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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

CrossLink: General Overview [Slides]

Problem: Traditional mesh generation approaches are labor intensive and have limited robustness when applied to parametric design exploration and optimization of complex geometries. While automatic mesh generation approaches exist, they tend to generate tetrahedral or mixed-hybrid meshes which are generally unsuitable for physics applications with strong shock waves, thin boundary layers, and strong gradients. In addition, simulations sizes in the billions of cells are becoming more common with traditional mesh generation methods quickly reaching scalability limits. Solution: CrossLink offers a topology-based mesh generation approach with unstructured block-filling methods and a scalable mesh generation engine. In addition, CrossLink incorporates a python-based API for seamless workflow integration and robust repeatability of the geometry handling and mesh generation process. This makes it ideal for parametric design study and optimization of complex geometries. Finally, future versions of CrossLink will offer a parametric mesh capability that optimizes a high-order mesh and enables reconstruction of the final mesh in memory by the physics solver.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1996

Automated clustering-based workload characterization

The demands placed on the mass storage systems at various federal agencies and national laboratories are continuously increasing in intensity. This forces system managers to constantly monitor the system, evaluate the demand placed on it, and tune it appropriately using either heuristics based on experience or analytic models. Performance models require an accurate workload characterization. This can be a laborious and time consuming process. It became evident from our experience that a tool is necessary to automate the workload characterization process. This paper presents the design and discusses the implementation of a tool for workload characterization of mass storage systems. The main features of the tool discussed here are: (1)Automatic support for peak-period determination. Histograms of system activity are generated and presented to the user for peak-period determination; (2) Automatic clustering analysis. The data collected from the mass storage system logs is clustered using clustering algorithms and tightness measures to limit the number of generated clusters; (3) Reporting of varied file statistics. The tool computes several statistics on file sizes such as average, standard deviation, minimum, maximum, frequency, as well as average transfer time. These statistics are given on a per cluster basis; (4) Portability. The tool can easily be used to characterize the workload in mass storage systems of different vendors. The user needs to specify through a simple log description language how the a specific log should be interpreted. The rest of this paper is organized as follows. Section two presents basic concepts in workload characterization as they apply to mass storage systems. Section three describes clustering algorithms and tightness measures. The following section presents the architecture of the tool. Section five presents some results of workload characterization using the tool.Finally, section six presents some concluding remarks.

Pentakalos, Odysseas I.↗
DOE OSTI2022

A Simple Condition for Sustained Super-Eddington Black Hole Growth

One of the most pressing questions in cosmology is how the black holes (BHs) powering quasars at high redshift grow to supermassive scales within a billion years of the Big Bang. Here we show that sustained super-Eddington accretion can be achieved for BHs with Eddington fractions f Edd ≳ 2/ϵ, where ϵ is the efficiency with which radiation is generated in the accretion process. In this regime, the radiation carries too little momentum to halt the accretion flow and the infalling gas traps the radiation. The BH growth then proceeds unimpeded until the gas supply is exhausted, in contrast to accretion at lower rates, which is limited by the radiation generated in the accretion process. The large gas supply available in massive high-redshift quasar host galaxies may be readily accreted onto seed BHs via this supply-limited mode of accretion, providing an explanation for how such supermassive BHs are assembled in the early universe. This sustained super-Eddington growth may also explain the short lifetimes inferred for the H ii regions surrounding high-redshift quasars, if the bulk of the BH growth occurs without the associated radiation escaping to ionize the intergalactic medium. It furthermore implies that a population of obscured rapidly growing BHs may be difficult to detect, perhaps explaining why so few quasars with Eddington fractions higher than a few have been observed. Finally, this simple condition for sustained super-Eddington growth can easily be implemented in cosmological simulations that can be used to assess in which environments it occurs.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1997

