Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Data aggregation”

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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

CMS Space Monitoring

During the first LHC run, CMS stored about one hundred petabytes of data. Storage accounting and monitoring help to meet the challenges of storage management, such as efficient space utilization, fair share between users and groups and resource planning. We present a newly developed CMS space monitoring system based on the storage metadata dumps produced at the sites. The information extracted from the storage dumps is aggregated and uploaded to a central database. A web based data service is provided to retrieve the information for a given time interval and a range of sites, so it can be further aggregated and presented in the desired format. The system has been designed based on the analysis of CMS monitoring requirements and experiences of the other LHC experiments. In this paper, we demonstrate how the existing software components of the CMS data placement system, PhEDEx, have been re-used, dramatically reducing the development effort.

Ratnikova, N.↗
DOE OSTI2016

Combined Effects of Temperature and Irradiation on Concrete Damage

Aggregate radiation-induced volumetric expansion (RIVE) is a predominant mechanism in the formation of mechanical damage in the hardened cement paste (hcp) of irradiated concrete under fast-neutron flux (Giorla et al. 2015). Among the operating conditions difference between test reactors and light water reactors (LWRs), the difference of irradiation flux and temperature is significant. While a temperature increase is quite generally associated with a direct, or indirect (e.g., by dehydration) loss of mechanical properties (Maruyama et al. 2014), we found that it causes a partial annealing of irradiation amorphization of α-quartz, hence, reducing RIVE rate. Based on data collected by Bykov et al. (1981), an incremental RIVE model coupling neutron fluence and temperature is developed. The elastic properties and coefficient of thermal expansion (CTE) of irradiated polycrystalline quartz are interpreted through analytical homogenization of experimental data on irradiated α-quartz published by Mayer and Lecomte (1960). Moreover, the proposed model, implemented in the meso-scale simulation code AMIE, is compared to experimental data obtained on ordinary concrete made of quartz/quartzite aggregate (Dubrovskii et al. 1967). Substantial discrepancy, in terms of damage and volumetric expansion developments, is found when comparing irradiation scenarios assuming constant flux and temperature, as opposed to more realistic test reactor operation conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2014

An open science resource for establishing reliability and reproducibility in functional connectomics

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Estimating Sediment Settling Velocities from a Theoretically Guided Data-Driven Approach

Sediment settling velocities are commonly estimated from analytical or process-based approaches. These approaches have theoretical constraints due to the incompletely resolved settling physics. A parametric data-driven approach was recently proposed without theoretical constraints, but it is limited by its mathematical assumptions. To overcome these limitations, here we apply a machine learning algorithm to an aggregated sediment settling experimental database and develops a nonparametric data-driven model to estimate the noncohesive sediment settling velocity in water. A cross-comparison against five process-based equations and a parametric data-driven equation demonstrates the higher accuracy and better consistency of the new model in estimating sediment settling velocities under various physical regimes. The new model also shows an easily implemented self-update capability by assimilating theoretical data derived from the process-based equations. The updated model, incorporating experimental and theoretical data of sediment settling processes, further improves the accuracy and reduces the uncertainty in estimating sediment settling velocities. This study demonstrates the capability of machine learning in sediment transport study and illustrates an alternative framework for other hydraulic engineering challenges.

42 ENGINEERING↗
DOE OSTI2020

A Step-Down Test Procedure for Wavelet Shrinkage Using Bootstrapping

Wavelet thresholding (or shrinkage) attempts to remove the noises existing in the signals while preserving inherent pattern characteristics in the reconstruction of true signals. For data-denoising purpose, we present a new wavelet thresholding procedure which employs the step-down testing idea of identifying active contrasts in unreplicated fractional factorial experiments. The proposed method employs bootstrapping methods to a step-down test for thresholding wavelet coefficients. By introducing the concept of a false discovery error rate in testing wavelet coefficients, we shrink the wavelet coefficients with p -values higher than the error rate. The error rate controls the expected proportion of wrongly accepted coefficients among chosen wavelet coefficients. Bootstrap samples are used to approximate the p -value for computational efficiency. We also present some guidelines for selecting the values of hyper-parameters which affect the performance in the step-down thresholding procedure. Based on some common testing signals and an air-conditioner sounds example, the comparison of our proposed procedure with other thresholding methods in the literature is performed. The analytical results show that the proposed procedure has a potential in data-denoising and data-reduction in a variety of signal reconstruction applications.

