Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Cluster 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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Modeling regional precipitation over the Indus River basin of Pakistan using statistical downscaling

Complex processes govern spatiotemporal distribution of precipitation within the high-mountainous headwater regions (commonly known as the upper Indus basin (UIB)), of the Indus River basin of Pakistan. Reliable precipitation simulations particularly over the UIB present a major scientific challenge due to regional complexity and inadequate observational coverage. Here, we present a statistical downscaling approach to model observed precipitation of the entire Indus basin, with a focus on UIB within available data constraints. Taking advantage of recent high altitude (HA) observatories, we perform precipitation regionalization using K-means cluster analysis to demonstrate effectiveness of low-altitude stations to provide useful precipitation inferences over more uncertain and hydrologically important HA of the UIB. We further employ generalized linear models (GLM) with gamma and Tweedie distributions to identify major dynamic and thermodynamic drivers from a reanalysis dataset within a robust cross-validation framework that explain observed spatiotemporal precipitation patterns across the Indus basin. Final statistical models demonstrate higher predictability to resolve precipitation variability over wetter southern Himalayans and different lower Indus regions, by mainly using different dynamic predictors. The modeling framework also shows an adequate performance over more complex and uncertain trans-Himalayans and the northwestern regions of the UIB, particularly during the seasons dominated by the westerly circulations. However, the cryosphere-dominated trans-Himalayan regions, which largely govern the basin hydrology, require relatively complex models that contain dynamic and thermodynamic circulations. Furthermore, we also analyzed relevant atmospheric circulations during precipitation anomalies over the UIB, to evaluate physical consistency of the statistical models, as an additional measure of reliability. Overall, our results suggest that such circulation-based statistical downscaling has the potential to improve our understanding towards distinct features of the regional-scale precipitation across the upper and lower Indus basin. Additionally, such understanding should help to assess the response of this complex, data-scarce, and climate-sensitive river basin amid future climatic changes, to serve communal and scientific interests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Zinc concentration affects the functional groups of microbial communities in sugarcane-cultivated soil

The addition of zinc (Zn) to the soil has significant gains in sugarcane productivity. The understanding of the consequences of zinc application requires insight into ecological aspects of changes in soil microbial communities. Here, we evaluated the effects of zinc application on richness, diversity, evenness and structure of microbial functional genes in sugarcane-cultivated soil, and response thresholds of abundance for groups of microbial functional genes on different zinc concentrations added to the soil (0, 5, 10 and 20 kg Zn ha -1 ) in two sugarcane fields in the Northeastern São Paulo state, Brazil. Using a high-density functional gene array termed GeoChip 5.0, which contains 101,796 distinct probes belonging to gene categories involved in zinc transport, secondary metabolism, stress, virulence and nutrient cycling, differences were observed in the functional gene structure of microbial communities and abundance of specific gene families with a threshold between 5 and 10 kg Zn ha -1 . However, the richness, diversity and evenness of functional genes did not differ across the zinc gradients in soil. Cluster analysis based on microbial functional subcategories revealed variation in abundances across the gradient of zinc concentrations in soil mainly for virulence, stress, secondary metabolism, and carbon- and phosphorus-cycling-related gene families. A threshold in abundance was observed between 5 and 10 kg Zn ha -1 with high abundance of microbial gene families associated with zinc transporter proteins, antioxidant enzymes, exoenzymes associated with infection, secretion proteins, virulence regulatory genes, carbon fixation, and phosphorus utilization in soils supplemented with up to 5 kg Zn ha -1 . In these same soils, reduced abundance was observed for gene families associated with methanogenesis, metabolism of halogen and carotenoid, and antiphagocytosis compared with soil supplements with 10 or 20 kg Zn ha -1 . The results suggest that changes in microbial functional gene structure and abundance could be used to evaluate the impact of soil management practices on soil microbial communities in sugarcane production fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

The role of histone tails in the nucleosome: A computational study

Histone tails play an important role in gene transcription and expression. We present here a systematic computational study of the role of histone tails in the nucleosome, using replica exchange molecular dynamics simulations with an implicit solvent model and different well-established force fields. We performed simulations for all four histone tails, H4, H3, H2A, and H2B, isolated and with inclusion of the nucleosome. The results confirm predictions of previous theoretical studies for the secondary structure of the isolated tails but show a strong dependence on the force field used. In the presence of the entire nucleosome for all force fields, the secondary structure of the histone tails is destabilized. Specific contacts are found between charged lysine and arginine residues and DNA phosphate groups and other binding sites in the minor and major DNA grooves. Using cluster analysis, we found a single dominant configuration of binding to DNA for the H4 and H2A histone tails, whereas H3 and H2B show multiple binding configurations with an equal probability. The leading stabilizing contribution for those binding configurations is the attractive interaction between the positively charged lysine and arginine residues and the negatively charged phosphate groups, and thus the resulting charge neutralization. Lastly, we present results of molecular dynamics simulations in explicit solvent to confirm our conclusions. Results from both implicit and explicit solvent models show that large portions of the histone tails are not bound to DNA, supporting the complex role of these tails in gene transcription and expression and making them possible candidates for binding sites of transcription factors, enzymes, and other proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Occupancy data analytics and prediction: A case study

