Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “factor analysis”

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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Analysis of Lung Tumor Motion in a Large Sample: Patterns and Factors Influencing Precise Delineation of Internal Target Volume

Purpose/Objective: To evaluate lung tumor motion during respiration and to describe factors affecting the range and variability of motion in patients treated with stereotactic ablative radiation therapy. Methods and Materials: Log file analysis from online respiratory tumor tracking was performed in 145 patients. Geometric tumor location in the lungs, tumor volume and origin (primary or metastatic), sex, and tumor motion amplitudes in the superior-inferior (SI), latero-lateral (LL), and anterior-posterior (AP) directions were recorded. Tumor motion variability during treatment was described using intrafraction/interfraction amplitude variability and tumor motion baseline changes. Tumor movement dependent on the tumor volume, position and origin, and sex were evaluated using statistical regression and correlation analysis. Results: After analysis of >500 hours of data, the highest rates of motion amplitudes, intrafraction/interfraction variation, and tumor baseline changes were in the SI direction (6.0 ± 2.2 mm, 2.2 ± 1.8 mm, 1.1 ± 0.9 mm, and −0.1 ± 2.6 mm). The mean motion amplitudes in the lower/upper geometric halves of the lungs were significantly different (P<.001). Motion amplitudes >15 mm were observed only in the lower geometric quarter of the lungs. Higher tumor motion amplitudes generated higher intrafraction variations (R=.86, P<.001). Interfraction variations and baseline changes >3 mm indicated tumors contacting mediastinal structures or parietal pleura. On univariate analysis, neither sex nor tumor origin (primary vs metastatic) was an independent predictive factor of different movement patterns. Metastatic lesions in women, but not men, showed significantly higher mean amplitudes (P=.03) and variability (primary, 2.7 mm; metastatic, 4.9 mm; P=.002) than primary tumors. Conclusion: Online tracking showed significant irregularities in lung tumor movement during respiration. Motion amplitude was significantly lower in upper lobe tumors; higher interfraction amplitude variability indicated tumors in contact with mediastinal structures, although adhesion to parietal pleura did not necessarily reduce tumor motion amplitudes. The most variable lung tumors were metastatic lesions in women.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS1977

Development of a multi-disciplinary ERTS user program in the state of Ohio

The author has identified the following significant results. Although the analysis of the results of presently - used methodology has showed inadequate correlation between the land use change and the various socio-economic factors, a system of analysis was produced that will aid in the future work in this area pointing out the adequacy of the land use data for producing models of desired accuracy. This testing methodology is applicable to other LANDSAT/land use interpretation systems.

Baldridge, P. E.↗
NASA NTRS2012

Design and Analysis of Subscale and Full-Scale Buckling-Critical Cylinders for Launch Vehicle Technology Development

NASA s Shell Buckling Knockdown Factor (SBKF) project has the goal of developing new analysis-based shell buckling design factors (knockdown factors) and design and analysis technologies for launch vehicle structures. Preliminary design studies indicate that implementation of these new knockdown factors can enable significant reductions in mass and mass-growth in these vehicles. However, in order to validate any new analysis-based design data or methods, a series of carefully designed and executed structural tests are required at both the subscale and full-scale levels. This paper describes the design and analysis of three different orthogrid-stiffeNed metallic cylindrical-shell test articles. Two of the test articles are 8-ft-diameter, 6-ft-long test articles, and one test article is a 27.5-ft-diameter, 20-ft-long Space Shuttle External Tank-derived test article.

Hilburger, Mark W.↗
DOE OSTI2021

Validation of non-negative matrix factorization for rapid assessment of large sets of atomic pair distribution function data

The use of the non-negative matrix factorization (NMF) technique is validated for automatically extracting physically relevant components from atomic pair distribution function (PDF) data from time-series data such as in situ experiments. The use of two matrix-factorization techniques, principal component analysis and NMF, on PDF data is compared in the context of a chemical synthesis reaction taking place in a synchrotron beam, applying the approach to synthetic data where the correct composition is known and on measured PDFs from previously published experimental data. The NMF approach yields mathematical components that are very close to the PDFs of the chemical components of the system and a time evolution of the weights that closely follows the ground truth. Lastly, it is discussed how this would appear in a streaming context if the analysis were being carried out at the beamline as the experiment progressed.

