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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

The clustering of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey DR14 quasar sample: anisotropic clustering analysis in configuration space

We explore the cosmological implications of anisotropic clustering measurements of the quasar sample from Data Release 14 (DR14) of the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey (eBOSS) in configuration space. The similar to 147000 quasar sample observed by eBOSS offers a direct tracer of the density field and bridges the gap of previous baryon acoustic oscillation measurements between redshift 0.8 < z < 2.2. By analysing the two-point correlation function characterized by clustering wedges xi(wi)(s) and multipolesi xi(l)(S), we measure the angular diameter distance, Hubble parameter, and cosmic structure growth rate. We define a systematic error budget for our measurements based on the analysis of N-body simulations and mock catalogues. Based on the DR14 large-scale structure quasar sample at the effective redshift z(eff) = 1.52, we find the growth rate of cosmic structure f sigma(8)(z(eff)) = 0.396 +/- 0.079, and the geometric parameters Dv(z)/r(d) = 26.47 +/- 1.23, and F-AP(Z) = 2.53 +/- 0.22, where the uncertainties include both statistical and systematic errors. These values are in excellent agreement with the best-fitting standard A cold dark matter model to the latest cosmic microwave background data from Planck.

cosmology↗
DOE OSTI2017

Self-consistent clustering analysis: an efficient multiscale scheme for inelastic heterogeneous materials

A predictive computational theory is shown for modeling complex, hierarchical materials ranging from metal alloys to polymer nanocomposites. The theory can capture complex mechanisms such as plasticity and failure that span across multiple length scales. This general multiscale material modeling theory relies on sound principles of mathematics and mechanics, and a cutting-edge reduced order modeling method named self-consistent clustering analysis (SCA) [Zeliang Liu, M.A. Bessa, Wing Kam Liu, “Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials,” Comput. Methods Appl. Mech. Engrg. 306 (2016) 319–341]. SCA reduces by several orders of magnitude the computational cost of micromechanical and concurrent multiscale simulations, while retaining the microstructure information. This remarkable increase in efficiency is achieved with a data-driven clustering method. Computationally expensive operations are performed in the so-called offline stage, where degrees of freedom (DOFs) are agglomerated into clusters. The interaction tensor of these clusters is computed. In the online or predictive stage, the Lippmann-Schwinger integral equation is solved cluster-wise using a self-consistent scheme to ensure solution accuracy and avoid path dependence. To construct a concurrent multiscale model, this scheme is applied at each material point in a macroscale structure, replacing a conventional constitutive model with the average response computed from the microscale model using just the SCA online stage. A regularized damage theory is incorporated in the microscale that avoids the mesh and RVE size dependence that commonly plagues microscale damage calculations. The SCA method is illustrated with two cases: a carbon fiber reinforced polymer (CFRP) structure with the concurrent multiscale model and an application to fatigue prediction for additively manufactured metals. For the CFRP problem, a speed up estimated to be about 43,000 is achieved by using the SCA method, as opposed to FE2, enabling the solution of an otherwise computationally intractable problem. The second example uses a crystal plasticity constitutive law and computes the fatigue potency of extrinsic microscale features such as voids. This shows that local stress and strain are capture sufficiently well by SCA. This model has been incorporated in a process-structure-properties prediction framework for process design in additive manufacturing.

Liu, Z.↗
DOE OSTI2022

Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements

Light detection and ranging (LiDAR) measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations, which encompass a breadth of atmospheric stability regimes and rotor thrust coefficients. The LiDAR measurements are clustered through the k-means algorithm, which enables identifying the most representative realizations of wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters. Considering the large number of LiDAR samples collected to probe the wake velocity field, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data and the associated supervisory control and data acquisition and meteorological data enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability. Furthermore, the cluster analysis of the LiDAR data allows for the identification of systematic off-design operations with a certain yaw misalignment of the turbine rotor with the mean wind direction.

