Search NASA⌕ Search

SEARCH · Search NASA

Results for “data intensive computing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

PyDREAM: high-dimensional parameter inference for biological models in python

Biological models contain many parameters whose values are difficult to measure directly via experimentation and therefore require calibration against experimental data. Markov chain Monte Carlo (MCMC) methods are suitable to estimate multivariate posterior model parameter distributions, but these methods may exhibit slow or premature convergence in high-dimensional search spaces. Here, we present PyDREAM, a Python implementation of the (Multiple-Try) Differential Evolution Adaptive Metropolis [DREAM(ZS)] algorithm developed by Vrugt and ter Braak (2008) and Laloy and Vrugt (2012). PyDREAM achieves excellent performance for complex, parameter-rich models and takes full advantage of distributed computing resources, facilitating parameter inference and uncertainty estimation of CPU-intensive biological models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Effective Missing Value Imputation Methods for Building Monitoring Data

To understand behaviors of natural and man-made events, such as energy consumption of buildings, which accounts for 40% of energy uses in the US, we deploy automated monitoring devices to record periodic observations. However, such experimental and observation data often contains problems and irregularities that have to be cleaned up before analyses. Due to various conditions affecting sensor operations, the communication channels, recording steps, or the recording media, the recorded data might have missing values, errors, or anomalous values. An effective way to clean up these problems is to replace these missing values, errors and anomalous values with expected values, a process generally known as imputation. In this work, we survey commonly used missing value imputation techniques and compare their performance on a set of building monitoring data. To compare the different types of sensor measurements with widely varying characteristics, we use normalized root mean squared error (NRMSE) as the key metric for the effectiveness of the imputation methods. We additionally consider periodicity and run time when considering comparing methods. Through extensive testing, we find that for small gap sizes, up to 8 consecutive missing values, linear interpolation performs the best; for larger gaps stretching up to 48 consecutive missing values, K-nearest neighbors provides the most accurate imputations; for even larger gaps, more computational intensive methods, such as matrix factorization, achieve the smallest NRMSE. Additionally, we observe that these computationally intensive algorithms not only provide accurate imputations for large gaps, but are also more robust across all types of sensors.

Cho, B↗
DOE OSTI2016

Direct comparison of elastic incoherent neutron scattering experiments with molecular dynamics simulations of DMPC phase transitions

Neutron scattering techniques have been employed to investigate 1,2-dimyristoyl-sn-glycero-3- phosphocholine (DMPC) membranes in the form of multilamellar vesicles (MLVs) and deposited, stacked multilamellar-bilayers (MLBs), covering transitions from the gel to the liquid phase. Neutron diffraction was used to characterise the samples in terms of transition temperatures, whereas elastic incoherent neutron scattering (EINS) demonstrates that the dynamics on the sub-macromolecular length-scale and pico- to nano-second time-scale are correlated with the structural transitions through a discontinuity in the observed elastic intensities and the derived mean square displacements. Molecular dynamics simulations have been performed in parallel focussing on the length-, time- and temperature-scales of the neutron experiments. They correctly reproduce the structural features of the main gel-liquid phase transition. Particular emphasis is placed on the dynamical amplitudes derived from experiment and simulations. Two methods are used to analyse the experimental data and mean square displacements. They agree within a factor of 2 irrespective of the probed time-scale, i.e. the instrument utilized. Mean square displacements computed from simulations show a comparable level of agreement with the experimental values, albeit, the best match with the two methods varies for the two instruments. Consequently, experiments and simulations together give a consistent picture of the structural and dynamical aspects of the main lipid transition and provide a basis for future, theoretical modelling of dynamics and phase behaviour in membranes. The need for more detailed analytical models is pointed out by the remaining variation of the dynamical amplitudes derived in two different ways from experiments on the one hand and simulations on the other.

Aoun, Bachir↗
NASA NTRS1990

Unsteady transonic Navier-Stokes computations for an oscillating wing using single and multiple zones

Modern jet transports and maneuvering tactical fighters operating in the transonic regime often give rise to time-dependent fluid physics that interact with flexible structural components, e.g., vortical flow, shocks, and separation. Efficient computational fluid dynamic (CFD) methods are required to study such computationally intensive problems. In this work a numerical method is presented to address this problem. Time-dependent, compressible, Navier-Stokes equations are used to simulate unsteady transonic flow about a three-dimensional rigid wing undergoing a forced periodic motion in angle of attack. An efficient, implicit, diagonal algorithm is utilized because of its low operation count per time step compared to other methods that solve systems of block matrix equations. The formal time accuracy is addressed theoretically and demonstrated numerically by comparison of computational results with experimental data. A zonal grid approach, capable of treating complex geometries, is presented and its time accuracy is demonstrated by comparing a zonal computation with a single grid computation and experimental data.