Occupants are a critical impact factor of building energy consumption. Numerous previous studies emphasized the role of occupants and investigated the interactions between occupants and buildings. However, a fundamental problem, how to learn occupancy patterns and predict occupancy schedule, has not been well addressed due to highly stochastic activities of occupants and insufficient data. This study proposes a data mining based approach for occupancy schedule learning and prediction in office buildings. The proposed approach first recognizes the patterns of occupant presence by cluster analysis, then learns the schedule rules by decision tree, and finally predicts the occupancy schedules based on the inducted rules. A case study was conducted in an office building in Philadelphia, U.S. Based on one-year observed data, the validation results indicate that the proposed approach significantly improves the accuracy of occupancy schedule prediction. The proposed approach only requires simple input data (i.e., the time series data of occupant number entering and exiting a building), which is available in most office buildings. Furthermore, this approach is practical to facilitate occupancy schedule prediction, building energy simulation and facility operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Linking human-building interactions in shared offices with personality traits

Occupant behavior influences office building energy performance. The level of human-building interactions (HBIs) in shared offices strongly influences building energy use and occupant well-being. This study explored the link between occupant personality types and their behaviors of sharing energy and environment control systems and interactions with their colleagues. Inspired by the Five-Factor Model (FFM), we classified HBI behaviors into four dimensions: willingness to share control, knowledge of control, group decision behavior, and adaptive strategies. These four variables can be mapped to the four personality traits proposed by the FFM: agreeableness, openness, extraversion, and conscientiousness. Our cluster analysis identified six behavioral patterns: average (17.7%), reserved (15.3%), environmentally friendly (16.6%), role model (24.2%), self-centered (17.2%), and mechanist (9.0%). We further applied association rules, a widely utilized machine learning technique, to discover how demographics, building-related contextual factors, and perception-attitudinal factors influence HBI behaviors. Country, control feature accessibility, and group dynamics were found to be the three most influential factors that determine occupants’ HBI behaviors. Finally, the study provides insights about building design and operation, as well as policy to promote socially and environmentally desirable HBI behaviors in a shared office environment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Simultaneous assessment of phase chemistry, phase abundance and bulk chemistry with statistical electron probe micro-analyses: Application to cement clinkers

According to recent developments in cement clinker engineering, the optimization of chemical substitutions in the main clinker phases offers a promising approach to improve both reactivity and grindability of clinkers. Thus, monitoring the chemistry of the phases may become part of the quality control at the cement plants, along with the usual measurements of the abundance of the mineralogical phases (quantitative X-ray diffraction) and the bulk chemistry (X-ray fluorescence). This paper presents a new method to assess these three complementary quantities with a single experiment. The method is based on electron microprobe spot analyses, performed over a grid located on a representative surface of the sample and interpreted with advanced statistical tools. This paper describes the method and the experimental program performed on industrial clinkers to establish the accuracy in comparison to conventional methods. -- Highlights: •A new method of clinker characterization •Combination of electron probe technique with cluster analysis •Simultaneous assessment of phase abundance, composition and bulk chemistry •Experimental validation performed on industrial clinkers.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Seasonal differences in trace element concentrations and distribution in Spartina alterniflora root tissue

This study uses nanometer-scale synchrotron X-ray nanofluorescence to investigate season differences in concentrations and distributions of major (Ca, K, S and P) and trace elements (As, Cr, Cu, Fe and Zn) in the root system of Spartina alterniflora collected from Jamaica Bay, New York, in April and September 2015. The root samples were cross-sectioned at a thickness of 10 μm. Selected areas in the root epidermis and endodermis were mapped with a sampling resolution of 100 and 200 nm, varying with the mapping areas. The results indicate that trace element concentrations in the epidermis and endodermis vary among the elements measured, possibly because of their different chemical properties or their ability to act as micronutrients for the plants. Elemental concentrations (As, Ca, Cr, Cu, Fe, K, P, S and Zn) within each individual root sample and between the root samples collected during two different seasons are both significantly different (p < 0.01). Furthermore, this study indicates that the nonessential elements (As and Cr) are significantly correlated (p < 0.01) with Fe, with high concentrations in the root epidermis, while others are not, implying that Fe may be a barrier to nonessential element transport in the root system. Hierarchy cluster analysis shows two distinct groups, one including As, Cr and Fe and the other the rest of the elements measured. Factor analysis also indicates that the processes and mechanisms controlling element transport in the root system can be different between the nutrient and nonessential elements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

