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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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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Big Data Analytic for Cascading Failure Analysis

With the challenges of increased grid dynamics and more variability of power generation from renewable energy sources, rapidly increasing complexity in the grid model, and abundant data from measurements and simulations, the requirements for computational analysis have also increased dramatically. Power system operators and engineers need to analyze more scenarios, extract meaningful information from a larger set of data, and respond more quickly when faced with these new challenges for large-scale applications, such as contingency analysis. This paper proposes a novel big data analysis approach for power system cascading analysis, prevention, and remediation. The developed techniques will be capable of cascading analysis, better assessment of the system’s vulnerability level, as well as proposing potential remediation. Case studies using IEEE 118-bus system and a 500-bus system, with a comparison against a commercial tool, validate the advantages of the developed big data approach: accurate prediction, and more importantly, faster and effective correction actions.The developed techniques could be further used for other power system applications.

big data, cascading analysis, corrective action, m↗
DOE OSTI2017

Unleashing the Power of Wide-Area Data Analytics for Enhanced Grid Visibility

We report that the operation of modern electric transmission systems relies increasingly on the use of advanced sensing and synchronized-measurement technologies. These technologies collect and collate a wide variety of data from disperse locations to monitor the health of network, obtain fast and accurate diagnosis of systemic changes, and detect signs of anomalies and instabilities at an early stage. In view of the far-reaching blackout risk from potential damages to critical transmission grid assets (due to severe disturbances, physical attacks, and extreme weather events), stealthy activities that compromise the operation of wide-area sensing, communication, and control systems could cripple the entire cyber-physical smart-grid infrastructure. To this end, achieving robust event identification, ensuring bulk system observability, and developing actionable intelligence (by fostering data-driven decision making under imminent threat of complex, multifaceted attacks and disruptions) remain key challenges for sustaining a resilient power grid operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Integration of Data Analytics with Plant System Health Program

This report summarizes the R&D activities of the Plant Health Management (PHM) project during fiscal year 2020 (FY20). This project focuses on the development of methods that integrate component health data and propagate such information at the system level to evaluate system sources of risk. This project development lives in cooperation with the Risk Informed Asset Management (RIAM) project. The RIAM project, in fact, uses the reliability models and data generated by the PHM project to optimize plant operations (e.g., maintenance/replacement schedule, optimal maintenance posture). This year’s activities for the PHM project focused mainly on the development of two classes of models. The first one includes a series of component reliability models which include aging, testing and maintenance. The second class includes a series of system reliability models which focus on the secondary side of exiting U.S. reactors. These models are based on fault tree logic structures such that they can be used by existing plant PRA software. We also started to focus on the management of health data and we tackled this issue in two directions. The first one focuses on the integration of monitor data with simulation models to assess component health. This approach is moving from a classical data based to a model+data based approach with the goal of improving higher component health information. The second direction focuses on linking equipment reliability data (e.g., maintenance/failure reports, component monitoring data) directly to system reliability models using a safety margin based language rather than a probability based language. The main advantage of a safety margin based language is that it can provide to system engineers more tangible information on system/component health and how it propagates to the system level.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography

Electron microscopy is undergoing a transition; from the model of producing only a few micrographs, through the current state where many images and spectra can be digitally recorded, to a new mode where very large volumes of data (movies, ptychographic and multi-dimensional series) can be rapidly obtained. In this paper, we discuss the application of so-called “big-data” methods to high dimensional microscopy data, using unsupervised multivariate statistical techniques, in order to explore salient image features in a specific example of BiFeO 3 domains. Remarkably, k-means clustering reveals domain differentiation despite the fact that the algorithm is purely statistical in nature and does not require any prior information regarding the material, any coexisting phases, or any differentiating structures. While this is a somewhat trivial case, this example signifies the extraction of useful physical and structural information without any prior bias regarding the sample or the instrumental modality. Further interpretation of these types of results may still require human intervention. Finally, however, the open nature of this algorithm and its wide availability, enable broad collaborations and exploratory work necessary to enable efficient data analysis in electron microscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Data Analytics for Designing Fe-9Cr Steels

Presented by NETL at 2019 Annual Project Review Meeting for Crosscutting, Rare Earth Elements, Gasification and Transformative Power Generation, 04/19

data science, alloy design↗
DOE OSTI2019

Occupancy sensing in buildings: A review of data analytics approaches

A review of the literature pertaining to building occupancy detection, counting, and tracking - three areas under the umbrella of building occupancy estimation - is presented with a focus on mathematical approaches and corresponding metrics. The idea is to provide the reader with a background on the hardware and techniques used for occupancy inference, subsequent to data collection, with emphasis placed on the algorithmic characterization of occupancy estimation. The various approaches employed by researchers to tackle the problem are surveyed and summarized, including: data collection, cleaning processes, algorithm utilization and categorization, as well as data structuring and organization. The scope of prediction and performance metrics are used to establish a benchmarking system through a comprehensive summary of (indoor) occupancy estimation, presented in the context of the mathematical tools utilized.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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 OSTI2021