42 ENGINEERING↗
DOE OSTI2026

Ion-Specific Effects on PuO 2 Nanoparticle Aggregation and Dissolution in Concentrated Electrolytes

Hydrolytic PuO 2 nanoparticles (NPs) are a dominant aqueous Pu-bearing phase in high ionic strength nuclear wastes, yet their reactivity in nonideal brines remains poorly constrained. We quantify how electrolyte identity and concentration control PuO 2 NP aggregation and ligand-assisted dissolution in acidic, high salinity solutions (NaCl, NaNO 3 , NaClO 4 , Na 2 SO 4 , Na 2 C 2 O 4 up to 5 M). A multitechnique workflow combining liquid scintillation counting (operationally defined aqueous [Pu]), scattering/electrokinetic measurements (aggregate size and zeta potential), and spectroscopy (UV–vis, XPS, Raman) resolves electrolyte-dependent partitioning between colloidal and molecular Pu species. Weakly coordinating anions (ClO 4 – , Cl – , NO 3 – ) largely preserve the (aggregated) nanoparticulate fraction but generate distinct dissolved species at high concentration, i.e., Pu(IV)–nitrato complexes in NaNO 3 and Pu(VI)–chloro complexes in NaCl. In contrast, stronger ligands substantially perturb PuO 2 NP stability: sulfate promotes partial dissolution to Pu(IV)–sulfate complexes at low concentration but reduces aqueous [Pu] at higher sulfate levels via secondary Pu(IV) sulfate formation, whereas oxalate drives strong dissolution to aqueous Pu–oxalate complexes. Aged NPs show similar trends with reduced aqueous fractions and more dominant aggregation mechanisms. In conclusion, these spectroscopically constrained speciation data provide a foundation for incorporating PuO 2 NP reactivity into thermodynamic and reactive transport models for high salinity waste and brine environments.

Aggregation↗
DOE OSTI2022

A Framework for Inverse Prediction Using Functional Response Data

Inverse prediction models have commonly been developed to handle scalar data from physical experiments. However, it is not uncommon for data to be collected in functional form. When data are collected in functional form, it must be aggregated to fit the form of traditional methods, which often results in a loss of information. For expensive experiments, this loss of information can be costly. In this study, we introduce the functional inverse prediction (FIP) framework, a general approach which uses the full information in functional response data to provide inverse predictions with probabilistic prediction uncertainties obtained with the bootstrap. The FIP framework is a general methodology that can be modified by practitioners to accommodate many different applications and types of data. We demonstrate the framework, highlighting points of flexibility, with a simulation example and applications to weather data and to nuclear forensics. Results show how functional models can improve the accuracy and precision of predictions.

42 ENGINEERING↗
DOE OSTI2014

DIRAQ: scalable in situ data- and resource-aware indexing for optimized query performance

Scientific data analytics in high-performance computing environments has been evolving along with the advancement of computing capabilities. With the onset of exascale computing, the increasing gap between compute performance and I/O bandwidth has rendered the traditional post-simulation processing a tedious process. Despite the challenges due to increased data production, there exists an opportunity to benefit from "cheap" computing power to perform query-driven exploration and visualization during simulation time. To accelerate such analyses, applications traditionally augment, post-simulation, raw data with large indexes, which are then repeatedly utilized for data exploration. However, the generation of current state-of-the-art indexes involves a compute- and memory-intensive processing, thus rendering them inapplicable in an in situ context. In this paper we propose DIRAQ, a parallel in situ, in network data encoding and reorganization technique that enables the transformation of simulation output into a query-efficient form, with negligible runtime overhead to the simulation run. DIRAQ's effective core-local, precision-based encoding approach incorporates an embedded compressed index that is 3---6 $$\times $$ ? smaller than current state-of-the-art indexing schemes. Its data-aware index adjustmentation improves performance of group-level index layout creation by up to 35 % and reduces the size of the generated index by up to 27 %. Moreover, DIRAQ's in network index merging strategy enables the creation of aggregated indexes that speed up spatial-context query responses by up to $$10\times $$ 10 ? versus alternative techniques. DIRAQ's topology-, data-, and memory-aware aggregation strategy results in efficient I/O and yields overall end-to-end encoding and I/O time that is less than that required to write the raw data with MPI collective I/O.

Computer Science↗
DOE OSTI2017

The POTRA domains of Toc75 exhibit chaperone-like function to facilitate import into chloroplasts