AIMSim : An accessible cheminformatics platform for similarity operations on chemicals datasets

The recent advances in deep learning, generative modeling, and statistical learning have ushered in a renewed interest in traditional cheminformatics tools and methods. Quantifying molecular similarity is essential in molecular generative modeling, exploratory molecular synthesis campaigns, and drug-discovery applications to assess how new molecules differ from existing ones. Further, most tools target advanced users and lack general implementations accessible to the larger community. In this work, we introduce Artificial Intelligence Molecular Similarity (AIMSim), an accessible cheminformatics platform for performing similarity operations on collections of molecules called molecular datasets. AIMSim provides a unified platform to perform similarity-based tasks on molecular datasets, such as diversity quantification, outlier and novelty analysis, clustering, dimensionality reduction, and inter-molecular comparisons. AIMSim implements all major binary similarity metrics and molecular fingerprints and is provided as a Python package that includes support for command-line use as well as a Graphical User Interface for code-free utilization with fully interactive plots.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Clustering and statistical analyses of air-conditioning intensity and use patterns in residential buildings

Energy conservation in residential buildings has gained increased attention due to its large portion of global energy use and potential of energy savings. Occupant behavior has been recognized as a key factor influencing the energy use and load diversity in buildings, therefore more realistic and accurate air-conditioning (AC) operating schedules are imperative for load estimation in equipment design and operation optimization. With the development of sensor technology, it became easier to access an increasing amount of heating/cooling data from thermal energy metering systems in residential buildings, which provides another possible way to understand building energy usage and occupant behaviors. However, except for cooling energy consumption benchmarking, there currently lacks effective and easy approaches to analyze AC usage and provide actionable insights for occupants. To fill this gap, this study proposes clustering analysis to identify AC use patterns of residential buildings, and develops new key performance indicators (KPIs) and data analytics to explore the AC operation characteristics using the long-term metered cooling energy use data, which is of great importance for inhabitants to understand their thermal energy use and save energy cost through adjustment of their AC use behavior. We demonstrate the proposed approaches in a residential district comprising 300 apartments, located in Zhengzhou, China. Main outcomes include: Representative AC use patterns are developed for three room types of residential buildings in the cold climate zone of China, which can be used as more realistic AC schedules to improve accuracy of energy simulation; Distributions of KPIs on household cooling energy usage are established, which can be used for household AC use intensity benchmarking and performance diagnoses.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Operation and performance of VRF systems: Mining a large-scale dataset

The energy consumption of air-conditioning systems has gained increasing attention as it contributes significantly to the global building energy use. The variable refrigerant flow (VRF) system is a common air-conditioning system applied widely in residential and office buildings in China. Understanding the actual operation and performance of VRF systems is fundamental for the energy-efficient design and operation of VRF systems. Previous research on VRF system operation used either limited field data covering certain building types and climate zones or used a questionnaire to obtain a larger dataset. However, they did not capture the wide applications of VRF systems quantitatively across all building types, climate zones, and operating conditions. To fill this gap, statistical and clustering analysis was conducted on the newly proposed key performance indicators of approximately 287,000 VRF systems for residential and commercial buildings in all five climate zones in China. In this work, the main findings are: (1) VRF systems are mainly used for cooling in all climate zones in China; (2) among all building types, the duration of use is lowest in residential buildings and highest in hotels and medical buildings; (3) the distribution of the ideal VRF cooling coefficient of performance (COP) is similar across all climate zones and building types; whereas the COPs of ideal VRF heating in the Severe Cold region and Cold regions are lower than those in other climate zones; and (4) partial load operations for VRF systems are common in residential buildings and office buildings due to the part-time-part-space operation mode. These findings can inform the actual application of VRF systems in China, supporting the design, operation, industry standard development, and performance optimization of VRF systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Using landscape typologies to model socioecological systems: Application to agriculture of the United States Gulf Coast

