing a multi-lingual wideband terminal (payload) and demonstrating connectivity to both government and commercial relay services. This paper provides a discussion of the progress toward NASA’s commercial space communications goals and consideration of key challenges and next steps.

commercialization↗
DOE OSTI2020

Multilevel Monte Carlo Predictions of First Passage Times in Three-Dimensional Discrete Fracture Networks: A Graph-Based Approach

In this paper, we present a method combining multilevel Monte Carlo (MLMC) and a graph-based primary subnetwork identification algorithm to provide estimates of the mean and variance of the distribution of first passage times in fracture media at significantly lower computational cost than standard Monte Carlo (MC) methods. Simulations of solute transport are performed using a discrete fracture network (DFN), and instead of using various grid resolutions for levels in the MLMC, which is standard practice in MLMC, we identify a hierarchy of subnetworks in the DFN based on the shortest topological paths through the network using a graph-based method. While the mean of these ensembles is of critical importance, the variance is also essential in fractured media where uncertainty is an overarching theme, and understanding variability across an ensemble is a requirement for safety assessments. The method provides good estimates of the mean and variance at two orders of magnitude lower computational cost than MC.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

A Spherical Earth Solution for TOA Lightning Location Retrieval

With the advent of high-speed digital computers, the method of chi square minimization is a highly practical means for analyzing a wide variety of (otherwise intractable) nonlinear inversion problems in applied mathematical physics. Little thought or effort is required to apply the chi square method to obtain quick and reasonable estimates of a solution, and the method offers a means to assess retrieval errors. Because the method is simple and practical it is sometimes hastily applied to problems that can be solved by formal analytic or quasi-analytic means. Presently, Global Atmospherics Inc. (GAI) finds the minimum of a chi square function to analyze time-of-arrival (TOA) and magnetic bearing data derived from the National Lightning Detection Network (NLDN); ellipsoidal Earth geometry is assumed. An analytic solution to this problem has not yet been attained, but the consideration and solving of less general problem statements might eventually lead to a final solution. In the present study, the problem of retrieving lightning ground-strike location on a spherical Earth surface using a network of 4 or more time-of-arrival (TOA) sensors is considered. It is shown that this problem has an analytic solution and therefore does not require the use of nonlinear estimation theory (such as the chi square method mentioned above). The mathematical robustness of the analytic solution is tested using computer-generated lightning sources and simulated TOA measurement errors. A quasi-analytic extension of the spherical Earth solution for an oblate spheroidal Earth geometry is considered in a related study. The incorporation of magnetic bearing information into these analytic solutions would lead to a general and elegant analytic retrieval scheme that would most likely replace the chi square estimation theory currently employed by Global Atmospherics Inc. (GAI).

Koshak, William J,↗
DOE OSTI2024

Optimization strategies for produced water networks with integrated desalination facilities

Optimal management and desalination of produced water is a major challenge for U.S. oil and gas development. Integrating rigorous desalination models into multi-period produced water network optimization problems presents several hurdles, which need to be tackled using advanced optimization strategies. Here, in this work, a novel multi-period produced water network formulation with separate solid and liquid flows is introduced to avoid singularities at zero flows. Rigorous steady state desalination models based on mechanical vapor recompression are embedded at the desalination sites in the network model. An integrated optimization formulation is developed to co-optimize the design of desalination units along with the operation of the network. Furthermore, a more robust approach based on the trust region filter method is developed to efficiently integrate complex desalination models into the multi-period planning problem. Both optimization approaches are demonstrated on a produced water network from the PARETO library (Drouven et al., 2022) using thermal desalination units. Our results show that while the TRF and integrated approaches have comparable solve times, the TRF approach has better performance reliability in terms of solver convergence. Furthermore, the optimal solution obtained by embedding rigorous models into the network is significantly different than when desalination costs are approximated using simple cost models, which motivates further research in this field.

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

Optimizing Non-Terrestrial Hybrid RF/FSO Links With Reinforcement Learning: Navigating Through Clouds

In the pursuit of ubiquitous broadband connectivity, there has been a significant shift towards the vertical expansion of communication networks into space, particularly through the exploitation of low Earth orbit (LEO) satellite constellations, which are favored for their relatively low latency. However, this approach faces many challenges that need to be addressed, including atmospheric turbulence, high path loss, and dynamic cloud formations. High-altitude pseudo-satellites (HAPS) have emerged as promising relaying layers between LEO satellites and ground stations, enhancing coverage, latency, and direct terrestrial user connectivity. While radio frequency (RF) bands suffer from congestion and limited bandwidth, free space optical (FSO) communications offer higher data rates, but are susceptible to misalignment and weather-induced signal degradation. To address these challenges, a hybrid RF/FSO approach has been proposed to take advantage of both technologies by dynamic switching between RF and FSO based on propagation channel conditions. This paper introduces a reinforcement learning-based algorithm designed to optimize the trajectory of HAPS, maneuver around cloudy areas, and seamlessly switch between the RF and FSO communication modes to maximize the achievable capacity. The proposed approach aims to maximize system performance by intelligently adapting to environmental conditions and offering a promising solution for next-generation space communication networks.

