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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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DOE OSTI2021

GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files

This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework and fictional input data, and a genetic optimization is included which determines optimal flash plant parameters. The inputs and outputs associated with the forecast and genetic optimization are included. The input and output files consist of data, configuration files, and plots. A link to the Physics-Guided Neural Networks (phygnn) GitHub repository is also included, which augments a traditional neural network loss function with a generic loss term that can be used to guide the neural network to learn physical or theoretical constraints. phygnn is used by the GOOML framework to help integrate its machine learning models into the relevant physics and engineering applications. Note that the data included in this submission are intended to provide a demonstration of GOOML's capabilities. Additional files that have not been released to the public are needed for users to run these models and reproduce these results. Units can be found in the readme data resource.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Generation and representation of synthetic smart meter data

Advanced energy algorithms running at big-data scale will be necessary to identify, realize, and verify energy savings to meet government and utility goals of building energy efficiency. Any algorithm must be well characterized and validated before it is trusted to run at these scales. Smart meter data from real buildings will ultimately be required for the development, testing, and validation of these energy algorithms and processes. However, for initial development and testing, smart meter data are difficult to work with due to privacy restrictions, noise from unknown sources, data accessibility, and other concerns which can complicate algorithm development and validation. This paper describes a new methodology to generate synthetic smart meter data of electricity use in buildings using detailed building energy modeling, which aims to capture the variability and stochastics of real energy use in buildings. The methodology can create datasets tailored to represent specific scenarios with known truth and controllable amounts of synthetic noise. Knowledge of ground truth also allows the development and validation of enhanced processes which leverage building metadata, such as building type or size (floor area), in addition to smart meter data. The methodology described in this paper includes the key influencing factors of real-world building energy use including weather data, occupant-driven loads, building operation and maintenance practices, and special events. Data formats to support workflows leveraging both synthetic meter data and associated metadata are proposed and discussed. Finally, example use cases of the synthetic meter data are described to illustrate potential applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Unleashing the Power of Industrial Big Data through Scalable Manual Labeling

Big Data plays a central role in the remarkable results achieved by Machine Learning (ML) and especially Deep Learning (DL) in the recent years. However, the difficulty in obtaining a reasonable amount of labeled samples limits ML/DL application in various domains, including industrial equipment and system monitoring. In this paper the need for methods that turn manual labeling into a scalable process is highlighted. A real world problem is analyzed for which weak supervision methods, successfully employed in other domains, did not produce acceptable results. An alternative approach based on clustering ensembles is described and tested, achieving good performance.

Paes Leao, Bruno↗
DOE OSTI2021

Facilitating better and faster simulations of aerosol-cloud interactions in Earth system models

Focal Area(s): 1. Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising a hierarchy of models. 2. Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: One major challenge that Earth system models (ESMs) face in providing credible prediction of the Earth system and its water cycle characteristics (e.g., mean state, variability, and extreme events) is to accurately simulate aerosol-cloud interactions (ACI). The physical, chemical, and dynamical processes affecting ACI are extremely complex and they range from nanoscale to planetary scale. In each model development cycle, scientists spend significant efforts investigating model deficiencies and uncertainties associated with aerosols (e.g., emissions, chemical processes, aerosol microphysics, and transport) and clouds (e.g., macrophysics, microphysics, turbulence, and large-scale circulation) in order to develop improved treatments. However, despite decades of active research, ACI is still a major source of uncertainty in climate projections, even though great progress has been made. Specific scientific challenges include: (i) Parameterizations are developed based on limited data; (ii) The complexity of a parameterization required for accurate predictions is not understood; (iii) Incomplete and unknown physics leads to errors in the fully coupled Earth system; and (iv) Complex physics is computationally too expensive to employ in ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Condition Monitoring of Wind Turbine Planetary Gearboxes Under Different Operating Conditions

