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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Minimum Detectable Quantity Calculation for Radiation Portal Monitors

The minimum detectable quantity of a radiation portal monitor is the smallest amount of radioactive material that can be detected passing through the monitor (with a specified detection probability). The minimum detectable quantity is a function of many factors, including isotope, velocity, distance between pillars, height of portal and vehicle, source distribution and position, background radiation, background suppression, detector volume and positions, detector efficiency, decision metrics and algorithms, the presence of NORM (naturally occurring radioactive material), and alarm thresholds. Experimentally testing a radiation portal monitor to failure for all combinations of these factors is extremely time-consuming and often cost prohibitive. This document outlines a straightforward method for quickly estimating the minimum detectable quantity for moving sources over a large variety of conditions with a minimal number of static measurements. The document also describes a proof of concept software application that the International Atomic Energy Agency has designed based on the described method that can be used by Member States to estimate a monitor’s minimum detectable quantity.

Blessinger, Christopher S.↗
DOE OSTI2021

Real-Time XFEL Data Analysis at SLAC and NERSC: a Trial Run of Nascent Exascale Experimental Data Analysis

X-ray scattering experiments using Free Electron Lasers (XFELs) are a powerful tool to determine the molecular structure and function of unknown samples (such as COVID-19 viral proteins). XFEL experiments are a challenge to computing in two ways: i) due to the high cost of running XFELs, a fast turnaround time from data acquisition to data analysis is essential to make informed decisions on experimental protocols; ii) data collection rates are growing exponentially, requiring new scalable algorithms. Here we report our experiences analyzing data from two experiments at the Linac Coherent Light Source (LCLS) during September 2020. Raw data were analyzed on NERSC's Cori XC40 system, using the Superfacility paradigm: our workflow automatically moves raw data between LCLS and NERSC, where it is analyzed using the software package CCTBX. We achieved real time data analysis with a turnaround time from data acquisition to full molecular reconstruction in as little as 10 min -- sufficient time for the experiment's operators to make informed decisions. By hosting the data analysis on Cori, and by automating LCLS-NERSC interoperability, we achieved a data analysis rate which matches the data acquisition rate. Furthermore, completing data analysis with 10 mins is a first for XFEL experiments and an important milestone if we are to keep up with data collection trends.

Blaschke, Johannes P.↗
NASA NTRSDate not supplied

Contact Multigraph Routing: Overview and Implementation

In Delay Tolerant Networking (DTN), the standard routing algorithm used to navigate time-varying networks has been Contact Graph Routing (CGR). In CGR, a globally distributed list of contacts, periods during which two DTN nodes may communicate, is used to construct a contact graph, in which contacts are vertices. A version of Dijkstra’s algorithm can then be used to find paths through this model of the timevarying network. However, since contact graphs may be large compared to the network, potentially growing with the square of the number of network nodes and linearly with the time interval represented, the resulting algorithm does not scale well with the size of the network or time. Any improvement to the routing algorithm will bring significant returns to scale. In a previous paper, we briefly introduced an alternative to the contact graph model for routing. This alternative model is based on a multigraph (a graph in which there may be multiple edges between a pair of vertices) where vertices represent network nodes instead of contacts. A version of Dijkstra’s algorithm in these multigraph models reduces the time needed to perform the same routing computations done in the existing CGR algorithm. Moreover, a modified version of Yen’s algorithm for multigraphs is included. Our variation of CGR, which we call Contact Multigraph Routing (CMR), provides an in-line replacement for the previously used pathfinding algorithms. This paper describes an implementation created based on the CMR approach, and experimental comparisons to traditional CGR are given. In addition, we explore some additional modifications to the routing pipeline traditionally assumed in CGR. These modifications range from the theoretical to the practical in terms of size and scope. We step forward our understanding of sheaftheoretic networking and describe how to model the routing pipeline using sheaves. We detail some enhanced route selection criteria that addresses some of the added complexity of DTNbased systems. We also include a future works section on future improvements and implementations that would be of service to the broader DTN community.

contact graph routing↗
NASA NTRS1993

A computational and experimental investigation of a delta wing with vertical tails

The flow over an aspect ratio 1 delta wing with twin vertical tails is studied in a combined computational and experimental investigation. This research is conducted in an effort to understand the vortex and fin interaction process. The computational algorithm used solves both the thin-layer Navier-Stokes and the inviscid Euler equations and utilizes a chimera grid-overlapping technique. The results are compared with data obtained from a detailed experimental investigation. The laminar case presented is for an angle of attack of 20 deg and a Reynolds number of 500,000. Good agreement is observed for the physics of the flow field, as evidenced by comparisons of computational pressure contours with experimental flow-visualization images, as well as by comparisons of vortex-core trajectories. While comparisons of the vorticity magnitudes indicate that the computations underpredict the magnitude in the wing primary-vortex-core region, grid embedding improves the computational prediction.