Modeling of the Coupled Magnetospheric and Neutral Wind Dynamos

Over the past four years of funding, SRI, in collaboration with the University of Texas at Dallas, has been involved in assessing the influence of thermospheric neutral winds on the electric field and current systems at high latitudes. The initial direction of the project was to perform a set of numerical experiments concerning the contribution of the magnetospheric and neutral wind dynamo processes, under specific boundary conditions, to the polarization electric field and/or the field-aligned current distribution at high latitudes. To facilitate these numerical experiments we developed a numerical scheme that relied on using output from the NCAR Thermosphere-Ionosphere General Circulation Model (NCAR-TIGCM), expanding them in the form of spherical harmonics and solving the dynamo equations spectrally. Once initial calculations were completed, it was recognized that the neutral wind contribution could be significant but its actual contribution to the electric field or currents depended strongly on the generator properties of the magnetosphere. Solutions to this problem are not unique because of the unknown characteristics of the magnetospheric generator, therefore the focus was on two limiting cases. One limiting case was to consider the magnetosphere as a voltage generator delivering a fixed voltage to the high-latitude ionosphere and allowing for the neutral wind dynamo to contribute only to the current system. The second limiting case was to consider the magnetosphere as a current generator and allowing for the neutral wind dynamo to contribute only to the generation of polarization electric fields. This work was completed and presented at the l994 Fall AGU meeting. The direction of the project then shifted to applying the Poynting flux concept to the high-latitude ionosphere. This concept was more attractive as it evaluated the influence of neutral winds on the high-latitude electrodynamics without actually having to determine the generator characteristics of the magnetosphere. The influence of the neutral wind was then determined not by estimating how much electric potential or current density it provides, but by determining the contribution of the neutral wind to the net electromagnetic energy transferred between the ionosphere and magnetosphere. The estimate of the net electromagnetic energy transfer and the role of the neutral winds proves to be a more fundamental quantity in studies of magnetosphere- ionosphere coupling also showed that by using electric and magnetic field measurements from the HILAT satellite, the Poynting flux could be a measurable quantity from polar-orbiting, low- altitude spacecraft. Through collaboration with Dr. Heelis and others at UTD and their expertise of the electric field measurements on the DE-B satellite, an extensive analysis was planned to determine the Poynting flux from the DE-B measurements in combination with a modeling effort to help interpret the observations taking into account the coupled magnetosphere-ionosphere.

Thayer, Jeffrey P.↗
DOE OSTI2020

Enzyme interactions on lignocellulosic biomass structure

This chapter shows that, although well studied over the years, the cellulose enzymatic hydrolysis process has still many obstacles to be overcome or minimized. High enzyme loading and relatively long incubation periods are generally required for effective cellulose saccharification to be achieved. These limitations make the process unattractive from an economic perspective. Most studies show that the conversion rate of cellulose hydrolysis decreases over reaction time due to several factors that may inhibit, deactivate, or slow the work of enzymes. Some of these factors are inhibition by the product generated; inhibition/deactivation of enzymes by by-products generated during pretreatment (PT); limitation of mass transfer; shear stress by high stirring; air–liquid interfacial area; and unproductive adsorption on lignin or other compounds present in the biomass. The interaction between cellulases and the lignin present in these biomasses has a major negative impact on the process, with the source of the raw material and the type of PT used having a strong influence on it. Finally, the use of nonenzymatic proteins seems to be the best way to mitigate this problem.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2019

Constraint-Based Off-Nominal Behavior Modeling for Europa Clipper

The risk analysis for the Europa Clipper mission evaluates the probability of mission failure based on the failure rates of individual components and dependencies among them. The probabilities are calculated by integrating over the intervals of time within which a fault occurs, accounting for an infinite number of cases. The response of the spacecraft to different faults can result in different schedules of activities, changing the intervals of integration. Europa currently uses models of spacecraft systems and components to simulate individual flight scenarios. The goal is to develop a framework for integrating, automating, and improving this modeling process. We describe an approach to generating the schedules for the different fault cases and determining the intervals for faults. It is not enough to just simulate individual cases because we are working with continuous variables that generate an infinite number of possible futures. Instead, we determine time windows within which certain faults can occur and use these time windows as bounds for integration. We found that determining these time windows is a constraint optimization problem. In order to represent these problems, we employ a language based on ontologies of behavior and scenarios. The language enables us to specify constraints in a simple, declarative syntax. A constraint-based analysis engine uses the declarative specification to identify bounds on system parameters and fill in details of behavior. For example, we created a detailed model of power generation, power use, and the corresponding effects on the battery in order to determine when an undervoltage fault can occur. An undervoltage during a trajectory correction maneuver requires that thrusting be interrupted for just enough time to recharge the battery such that the maneuver can be completed within battery limits. This behavior is generated based on the model to minimize the interruption time. For certain scenarios the constraint optimization problems were simple enough to be solved by hand, but the framework made the process substantially faster. It also produced solutions to other problems that we could not solve by hand or with existing tools and allowed us to generate and run many scenarios at once. The scenario language and engine greatly simplified the process of identifying time bounds and separating cases.

