Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Indexes”

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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Applying the index of watershed integrity to the Matanuska–Susitna basin

The Matanuska-Susitna Borough is the fastest growing region in the State of Alaska and is impacted by a number of human activities. We conducted a multiscale assessment of the stressors facing the borough by developing and mapping the Index of Watershed Integrity (IWI) and Index of Catchment Integrity (the latter considers stressors in areas surrounding individual stream segments exclusive of upstream areas). The assessment coincided with the borough’s stormwater management planning. We adapted the list of anthropogenic stressors used in the original conterminous United States IWI application to reflect the borough’s geography, human activity, and data availability. This analysis also represents an early application of the NHDPlus High Resolution geospatial framework and the first use of the framework in an IWI study. We also explored how remediation of one important stressor, culverts, could impact watershed integrity at the catchment and watershed scales. Overall, we found that the integrity scores for the Matanuska– Susitna basin were high compared to the conterminous United States. Low integrity scores did occur in the rapidly developing Wasilla–Palmer core area. We also found that culvert remediation had a larger proportional impact in catchments with fewer stressors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Analyzing the cosmic web environment in the vicinity of grand-design and flocculent spirals with local geometric index

We explore the environment of a combined set of 367 grand-design and 619 flocculent spiral galaxies. We introduce a novel estimator called the local geometric index to quantify the morphology of the local environment of these 986 spirals. The local geometric index allows us to classify the environment of galaxies into voids, sheets, filaments, and clusters. We find that grand-designs are mostly located in dense environments like clusters and filaments (∼ 78%), whereas the fraction of the flocculents lying in sparse environments like voids and sheets is significantly higher (> 10%) than that of the grand-designs. A p-value < 10 -3 from a Kolmogorov-Smirnov test indicates that our results are statistically significant at 99.9% confidence level. Further, we note that dense environments with large tidal flows are dominated by the grand-designs. On the other hand, low-density environments such as sheets and voids favor the growth of flocculents.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Indexed improvements for real-time trotter evolution of a (1 + 1) field theory using NISQ quantum computers

Today's quantum computers offer the possibility of performing real-time calculations for quantum field theory scattering processes motivated by high energy physics. In order to follow the successful roadmap which has been established for the calculation of static properties at Euclidean time, it is crucial to develop new algorithmic methods to deal with the limitations of current noisy intermediate-scale quantum (NISQ) devices and to establish quantitative measures of the progress made with different devices. In this paper, we report recent progress in these directions. We show that nonlinear aspects of the trotter errors allow us to take much larger step then suggested by low-order analysis. This is crucial to reach physically relevant time scales with today's NISQ technology. We propose to use an index averaging absolute values of the difference between the accurately calculated trotter evolution of site occupations and their actual measurements on NISQ machines (G index) as a measure to compare results that have been obtained from different hardware platforms. Using the transverse Ising model in one spatial dimension with four sites we apply this metric across several hardware platforms. We study the results including readout mitigation and Richardson extrapolations and show that the mitigated measurements are very effective based on the analysis of the trotter step size modifications. Furthermore, we discuss how this advance in the trotter step size procedures can improve quantum computing physics scattering results and how this technical advance can be applied to other machines and noise mitigation methods.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Index Theorems, Generalized Hall Currents, and Topology for Gapless Defect Fermions

In this work, we show how the index of the fermion operator from the Euclidean action can be used to uncover the existence of gapless modes living on defects (such as edges and vortices) in topological insulators and superconductors. The 1-loop Feynman diagram that computes the index reveals an analog of the quantum Hall current flowing on and off the defect—even in systems without conserved currents or chiral anomalies—and makes explicit the interplay between topology in momentum and coordinate space. We provide several explicit examples.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

GIWAXS-SIIRkit: scattering intensity, indexing and refraction calculation toolkit for grazing-incidence wide-angle X-ray scattering of organic materials