Krist, Sherrie L.↗
NASA NTRS2004

A Computational and Experimental Investigation of a Delta Wing with Vertical Tails

The flow over an aspect ratio 1 delta wing with twin vertical tails is studied in a combined computational and experimental investigation. This research is conducted in an effort to understand the vortex and fin interaction process. The computational algorithm used solves both the thin-layer Navier-Stokes and the inviscid Euler equations and utilizes a chimera grid-overlapping technique. The results are compared with data obtained from a detailed experimental investigation. The laminar case presented is for an angle of attack of 20 and a Reynolds number of 500; 000. Good agreement is observed for the physics of the flow field, as evidenced by comparisons of computational pressure contours with experimental flow-visualization images, as well as by comparisons of vortex-core trajectories. While comparisons of the vorticity magnitudes indicate that the computations underpredict the magnitude in the wing primary-vortex-core region, grid embedding improves the computational prediction.

Krist. Sherrie L.↗
NASA NTRSDate not supplied

Multiscale Modeling of Reconstructed Tricalcium Silicate using NASA Multiscale Analysis Tool

To study microstructure characteristics of cementitious materials hydrated in space; previously, cement binder formations were processed under microgravity conditions and was further compared against ground-based experiments. For accurate estimation of process-structure-property linkage, particularly on samples hydrated in the microgravity environment, it is desired to have a high-fidelity volumetric representation of the microstructure. However, owing to small sample size and high porosity of the space-returned samples, conventional experimental characterization techniques are not viable. Hence, a deep learning-based reconstruction algorithm was employed to obtain high fidelity 3D volumes from sparse high resolution 2D Scanning Electron Microscopy (SEM) images, as inputs to micromechanics-based modeling. This machine learning-based reconstruction methodology validated against low-order statistical descriptors, captured the microstructural topology of both sample types (ground, 1g and microgravity, μg). Due to the lack of gravity, hydration products of the samples processed in space differed from those processed-on ground. Such AI-generated virtual samples were analyzed in a multiscale recursive micromechanics approach using the NASA Multiscale Analysis Tool (NASMAT). Here, we present a methodology to rapidly integrate and evaluate these AI-generated volumes in NASMAT. The synthesized microstructural volumes are directly employed as Representative Volume Elements (RVEs) to preserve the fidelity (1 pixel = 0.54 m). Invariably, analysis of such largescale problems (5123 voxels) requires huge amount of computational resources. By taking advantage of the NASMAT architecture, we also focused on systematic multiscale integration of these AI-reconstructed virtual volumes to reduce the computational demands. In this work, this methodology is demonstrated on the ground-based, 1g samples. The estimated stiffness value of 15.90 GPa is comparable to experimentally obtained modulus of hydrated tricalcium silicate sample. The workflow presented here paves the way for utilizing the NASMAT tool to perform multiscale analyses of other multi-phase material systems using either 3D virtual datasets synthesized using AI or obtained via micro-CT.

Machine Learning↗
NASA NTRS2020

ANC of Radiated Sound from a Small UAS Rotor using Virtual Error Sensors

Small unmanned aerial systems (UAS) are becoming increasingly common for private, military, and commercial use, increasing community noise exposure. Reducing the noise produced by UAS could help improve community acceptance. Active noise control (ANC) might be used to attenuate noise produced by UAS, however, traditional ANC systems require a physical sensor in the far-field, which is not feasible. This paper assesses a virtual error sensing (VES) method that eliminates the need for a far-field sensor. This paper describes the proposed VES strategy, and presents numerical simulations and experimental results that highlight the benefits and limitations of the approach. Results for the VES system alone and with an ANC approach are presented and discussed. Experimental testing focused on attenuating the tonal noise produced by one 2-bladed rotor with a tip radius of 11.9 cm. Pressure variations caused by blade rotation were measured in the near and far-field using electret microphones and externally polarized condenser microphones, respectively. The filtered-x least mean squares algorithm was used in conjunction with the VES system to attenuate the far-field response. Experimental results show reductions between 6-13 dB at varying far-field locations and rotation rates.