actor-critic algorithm↗
DOE OSTI2021

Stabilization of the 81-channel coherent beam combination using machine learning

We develop a rapidly converging algorithm for stabilizing a large channel-count diffractive optical coherent beam combination. An 81-beam combiner is controlled by a novel, machine-learning based, iterative method to correct the optical phases, operating on an experimentally calibrated numerical model. A neural-network is trained to detect phase errors based on interference pattern recognition of uncombined beams adjacent to the combined one. Due to the non-uniqueness of solutions in the full space of possible phases, the network is trained within a limited phase perturbation/error range. This also reduces the number of samples needed for training. Simulations have proven that the network can converge in one step for small phase perturbations. When the trained neural-network is applied to a realistic case of 360 degree full range, an iterative scheme exploits random walking at the beginning, with the accuracy of prediction on phase feedback direction, to allow the neural-network to step into the training range for fast convergence. This neural-network-based iterative method of phase detection works tens of times faster than the commonly used stochastic parallel gradient descent approach (SPGD) using a single-detector and random dither when both are tested with random phase perturbations.

Wang, Dan↗
DOE OSTI2020

Heterogeneous Machine Learning on High Performance Computing for End to End Driving of Autonomous Vehicles

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving. For machines though, this gap is guarded by at least two challenges: 1) machine learning techniques remain brittle and unable to generalize to a wide range of scenarios, and 2) effective training data that enhances generalization and generates the desired driving behavior. Further, each challenge can be computationally intensive on its own thereby exasperating the gap. Moreover, is has not yet been shown that a single form of learning or training is capable of addressing a large range of scenarios. As a result, solving the first challenge does not inherently solve the second and vice versa. The work described here discusses an approach to address the first challenge that would also provide a foundation for solving the second. Our approach utilizes a combination of conditional imitation learning with a static dataset, reinforcement learning with a simulation environment, and high-performance computing to train a neural network. As a result, this reduces the “time to solution” from to the existing techniques for autonomous driving and provides an extensible framework to address the second key challenge.

autonomous vehicles↗
DOE OSTI2023

Operation Optimization Using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

Kim, Junyung↗
DOE OSTI2013

Continuum Modeling and Control of Large Nonuniform Wireless Networks via Nonlinear Partial Differential Equations

We introduce a continuum modeling method to approximate a class of large wireless networks by nonlinear partial differential equations (PDEs). This method is based on the convergence of a sequence of underlying Markov chains of the network indexed by N , the number of nodes in the network. As N goes to infinity, the sequence converges to a continuum limit, which is the solution of a certain nonlinear PDE. We first describe PDE models for networks with uniformly located nodes and then generalize to networks with nonuniformly located, and possibly mobile, nodes. Based on the PDE models, we develop a method to control the transmissions in nonuniform networks so that the continuum limit is invariant under perturbations in node locations. This enables the networks to maintain stable global characteristics in the presence of varying node locations.

Zhang, Yang↗
DOE OSTI2023

Confidentiality-preserving machine learning algorithms for soft-failure detection in optical communication networks

Automated fault management is at the forefront of next-generation optical communication networks. The increase in complexity of modern networks has triggered the need for programmable and software-driven architectures to support the operation of agile and self-managed systems. In these scenarios, the European Telecommunications Standards Institute zero-touch network and service management approach is imperative. The need for machine learning algorithms to process the large volume of telemetry data brings safety concerns as distributed cloud-computing solutions become the preferred approach for deploying reliable communication network automation. This paper’s contribution is twofold. First, we propose a simple yet effective method to guarantee the confidentiality of the telemetry data based on feature scrambling. The method allows the operation of third-party computational services without direct access to the full content of the collected data. Additionally, the effectiveness of four unsupervised machine learning algorithms for soft-failure detection is evaluated when applied to the scrambled telemetry data. The methods are based on factor analysis, principal component analysis, nonlinear principal component analysis, and singular value decomposition. Most dimensionality reduction algorithms have the common property that they can maintain similar levels of fault classification performance while hiding the data structure from unauthorized access. Evaluations of the proposed algorithms demonstrate this capability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Polyelectrolyte induced formation of silver nanoparticles in copolymer hydrogel and their application as catalyst

Highlights: • A simple route for the in situ preparation of Ag nanoparticles has been developed. • The Ag loaded hydrogel showed catalytic activity for reduction of 4-nitrophenol. • The catalyst can be recovered by simple separation and showed good recyclability. - Abstract: A simple route for the in situ preparation of catalytically active Ag nanoparticles (NPs) in hydrogel networks has been developed. The electronegativity of the amide and carboxyl groups on the poly(acrylamide-co-acryl acid) chains caused strong binding of the Ag{sup +} ions which made the ions distribute uniformly inside the hydrogels. When the Ag{sup +} loaded hydrogels were immersed in NaBH{sub 4} solution, the Ag{sup +} ions on the polymer networks were reduced to Ag NPs. The resultant hydrogel showed good catalytic activity for the reduction of a common organic pollutant, 4-nitrophenol, with sodium borohydride. A kinetic study of the catalytic reaction was carried out and a possible reason for the decline of the catalytic performance with reuse is proposed.

77 NANOSCIENCE AND NANOTECHNOLOGY↗