Grazing-incidence wide-angle X-ray scattering (GIWAXS) has become an increasingly popular technique for quantitative structural characterization and comparison of thin films. For this purpose, accurate intensity normalization and peak position determination are crucial. At present, few tools exist to estimate the uncertainties of these measurements. Here, a simulation package is introduced called GIWAXS-SIIRkit , where SIIR stands for scattering intensity, indexing and refraction. Furthermore, the package contains several tools that are freely available for download and can be executed in MATLAB. The package includes three functionalities: estimation of the relative scattering intensity and the corresponding uncertainty based on experimental setup and sample dimensions; extraction and indexing of peak positions to approximate the crystal structure of organic materials starting from calibrated GIWAXS patterns; and analysis of the effects of refraction on peak positions. Additionally, each tool is based on a graphical user interface and designed to have a short learning curve. A user guide is provided with detailed usage instruction, tips for adding functionality and customization, and exemplary files.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Analysis of Historical Power Outages of the United States and the National Risk Index

Several works have been documented in the literature to study the societal effect of power outages and to analyze their correlation with the Social Vulnerability Index (SVI). However, the relationship between National Risk Index (NRI) and power outages is yet to be explored. This work analyzes the NRI indices such as Risk, Expected Annual Loss, Social Vulnerability, and Community Resilience with several resilience metrics such as event duration, impact duration, recovery duration, impact level, impact rate, recovery rate, recovery to impact ratio, and area under the outage curves to see the correlation of NRI indices with the resilience metrics. The results show that NRI indices such as Risk and Expected Annual Loss increase with the increase of event duration, impact duration, and recovery duration. All Other metrics are indifferent to the change in the Risk and EAL ratings. The results also show that there is no strong relationship between all the metrics and community resilience and social vulnerability. This work also performed the sensitivity analysis of the extreme event selection process. This sensitivity analysis reveals that the way of identifying extreme events has a significant impact on the evaluation of the events.

Bhusal, Narayan↗
DOE OSTI2020

An Integrated Indexing and Search Service for Distributed File Systems

Data services such as search, discovery, and management in scalable distributed environments have traditionally been decoupled from the underlying file systems, and are often deployed using external databases and indexing services. However, modern data production rates, looming data movement costs, and the lack of metadata, entail revisiting the decoupled file system-data services design philosophy. In this article, we present TagIt, a scalable data management service framework aimed at scientific datasets, which can be integrated into prevalent distributed file system architectures. A key feature of TagIt is a scalable, distributed metadata indexing framework, which facilitates a flexible tagging capability to support data discovery. Furthermore, the tags can also be associated with an active operator, for pre-processing, filtering, or automatic metadata extraction, which we seamlessly offload to file servers in a load-aware fashion. We have integrated TagIt into two popular distributed file systems, i.e., GlusterFS and CephFS. Our evaluation demonstrates that TagIt can expedite data search operation by up to 10× over the extant decoupled approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Streaming Data Reorganization at Scale with DeltaFS Indexed Massive Directories

We report complex storage stacks providing data compression, indexing, and analytics help leverage the massive amounts of data generated today to derive insights. It is challenging to perform this computation, however, while fully utilizing the underlying storage media. This is because, while storage servers with large core counts are widely available, single-core performance and memory bandwidth per core grow slower than the core count per die. Computational storage offers a promising solution to this problem by utilizing dedicated compute resources along the storage processing path. We present DeltaFS Indexed Massive Directories (IMDs), a new approach to computational storage. DeltaFS IMDs harvest available (i.e., not dedicated) compute, memory, and network resources on the compute nodes of an application to perform computation on data. We demonstrate the efficiency of DeltaFS IMDs by using them to dynamically reorganize the output of a real-world simulation application across 131,072 CPU cores. DeltaFS IMDs speed up reads by 1,740x while only slightly slowing down the writing of data during simulation I/O for in situ data processing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Application of the Chloride Susceptibility Index to Study the Effects of Ni, Cr, Mn and Mo on the Repassivation of Stainless Steels