Chaderjian, Neal M.↗
NASA NTRS1995

The Ozonide Anion: A Theoretical Study

Complete active space self-consistent field (CASSCF) and CASSCF second order perturbation theory (CASPT2) methods have been used for the geometry optimization and calculation of harmonic and fundamental frequencies of the ozonide ion O3(-) and the ozonide lithium complex Li(+)O3(-). For O3(-) harmonic frequencies have also been obtained using the coupled-cluster method, CCSD(T). Infrared intensities are computed from dipole moment derivatives at the CASSCF level. The predicted equilibrium geometry for O3(-) is: Roo = 1.361 Angstroms and angle ooo = 115.4 degrees, and the fundamental frequencies are: nu(sub 1) = 989 per centimeter, nu(sub 2) 556 per centimeter, nu(sub 3) = 870 per centimeter (experimental values are: Roo =1.36 plus or minus 0.02 Angstroms, angle (ooo) = 111.8 plus or minus 2.0 degrees, nu(sub 1) = 975(50) per centimeter, nu(sub 2) = 550(50) per centimeter, nu(sub 3) = 880(50) per centimeter). Corresponding data for the lithium ozonide complex have also been obtained. The presented data contradict the previous interpretation of the IR and Raman spectrum obtained after deposition of ozone in N2, argon, or neon matrices with atomic beams of alkali metals. The presence of the lithium cation raises the asymmetric stretch frequency to about 940 per centimeter, which is contradictory to assumptions made in the assignments of the matrix spectra. Calculations made in a dielectric medium strongly suggest that the effect of the matrix on the IR spectrum is small for O3(-) itself. The dissociation and atomization energies of O3(-) are found to be in agreement with experiment.

Borowski, Poitr↗
NASA NTRS2004

Improving Rotor-Stator Interaction Noise Code Through Analysis of Input Parameters

There are two major sources of aircraft noise. The first is from the airframe and the second is from the engines. The focus of the acoustics branch at NASA Glenn is on the engine noise sources. There are two major sources of engine noise; fan noise and jet noise. Fan noise, produced by rotating machinery of the engine, consists of both tonal noise, which occurs at discrete frequencies, and broadband noise, which occurs across a wide range of frequencies. The focus of my assignment is on the broadband noise generated by the interaction of fan flow turbulence and the stator blades. such as the sweep and stagger angles and blade count, as well as the flow parameters such as intensity of turbulence in the flow. The tool I employed in this work is a computer program that predicts broadband noise from fans. The program assumes that the complex shape of the curved blade can be represented as a single flat plate, allowing it to use fairly simple equations that can be solved in a reasonable amount of time. While the results from such representation provided reasonable estimates of the broadband noise levels, they did not usually represent the entire spectrum accurately. My investigation found that the discrepancy between data and theory can be improved if the leading edge and the trailing edge of the blade are treated separately. Using this approach, I reduced the maximum error in noise level from a high of 30% to less than 5% for the cases investigated. Detailed results of this investigation will be discussed at my presentation. The objective of this study is to investigate the influence of geometric parameters

Unton, Timothy J.↗
DOE OSTI2018

Do energy costs really affect commercial mortgage default risk? New results and implications for energy efficiency investments [PowerPoint]

This paper presents new results on the link between energy factors and commercial mortgage default. First, we summarize results from an empirical analysis of the impact of source energy use intensity (EUI) and electricity prices on mortgage default. We used a unique data set that merges building-level energy use data from city benchmarking ordinances and financial data for commercial mortgages on the same buildings. We found that building source EUI and electricity price are statistically and economically associated with commercial mortgage defaults. Next, we present five case studies on the impact of energy use and price variations on default risk: three office buildings, a hotel, and a multi-family residential building. We use the empirical model coefficients to compute the default risk impacts due to variations in source EUI and electricity price over the course of the mortgage term. We found that variations in source EUI could raise or lower the default rates in these properties by between 5% and 40%, depending on the property type and geography. Electricity pricing has an even greater effect – roughly 60% change in default rate in the Denver area and nearly 90% in northern California. Finally, we propose an energy risk score that lenders can use to assess energy risk and inform mortgage terms. The score is being developed and piloted in collaboration with three mortgage lenders. We conclude with implications of this score as a market signal and mechanism for incentivizing energy efficiency investments through the commercial mortgage channel.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation

Purpose: To predict microsatellite instability (MSI) status of colon cancer on preoperative CT imaging using radiomic analysis. Methods: This retrospective study involved radiomic analysis of preoperative CT imaging of patients who underwent resection of stage II–III colon cancer from 2004 to 2012. A radiologist blinded to MSI status manually segmented the tumor region on CT images. 254 Intensity-based radiomic features were extracted from the tumor region. Three prediction models were developed with (1) only clinical features, (2) only radiomic features, and (3) “combined” clinical and radiomic features. Patients were randomly separated into training (n = 139) and test (n = 59) sets. The model was constructed from training data only; the test set was reserved for validation only. Model performance was evaluated using AUC, sensitivity, specificity, PPV, and NPV. Results: Of the total 198 patients, 134 (68%) patients had microsatellite stable tumors and 64 (32%) patients had MSI tumors. The combined model performed slightly better than the other models, predicting MSI with an AUC of 0.80 for the training set and 0.79 for the test set (specificity = 96.8% and 92.5%, respectively), whereas the model with only clinical features achieved an AUC of 0.74 and the model with only radiomic features achieved an AUC of 0.76. The model with clinical features alone had the lowest specificity (70%) compared with the model with radiomic features alone (95%) and the combined model (92.5%). Conclusions: Preoperative prediction of MSI status via radiomic analysis of preoperative CT adds specificity to clinical assessment and could contribute to personalized treatment selection.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Predicting Atomistic Transitions with Transformers

Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation techniques used to find these transitions are extremely computationally intensive. Even with large-scale, accelerated material simulations, the computational cost constrains the applicable domain in practice. Machine learning models, with the potential to learn the complex emergent behaviors governing atomistic transitions as a fast surrogate model, have great promise to predict transitions with a vastly reduced computational cost. Here, we demonstrate how transformers can be trained to predict atomistic transitions in nano-clusters. We show how we evaluate physical validity of the predictions and how a multitude of additional, different microstates can be generated by slightly varying the data provided to the model.

36 MATERIALS SCIENCE↗
DOE OSTI2015

MO-G-304-02: Knowledge Based DVH Prediction Using a Geometric Dose Transform

Purpose: To demonstrate a novel method for predicting patient dose-volume histograms (DVHs) using a prior database of optimized radiotherapy treatment plans. Such predicted DVHs could be useful for automating treatment planning. Methods: Our initial demonstration utilized a database of 100 prostate intensity-modulated radiotherapy (IMRT) data-sets. Each data-set contained a CT image with contours of the planning target volume (PTV), rectum, and bladder, the parameters of a clinically approved IMRT plan, and a corresponding simulated dose distribution. We applied a novel geometric transformation to remove the influence of the PTV size, shape, and location on the dose distribution. We termed the transformed distribution the geometrically normalized dose distribution (GNDD). This normalization transform was applied to 80 data-sets randomly selected from the database, and a population GNDD was computed as the average. Next, the population GNDD was mapped onto each of the remaining 20 patient datasets using the reverse of the geometric normalization transform, and predicted DVHs were calculated from the reverse transformed dose distributions (GNDD-DVHs). In addition, a state of the art machine learning based method from the literature was tested for comparison. Results: DVH prediction accuracy was quantified by calculating the relative root mean squared error (rRMSE) on predicted DVHs for the 20 test patients using their known DVHs. For bladder, rectum, and PTV average rRMSEs for the GNDD method were 9.7 ± 4.2%, 13.9 ± 6.0%, and 2.3 ± 0.5% respectively. Prediction results using GNDD were roughly equivalent to that from the machine learning method. Conclusion: We developed a new method for predicting DVH curves from a database of prior patient plans. We demonstrated that our simple approach achieves accuracy comparable to a method using a complicated machine learning based approach.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

The antibody mining toolbox: An open source tool for the rapid analysis of antibody repertoires