36 MATERIALS SCIENCE↗
NASA NTRS2009

Parametric Analysis of a Hover Test Vehicle using Advanced Test Generation and Data Analysis

Large complex aerospace systems are generally validated in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. This is due to the large parameter space, and complex, highly coupled nonlinear nature of the different systems that contribute to the performance of the aerospace system. We have addressed the factors deterring such an analysis by applying a combination of technologies to the area of flight envelop assessment. We utilize n-factor (2,3) combinatorial parameter variations to limit the number of cases, but still explore important interactions in the parameter space in a systematic fashion. The data generated is automatically analyzed through a combination of unsupervised learning using a Bayesian multivariate clustering technique (AutoBayes) and supervised learning of critical parameter ranges using the machine-learning tool TAR3, a treatment learner. Covariance analysis with scatter plots and likelihood contours are used to visualize correlations between simulation parameters and simulation results, a task that requires tool support, especially for large and complex models. We present results of simulation experiments for a cold-gas-powered hover test vehicle.

Gundy-Burlet, Karen↗
DOE OSTI2015

Inverse patchy colloids with two and three patches. Analytical and numerical study

We propose an analytical solution of the multi-density Ornstein-Zernike equation supplemented by the associative Percus-Yevick closure relations specifically designed to describe the equilibrium properties of the novel class of patchy colloidal particles represented by the inverse patchy colloids with arbitrary number of patches. Using Baxter’s factorization method, we reduce solution of the problem to the solution of one nonlinear algebraic equation for the fraction of the particles with one non-bonded patch. We present closed-form expressions for the structure (structure factor) and thermodynamic (internal energy) properties of the system in terms of this fraction (and parameters of the model). We perform computer simulation studies and compare theoretical and computer simulation predictions for the pair distribution function, internal energy, and number of single and double bonds formed in the system, for two versions of the model, each with two and three patches. We consider the models with formation of the double bonds blocked by the patch-patch repulsion and the models without patch-patch repulsion. In general very good agreement between theoretical and computer simulation results is observed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2004

Use of Radon for Evaluation of Atmospheric Transport Models: Sensitivity to Emissions

This paper presents comparative analyses of atmospheric radon (Rn) distributions simulated using different emission scenarios and the observations. Results indicate that the model generally reproduces observed distributions of Rn but there are some biases in the model related to differences in large-scale and convective transport. Simulations presented here use an off-line three-dimensional chemical transport model driven by assimilated winds and two scenarios of Rn fluxes (atom/cm s) from ice-free land surfaces: (A) globally uniform flux of 1.0, and (B) uniform flux of 1.0 between 60 deg. S and 30 deg. N followed by a sharp linear decrease to 0.2 at 70 deg. N. We considered an additional scenario (C) where Rn emissions for case A were uniformly reduced by 28%. Results show that case A overpredicts observed Rn distributions in both hemispheres. Simulated northern hemispheric (NH) Rn distributions from cases B and C compare better with the observations, but are not discernible from each other. In the southern hemisphere, surface Rn distributions from case C compare better with the observations. We performed a synoptic scale source-receptor analysis for surface Rn to locate regions with ratios B/A and B/C less than 0.5. Considering an uncertainty in regional Rn emissions of a factor of two, our analysis indicates that additional measurements of surface Rn particularly during April-October and north of 50 deg. N over the Pacific as well as Atlantic regions would make it possible to determine if the proposed latitude gradient in Rn emissions is superior to a uniform flux scenario.