17 WIND ENERGY↗
DOE OSTI2014

Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster analysis results were similar to SPECT RNA phase analysis (ROC AUC = 0.78, p = 0.73 vs cluster AUC; sensitivity/specificity = 59%/89%) and PET scar size analysis (ROC AUC = 0.73, p = 1.0 vs cluster AUC; sensitivity/specificity = 76%/67%). Conclusions: A SPECT RNA cluster analysis algorithm was developed for the prediction of CRT outcome. Cluster analysis results produced results equivalent to those obtained from Fourier and scar analysis.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1987

Cluster analysis of multiple planetary flow regimes

A modified cluster analysis method was developed to identify spatial patterns of planetary flow regimes, and to study transitions between them. This method was applied first to a simple deterministic model and second to Northern Hemisphere (NH) 500 mb data. The dynamical model is governed by the fully-nonlinear, equivalent-barotropic vorticity equation on the sphere. Clusters of point in the model's phase space are associated with either a few persistent or with many transient events. Two stationary clusters have patterns similar to unstable stationary model solutions, zonal, or blocked. Transient clusters of wave trains serve as way stations between the stationary ones. For the NH data, cluster analysis was performed in the subspace of the first seven empirical orthogonal functions (EOFs). Stationary clusters are found in the low-frequency band of more than 10 days, and transient clusters in the bandpass frequency window between 2.5 and 6 days. In the low-frequency band three pairs of clusters determine, respectively, EOFs 1, 2, and 3. They exhibit well-known regional features, such as blocking, the Pacific/North American (PNA) pattern and wave trains. Both model and low-pass data show strong bimodality. Clusters in the bandpass window show wave-train patterns in the two jet exit regions. They are related, as in the model, to transitions between stationary clusters.

Mo, Kingtse↗
NASA NTRS1988

Cluster analysis of multiple planetary flow regimes

A modified cluster analysis method developed for the classification of quasi-stationary events into a few planetary flow regimes and for the examination of transitions between these regimes is described. The method was applied first to a simple deterministic model and then to a 500-mbar data set for Northern Hemisphere (NH), for which cluster analysis was carried out in the subspace of the first seven empirical orthogonal functions (EOFs). Stationary clusters were found in the low-frequency band of more than 10 days, while transient clusters were found in the band-pass frequency window between 2.5 and 6 days. In the low-frequency band, three pairs of clusters determined EOFs 1, 2, and 3, respectively; they exhibited well-known regional features, such as blocking, the Pacific/North American pattern, and wave trains. Both model and low-pass data exhibited strong bimodality.

Mo, Kingtse↗
DOE OSTI2016

Data depth based clustering analysis

Here, this paper proposes a new algorithm for identifying patterns within data, based on data depth. Such a clustering analysis has an enormous potential to discover previously unknown insights from existing data sets. Many clustering algorithms already exist for this purpose. However, most algorithms are not affine invariant. Therefore, they must operate with different parameters after the data sets are rotated, scaled, or translated. Further, most clustering algorithms, based on Euclidean distance, can be sensitive to noises because they have no global perspective. Parameter selection also significantly affects the clustering results of each algorithm. Unlike many existing clustering algorithms, the proposed algorithm, called data depth based clustering analysis (DBCA), is able to detect coherent clusters after the data sets are affine transformed without changing a parameter. It is also robust to noises because using data depth can measure centrality and outlyingness of the underlying data. Further, it can generate relatively stable clusters by varying the parameter. The experimental comparison with the leading state-of-the-art alternatives demonstrates that the proposed algorithm outperforms DBSCAN and HDBSCAN in terms of affine invariance, and exceeds or matches the ro-bustness to noises of DBSCAN or HDBSCAN. The robust-ness to parameter selection is also demonstrated through the case study of clustering twitter data.

58 GEOSCIENCES↗
NASA NTRS1989

Cluster analysis of the hot subdwarfs in the PG survey

Application of cluster analysis to the hot subdwarfs in the Palomar Green (PG) survey of faint blue high-Galactic-latitude objects is assessed, with emphasis on data noise and the number of clusters to subdivide the data into. The data used in the study are presented, and cluster analysis, using the CLUSTAN program, is applied to it. Distances are calculated using the Euclidean formula, and clustering is done by Ward's method. The results are discussed, and five groups representing natural divisions of the subdwarfs in the PG survey are presented.

Thejll, Peter↗
DOE OSTI2021

Cluster analysis of occupancy schedules in residential buildings in the United States