The effects of Ni, Cr, Mn and Mo on the very earliest stages of repassivation of stainless steels are quantified using the Chloride Susceptibility Index (CSI), which is an ab initio-based index for the evaluation of repassivation tendency. The quinary system of Fe-Ni-Cr-Mn-Mo is studied with density functional theory analysis and an electrochemisorption model developed previously by the authors, which are required to determine the CSI. The adsorption energies of O and Cl to different surface configurations are calculated, and then surface coverage maps of different species on the surface are obtained from the adsorption energies based on the Langmuir isotherm. Finally, CSI is calculated for different compositions of stainless steels. It is found that the effect of alloying elements on promoting repassivation of Fe alloys is in the order of Mn > ≈Ni > Cr > Mo when solute composition is less than 28 wt.%. A strong synergy is found between Cr and Mo such that a combination of these two elements at a certain ratio can give an optimal (low) CSI. Here, the usage of CSI for evaluating repassivation tendency of CRAs is validated by experimental measured repassivation potential, which shows a strong monotonic negative relation with CSI.

36 MATERIALS SCIENCE↗
DOE OSTI2023

OpenStudio®-ERI (Energy Rating Index (ERI) Workflow) [SWR-18-72] 1.7.0 Released 12/14/2023

The OpenStudio®-ERI project allows calculating an Energy Rating Index (ERI) using the Department of Energy's open-source OpenStudio®/EnergyPlus® simulation platform. The building description is provided in an HPXML file format. The project supports: - ANSI/RESNET/ICC 301© Standard for the Calculation and Labeling of the Energy Performance of Dwelling and Sleeping Units using an Energy Rating Index - ENERGY STAR Certification System for Homes and Apartments Using an ERI Compliance Path - IECC ERI Compliance Alternative (Section R406) - DOE ZERH Certification Using an ERI Compliance Path For more information on running simulations, generating HPXML files, etc., please visit the documentation at https://openstudio-eri.readthedocs.io/en/latest/

Horowitz, Scott↗
DOE OSTI2020

Psychological Distress and Alzheimer’s Disease Mortality in the United States: Results from the 1997–2014 National Health Interview Survey-National Death Index Record Linkage Study

Objective: This study examines the association between psychological distress and Alzheimer’s disease mortality among US adults aged ≥45. Methods: We analyzed the Kessler 6-item psychological distress scale as a risk factor for Alzheimer’s mortality using the pooled 1997–2014 National Health Interview Survey (NHIS)- National Death Index (NDI) database ( N = 265,089). Cox regression was used to model mortality as a function of psychological distress and sociodemographic and behavioral covariates. Results: The Alzheimer’s mortality risk was 97% higher (HR = 1.97; 95% confidence interval [CI] = 1.37, 2.84) in adults with serious psychological distress compared with those without psychological distress, controlling for sociodemographic covariates. The relative mortality risk remained statistically significant (HR = 1.49; 95% CI = 1.04, 2.13) after additional adjustment for smoking, alcohol consumption, health status, activity limitation, and body mass index. Discussion: US adults had significantly higher risks of Alzheimer’s disease mortality at higher psychological distress levels. These findings underscore the significance of addressing psychological well-being as a strategy for reducing Alzheimer’s disease mortality.

Singh, Gopal K.↗
DOE OSTI2025

Development of the Contamination Distribution Centered Toxics Mobility Vulnerability Index in the Beaumont–Port Arthur Region of Texas