Informing the planning of rotating power outages in heat waves through data analytics of connected smart thermostats for residential buildings

Abstract With climate change, heat waves have become more frequent and intense. Rotating power outages happen when the power supply is unable to meet the cooling demand increase resulting from extreme high temperatures. Power outages during heat waves expose residents to high risks of overheating. In this study, we propose a novel data-driven inverse modelling approach to inform decision makers and grid operators on planning rotating power outages. We first infer the building thermal characteristics using the connected smart thermostat data, and used the estimated thermal dynamics to simulate the thermal resilience during a heat wave event. Our proposed method was tested for the California power outage in August 2020 by using the open source Ecobee Donate Your Data dataset. We found in California the power outage should not last more than two hours during heat waves to avoid overheating risks. Informing the residents in advance so they can prepare for it through pre-cooling is a simple but effective strategy to expand the acceptable power outage duration. In addition to assisting power outage planning, the proposed method can be used for other applications, such as to evaluate a building energy efficiency policy, to examine fuel poverty, and to estimate the load shifting potential of building stocks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

COVID-19: Spatiotemporal social data analytics and machine learning for pandemic exploration and forecasting

This task focused on developing a preliminary approach to use machine learning (ML) to explore the relationship between county-level societal variables and COVID-19 parameters, including COVID-19 cases rates and counts and COVID-19 death rates and counts. The objective was to develop and test a prototype approach for linking COVID-19 and county-level data. The task focused on enhancing and applying existing LANL ML techniques to COVID-19. Our novel ML methods have been a subject of a recently approved U.S. patent. The codes based on these methods are already open-source released. Our ML tools (NMFk/NTFk) are applied to extract hidden features (signals, waves) in the analyzed datasets and automatically identify their optimal number. The features are extracted by identifying counties that have similarities between the county-level societal variables and the COVID-19 parameters. These demonstration analyses will facilitate the ongoing pandemic simulations and predictions performed by Los Alamos other institutions, as well as lay the groundwork for future work.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Deep Data Analytics in Structural and Functional Imaging of Nanoscale Materials

Recent advances in scanning probe microscopy and scanning transmission electron microscopy have opened unprecedented opportunities in probing the materials structural parameters and electronic properties in real space on a picometre-scale. At the same time, the ability of modern day microscopes to quickly produce large, high-resolution datasets has created a challenge for rapid physics-guided analysis of data that typically contain several hundreds to several thousand atomic or molecular units per image. Here it is demonstrated how the advanced statistical analysis and machine learning techniques can be used for extracting relevant physical and chemical information from microscope data on multiple functional materials. Specifically, the following three case studies are discussed (i) application of a combination of convolutional neural network and Markov model for analyzing positional and orientational order in molecular self-assembly; (ii) a combination of sliding window fast Fourier transform, Pearson correlation matrix and canonical correlation analysis methods to study the relationships between lattice distortions and electron scattering patterns in graphene; (iii) application of a non-negative matrix factorization with physics-based constraints and Moran’s analysis of spatial associations to extracting electronic responses linked to different types of structural domains from multi-modal imaging datasets on iron-based superconductors. The approaches demonstrated here are universal in nature and can be applied to a variety of microscopic measurements on different materials.

Ziatdinov, Maxim↗
DOE OSTI2015

Identification of Chemical Attribution Signatures of Fentanyl Syntheses Using Multivariate Statistical Analysis of Orthogonal Analytical Data

Attribution of the origin of an illicit drug relies on identification of compounds indicative of its clandestine production and is a key component of many modern forensic investigations. The results of these studies can yield detailed information on method of manufacture, starting material source, and final product - all critical forensic evidence. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic fentanyl, N-(1-phenylethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods, all previously published fentanyl synthetic routes or hybrid versions thereof, were studied in an effort to identify and classify route-specific signatures. 160 distinct compounds and inorganic species were identified using gas and liquid chromatographies combined with mass spectrometric methods (GC-MS and LCMS/ MS-TOF) in conjunction with inductively coupled plasma mass spectrometry (ICPMS). The complexity of the resultant data matrix urged the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 87 route-specific CAS were classified and a statistical model capable of predicting the method of fentanyl synthesis was validated and tested against CAS profiles from crude fentanyl products deposited and later extracted from two operationally relevant surfaces: stainless steel and vinyl tile. This work provides the most detailed fentanyl CAS investigation to date by using orthogonal mass spectral data to identify CAS of forensic significance for illicit drug detection, profiling, and attribution.