Agricultural enterprises are vulnerable to the effects of climate variability and change. Improved understanding of the determinants of vulnerability and adaptive capacity in agricultural systems is important for projecting and managing future climate risk. At present, three analytical tools dominate methodological approaches to understanding agroecological vulnerability to climate: process-based crop models, empirical crop models, and integrated assessment models. A common weakness of these approaches is their limited treatment of socio-economic conditions and human agency in modeling agroecological processes and outcomes. This study proposes a framework that uses spatial cluster analysis to generate regional socioecological typologies that capture geographic variance in regional agricultural production and enable attribution of that variance to climatic, topographic, edaphic, and socioeconomic components. This framework was applied to historical corn production (1986-2010) in the U.S. Gulf of Mexico region as a testbed. The results demonstrate that regional socioeconomic heterogeneity is an important driving force in human dominated ecosystems, which we hypothesize, is a function of the link between socioeconomic conditions and the adaptive capacity of agricultural systems. Meaningful representation of future agricultural responses to climate variability and change is contingent upon understanding interactions among biophysical conditions, socioeconomic conditions, and human agency their incorporation in predictive models.

58 GEOSCIENCES↗
DOE OSTI2020

Grain detachment and transport clogging during mineral dissolution in carbonate rocks with permeable grain boundaries

In this paper, we explore the effect of permeable grain boundaries on carbonate rock dissolution in order to improve our understanding of grain detachment and migration during reactive flow. To do so we investigated the effects of grain size and enhanced reactivity along grain boundaries on global dissolution kinetics. Variations of permeability and porosity were calculated, and exponential relationships were observed in fractured rocks. Our model employed a reactive transport framework based on the Darcy-Brinkman equation to simulate calcite dissolution in carbonate rocks composed of microporous grains and, included fluid transport along grain boundaries. The model includes fluid flow, solute transport by advection–diffusion, heterogeneous reaction between fluid and minerals and grain detachment with subsequent grain transport in macropores. The migration of solid particles due to dissolution was based on cluster analysis and local movement, and the results show that grain detachment can lead to significant decreases in permeability due to the clogging of transport pathways. Microporous media with smaller grain sizes (fine grains in this study) showed a higher average reaction rate than those with coarser grains. In addition, as the overall rates of geochemical processes are commonly affected by the presence of texture heterogeneities such as fractures, a single fracture (macropore) introduced into a microporous matrix composed of permeable grains and grain boundaries was modelled as a large channel connecting the inlet and outlet of the simulation domain. It was found that macropore clogging by grain detachment temporary decreased permeability, lowering the long-term global reaction rates. Lastly, we observed that increases in flow rate can reduce detachment and fracture clogging by reducing local dissolution along grain boundaries.

58 GEOSCIENCES↗
DOE OSTI2026

Investigating the Determinants of Household Capabilities Burden During Power Outages: The Case of Winter Storm Uri

Existing research primarily uses census data to identify the vulnerability of communities to hazards. These vulnerability indices provide aggregated data and are not hazard-specific nor well-validated with post-event data. In contrast, our study uses household survey data (n=1065) to understand which Texan households suffered the greatest loss of their capabilities due to power outages and other utility service disruptions during Winter Storm Uri. Inspired by the Capabilities Approach, our measures of burden include the number of household capability types disrupted during the outages (e.g., cooking, heating, refrigeration), the severity of impact for each disrupted capability, and the additional time and financial costs of coping with these disruptions. We perform a clustering analysis, and find two distinct groups in our data, consisting of ‘lesser burden' and ‘heavier burden' households. Results indicate that the households experiencing the heaviest capabilities burden were most likely to experience longer power outages and the loss of water services. They were also more likely to have a Hispanic-Latino household member, lack access to a generator, live in a rented home, have larger households with more young children, fewer adults over 65, lower household incomes, been impacted by the COVID-19 pandemic, and more family characteristics that made life harder. We also fit a logistic regression model to assess the role of outage, household, and community characteristics in predicting differences in capabilities burden. Our results offer insights into enumerating the consequences of utility service disruptions on households, which can inform more targeted and equitable resilience strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Precipitation regime classification for the Mojave Desert: Implications for fire occurrence

Mojave Desert ecosystem processes are dependent upon the amount and seasonality of precipitation. Multi-decadal periods of drought or above-average rainfall affect landscape vegetation condition, biomass and susceptibility to fire. The seasonality of precipitation events can also affect the likelihood of lightning, a key ignition source for fires. To develop an understanding of precipitation regimes and fire patterns we used monthly average precipitation data and GIS data representing burned areas from 1971-2010. We applied a K-means cluster analysis to the monthly precipitation data identifying three distinct precipitation seasons; winter (October – March), spring (April-June) and summer (July-September) and four discrete precipitation regimes within the Mojave ecoregion.