virtual error sensor↗
NASA NTRS1998

Prediction Based Design of Fire Detection for Buildings with Ceiling Heights between 9m and 18m

The purpose of this paper is to provide the experimental and theoretical background necessary to extend guidelines to ceiling heights between 9 m and 18 m. Based on the results of experiments conducted in 15 m and 22 m high hangars, detector activation thresholds and detector spacing are analyzed for both smoke and heat detectors. Only ceiling mounted detection devices are analyzed in this paper. In addition to the detector threshold study, the predictive capabilities of computer fire model simulations were compared with experimental results. This comparison, which is based on 12 fire tests, resulted in the development of a new ceiling jet algorithm to model phenomena which had not been included in previous algorithms. The improved algorithm provides a better representation of the development of the ceiling jet temperature to a growing hot layer and a better estimation of plume centerline temperature. Guidelines are examined, based on the experimental results, for fire detector spacing, placement, and sensitivity. Recommendations concerning the use of computer fire models at these heights are made as a function of fire size and hot layer development. The role of draft curtains is discussed and their impact on detector activation is demonstrated.

Davis, W. D.↗
DOE OSTI2020

ConnectIt: a framework for static and incremental parallel graph connectivity algorithms

Connected components is a fundamental kernel in graph applications. The fastest existing multicore algorithms for solving graph connectivity are based on some form of edge sampling and/or linking and compressing trees. However, many combinations of these design choices have been left unexplored. In this paper, we design the ConnectIt framework, which provides different sampling strategies as well as various tree linking and compression schemes. ConnectIt enables us to obtain several hundred new variants of connectivity algorithms, most of which extend to computing spanning forest. In addition to static graphs, we also extend ConnectIt to support mixes of insertions and connectivity queries in the concurrent setting. We present an experimental evaluation of ConnectIt on a 72-core machine, which we believe is the most comprehensive evaluation of parallel connectivity algorithms to date. Compared to a collection of state-of-the-art static multicore algorithms, we obtain an average speedup of 12.4x (2.36x average speedup over the fastest existing implementation for each graph). Using ConnectIt, we are able to compute connectivity on the largest publicly-available graph (with over 3.5 billion vertices and 128 billion edges) in under 10 seconds using a 72-core machine, providing a 3.1x speedup over the fastest existing connectivity result for this graph, in any computational setting. For our incremental algorithms, we show that our algorithms can ingest graph updates at up to several billion edges per second. To guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies. Finally, we show how the techniques in ConnectIt can be used to speed up two important graph applications: approximate minimum spanning forest and SCAN clustering.

Computer Science↗
NASA NTRS1990

Navier-Stokes computations for aerodynamic configurations at high angles of attack

Navier-Stokes computations at high angles of attack over aerodynamic configurations are presented using an implicit finite-volume algorithm. The spatial differencing is upwind-biased for the convective and pressure terms and central for the shear stress and heat transfer terms. The equations are relaxed to steady state with a spatially factored implicit algorithm. In order to treat general geometries, a multiblock patched-grid framework is implemented. Applications and detailed comparisons with experimental data are made for two simple but representative geometric shapes: (1) a highly swept delta wing and (2) a prolate spheroid of 6:1 length-to-diameter. Recent extensions of the algorithm to compute the flow over an F-18 forebody-strake configuration are shown, including comparisons with wind tunnel and flight test results. Comparisons across the range of Reynolds number for all cases indicate that either low Reynolds number or high Reynolds number flows are simulated well, but that flows at intermediate Reynolds number cannot be simulated accurately without a detailed knowledge of the transitional zone between laminar and turbulent flow.

Thomas, J. L.↗
DOE OSTI2020

Designing a parallel Feel-the-Way clustering algorithm on HPC systems

This paper introduces a new parallel clustering algorithm, named Feel-the-Way clustering algorithm, that provides better or equivalent convergence rate than the traditional clustering methods by optimizing the synchronization and communication costs. Our algorithm design centers on how to optimize three factors simultaneously: reduced synchronizations, improved convergence rate, and retained same or comparable optimization cost. To compare the optimization cost, we use the Sum of Square Error (SSE) cost as the metric, which is the sum of the square distance between each data point and its assigned clusters. Compared with the traditional MPI k-means algorithm, the new Feel-the-Way algorithm requires less communications among participating processes. As for the convergence rate, the new algorithm requires fewer number of iterations to converge. As for the optimization cost, it obtains the SSE costs that are close to the k-means algorithm. In the paper, we first design the full-step Feel-the-Way k-means clustering algorithm that can significantly reduce the number of iterations that are required by the original k-means clustering method. Next, we improve the performance of the full-step algorithm by adopting an optimized sampling-based approach, named reassignment-history-aware sampling. Our experimental results show that the optimized sampling-based Feel-the-Way method is significantly faster than the widely used k-means clustering method, and can provide comparable optimization costs. More extensive experiments with several synthetic datasets and real-world datasets (e.g., MNIST, CIFAR-10, ENRON, and PLACES-2) show that the new parallel algorithm can outperform the open source MPI k-means library by up to 110% on a high-performance computing system using 4,096 CPU cores. In addition, the new algorithm can take up to 51% fewer iterations to converge than the k-means clustering algorithm.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1999