Gupta, Mohan L.↗
NASA NTRS1997

Advanced Power System Analysis Capabilities

As a continuing effort to assist in the design and characterization of space power systems, the NASA Lewis Research Center's Power and Propulsion Office developed a powerful computerized analysis tool called System Power Analysis for Capability Evaluation (SPACE). This year, SPACE was used extensively in analyzing detailed operational timelines for the International Space Station (ISS) program. SPACE was developed to analyze the performance of space-based photovoltaic power systems such as that being developed for the ISS. It is a highly integrated tool that combines numerous factors in a single analysis, providing a comprehensive assessment of the power system's capability. Factors particularly critical to the ISS include the orientation of the solar arrays toward the Sun and the shadowing of the arrays by other portions of the station.

Source record↗
NASA NTRS2015

Acoustic Liner Drag: A Parametric Study of Conventional Configurations

Interest in the characterization of the aerodynamic drag performance of acoustic liners has increased in the past several years. This paper details experiments in NASA Langley's Grazing Flow Impedance Tube to quantify the relative drag of several conventional perforate-over-honeycomb liner configurations. For a fixed porosity, facesheet hole diameter and cavity depth are varied to study the effect of each. These configurations are selected to span the range of conventional liner geometries used in commercial aircraft engines. Detailed static pressure and acoustic measurements are made for grazing flows up to M=0.5 at 140 dB SPL for tones between 400 and 2800 Hz. These measurements are used to calculate a resistance factor (λ) for each configuration. Analysis shows a correlation between perforate hole size and the resistance factor but cavity depth seems to have little influence. Acoustic effects on liner drag are observed to be limited to the lower Mach numbers included in this investigation.

Howerton, Brian M.↗
DOE OSTI2016

Reducing Computation and Communication in Scientific Computing: Connecting Theory to Practice.

This report summarizes the work produced as part of a Truman Fellowship appointment and its associated LDRD project. The overall goal of the project was to develop better algorithms and implementations for key computational kernels within the field of scientific computing by designing them to be communication efficient, moving as little data as possible. The primary problem of interest was dense matrix multiplication; other computations that were addressed include sparse matrix-matrix multiplication, QR factorization, solving symmetric linear systems, and the symmetric eigendecomposition. The project also involved the study of computations at the intersection of scientific computing and data analysis, including nonnegative matrix factorization for discovering latent factors, Tucker tensor decomposition for data compression, and sampling methods for similarity search.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Accuracy limitations for composition analysis by XPS using relative peak intensities: LiF as an example

Although precision in XPS can be excellent, allowing small changes to be easily observed, obtaining accurate absolute elemental composition of a solid material from relative peak intensities is generally much more problematical, involving many factors: background removal; differing analysis depths at different photoelectron kinetic energies; possible angular distribution effects; calibration of the instrument transmission function, and variations of the distribution of the photoelectron intensity between “main” peaks (those usually used for analysis) and associated substructure following the main peak, as a function of the chemical bonding of the elements concerned. The last item, coupled with the use of photoionization cross-sections and/or relative sensitivity factors, is the major subject of this paper, though it is necessary to consider the other items also, using LiF as a test case. The results show that the above issues, which are relevant to differing degrees in most XPS analyses, present significant challenges to highly accurate XPS quantification.

Brundle, Christopher R.↗
DOE OSTI2016

SU-F-P-39: End-To-End Validation of a 6 MV High Dose Rate Photon Beam, Configured for Eclipse AAA Algorithm Using Golden Beam Data, for SBRT Treatments Using RapidArc