Digitally enhanced services for wind power could reduce Operations and Maintenance (O&M) costs as well as the Levelised Cost Of Energy (LCOE). Therefore, there is a continuous need for advanced monitoring techniques which can exploit the opportunities of Internet of Things (IoT) and Big Data Analytics, revolutionizing the future of the energy sector. The heart of wind turbines is a rather complex epicyclic gearbox. Among others, extremely critical gearbox components which are often responsible for machinery stops are the rolling element bearings. The vibration signatures of bearings are rather weak compared to other components, such as gears, and as a result an extended number of signal processing techniques and tools have been proposed during the last decades, focusing towards accurate, early, and on time bearing fault detection with limited false alarms and missed detections. Envelope Analysis is one of the most important methodologies, where an envelope of the vibration signal is estimated usually after filtering around a frequency band excited by impacts due to the bearing faults. Different tools, such as Kurtogram, have been proposed in order to accurately select the optimum filter parameters (center frequency and bandwidth). Cyclic Spectral Correlation and Cyclic Spectral Coherence, based on Cyclostationary Analysis, have been proved as very powerful tools for condition monitoring. The monitoring techniques seem to have reached a mature level in case a machinery operates under steady speed and load. On the other hand, in case the operating conditions change, it is still unclear whether the change of the monitoring indicators is due to the change of the health of the machinery or due to the change of the operating parameters. Recently, the authors have proposed a new tool called IESFOgram, which is based on Cyclic Spectral Coherence and can automatically select the filtering band. Furthermore, the Cyclic Spectral Coherence is integrated along the selected frequency band leading to an Improved Envelope Spectrum. In this paper, the performance of the tool is evaluated and further extended on cases operating under different speeds and different loads. The effectiveness of the methodology is tested and validated on the National Renewable Energy Laboratory (NREL) wind turbine gearbox vibration condition monitoring benchmarking data set which includes various faults with different levels of diagnostic complexity as well as various speed and load operating conditions.

17 WIND ENERGY↗
DOE OSTI2025

Release of ENDF81SaB: ENDF/B-VIII.1-Based ACE Data Files for Thermal Scattering

On August 30, 2024, the National Nuclear Data Center (NNDC) released the ENDF/B-VIII.1 nuclear data library. The library was released in the standard Evaluated Nuclear Data File (ENDF) format. These files can be accessed on the NNDC's website (www.nndc.bnl.gov). The files provided in the thermal neutron scattering sublibrary were processed into A Compact ENDF (ACE)-formatted files, verified, and validated by the XCP-5 Nuclear Data Team, resulting in the ENDF81SaB application library. This report details the processing of these files and the quality assurance approach taken. This is not intended to be a full validation effort; rather, this library is intended to simply reproduce the released files for further validation testing by the community. The validation basis and details of the evaluations are documented in the forthcoming ``Big Paper''.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Experimentally Constrained (\(n,\gamma \)) Reaction Rates Relevant to r- and i-Process Nucleosynthesis

The element distribution that we observe in the Universe today, tells a fascinating story of nucleosynthesis events that have taken place throughout the 13.7-billion-years-long history starting with the Big Bang. It is known for a long time that radiative neutron-capture reactions play a major role in synthesizing elements heavier than iron. However, many questions remain when it comes to our understanding of neutron-capture processes in extreme stellar environments. In particular, the intermediate and rapid neutron-capture processes are very challenging to describe, as there exist little or no data on the much-needed neutron-capture rates. In this work we discuss possibilities to obtain indirect, experimental constrains on these rates by means of the Oslo and the β-Oslo methods.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Automated System-wide Event Detection and Classification Using Machine Learning on Synchrophasor Data