Ocean Observations with EOS/MODIS: Algorithm Development and Post Launch Studies

This separation has been logical thus far; however, as launch of AM-1 approaches, it must be recognized that many of these activities will shift emphasis from algorithm development to validation. For example, the second, third, and fifth bullets will become almost totally validation-focussed activities in the post-launch era, providing the core of our experimental validation effort. Work under the first bullet will continue into the post-launch time frame, driven in part by algorithm deficiencies revealed as a result of validation activities. Prior to the start of the 1999 fiscal year (FY99) we were requested to prepare a brief plan for our FY99 activities. This plan is included as Appendix 1. The present report describes the progress made on our planned activities.

Gordon, Howard R.↗
NASA NTRS1993

Experimental evaluation of the certification-trail method

Certification trails are a recently introduced and promising approach to fault-detection and fault-tolerance. A comprehensive attempt to assess experimentally the performance and overall value of the method is reported. The method is applied to algorithms for the following problems: huffman tree, shortest path, minimum spanning tree, sorting, and convex hull. Our results reveal many cases in which an approach using certification-trails allows for significantly faster overall program execution time than a basic time redundancy-approach. Algorithms for the answer-validation problem for abstract data types were also examined. This kind of problem provides a basis for applying the certification-trail method to wide classes of algorithms. Answer-validation solutions for two types of priority queues were implemented and analyzed. In both cases, the algorithm which performs answer-validation is substantially faster than the original algorithm for computing the answer. Next, a probabilistic model and analysis which enables comparison between the certification-trail method and the time-redundancy approach were presented. The analysis reveals some substantial and sometimes surprising advantages for ther certification-trail method. Finally, the work our group performed on the design and implementation of fault injection testbeds for experimental analysis of the certification trail technique is discussed. This work employs two distinct methodologies, software fault injection (modification of instruction, data, and stack segments of programs on a Sun Sparcstation ELC and on an IBM 386 PC) and hardware fault injection (control, address, and data lines of a Motorola MC68000-based target system pulsed at logical zero/one values). Our results indicate the viability of the certification trail technique. It is also believed that the tools developed provide a solid base for additional exploration.

Sullivan, Gregory F.↗
DOE OSTI2020

Far-field thermal imaging below diffraction limit

Non-uniform self-heating and temperature hotspots are major concerns compromising the performance and reliability of submicron electronic and optoelectronic devices. At deep submicron scales where effects such as contact-related artifacts and diffraction limits accurate measurements of temperature hotspots, non-contact thermal characterization can be extremely valuable. In this work, we use a Bayesian optimization framework with generalized Gaussian Markov random field (GGMRF) prior model to obtain accurate full-field temperature distribution of self-heated metal interconnects from their thermoreflectance thermal images (TRI) with spatial resolution 2.5 times below Rayleigh limit for 530nm illumination. Finite element simulations along with TRI experimental data were used to characterize the point spread function of the optical imaging system. In addition, unlike iterative reconstruction algorithms that use ad hoc regularization parameters in their prior models to obtain the best quality image, we used numerical experiments and finite element modeling to estimate the regularization parameter for solving a real experimental inverse problem.