Purpose: To use end-to-end testing to validate a 6 MV high dose rate photon beam, configured for Eclipse AAA algorithm using Golden Beam Data (GBD), for SBRT treatments using RapidArc. Methods: Beam data was configured for Varian Eclipse AAA algorithm using the GBD provided by the vendor. Transverse and diagonals dose profiles, PDDs and output factors down to a field size of 2×2 cm2 were measured on a Varian Trilogy Linac and compared with GBD library using 2% 2mm 1D gamma analysis. The MLC transmission factor and dosimetric leaf gap were determined to characterize the MLC in Eclipse. Mechanical and dosimetric tests were performed combining different gantry rotation speeds, dose rates and leaf speeds to evaluate the delivery system performance according to VMAT accuracy requirements. An end-to-end test was implemented planning several SBRT RapidArc treatments on a CIRS 002LFC IMRT Thorax Phantom. The CT scanner calibration curve was acquired and loaded in Eclipse. PTW 31013 ionization chamber was used with Keithley 35617EBS electrometer for absolute point dose measurements in water and lung equivalent inserts. TPS calculated planar dose distributions were compared to those measured using EPID and MapCheck, as an independent verification method. Results were evaluated with gamma criteria of 2% dose difference and 2mm DTA for 95% of points. Results: GBD set vs. measured data passed 2% 2mm 1D gamma analysis even for small fields. Machine performance tests show results are independent of machine delivery configuration, as expected. Absolute point dosimetry comparison resulted within 4% for the worst case scenario in lung. Over 97% of the points evaluated in dose distributions passed gamma index analysis. Conclusion: Eclipse AAA algorithm configuration of the 6 MV high dose rate photon beam using GBD proved efficient. End-to-end test dose calculation results indicate it can be used clinically for SBRT using RapidArc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

A Deep Learning Approach for TNC Trip Demand Prediction Considering Spatial-Temporal Features: Preprint

Ride-hailing or transportation network companies (TNCs), such as Uber, Lyft, DiDi Chuxing, or RideAustin, are emerging as a new and disruptive on-demand mobility service in recent years. However, the methods for developing predictive analytics to explore the nature and dynamics of TNCs across cities in the United States are still nascent due to the lack of publicly available data. Recently available public datasets on TNCs by RideAustin offer a unique opportunity to examine spatial, temporal, environmental, and special event factors associated with TNC trip demand. This study explores the use of a deep learning approach - Long Short-Term Memory (LSTM) - to predict TNC trip demand at the ZIP Code level using data from Austin, Texas. The analysis includes key predictive factors such as time of day, day of week, precipitation, and temperature, indicating their corresponding associations with TNC trip demand. Results from initial analysis show that LSTM is able to predict the TNC trip demand for the upcoming hour accurately. LSTM, when compared to other prediction methods, such as historical average and instantaneous trip demand, reduces the mean absolute error (MAE) of the model predictions by 37% and 24%, respectively. This novel method offers significant potential for scaling up and/or replicability across cities where data are available for understanding TNC trip demand to inform emerging mobility system operators. Predicting trip demand can help TNC drivers make informed decisions on how to be more efficient by maximizing passenger pickups and minimizing wait times and deadheading.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Efficient Computation of Free Energy Surfaces of Diels–Alder Reactions in Explicit Solvent at Ab Initio QM/MM Level

For Diels–Alder (DA) reactions in solution, an accurate and converged free energy (FE) surface at ab initio (ai) quantum mechanical/molecular mechanical (QM/MM) level is imperative for the understanding of reaction mechanism. However, this computation is still far too expensive. In a previous work, we proposed a new method termed MBAR+wTP, with which the computation of the ai FE profile can be accelerated by several orders of magnitude via a three-step procedure: (I) an umbrella sampling (US) using a semi-empirical (SE) QM/MM Hamiltonian is performed; (II) the FE profile is generated using the Multistate Bennett Acceptance Ratio (MBAR) analysis; and (III) a weighted Thermodynamic Perturbation (wTP) from the SE Hamiltonian to the ai Hamiltonian is performed to obtain the ai QM/MM FE profile using weight factors from the MBAR analysis. In this work, this method is extended to the calculations of two-dimensional FE surfaces of two Diels–Alder reactions of cyclopentadiene with either acrylonitrile or 1-4-naphthoquinone at ai QM/MM level. The accurate activation free energies at the ai QM/MM level, which are much closer to the experimental measurements than those calculated by other methods, indicate that this MBAR+wTP method can be applied in the studies of complex reactions in condensed phase with much-enhanced efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Evaluating Contaminant Transport in the Vadose Zone to Support Remedy Decisions at Aqueous-Waste Disposal Sites - 16195