As the number of phasor measurement units (PMUs) deployed in a power system increases, and their data volume streamed to the control canter intensifies, operators are facing challenges related to the analysis of such data, which need to be observed and responded to as the measurements are displayed in the Control Room. Humans are generally unable to process such large amount of data efficiently and rapidly. There is an apparent need for automated ways to analyze the data, extract actionable information about occurrence of specific events, and characterize the events quickly and cost effectively. This paper discusses the use of machine learning (ML) to facilitate such tasks by providing automated, highly computationally efficient, and cost-effective ways of extracting actionable information from synchrophasor big data in real-time. We developed Big Data Smart (BDSmart) ML-based prototype tool for the Control Room use that automatically analyses data properties from synchrophasor system measurements taken across the three grid Interconnections in the USA (Western, Eastern and ERCOT). The data collected from several hundreds of PMUs located across the Interconnections over a period of two years have been made available for our extensive study. As a result, we were able to identify a number of big data properties that influence how ML methodology is applied to select, develop, train and test the data models that can eventually be used for the tool implementation. The resulting set of candidate algorithms spans unsupervised, supervised, semi-supervised and transfer-learning approaches. Many ML techniques, such as decision trees, multinomial logistic regression, feed-forward neural networks, K-nearest neighbor, multiclass support vector machine, and single and multi-channel convolutional neural networks, are implemented, and their performance is examined. We offer the results from testing the data models. The novelty of our study is in the approaches for bad data detection and mitigation, selection of a simplified feature for event detection, and data label improvements. As a result, we came up with a list of recommendations for the utilities on how to improve the PMU recording practices to cater to the future ML applications aimed at automating the analysis of synchrophasor data.

Synchrophasors, Machine Learning, System-wide Even↗
DOE OSTI2026

Vanderbilt CMS Heavy-Ion Tier-2 Facility (Final Report)

The CMS experiment at the LHC at CERN in Geneva, Switzerland spends a portion of its running time colliding heavy ions. These heavy ion collisions are a key probe of a state of matter known as a Quark Gluon Plasma, which is a state of matter found in the very early universe 10s of microseconds after the big bang. These collision data are recorded by the CMS experiment and then undergo several stages of refinement to extract physics properties of the collisions from the raw data recorded by the various detector subsystems in CMS. In addition, copies of these datasets are stored on tape archival storage for data preservation. This award funds the deployment, maintenance, and operation of a computing facility at Vanderbilt University which processes, stores, and analyzes these collision data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Introduction to the special issue on smart transportation

Transportation is getting smarter and smarter, with the prominence of connected automated vehicle technologies in the global auto industry’s near-term growth strategies, of big data analytics and unprecedented access to sensing data of mobility, and of integration of this analytics into the optimization of mobility and transport. Further, these developments are setting off a wave of smart transportation innovations, which are featured by new methods and applications driven by various forms of sensor data such as GPS, CAN bus, LIDA, images, etc. At the same time, complexities surrounding the use, conflation, and processing of disparate data in near real-time is of essence for the design and development of futuristic smart transportation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Materials informatics: From the atomic-level to the continuum

We report that in recent years materials informatics, which is the application of data science to problems in materials science and engineering, has emerged as a powerful tool for materials discovery and design. This relatively new field is already having a significant impact on the interpretation of data for a variety of materials systems, including those used in thermoelectrics, ferroelectrics, battery anodes and cathodes, hydrogen storage materials, polymer dielectrics, etc. Its practitioners employ the methods of multivariate statistics and machine learning in conjunction with standard computational tools (e.g., density-functional theory) to, for example, visualize and dimensionally reduce large data sets, identify patterns in hyperspectral data, parse microstructural images of polycrystals, characterize vortex structures in ferroelectrics, design batteries and, in general, establish correlations to extract important physics and infer structure-property-processing relationships. In this Overview, we critically examine the role of informatics in several important materials subfields, highlighting significant contributions to date and identifying known shortcomings. We specifically focus attention on the difference between the correlative approach of classical data science and the causative approach of physical sciences. From this perspective, we also outline some potential opportunities and challenges for informatics in the materials realm in this era of big data.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning

Abstract The National Water Model (NWM) is a key tool for flood forecasting, planning, and water management. Key challenges facing the NWM include calibration and parameter regionalization when confronted with big data. We present two novel versions of high‐resolution (∼37 km 2 ) differentiable models (a type of hybrid model): one with implicit, unit‐hydrograph‐style routing and another with explicit Muskingum‐Cunge routing in the river network. The former predicts streamflow at basin outlets whereas the latter presents a discretized product that seamlessly covers rivers in the conterminous United States (CONUS). Both versions use neural networks to provide a multiscale parameterization and process‐based equations to provide a structural backbone, which were trained simultaneously (“end‐to‐end”) on 2,807 basins across the CONUS and evaluated on 4,997 basins. Both versions show great potential to elevate future NWM performance for extensively calibrated as well as ungauged sites: the median daily Nash‐Sutcliffe efficiency of all 4,997 basins is improved to around 0.68 from 0.48 of NWM3.0. As they resolve spatial heterogeneity, both versions greatly improved simulations in the western CONUS and also in the Prairie Pothole Region, a long‐standing modeling challenge. The Muskingum‐Cunge version further improved performance for basins >10,000 km 2 . Overall, our results show how neural‐network‐based parameterizations can improve NWM performance for providing operational flood predictions while maintaining interpretability and multivariate outputs. The modeling system supports the Basic Model Interface (BMI), which allows seamless integration with the next‐generation NWM. We also provide a CONUS‐scale hydrologic data set for further evaluation and use.

Song, Yalan [Civil and Environmental Engineering T↗
DOE OSTI2019

A Scalable Graph Analytics Framework for Programming with Big Data in R (pbdR)

Many disciplines such as biology, economics, engineering, physics, and the social sciences represent their data as graphs to capture patterns, trends, and associations. There are are many commercially available graph libraries in different programming languages to analyze these complex graphs. But there is no distributed graph library package in R - the popular statistical programming language to analyze graphs that bigger than a single machine's memory. Many domain experts prefer R over the numerous other alternatives. Towards this, we present a distributed graph analytics framework for R called programming with big graph using R (pBGR.) Our proposed framework leverages the Programming with Big Data in R (pbdR) ecosystem that provides scalable R packages for distributed computing in data science. We present an early prototype implementation of this framework using the distributed-memory parallel graph library CombBLAS and evaluate the framework's performance on leadership class computing platforms. Our experimental results demonstrate that the proposed framework is capable of performing large-scale parallel graph mining through the easyto-use R language. This enhanced graph processing capability coupled with other statistical tools already available in R, should be valuable to many domain experts.

Hasan, S M Shamimul↗
DOE OSTI2020

Global gross primary production simulated by three terrestrial biosphere models to analyse smoothing of limiting rates

Processed global terrestrial gross primary production (GPP) from three terrestrial biosphere models (TBMs). Two simulations were run with each model, one that selects the rate limiting process of photosynthesis based on the minimum of the potential rates (following Farquhar et al. 1980) and the other that uses quadratic smoothing between the potential rates (following Collatz et al. 1991). The three TBMs are: 1) the Energy Exascale Earth System Model (E3SM) land model (ELM) (release: v1.1.0) (Burrows et al., 2020), a coupled carbon, nitrogen, and phosphorus model with sun/shade big-leaf canopy photosynthesis scaling. 2) The Functionally Assembled Terrestrial Ecosystem Simulator (FATES, tag: sci.1.30.0_api.8.0.0) (Koven et al., 2019), coupled with the Community Land Model (CLM, version 5; Lawrence et al., 2019), a carbon-only vegetation demography model with multi-leaf and multi-canopy layers for scaling photosynthesis, and with a leaf area index (LAI) optimisation scheme. 3) The Sheffield Dynamic Global Vegetation Model (SDGVM, tag: Walkeretal2020_GCB), a carbon-only model with multi-leaf-layer canopy photosynthesis scaling and also with an LAI optimisation scheme (Walker et al., 2017; Woodward & Lomas, 2004). These data were generated to evaluate the effect of Collatz smoothing of limiting rates on simulated GPP across a range of terrestrial biosphere models. Analysis of results can be found in Walker et al. (2020). This dataset is comprised of R scripts to process data and produce figures and .RDS files, a binary format specific to R and read with the 'readRDS' function.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Using Likwid and Byfl to Benchmark Hardware Performance