42 ENGINEERING↗
DOE OSTI2020

Enhancing nuclear data validation analysis by using machine learning

In this work, we demonstrate how machine learning (ML) techniques can be used as an auxiliary tool for nuclear data validation analysis. The output of the ML analysis can inform evaluators and validators on the quality, or lack thereof, of specific nuclear data and benchmark experiments with respect to simulating these experimental benchmark values. To this end, measured and simulated effective neutron multiplication factors, k eff , of 875 selected ICSBEP critical assemblies and the latter's sensitivities with respect to nuclear data as well as benchmarks' features (e.g., material nuclide, core geometry) are used as input for a random forest (RF) regression model. The RF is used to encode the complex inter-dependencies between thousands of nuclear data features (i.e., sensitivity profiles and aspects describing the measurements) and 875 simulated and experimental k eff values in order to understand what nuclear data features are most informative for predicting bias. The complexity of relationships and high-dimensional space is difficult-to-impossible to search fully using simply expert judgment. As a first proof-of-concept—step, it is demonstrated that this technique is able to correctly trace large discrepancies between simulated and experimental k eff back to fabricated shortcomings in nuclear data that were perturbed to simulated k eff values. In a second, real-case scenario, step, the RF algorithm is used to validate the ENDF/B-VIII.0 library in comparison to ENDF/B-VII.1 nuclear data. One case is showcased where the chosen ML algorithms highlighted nuclear data (the 19 F(n,inl) cross section from 0.4–0.9 MeV) that are shown to be problematic by comparing them to associated differential experimental data and nuclear data from other libraries. In addition to that, it is shown that the RF results point towards poor benchmark experiments and associated underestimated uncertainties (e.g., the PU-SOL-THERM-028 series). However, using the RF algorithm for validating nuclear data with respect to k eff is currently limited to pinpointing groups of questionable nuclear data due to the inherent correlations between features introduced by the nuclear data themselves and how k eff is simulated. Due to this, we recommend that the ML methods presented be used to augment—rather than replace—the expert knowledge of evaluators and validators.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Nonintuitive Surface Self-Assembly of Functionalized Molecules on Ag(111)

The fabrication of nanomaterials involves selfordering processes of functional molecules on inorganic surfaces. To obtain specific molecular arrangements, a common strategy is to equip molecules with functional groups. However, focusing on the functional groups alone does not provide a comprehensive picture. Especially at interfaces, processes that govern self-ordering are complex and involve various physical and chemical effects, often leading to unexpected structures, as we showcase here on the example of a homologous series of quinones on Ag(111). Naively, one could expect that such quinones, which all bear the same functionalization, form similar motifs. In salient contrast, our joint theoretical and experimental study shows that profoundly different structures are formed. Using a machine-learning-based structure search algorithm, we find that this is due to a shift of the balance of three antagonizing driving forces: adsorbate-substrate interactions governing adsorption sites, adsorbate-adsorbate interactions favoring close packing, and steric hindrance inhibiting certain otherwise energetically beneficial molecular arrangements. The theoretical structures show excellent agreement with our experimental characterizations of the organic/ inorganic interfaces, both for the unit cell sizes and the orientations of the molecules within. The nonintuitive interplay of similarly important interaction mechanisms will continue to be a challenging aspect for the design of functional interfaces. With a detailed examination of all driving forces, we are, however, still able to devise a design principle for self-assembly of functionalized molecules.

structure prediction↗
DOE OSTI2025

Exploring Electrode-Level State-of-Charge and State-of-Health Dynamics in Lithium-Ion Battery Cells: Modeling and Experimental Identification

A computationally efficient model serves as a critical prerequisite for battery performance analysis and advanced battery management algorithm design. Although battery models that capture cell-level behavior have been widely explored in existing literature, electrode-level battery models have received much lesser attention till to date. However, such electrode-level models can significantly increase battery performance and life by enabling electrode-level health-conscious control. Such electrode-level control can effectively expand usable energy and power limits of the battery cells by utilizing the knowledge of individual electrodes' charge and health. In this context, this paper presents a comprehensive battery model developed with a reference electrode insertion that captures (i) electrode-level charge/discharge dynamics, (ii) stoichiometric and temporal dependencies of electrode-level resistances, (iii) solid electrolyte interface (SEI) layer growth as key degradation phenomenon, and (iv) capacity fade and resistance rise in each electrode due to nominal battery aging. The proposed model is identified, and a preliminary validation is performed utilizing terminal voltage and negative electrode potential data collected from a pouch cell under one continuous cycling and accelerated aging conditions where the cell experienced 14% capacity loss.

aging↗
NASA NTRS1987

Non-linear identification of a squeeze-film damper

Described is an experimental study to identify the damping laws associated with a squeeze-film vibration damper. This is achieved by using a non-linear filtering algorithm to process displacement responses of the damper ring to synchronous excitation and thus to estimate the parameters in an nth-power velocity model. The experimental facility is described in detail and a representative selection of results is included. The identified models are validated through the prediction of damper-ring orbits and comparison with observed responses.

Stanway, Roger↗