This study advances the Toxics Mobility Inventory (TMI) and the Toxics Mobility Vulnerability Index (TMVI) to develop a new tool to assess the movement of hazardous substances and their implications for vulnerable communities. It emphasizes the need to include contamination distribution variables in such indices to address disproportionate impacts and more accurately reflect vulnerability. The study uses the TMI framework and TMVI methodology in the Beaumont–Port Arthur region of Texas, also integrating contamination distribution considerations into the analysis to develop a new framework and process. The new Contamination Distribution Centered Toxics Mobility Vulnerability Index (CDC-TMVI) consolidates climate change and topography variables into a broader built environment vulnerability category while introducing a contamination sources category. Using ArcGIS Pro and ToxPi tools, the study evaluates 27 geospatial variables across four categories: built environment vulnerability, social vulnerability, health outcomes, and contamination sources. The results indicate significant contributions from contamination and social vulnerability variables, highlighting areas with higher risks of flooding and air pollution. This article advocates for future research and policy efforts to enhance the integration of contamination sources and their spatial distributions into toxics mobility assessments to better protect vulnerable populations. Furthermore, the unique methodology and findings serve as a basis for developing targeted measures and strategic planning to improve environmental health.

contamination↗
DOE OSTI2026

Application of artificial intelligence methods in the international roughness index prediction of rigid and composite pavements: a systematic review

The International Roughness Index (IRI) is a widely adopted metric for quantifying pavement roughness, directly influencing vehicle safety, ride comfort, and overall roadway performance. In recent years, the use of Machine Learning (ML) models for IRI prediction has gained momentum, with the goal of improving the allocation of maintenance and rehabilitation resources by enabling accurate assessments of pavement conditions. Most prior reviews, however, have concentrated on flexible pavements, leaving a notable gap regarding rigid and composite pavements. To address this gap, the present study conducts a systematic review of Artificial Intelligence (AI) methods applied to IRI prediction for rigid and composite pavements. Literature published between 2004 and 2025 is synthesized to highlight prevailing trends, methodological contributions, and directions for future research. Particular attention is given to the types of models employed, the datasets used for training and validation, and the role of input variables and data-processing strategies. Across the included studies, ensemble learning methods (especially gradient boosting variants such as XGBoost), artificial neural networks, and hybrid architectures frequently achieved high predictive skill, with several models reporting test-set coefficients of determination approaching 0.9–0.96, indicating strong potential for capturing the influence of traffic, pavement structure, and climatic factors. Since these results are obtained from heterogeneous datasets and evaluation protocols, they are interpreted qualitatively rather than as strict cross-study rankings. Analysis of input variables revealed that pavement age and initial IRI were included in 91% (21 of 23) and 78% (18 of 23) of studies, respectively. Climatic variables such as the freezing index appeared in 57% (13 of 23), while traffic-related factors were considered in 65% (15 of 23). The findings underscore the importance of standardized, high-quality datasets, such as those from the Long-Term Pavement Performance (LTPP) program, along with data consistency, model interpretability, computational efficiency, and replicability in enhancing IRI prediction. Future research should focus on incorporating input variable selection techniques to identify the most influential predictors, thereby improving accuracy and robustness. Integrating these approaches with advanced non-linear data-driven models, coupled with robust hyperparameter optimization, holds considerable promise for strengthening the reliability of IRI prediction and supporting resilient pavement management strategies.

42 ENGINEERING↗
DOE OSTI2021

Single-shot measurement of the nonlinear refractive index of air at 92 µm with a picosecond terawatt CO 2 laser

We developed a simple, accurate single-shot method to determine the nonlinear refractive index of air by measuring the evolution of the spatial shape of a laser beam propagating through the atmosphere. A distinctive feature of this new method, which relies on a modified Fresnel propagation model for data analysis, is the use of a hard aperture for producing a welldefined, high-quality beam from a comparatively nonuniform quasi-flat-top beam which is typical for highpeak-power lasers. The nonlinear refractive index of air for a very short (2 ps) long-wave infrared (LWIR) laser pulse was measured for the first time, yielding n 2 = 3.0 × 10 -23 m 2 /W at 9.2 µm. This result is 40% lower than a corresponding measurement with longer (200 ps) LWIR pulses at a similar wavelength.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Dual-mode fiber-optic refractive-index gas thermometry with primary anchoring and continuous temperature tracking