Protein trafficking across membranes is an essential function in cells; however, the exact mechanism for how this occurs is not well understood. In the endosymbionts, mitochondria and chloroplasts, the vast majority of proteins are synthesized in the cytoplasm as preproteins and then imported into the organelles via specialized machineries. In chloroplasts, protein import is accomplished by the TOC (translocon on the outer chloroplast membrane) and TIC (translocon on the inner chloroplast membrane) machineries in the outer and inner envelope membranes, respectively. TOC mediates initial recognition of preproteins at the outer membrane and includes a core membrane channel, Toc75, and two receptor proteins, Toc33/34 and Toc159, each containing GTPase domains that control preprotein binding and translocation. Toc75 is predicted to have a β-barrel fold consisting of an N-terminal intermembrane space (IMS) domain and a C-terminal 16-stranded β-barrel domain. Here we report the crystal structure of the N-terminal IMS domain of Toc75 from Arabidopsis thaliana , revealing three tandem polypeptide transport-associated (POTRA) domains, with POTRA2 containing an additional elongated helix not observed previously in other POTRA domains. Functional studies show an interaction with the preprotein, preSSU, which is mediated through POTRA2-3. POTRA2-3 also was found to have chaperone-like activity in an insulin aggregation assay, which we propose facilitates preprotein import. Furthermore, our data suggest a model in which the POTRA domains serve as a binding site for the preprotein as it emerges from the Toc75 channel and provide a chaperone-like activity to prevent misfolding or aggregation as the preprotein traverses the intermembrane space.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Ion Dynamics in Ionic-Liquid-Based Li-Ion Electrolytes Investigated by Neutron Scattering and Dielectric Spectroscopy

A detailed understanding of the diffusion mechanisms of ions in pure and doped ionic liquids remains an important aspect in the design of new ionic-liquid electrolytes for energy storage. Here, to gain more insight into the widely used imidazolium-based ionic liquids, the relationship between viscosity, ionic conductivity, diffusion coefficients, and reorientational dynamics in the ionic liquid 3-methyl-1-methylimidazolium bis(trifluoromethanesulfonyl)imide (DMIM-TFSI) with and without lithium bis(trifluoromethanesulfonyl)imide (Li-TFSI) was examined. The diffusion coefficients for the DMIM + cation and the role of ion aggregates were investigated by using the quasielastic neutron scattering (QENS) and neutron spin echo techniques. Two diffusion mechanisms are observed for the DMIM + cation with and without Li-TFSI, that is, translational and local. The data additionally suggest that Li + ion transport along with ion aggregates, known as the vehicle mechanism, may play a significant role in the ion diffusion process. These dielectric-spectroscopy investigations in a broad temperature and frequency range reveal a typical α–β-relaxation scenario. The α relaxation mirrors the glassy freezing of the dipolar ions, and the β relaxation exhibits the signatures of a Johari–Goldstein relaxation. In contrast to the translational mode detected by neutron scattering, arising from the decoupled faster motion of the DMIM + ions, the α relaxation is well coupled to the dc charge transport, that is, the average translational motion of all three ion species in the material. Finally, the local diffusion process detected by QENS is only weakly dependent on temperature and viscosity and can be ascribed to the typical fast dynamics of glass-forming liquids.

25 ENERGY STORAGE↗
DOE OSTI2021

Cryo-electron tomography provides topological insights into mutant huntingtin exon 1 and polyQ aggregates

Huntington disease (HD) is a neurodegenerative trinucleotide repeat disorder caused by an expanded poly-glutamine (polyQ) tract in the mutant huntingtin (mHTT) protein. The formation and topology of filamentous mHTT inclusions in the brain (hallmarks of HD implicated in neurotoxicity) remain elusive. Using cryo-electron tomography and subtomogram averaging, here we show that mHTT exon 1 and polyQ-only aggregates in vitro are structurally heterogenous and filamentous, similar to prior observations with other methods. Yet, we find filaments in both types of aggregates under ~2 nm in width, thinner than previously reported, and regions forming large sheets. In addition, our data show a prevalent subpopulation of filaments exhibiting a lumpy slab morphology in both aggregates, supportive of the polyQ core model. This provides a basis for future cryoET studies of various aggregated mHTT and polyQ constructs to improve their structure-based modeling as well as their identification in cells without fusion tags.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Data-driven agent-based modeling, with application to rooftop solar adoption

Agent-based modeling is commonly used for studying complex system properties emergent from interactions among many agents. We present a novel data-driven agent-based modeling framework applied to forecasting individual and aggregate residential rooftop solar adoption in San Diego county. Our first step is to learn a model of individual agent behavior from combined data of individual adoption characteristics and property assessment. We then construct an agent-based simulation with the learned model embedded in artificial agents, and proceed to validate it using a holdout sequence of collective adoption decisions. We demonstrate that the resulting agent-based model successfully forecasts solar adoption trends and provides a meaningful quantification of uncertainty about its predictions. We utilize our model to optimize two classes of policies aimed at spurring solar adoption: one that subsidizes the cost of adoption, and another that gives away free systems to low-income house- holds. We find that the optimal policies derived for the latter class are significantly more efficacious, whereas the policies similar to the current California Solar Initiative incentive scheme appear to have a limited impact on overall adoption trends.