The energy performance of residential buildings significantly depends on the building occupants’ behavior, which can be highly variable. When the heating, ventilation and air conditioning (HVAC) system is controlled based on the presence or absence of occupants in a building, occupant behavior is of even further importance to its energy performance. In current practice, building energy simulation tools generally use a single occupancy profile to represent the building’s occupancy schedule, the schedule of which is considered to be the same, regardless of the type of household being modeled. Thus, there is significant potential for improvement to allow for more flexibility and accuracy in calculation of occupancy. The objective of this study is to assess the variations in the typical types of occupancy schedules followed by the U.S. population using cluster analysis. American Time Use Survey data, which statically represents the overall U.S. population’s activities, across 12 years (2006–2017), is used. The ATUS data is segregated into smaller groups based on age and weekday/weekend, then divided into activities that are considered “at home” and “away from home”, which are mapped to the presence or non-presence of occupants in the home. Cluster analysis is then used to identify common types of occupancy schedule patterns for each age group. Three main types of patterns are obtained from cluster analysis for each age group, which together represent approximately 88% of people in the United States. The output of the cluster analysis is further analyzed to evaluate the variation in characteristics, including the number of times leaving home, time of day when leaving the home, and the timespan of absence from the home. The results of this study provide detailed insights on how typical occupants in the United States spend their time in residential spaces which can be used to create occupancy profiles for residential buildings. Finally, these occupancy profiles could be utilized inform an assessment of the energy use impact of occupancy-based controls of energy consuming systems and technologies.

42 ENGINEERING↗
NASA NTRS1981

ICAP - An Interactive Cluster Analysis Procedure for analyzing remotely sensed data

An Interactive Cluster Analysis Procedure (ICAP) was developed to derive classifier training statistics from remotely sensed data. ICAP differs from conventional clustering algorithms by allowing the analyst to optimize the cluster configuration by inspection, rather than by manipulating process parameters. Control of the clustering process alternates between the algorithm, which creates new centroids and forms clusters, and the analyst, who can evaluate and elect to modify the cluster structure. Clusters can be deleted, or lumped together pairwise, or new centroids can be added. A summary of the cluster statistics can be requested to facilitate cluster manipulation. The principal advantage of this approach is that it allows prior information (when available) to be used directly in the analysis, since the analyst interacts with ICAP in a straightforward manner, using basic terms with which he is more likely to be familiar. Results from testing ICAP showed that an informed use of ICAP can improve classification, as compared to an existing cluster analysis procedure.

Wharton, S. W.↗
DOE OSTI2019

The Application of the OPTICS Algorithm to Cluster Analysis in Atom Probe Tomography Data

Atom probe tomography (APT) is a powerful technique to characterize buried 3D nanostructures in a variety of materials. Accurate characterization of those nanometer-scale clusters and precipitates is of great scientific significance to understand the structure-property relationships and the microstructural evolution. The current widely used cluster analysis method, a variant of the DBSCAN algorithm, can only accurately extract clusters of the same atomic density, neglecting several experimental realities, such as density variations within and between clusters and the non-uniformity of the atomic density in the APT reconstruction itself (e.g. crystallographic poles and other field evaporation artifacts). The clustering results of DBSCAN-like method rely heavily on multiple input parameters, but ideal selection of those parameters is challenging and oftentimes ambiguous. In this study, we present a new cluster analysis method consisting of a well-known algorithm, called OPTICS, and a cluster extraction algorithm we developed to enable accurate detection of clusters of varying atomic density in APT data. The new method requires only one free parameter, and other inputs can be estimated based on physical parameters of the material. The effectiveness of this method is demonstrated by application to several small-scale model datasets and a real APT dataset obtained from an oxide-dispersion strengthened ferritic alloy specimen. The method’s sensitivity with respect to parameter selection is discussed and compared with the current widely used methods.

atom probe tomography, cluster analysis, density-b↗
DOE OSTI2020

Clustering Analysis of Commercial Vehicles Using Automatically Extracted Features from Time Series Data

Standard of practice approaches to time series cluster analysis involve careful feature engineering, often utilizing expert input to tune and select features by hand. In many cases, expert input may not be readily available, or there may not yet exist a community consensus on the ideal features for a given application. This paper compares the results of several cluster analysis methods, using both hand selected features and those extracted automatically, when applied to large geospatial time series telematics data from commercial trucking fleets. The impacts of feature selection, dimensionality reduction, and choice of clustering algorithm on the quality of clustering results are explored. Results from this analysis confirm prior results that domain agnostic features are competitive with the hand engineered features with respect to clustering quality metrics. These results also provide new insight into the most successful strategies for identifying structure in large unstructured vehicle telematics data, and suggest that time series clustering using automatic feature extraction can be an effective approach to extract structure from large scale geospatial time series data in cases when hand selected features are not available.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1984

Applications of cluster analysis to satellite soundings

The advantages of the use of cluster analysis in the improvement of satellite temperature retrievals were evaluated since the use of natural clusters, which are associated with atmospheric temperature soundings characteristic of different types of air masses, has the potential for improving stratified regression schemes in comparison with currently used methods which stratify soundings based on latitude, season, and land/ocean. The method of discriminatory analysis was used. The correct cluster of temperature profiles from satellite measurements was located in 85% of the cases. Considerable improvement was observed at all mandatory levels using regression retrievals derived in the clusters of temperature (weighted and nonweighted) in comparison with the control experiment and with the regression retrievals derived in the clusters of brightness temperatures of 3 MSU and 5 IR channels.