In vitro selection has been an essential tool in the development of recombinant antibodies against various antigen targets. Deep sequencing has recently been gaining ground as an alternative and valuable method to analyze such antibody selections. The analysis provides a novel and extremely detailed view of selected antibody populations, and allows the identification of specific antibodies using only sequencing data, potentially eliminating the need for expensive and laborious low-throughput screening methods such as enzyme-linked immunosorbant assay. The high cost and the need for bioinformatics experts and powerful computer clusters, however, have limited the general use of deep sequencing in antibody selections. Here, we describe the AbMining ToolBox, an open source software package for the straightforward analysis of antibody libraries sequenced by the three main next generation sequencing platforms (454, Ion Torrent, MiSeq). The ToolBox is able to identify heavy chain CDR3s as effectively as more computationally intense software, and can be easily adapted to analyze other portions of antibody variable genes, as well as the selection outputs of libraries based on different scaffolds. The software runs on all common operating systems (Microsoft Windows, Mac OS X, Linux), on standard personal computers, and sequence analysis of 1–2 million reads can be accomplished in 10–15 min, a fraction of the time of competing software. Use of the ToolBox will allow the average researcher to incorporate deep sequence analysis into routine selections from antibody display libraries.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1984

Theoretical studies of X-ray emission from supernova remnants and other non-equilibrium astrophysical plasmas

To improve the accuracy of thermal X-ray emission spectrum predictions, new computations of electron excitation rates, dielectric recombination rates, and updated codes were applied to existing data on supernova remnants and O star winds. Because of its general predominance along with low ionization and recombination rates, Fe(XVII) was analyzed with the inclusion of resonances to the excitation rate data. Corrections to the existing dielectric recombination rates based on detailed Fe (XVII) calculations and the more approximate Quantum Defect Method were accomplished. by using Distorted Wave ionization rates and reliable excitation rates, models for X-ray, ultraviolet, and optical emission line intensities of non-radiative shock wave can be constructed that supply data on shock velocity, preshock ionization states, and elemental abundances. The radiative driving force that drives the shock wave is calculated through line opacities of the shocked gas. X-ray emission spectra are also calculated for O star winds.

Dupree, A. K.↗
NASA NTRS1993

Turbulence modelling of flow fields in thrust chambers

Following the consensus of a workshop in Turbulence Modelling for Liquid Rocket Thrust Chambers, the current effort was undertaken to study the effects of second-order closure on the predictions of thermochemical flow fields. To reduce the instability and computational intensity of the full second-order Reynolds Stress Model, an Algebraic Stress Model (ASM) coupled with a two-layer near wall treatment was developed. Various test problems, including the compressible boundary layer with adiabatic and cooled walls, recirculating flows, swirling flows, and the entire SSME nozzle flow were studied to assess the performance of the current model. Detailed calculations for the SSME exit wall flow around the nozzle manifold were executed. As to the overall flow predictions, the ASM removes another assumption for appropriate comparison with experimental data to account for the non-isotropic turbulence effects.

Chen, C. P.↗
NASA NTRS1993

Development of an algebraic stress/two-layer model for calculating thrust chamber flow fields

Following the consensus of a workshop in Turbulence Modeling for Liquid Rocket Thrust Chambers, the current effort was undertaken to study the effects of second-order closure on the predictions of thermochemical flow fields. To reduce the instability and computational intensity of the full second-order Reynolds Stress Model, an Algebraic Stress Model (ASM) coupled with a two-layer near wall treatment was developed. Various test problems, including the compressible boundary layer with adiabatic and cooled walls, recirculating flows, swirling flows and the entire SSME nozzle flow were studied to assess the performance of the current model. Detailed calculations for the SSME exit wall flow around the nozzle manifold were executed. As to the overall flow predictions, the ASM removes another assumption for appropriate comparison with experimental data, to account for the non-isotropic turbulence effects.

Chen, C. P.↗
NASA NTRS2004

Anomolous Fatigue Crack Growth Phenomena in High-Strength Steel

The growth of a fatigue crack through a material is the result of a complex interaction between the applied loading, component geometry, three-dimensional constraint, load history, environment, material microstructure and several other factors. Previous studies have developed experimental and computational methods to relate the fatigue crack growth rate to many of the above conditions, with the intent of discovering some fundamental material response, i.e. crack growth rate as a function of something. Currently, the technical community uses the stress intensity factor solution as a simplistic means to relate fatigue crack growth rate to loading, geometry and all other variables. The stress intensity factor solution is a very simple linear-elastic representation of the continuum mechanics portion of crack growth. In this paper, the authors present fatigue crack growth rate data for two different high strength steel alloys generated using standard methods. The steels exhibit behaviour that appears unexplainable, compared to an aluminium alloy presented as a baseline for comparison, using the stress intensity factor solution.