Agent-based modeling↗
DOE OSTI2018

The future of Sinbad: learning from the best practices of ICSBEP - 24622

Two projects sanctioned by the Organisation for Economic Co-operation and Development (OECD) Nuclear Energy Agency (NEA) have over two decades of experience established comprehensive data sets in handbooks supporting criticality safety and reactor physics. The International Criticality Safety Benchmark Evaluation Project (ICSBEP), and later the International Reactor Physics Experiment Evaluation Project (IRPhEP) that was patterned after the success of the ICSBEP, serves as an example of quality and excellence in preserving our nuclear data heritage and establishing benchmark standards upon which current and future modeling, validation, and safety efforts can be supported. Many of the best practices and lessons learned from these benchmark projects can be adopted and adapted for use with the Shielding Integral Benchmark Archive and Database (SINBAD) to prepare higher quality shielding benchmark data to effectively expand the availability of qualified experimental data available to the nuclear community. The unique experiments performed and aggregated within SINBAD represent characteristic measurements, techniques, and applications not captured with the specificity of nuclear criticality safety or traditional reactor physics applications. Application of evaluated SINBAD benchmark data would challenge and enhance our current nuclear data and modeling and simulation strategies. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

A Mountain Glacier Perspective on the Bipolar Seesaw

A global record of mountain glacier terminations during the last deglaciation (∼19–11 ka) dated by a large, uncurated data set of cosmogenic-nuclide exposure ages highlights a statistically significant asynchrony in termination ages between the Northern and Southern Hemispheres. This interhemispheric offset in the timing of glacier terminations is consistent with previously correlated ice core records that show a systematic interhemispheric lag in the timing of abrupt climate events, with the Southern Hemisphere leading the Northern Hemisphere by ∼300–3,500 years. Our analysis (a) aggregates cosmogenic-nuclide exposure ages from a global data set of moraines to discern climatically driven peaks in moraine emplacement events, and (b) utilizes a Monte Carlo simulation based on a null hypothesis that moraine emplacement is interhemispherically synchronous to estimate the statistical significance of the observed offset. The observed lag of Northern Hemisphere emplacement events compared to the Southern Hemisphere is statistically significant and is consistent with the “bipolar seesaw” pattern observed in ice core records.

58 GEOSCIENCES↗
DOE OSTI2025

ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis

Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. Here, to address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features. Methods: Using data from 12.5 million Veterans Affairs patients, ARCH first derives embedding vectors and generates similarities along with associated p-values to measure the strength of relatedness between clinical features with statistical certainty quantification. Next, ARCH performs a sparse embedding regression to remove indirect linkage between features to build a sparse KG. Finally, ARCH was validated on various clinical tasks, including detecting known relationships between entity pairs, predicting drug side effects, disease phenotyping, as well as sub-typing Alzheimer’s disease patients. Results: ARCH produces high-quality clinical embeddings and KG for over 60,000 codified and narrative EHR concepts. The KG and embeddings are visualized in the R-shiny powered web-API.3 ARCH achieved high accuracy in detecting EHR concept relationships, with AUCs of 0.926 (codified) and 0.861 (NLP) for similar EHR concepts, and 0.810 (codified) and 0.843 (NLP) for related pairs. It detected drug side effects with a 0.723 AUC, which improved to 0.826 after fine-tuning. Using both codified and NLP features, the detection power increased significantly. Compared to other methods, ARCH has superior accuracy and enhances weakly supervised phenotyping algorithms’ performance. Notably, it successfully categorized Alzheimer’s patients into two subgroups with varying mortality rates. Conclusion: The proposed ARCH algorithm generates large-scale high-quality semantic representations and knowledge graph for both codified and NLP EHR features, useful for a wide range of predictive modeling tasks.

Electronic health records↗
DOE OSTI2022

Ionic Liquid Sheath Stabilizes Atomically Dispersed Reduced Graphene Aerogel‐Supported Iridium Complexes during Ethylene Hydrogenation Catalysis

Abstract An atomically dispersed reduced graphene aerogel (rGA)‐supported iridium catalyst having reactive ethylene ligands was synthesized at an iridium loading of 9.9 wt % and coated with an ionic liquid, 1‐ethyl‐3‐methylimidazolium acetate ([EMIM][OAc]). Continuous‐scan X‐ray absorption spectra demonstrated that the iridium remained site‐isolated in flowing equimolar C 2 H 4 and H 2 during a temperature ramp to 100 °C. The data further showed the lack of detectable iridium aggregation when the feed was H 2 ‐rich or even pure H 2 at 100 °C. An Arrhenius plot determined for ethylene hydrogenation catalysis with the sample in flowing equimolar ethylene and hydrogen showed no variation in the apparent activation energy at temperatures up to 100 °C, confirming that the active sites remained intact at the higher temperatures. The results point to opportunities for overcoming the stability limitations of atomically dispersed supported noble metal catalysts by choice of electron‐donor supports and ionic liquid sheaths.

Kurtoğlu‐Öztulum, Samira F.↗
DOE OSTI2017

Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power time series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.

17 WIND ENERGY↗