Inorganic contaminants in aqueous waste disposed at land surface have a potential to contaminate groundwater. However, contaminant concentrations are attenuated during transport through the vadose zone. Quantifying contaminant attenuation and the resulting temporal concentration profile of in underlying groundwater is important for assessing the need for, and type of, remediation in the vadose zone and groundwater. An approach was developed for evaluating vadose zone transport and attenuation of aqueous wastes containing inorganic (non-volatile) contaminants that were disposed at land surface (i.e., directly to the ground in cribs, trenches, tile fields, etc.) and their effect on underlying groundwater. The approach provides a structured method to estimate transport of contaminants through the vadose zone and the resulting temporal profile of groundwater contaminant concentrations. A series of numerical model simulations was applied to investigate how different factors affect contaminant transport in the vadose zone and the conditions for which individual factors are important. This analysis demonstrated that, while water flow and contaminant transport can be complex during aqueous-waste disposal, conditions will change over time after disposal ceases and fewer factors will control the contaminant transport rate and resulting temporal profile of groundwater contaminant concentrations. Data verifying that these conditions have been reached can be provided by a few key metrics such as the vertical profile of moisture content, the temporal profile of recharge at the site, and basic information about waste disposal and subsurface properties. Under these conditions, and with information about specific contaminant transport parameters (e.g., for partitioning), estimates of contaminant transport can be made and used to support remedy decisions. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Identifying key factors of the seawater intrusion model of Dagu river basin, Jiaozhou Bay

Highlights: • The key factors of seawater intrusion model is identified by mutual entropy analysis. • Precipitation rate and groundwater pumping are the key factors for seawater intrusion. • The hydraulic conductivity of zone 1 has significant influence on seawater intrusion. Seawater intrusion is a complex groundwater - seawater interaction process, and it is influenced by many factors from ground surface to underground, from groundwater to seawater. Generally, for seawater intrusion model, some model parameters and boundary conditions are always specified by model users’ personal experiences or literature's reference value. The defective model would damage the groundwater management for controlling and preventing seawater intrusion when making decisions are based on this model. In order to improve the reliability of seawater intrusion model, the influences of model inputs on output should be identified prior at optimizing model inputs. Dagu river basin, Jiaozhou Bay is one of the most serious areas of seawater intrusion in China, and it is chosen as the study area in this study. The seawater intrusion model of Dagu river basin is built based on a general program SEAWAT4. The key influence factors of model output are analyzed by two sensitivity analysis methods, i.e., stepwise regression and mutual entropy. The results demonstrated that the most important influence factors which have largest sensitivities to groundwater Cl{sup -} concentration are the precipitation rate and groundwater pumping in agriculture area. In addition, the hydraulic conductivity of zone 1 has a non-negligible influence on seawater intrusion process. Stepwise regression analysis is capable of identifying most important influence factor, and it can’t handle complicated nonlinear input-output relationship. Mutual entropy analysis is reliable for identifying the influence factors for complex seawater intrusion model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Factor of spontaneous emission into the optical dielectric waveguide mode

Within the framework of a complete vector analysis, an expression for the factor of spontaneous emission into the transverse mode of a dielectric waveguide is found. The results are compared with those previously obtained in the framework of the scalar approximation. It is shown that their most significant quantitative difference is possible for waveguides used in diode lasers and amplifiers in which the transverse beam size is commensurable with the wavelength. The conditions for the applicability of the scalar approximation for calculating the spontaneous emission factor are obtained. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