Munteanu, M. J.↗
NASA NTRS1980

ICAP: An Interactive Cluster Analysis Procedure for analyzing remotely sensed data

An Interactive Cluster Analysis Procedure (ICAP) was developed to derive classifier training statistics from remotely sensed data. The algorithm interfaces the rapid numerical processing capacity of a computer with the human ability to integrate qualitative information. Control of the clustering process alternates between the algorithm, which creates new centroids and forms clusters and the analyst, who evaluate and elect to modify the cluster structure. Clusters can be deleted or lumped pairwise, or new centroids can be added. A summary of the cluster statistics can be requested to facilitate cluster manipulation. The ICAP was implemented in APL (A Programming Language), an interactive computer language. The flexibility of the algorithm was evaluated using data from different LANDSAT scenes to simulate two situations: one in which the analyst is assumed to have no prior knowledge about the data and wishes to have the clusters formed more or less automatically; and the other in which the analyst is assumed to have some knowledge about the data structure and wishes to use that information to closely supervise the clustering process. For comparison, an existing clustering method was also applied to the two data sets.

Wharton, S. W.↗
DOE OSTI2020

Characterization of Surface Heterogeneity-Induced Convection Using Cluster Analysis

Land-atmosphere interactions and boundary layer processes often control the formation of shallow clouds and subsequently deep convective precipitation over the Southern Great Plains. In this study, we examine the impacts of large-scale advection on the cloud populations and land-atmospheric coupling observed during the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) field campaign in 2016. In this work, we performed two Large Eddy Simulations (LES) using the Weather Research and Forecasting Model for a day in which the transition from clear-sky to shallow precipitating clouds and isolated deep convection was observed. The control simulation reproduced the overall distribution of cloud populations by using realistic soil conditions with an interactive land model. In the sensitivity simulation where large-scale advection is removed, a strong relationship between the land and boundary layer is found. To study the timing, location, and intensity of convective initiation and the relationship of clouds with land surface properties, a cluster analysis of equivalent potential temperature is performed for the simulation without large-scale advection. That analysis shows that convective clouds first form over regions with higher surface sensible heat flux. Precipitation from those convective clouds likely triggers new updrafts nearby about two hours later through the lifting associated with cold pools. Furthermore, the cluster analysis also shows that in addition to the spatial pattern of soil moisture, land use and soil texture in western Oklahoma also influence the location of convective initiation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Code Artifact for: Clustering Analysis of Commercial Vehicles Using Automatically Extracted Features from Time Series Data [SWR-21-96]

This repository contains data ingestion, feature extraction, and analysis code used in NREL Technical report "Clustering Analysis of Commercial Vehicles Using Automatically Extracted Features from Time Series Data." The code is written in Python. The ETL and feature extraction code must be run in a Spark context. The analysis code can be run without Spark, provided you have pre-computed features in a CSV file. Analysis code related to the NREL Technical Report NREL/TP-2C00-74212. Includes PySpark functions to perform trip segmentation and feature extraction over big time series data in Apache Spark. Includes "domain specific" features such as Aerodynamic Speed (ft/s), Characteristic Acceleration (ft/s2), Percent Below 55 (%), Percent Zero (%), Stops Per Mile, Average Speed (mph), Maximum Speed (mph), and Speed Standard Deviation (mph). Includes Pyspark UDF to compute "domain agnostic" features using the TSFresh library. This software record also includes the analysis notebooks and code to generate the results in the previously mentioned technical report.

Perr-Sauer, Jordan↗
DOE OSTI2017

Testing the Technique for the Cluster Analysis of Acoustic Emission Pulse Arrays under the Formation of a Conical Glass Granulate Pile

The technique for the cluster analysis and the classification of registered acoustic emission (AE) data arrays was used for the determination of the isolated phases upon the transition from the regular to the avalanche process of damage accumulation in a physical simulation model. The damage accumulation phases were modeled in the process of formation of a conical glass granulate pile. Their registration was performed by an AE control system synchronized with a high-speed video camera.

36 MATERIALS SCIENCE↗