This paper outlines a benchmarking study conducted during my internship at LANL, focusing on CPU (Computer Processing Unit) and program performance assessment. The primary goal was to gather memory access data using three methods across five polybench kernels The data gathered would then be used to compare and contrast to one another and calculate operational intensity for performance comparisons. Benchmarking tools like Byfl and Likwid were employed, with Byfl offering hardware-independent data through LLVM compiler communication and Likwid directly interacting with computer hardware. The study considered various benchmarking factors, including optimization levels, Big O notation ((n)), CPU diversity and specific kernel equations. Big O notation was utilized to simplify code complexity, with detailed breakdwons of operations and memory components for each polybench application. Specific O(n) equations enabled nuanced kernel compariosns, facilitating the identification of performance variations. CPU efficiency assessments were conducted using Likwid tests on two CPUs. The central focus on code optimization aimed at achieving higher speeds and reduced memory usage through streamlined code. Future work propsoes creating a roofline model, synthesizing benchmarking data into a comprehensive data graph to assist in optimizing code and improving hardware performance. The potential impact on the laboratory or national mission was underscored, emphasizing the importance of optimizing applications and hardware to conserve resources and accelerate program execution. The specific relevance to LANL’s operations in math-intensive fields such as Nuclear Fission, Space Exploration, and Nanotechnology highlights the necessity of efficient benchmarking for resource conservation and proram speed. Overall, this study contributes to the understanding of CPU and program performance, providing insights for future optimization efforts in a laboratory setting

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

An Interactive Visualization Tool for Large-Scale Building Stock Modeling: Preprint

Recent advancements in data science and high-performance computing are making it easier to run millions of building simulations, but meaningful visualization of such large datasets remains a challenge. This paper presents a new tool developed to view the results of large-scale OpenStudio simulations of national, regional, or local building stocks. The tool processes millions of simulations to calculate measure savings, utility bills, carbon emissions, primary energy, and cost-effectiveness metrics at a high geographic resolution. Interactive visualizations of the building characteristics, consumption, and measure savings data include proportional symbol maps and histogram plots and can be filtered by any building characteristic.

big data↗
DOE OSTI2022

A Multi-Branch Decoder Network Approach to Adaptive Temporal Data Selection and Reconstruction for Big Scientific Simulation Data

A key challenge in scientific simulation is that the simulation outputs often require intensive I/O and storage space to store the results for effective post hoc analysis. This article focuses on a quality-aware adaptive temporal data selection and reconstruction problem where the goal is to adaptively select simulation data samples at certain key timesteps in situ and reconstruct the discarded samples with quality assurance during post hoc analysis. This problem is motivated by the limitation of current solutions that a significant amount of simulation data samples are either discarded or aggregated during the sampling process, leading to inaccurate modeling of the simulated phenomena. Two unique challenges exist: 1) the sampling decisions have to be made in situ and adapted to the dynamics of the complex scientific simulation data; 2) the reconstruction error must be strictly bounded to meet the application requirement. To address the above challenges, we develop DeepSample , an error-controlled convolutional neural network framework, that jointly integrates a set of coherent multi-branch deep decoders to effectively reconstruct the simulation data with rigorous quality assurance. The results on two real-world scientific simulation applications show that DeepSample significantly outperforms other state-of-the-art methods on both sampling efficiency and reconstructed simulation data quality.

Zhang, Yang↗
DOE OSTI2019

Provenance–aware workflow for data quality management and improvement for large continuous scientific data streams

Data quality assessment, management and improvement is integral part of any big data intensive scientific research to ensure accurate, reliable and reproducible science discoveries. The task of maintaining the quality of data, however, is non-trivial and pose a challenge for a program like Department of Energy’s Atmospheric Radiation Measurement (ARM) that collects data from hundreds of instruments across the world,and distributes thousands of streaming data products that are continuously growing in near-real-time in an archive 1.7 Petabyte in size and growing. We present a computational data processing workflow to collect the data quality issue via an easy and intuitive web-based portal that allows reporting of any quality issues for any site, facility or instruments at a granularity down to individual variable in the data files. Portal allows instrument specialists and scientists to provide corrective actions in form of symbolic equation. A parallel processing framework applies the data improvement to large volume of data in efficient parallel environment, while optimizing data transfer and file I/O operations. Corrected files are systematically versioned and archived. A provenance tracking module tracks and records any change made to the data during its entire life cycle which are communicated transparently to the scientific users. Developed in Python using open source technologies, software architecture enables efficient and fast management and improvement of data in an operational data center environment.

Kumar, Jitendra↗