Forth, Scott C.↗
NASA NTRS2005

Parachute Testing for the NASA X-38 Crew Return Vehicle

NASA's X-38 program was an in-house technology demonstration program to develop a Crew Return Vehicle (CRV) for the International Space Station capable of returning seven crewmembers to Earth when the Space Shuttle was not present at the station. The program, managed out of NASA's Johnson Space Center, was started in 1995 and was cancelled in 2003. Eight flights with a prototype atmospheric vehicle were successfully flown at Edwards Air Force Base, demonstrating the feasibility of a parachute landing system for spacecraft. The intensive testing conducted by the program included testing of large ram-air parafoils. The flight test techniques, instrumentation, and simulation models developed during the parachute test program culminated in the successful demonstration of a guided parafoil system to land a 25,000 Ib spacecraft. The test program utilized parafoils of sizes ranging from 750 to 7500 p. The guidance, navigation, and control system (GN&C) consisted of winches, laser or radar altimeter, global positioning system (GPS), magnetic compass, barometric altimeter, flight computer, and modems for uplink commands and downlink data. The winches were used to steer the parafoil and to perform the dynamic flare maneuver for a soft landing. The laser or radar altimeter was used to initiate the flare. In the event of a GPS failure, the software navigated by dead reckoning using the compass and barometric altimeter data. The GN&C test beds included platforms dropped from cargo aircraft, atmospheric vehicles released from a 8-52, and a Buckeye powered parachute. This paper will describe the test program and significant results.

Stein, Jenny M.↗
DOE OSTI2021

Discrete Green’s functions and spectral graph theory for computationally efficient thermal modeling

Here, this work concerns solutions of the heat equation with the spectral graph method, for which the temperature is defined at discrete points in the domain and the spatial relationship among the points is described by a graph. The heat equation on the graph is solved using matrix techniques involving the eigenvectors and eigenvalues of the Laplacian matrix. The spectral graph approach precludes the computationally intensive meshing and numerous time-integration steps of the finite element method. In the present work, the spectral graph method is extended to include heat loss at the boundaries with a generalized boundary condition, and physics-based edge weights are introduced which simplify the calibration process. From this approach a discrete Green’s function is defined which allows for solutions under a variety of heating conditions including: space-varying initial conditions; time-and-space varying internal heating; and, time-and-space-varying heating at boundaries of type 1 (Dirichlet), type 2 (Neumann) and type 3 (Robin). Results are provided for benchmark heat transfer problems in one spatial dimension and in three spatial dimensions, and verification is provided by comparison with exact analytical solutions and finite difference solutions. The spectral graph method converges within 0.4% error of the analytical solution. The practical utility of the approach is demonstrated by thermal simulation of a multilayer additive manufacturing process. The spectral graph results are compared to experimentally-obtained temperature data for two metal parts, with error less than 5% of the experimental measurements, with computation time less than one minute on a desktop computer.

36 MATERIALS SCIENCE↗
DOE OSTI2023

WellSTIC: A Cost-Effective Sensor for Performing Point Dilution Tests to Measure Groundwater Velocity in Shallow Aquifers

Many individual measurement points are required to characterize groundwater velocity within an aquifer. Groundwater velocity is most commonly measured using a network of >5 cm diameter monitoring wells, which, if not already present at a site, are expensive and labor-intensive to install. Drive-point piezometers—simple, cost-effective wells that can be installed by hand—are a common tool for sampling groundwater in shallow, alluvial aquifers, but most groundwater velocity measurement techniques require equipment that is too large for these narrow (usually <2 cm inside diameter) piezometers. In this technical note, we introduce a low-cost sensor and well packer system (<$90 USD) for performing point dilution tests in narrow piezometers. Field data show that the magnitude of groundwater velocity measured with this technique agrees with velocities computed from natural gradient tracer tests. Additionally, with proper calibration, these sensors can be used to continuously monitor in-well specific conductance, either during inter-well tracer tests with saline tracers or for water quality monitoring. Finally, this system is a viable tool for rapid assessment of the magnitude of groundwater velocity in shallow aquifers.

54 ENVIRONMENTAL SCIENCES↗