Extending fiber-optic refractive-index gas thermometry beyond primary-only operation, we report a dual-mode argon-filled Fabry-Perot thermometer in which the same cavity first establishes a primary temperature anchor and then performs continuous secondary-mode tracking. The primary anchor is obtained from two-pressure fringe shifts using a virial-coefficient refractivity model. During secondary operation, pressure cycling is stopped, and temperature is recovered from the monitored fringe wavelength and gas pressure using a model that includes argon refractivity, pressure-induced cavity strain, and fused-silica thermal expansion. From 310 to 1013 K, secondary-mode temperatures agreed with a reference thermocouple with 0.21 K RMSE and 0.47 K maximum error; 45-min natural furnace cooldowns were also tracked from nearby anchors with close agreement. The dual-mode method retains the empirical-calibration-free primary measurement as an in-situ reference while enabling simpler continuous tracking between anchors, addressing a practical limitation of primary-only fiberoptic refractive-index gas thermometry for hightemperature applications.

Syed, Musaddeque [Michigan State University, East ↗
DOE OSTI2020

Optically anisotropic porous silicon microlenses with tunable refractive indexes and birefringence profiles

The effect of spatially varying birefringence on the focusing behavior of porous silicon (PSi) and porous silicon dioxide (PSiO 2 ) gradient refractive index (GRIN) lenses is investigated. Both materials attain broad, tunable refractive indexes and birefringence profiles, with PSi having a maximum birefringence of ~0.26 and PSiO 2 a reduced maximum birefringence of ~0.03 at 633 nm. These GRIN lenses exhibit polarization-dependent split focusing behavior, wherein the divergence angle between the twin foci increases with the birefringence gradient. PSi’s large birefringence allows the divergence angle to be tuned such that light focuses away from the center of the lens. These GRIN elements demonstrate how tunable birefringent materials can be used to engineer polarization-selective optical responses.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Real-time structural motif searching in proteins using an inverted index strategy

Biochemical and biological functions of proteins are the product of both the overall fold of the polypeptide chain, and, typically, structural motifs made up of smaller numbers of amino acids constituting a catalytic center or a binding site that may be remote from one another in amino acid sequence. Detection of such structural motifs can provide valuable insights into the function(s) of previously uncharacterized proteins. Technically, this remains an extremely challenging problem because of the size of the Protein Data Bank (PDB) archive. Existing methods depend on a clustering by sequence similarity and can be computationally slow. We have developed a new approach that uses an inverted index strategy capable of analyzing >170,000 PDB structures with unmatched speed. The efficiency of the inverted index method depends critically on identifying the small number of structures containing the query motif and ignoring most of the structures that are irrelevant. Our approach (implemented at motif.rcsb.org ) enables real-time retrieval and superposition of structural motifs, either extracted from a reference structure or uploaded by the user. Herein, we describe the method and present five case studies that exemplify its efficacy and speed for analyzing 3D structures of both proteins and nucleic acids.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Fabrication-conscious neural network based inverse design of single-material variable-index multilayer films

Multilayer films with continuously varying indices for each layer have attracted great deal of attention due to their superior optical, mechanical, and thermal properties. However, difficulties in fabrication have limited their application and study in scientific literature compared to multilayer films with fixed index layers. In this work we propose a neural network based inverse design technique enabled by a differentiable analytical solver for realistic design and fabrication of single material variable-index multilayer films. This approach generates multilayer films with excellent performance under ideal conditions. We furthermore address the issue of how to translate these ideal designs into practical useful devices which will naturally suffer from growth imperfections. By integrating simulated systematic and random errors just as a deposition tool would into the optimization process, we demonstrated that the same neural network that produced the ideal device can be retrained to produce designs compensating for systematic deposition errors. Furthermore, the proposed approach corrects for systematic errors even in the presence of random fabrication imperfections. The results outlined in this paper provide a practical and experimentally viable approach for the design of single material multilayer film stacks for an extremely wide variety of practical applications with high performance.

36 MATERIALS SCIENCE↗