classification↗
DOE OSTI2021

Alpha Shape Analysis (ASA) Framework for Post- Clustering Property Determination in Atom Probe Tomographic Data

While application of clustering algorithms to atom probe tomography data have enabled quantification of solute clusters in terms of number density, size, and subcomposition there exist other properties (e.g., volume, surface area, and composition) that are better determined by defining an interface between the cluster and the surrounding matrix. The limitation in composition results from an ion selection step where the expected matrix ion types are omitted from the cluster search algorithm to enhance the contrast between the matrix and cluster and to reduce the complexity of the search. Previously, composition determination within solute clusters has utilized a secondary envelopment and erosion step on top of conventional methods such as maximum separation. In this work, we present a novel stochastic method that combines the particle identification fidelity of a conventional clustering algorithm with the analytical flexibility of mesh-based approaches through the generation of alpha shapes for each identified cluster. Furthermore, the corresponding mesh accounts for concave components of the clusters and determines the volume and surface area of the clusters; additionally, the mesh boundary is utilized to update the total composition according to the internal ions.

42 ENGINEERING↗
DOE OSTI2018

Nanoscale Growth Kinetics of Cu(In,Ga)Se 2 Absorbers

Here, we investigate the kinetics of elemental segregation during the synthesis of Cu(In 1–x ,Ga x )Se 2 (CIGS) thin films via a rapid thermal growth process. Changes in elemental concentration are mapped rapidly via in situ X-ray fluorescence (XRF) microscopy. In situ XRF enables the quantification of Cu, In, Ga, and Se concentrations with 200 nm spatial resolution over a region of 7 μm × 7 μm, large enough to map multiple CIGS grains. Reacting elemental precursor stacks at 500, 550, and 600 °C, we find that copper is the driving force for α-CIGS phase formation at a rate of 17 nmol/cm 2 /min at 500 °C. Because of the thermal expansion of the substrate, cluster analysis and classification algorithms were employed to track compositionally similar regions over time and across samples measured at different temperatures. Beyond the direct application of these results for the optimization of CIGS manufacturing, these in situ experiments demonstrate the feasibility of quantitative compositional evaluation at the nanoscale during the synthesis of technologically relevant materials. In combination with advanced analysis methods, this example shows how correlative X-ray microscopy can contribute to the solution of the materials paradigm, relating synthesis to composition and properties at the nanoscale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Nanoscale Magnetic Ordering Dynamics in a High Curie Temperature Ferromagnet

Thermally driven transitions between ferromagnetic and paramagnetic phases are characterized by critical behavior with divergent susceptibilities, long-range correlations, and spin dynamics that can span kHz to GHz scales as the material approaches the critical temperature T c , but it has proven technically challenging to probe the relevant length and time scales with most conventional measurement techniques. In this study, we employ scanning nitrogen-vacancy center based magnetometry and relaxometry to reveal the critical behavior of a high-T c ferromagnetic oxide near its Curie temperature. Cluster analysis of the measured temperature-dependent nanoscale magnetic textures points to a 3D universality class with a correlation length that diverges near T c . Meanwhile, the temperature-dependent spin dynamics, measured through all optical relaxometry suggest that the phase transition is in the XY universality class. In conclusion, our results capture both static and dynamic aspects of critical behavior, providing insights into universal properties that govern phase transitions in magnetic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Two-Dimensional Cadmium Chloride Nanosheets in Cadmium Telluride Solar Cells

In this paper we make use of a liquid nitrogen-based thermomechanical cleavage technique and a surface analysis cluster tool to probe in detail the tin oxide/emitter interface at the front of completed CdTe solar cells. We show that this thermomechanical cleavage occurs within a few angstroms of the SnO 2 /emitter interface. An unexpectedly high concentration of chlorine at this interface, ~20%, was determined from a calculation that assumed a uniform chlorine distribution. Angle-resolved X-ray photoelectron spectroscopy was used to further probe the structure of the chlorine-containing layer, revealing that both sides of the cleave location are covered by one-third of a unit cell of pure CdCl 2 , a thickness corresponding to about one Cl-Cd-Cl molecular layer. We interpret this result in the context of CdCl 2 being a true layered material similar to transition-metal dichalcogenides. Exposing cleaved surfaces to water shows that this Cl-Cd-Cl trilayer is soluble, raising questions pertinent to cell reliability. Our work provides new and unanticipated details about the structure and chemistry of front surface interfaces and should prove important to improving materials, processes, and reliability of next-generation CdTe-based solar cells.

14 SOLAR ENERGY↗