Everline, Chester J.↗
DOE OSTI2025

Influence of Control and Limiter Schemes on Sequence-Domain Fault Models of Grid-Forming Inverter-Interfaced Distributed Generators

Unlike synchronous generators, the fault response of grid-forming (GFM) inverter-interfaced distributed generators (IIDGs) is notably governed by the selection of control and current limiting strategies rather than inherent physical traits. While recent research has focused on the sequence domain fault model of GFM IIDGs, a research gap exists in elucidating the influence of control and current limiting schemes on this model's characteristics. This article aims to fill this void by examining how different control and current limiting schemes influence the positive and negative sequence impedances in the phasor-domain fault model of GFM IIDGs. This investigation encompasses droop-based, virtual synchronous machine-based, and virtual oscillator-based reference generation controls alongside rotating and stationary reference-frame-based voltage controls. Furthermore, saturation-based, latching-based, circular and virtual impedance-based current limiting schemes are analyzed. To achieve this goal, a thorough numerical simulation study is conducted. Findings indicate that outer reference generation controls exhibit minimal impact. Conversely, the choice of voltage control and various current limiting schemes emerge as the predominant factors shaping the sequence models of GFM IIDGs. These analyses and results are instrumental in devising reliable protection strategies within inverter-based grids, as a comprehensive understanding of electrical elements in the sequence domain is imperative for effective protective measures.

current limiters↗
DOE OSTI2020

Radial radio frequency (RF) electron guns

Radial radio frequency (RF) electron guns and radial RF electron gun systems are provided that are capable of generating an electron beam that can propagate either radially inward, towards the axis of a cylinder, or radially outward from the axis. A beam source capable of generating a radially inwardly propagating electron beam, while perhaps not particularly useful as a source for a higher-energy accelerator, offers potential advantages for materials processing, as the geometry allows irradiation from all sides of an enclosed material flow with a single structure. Other potential applications include, but are not limited to, atmospheric plasma generation, radiation damage testing, and possibly, novel electron lens-type devices for hadron accelerators.

Lewellen, John↗
DOE OSTI2022

Improving Seasonal Forecast Using Probabilistic Deep Learning

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits relies on improving general circulation model (GCM) based dynamical forecast systems. To improve dynamical seasonal forecasts, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge costs in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. Here, we develop a probabilistic deep learning-based statistical forecast methodology, drawing on a wealth of climate simulations to enhance seasonal forecast capability and forecast diagnosis. By explicitly modeling the internal climate variability and GCM formulation differences, the proposed Conditional Generative Forecasting (CGF) methodology enables bypassing crucial barriers in dynamical forecast, and offers a top-down viewpoint to examine how complicated GCMs encode the seasonal predictability information. We apply the CGF methodology for global seasonal forecast of precipitation and 2 m air temperature, based on a unique data set consisting 52,201 years of climate simulation. Results show that the CGF methodology can faithfully represent the seasonal predictability information encoded in GCMs. We successfully apply this learned relationship in real-world seasonal forecast, achieving competitive performance compared to dynamical forecasts. Using this CGF as benchmark, we reveal the impact of insufficient forecast spread sampling that limits the skill of the considered dynamical forecast system. Finally, we introduce different strategies for composing ensembles using the CGF methodology, highlighting the potential for leveraging the strengths of multiple GCMs to achieve advantgeous seasonal forecast.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Sunsetting coal power in China

Reducing CO 2 emissions from coal-fired electricity generation in China is critical to limit global warming. Long-term projections of China's electricity supply tend to assume that coal generation will be a mainstay of China's electricity system through 2050, due to limitations in the scalability of hydropower, nuclear, and natural gas generation and the commercial availability of carbon capture and storage. This paper examines the resource, economic, and institutional implications of reducing and replacing coal generation in China with mostly renewable energy and energy storage by 2040. We find that the scale of solar, wind, and storage resources needed to do so is on the order of 100–150 GW/year of solar and wind capacity and 15 GW/year of energy storage from 2020 to 2025, growing to 250 GW/year and 90 GW/year, respectively, from 2025 to 2040. We then also evaluate the sensitivities if coal plants are retired by 2050.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS1983

Population Control of Self-Replicating Systems: Option C

From the conception and development of the theory of self-replicating automata by John von Neumann, others have expanded on his theories. In 1980, Georg von Tiesenhausen and Wesley A. Darbro developed a report which is a "first' in presenting the theories in a conceptualized engineering setting. In that report several options involving self-replicating systems are presented. One of the options allows each primary to generate n replicas, one in each sequential time frame after its own generation. Each replica is limited to a maximum of m ancestors. This study involves determining the state vector of the replicas in an efficient manner. The problem is cast in matrix notation, where F = fij is a non-diagonalizable matrix. Any element fij represents the number of elements of type j = (c,d) in time frame k+1 generated from type i = (a,b) in time frame k. It is then shown that the state vector is: bar F(k)=bar F (non-zero) X F sub K = bar F (non-zero) xmx J sub kx m sub-1 where J is a matrix in Jordan form having the same eigenvalues as F. M is a matrix composed of the eigenvectors and the generalized eigenvectors of F.