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

Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Part Three: Model Building at the Layer Level

To ensure the usefulness of simulation technologies in practice, their credibility needs to be established with Uncertainty Quantification (UQ) methods. In this project smart proxy is introduced to significantly reduce the computational cost of conducting large number of multiphase CFD simulations. Smart proxy for CFD models are developed using pattern recognition capabilities of Artificial Intelligence (AI), Machine Learning (LM) and Data Mining (DM) technologies. Several CFD simulation runs with different inlet air velocities for a rectangular fluidized bed are used to create a smart CFD proxy that is capable of replicating the CFD results for a wide variety of inlet velocities. The smart CFD proxy is validated with blind CFD runs (CFD runs that have not played any role during the development [training, calibration and validation] of the smart CFD proxy). In our earlier work, CFD data was used to train ANN at the cell level. That is an ANN was trained for each computational cell used in the CFD simulations. In the present work, the ANN is constructed and trained based on cross sectional area average of each variable, such as pressure, velocities and volume fraction (Layer Level). This leads to improvements in the training time at the expense of less spatial resolution. The resulting trained ANN provides spatially average value of parameters of interest, along the length of the fluidized bed. Upon completion of this project, UQ studies that rely on hundreds or thousands of smart CFD proxy runs can be accomplished in minutes. Following figure demonstrates a validation example (blind CFD run) showing the results from the MFiX and the smart CFD proxy for pressure drop across a fluidized bed at time-step of 1400 (the layer number corresponds to the vertical location in the bed).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Data Analytics for Residential PV from Permit to Interconnect (Final Technical Report)

The main objective of this research is to provide novel insights into the effects of permitting, inspection, and interconnection (PII) processes on PV system installations—and in particular, into the relationship between PII processes and adoption timelines. This research can then be used to clarify the potential effect of various process changes on reducing PII timelines, customer cancellation rates, and related costs nationwide. NREL completed this research by assembling a data set of distributed, largely residential rooftop solar systems less than 50 kilowatts in size from participating solar installers. NREL produced five publications describing the effects that PII processes can have on adoption timelines nationwide, in addition to publishing an interactive data viewer with five years of PII cycle time data. This tool can be used by stakeholders to identify potential adoption timelines by local government.

14 SOLAR ENERGY↗
DOE OSTI2018

Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Report Two: Model Building at the Cell Level

To ensure the usefulness of simulation technologies in practice, their credibility needs to be established with Uncertainty Quantification (UQ) methods. In this project, smart proxy is introduced to significantly reduce the computational cost of conducting large number of multiphase CFD simulations, which is typically required for non-intrusive UQ analysis. Smart proxy for CFD models are developed using pattern recognition capabilities of Artificial Intelligence (AI) and Data Mining (DM) technologies. Several CFD simulation runs with different inlet air velocities for a rectangular fluidized bed are used to create a smart CFD proxy that is capable of replicating the CFD results for the entire geometry and inlet velocity range. The smart CFD proxy is validated with blind CFD runs (CFD runs that have not played any role during the development of the smart CFD proxy). The developed and validated smart CFD proxy generates its results in seconds with reasonable error (less than 10%). Upon completion of this project, UQ studies that rely on hundreds or thousands of smart CFD proxy runs can be accomplished in minutes. Following figure demonstrates a validation example (blind CFD run) showing the results from the MFiX simulation and the smart CFD proxy for pressure distribution across a fluidized bed at a given time-step (the layer number corresponds to the vertical location in the bed).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Classification of orthostatic intolerance through data analytics

Imbalance in the autonomic nervous system can lead to orthostatic intolerance manifested by dizziness, lightheadedness, and a sudden loss of consciousness (syncope); these are common conditions, but they are challenging to diagnose correctly. Uncertainties about the triggering mechanisms and the underlying pathophysiology have led to variations in their classification. This study uses machine learning to categorize patients with orthostatic intolerance. Here we use random forest classification trees to identify a small number of markers in blood pressure, and heart rate time-series data measured during head-up tilt to (a) distinguish patients with a single pathology and (b) examine data from patients with a mixed pathophysiology. Next, we use Kmeans to cluster the markers representing the time-series data. We apply the proposed method analyzing clinical data from 186 subjects identified as control or suffering from one of four conditions: postural orthostatic tachycardia (POTS), cardioinhibition, vasodepression, and mixed cardioinhibition and vasodepression. Classification results confirm the use of supervised machine learning. We were able to categorize more than 95% of patients with a single condition and were able to subgroup all patients with mixed cardioinhibitory and vasodepressor syncope. Clustering results confirm the disease groups and identify two distinct subgroups within the control and mixed groups. The proposed study demonstrates how to use machine learning to discover structure in blood pressure and heart rate time-series data. The methodology is used in classification of patients with orthostatic intolerance. Diagnosing orthostatic intolerance is challenging, and full characterization of the pathophysiological mechanisms remains a topic of ongoing research. This study provides a step toward leveraging machine learning to assist clinicians and researchers in addressing these challenges.

60 APPLIED LIFE SCIENCES↗