Mccord, R. L.↗
NASA NTRS1989

Trajectory generation of space telerobots

The purpose is to review a variety of trajectory generation techniques which may be applied to space telerobots and to identify problems which need to be addressed in future telerobot motion control systems. As a starting point for the development of motion generation systems for space telerobots, the operation and limitations of traditional path-oriented trajectory generation approaches are discussed. This discussion leads to a description of more advanced techniques which have been demonstrated in research laboratories, and their potential applicability to space telerobots. Examples of this work include systems that incorporate sensory-interactive motion capability and optimal motion planning. Additional considerations which need to be addressed for motion control of a space telerobot are described, such as redundancy resolution and the description and generation of constrained and multi-armed cooperative motions. A task decomposition module for a hierarchical telerobot control system which will serve as a testbed for trajectory generation approaches which address these issues is also discussed briefly.

Lumia, R.↗
DOE OSTI2022

Examination of Ac-225 production from Ra-226 using fast reactor JOYO

In this study, the authors investigated the method of producing the radionuclide Ac{sup 225} used for targeted alpha therapy (TAT). Currently, Ac{sup 225} is mainly generated from ORNL's Th{sup 229} generator, and the annual production amount is limited to about 63 GBq, and methods for generating it using accelerators are under development in each country. The method using an accelerator has the advantage of being able to generate Ac{sup 225} from a small amount of target nuclides with high efficiency but has the disadvantage of not being able to irradiate a large amount of target nuclides at once due to the small irradiation area. Therefore, the authors investigated a method to generate Ac{sup 225} by neutron irradiation of Ra{sup 226} as a target nuclide using the experimental fast reactor JOYO, which has abundant neutrons and a large loading region. Irradiation of Ra{sup 226} with fast neutrons causes a (n, 2n) reaction to generate Ra{sup 225}, and then decay to produce Ac{sup 225}. In addition, although harmful Ac{sup 227} is also produced at the same time by the (n,γ) reaction, first of all the actinium isotope is chemically separated and eliminated. Since the remaining Ra{sup 225} collapses and Ac{sup 225} is produced, pure Ac{sup 225} can be extracted by performing chemical separation again. As a result of the analysis, 1 g of Ra{sup 226} is irradiated with JOYO for 60 days, and milking is performed 4 times every 17.5 days. By doing these three times a year, it was found that about 50 GBq of Ac{sup 225} was generated. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
NASA NTRS2004

Improving Evolvability through Generative Representations

One of the main limitations of computer automated design systems is the representation used for encoding designs. Using computer programs as an analogy, representations can be thought of as having the properties of combination, control-flow and abstraction. Generative representations are those which have the ability to reuse elements in an encoding through either iteration, a form of control-flow, or abstraction. Here we argue that generative representations improve the evolvability of designs by capturing design dependencies in a way that makes them easier to change, and we support this with examples from two design substrates.

Hornby, Gregory S.↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), has typically limited machine learning (ML) in space studies and further study of radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNAseq) data from 6 mouse liver GeneLab datasets (GLDS) with a total of 113 spaceflight and ground-control samples to determine top features relevant to spaceflight including the effect of radiation exposure. Data was normalized within each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. The top MRMR features were used to predict spaceflight vs. ground-control samples using a Random Forest (RF) classifier with 5-fold cross validation (CV). The ML-based gene sets were further compared against differential gene expression results from individual GLDS. CV training using the top 100 MRMR genes show averages of 86% accuracy and 0.95 AUC value on the validation set over 5 folds (Figure 1A). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 811 or 68 DEGs overlapping between at least 2 or 3 studies, respectively (Figure 1B). Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism. Set analysis between the MRMR features and the DEGs showed 60 or 8 genes overlapping with at least 1 or 2 studies, respectively. MRMR feature selection and ensemble ML methods (e.g. RF) improve performance relative to a Naïve Bayes classifier when NGS data sets are analyzed. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise ratio. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from RNASeq analysis. Non-intersecting sets introduce opportunity to explore spaceflight relevant genes and implementing ML methods across existing NGS datasets may overcome sample size limitations. ML coupled with existing analytical methods enhances understanding of disease by revealing common underlying pathways across datasets.

Machine Learning↗
NASA NTRS1993

Self-mode-locked chromium-doped forsterite laser generates 50-fs pulses

Stable transform-limited (delta nu-delta tau = 0.32) femtosecond pulses with a FWHM of 50 fs were generated from a self-mode-locked chromium-doped forsterite laser. The forsterite laser was synchronously pumped by a CW mode-locked Nd:YAG (82 MHz) laser that generated picosecond pulses (200-300 ps) and provided the starting mechanism for self-mode-locked operation. Maximum output power was 45 mW for 3.9 W of absorbed pumped power with the use of an output coupler with 1 percent transmission. The self-mode-locked forsterite laser was tuned from 1240 to 1270 nm.

Seas, A.↗