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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Pierre Auger Observatory open data

The Pierre Auger Collaboration has embraced the concept of open access to their research data since its foundation, with the aim of giving access to the widest possible community. A gradual process of release began as early as 2007 when 1% of the cosmic-ray data was made public, along with 100% of the space-weather information. In February 2021, a portal was released containing 10% of cosmic-ray data collected by the Pierre Auger Observatory from 2004 to 2018, during the first phase of operation of the Observatory. The Open Data Portal includes detailed documentation about the detection and reconstruction procedures, analysis codes that can be easily used and modified and, additionally, visualization tools. Since then, the Portal has been updated and extended. In 2023, a catalog of the highest-energy cosmic-ray events examined in depth has been included. A specific section dedicated to educational use has been developed with the expectation that these data will be explored by a wide and diverse community, including professional and citizen scientists, and used for educational and outreach initiatives. This paper describes the context, the spirit, and the technical implementation of the release of data by the largest cosmic-ray detector ever built and anticipates its future developments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Imaging Bias‐Driven Domain Wall Motion With Scanning Oscillator Piezoresponse Force Microscopy

Understanding ferroelectric domain wall dynamics at the nanoscale across a broad range of timescales requires measuring domain wall position under different applied electric fields. The success of piezoresponse force microscopy (PFM) as a tool to apply local electric fields at different positions and imaging their changing position, together with the information obtained from associated switching spectroscopies has fueled numerous studies of the dynamics of ferroelectric domains to determine the impact of intrinsic parameters such as crystalline order, defects and pinning centers, as well as boundary conditions such as environment. However, the investigation of sub-coercive reversible domain wall vibrational modes requires the development of new tools that enable visualizing domain wall motion under varying applied fields with high temporal and spatial resolution while also accounting for spurious electrostatic effects. Here, scanning oscillator piezoresponse force microscopy extends the investigation of domain wall dynamics to new regimes, providing direct visualization of domain wall position as a function of an external electric field that varies in time and location. In conclusion, this enables studying the energetics of field-driven ferroelectric domain wall motion, which is shown to obey a thermally activated flow regime in the millisecond timescale.

Piezoresponse Force Microscopy↗
DOE OSTI2024

Stroboscopic x-ray diffraction microscopy of dynamic strain in diamond thin-film bulk acoustic resonators for quantum control of nitrogen-vacancy centers

Bulk-mode acoustic waves in a crystalline material exert lattice strain through the thickness of the sample, which couples to the spin Hamiltonian of defect-based qubits such as the nitrogen-vacancy (N-V) center defect in diamond. This mechanism has previously been harnessed for unconventional quantum spin control, spin decoherence protection, and quantum sensing. Bulk-mode acoustic wave devices are also important in the microelectronics industry as microwave filters. A key challenge in both applications is a lack of appropriate operando microscopy tools for quantifying and visualizing gigahertz-frequency dynamic strain. Here, in this work, we directly image acoustic strain within N-V center-coupled diamond thin-film bulk acoustic wave resonators using stroboscopic scanning hard x-ray diffraction microscopy at the Advanced Photon Source. The far-field scattering patterns of the nanofocused x-ray diffraction encode strain information entirely through the illuminated thickness of the resonator. These patterns have a real-space spatial variation that is consistent with the bulk strain’s expected modal distribution and a momentum-space angular variation from which the strain amplitude can be quantitatively deduced. We also perform optical measurements of strain-driven Rabi precession of of the N-V center spin ensemble, providing an additional quantitative measurement of the strain amplitude. As a result, we directly measure one of the six N-V spin-stress coupling parameters, 𝑏 =2.73⁢(2) MHz/GPa, by correlating these measurements at the same spatial position and applied microwave power. Our results demonstrate a unique technique for directly imaging ac lattice strain in micromechanical structures and provide a direct measurement of a fundamental constant for the N-V center defect spin Hamiltonian.

acoustic techniques↗
DOE OSTI2023

MetaboDirect: an analytical pipeline for the processing of FT-ICR MS-based metabolomic data

Background: Microbiomes are now recognized as the main drivers of ecosystem function ranging from the oceans and soils to humans and bioreactors. However, a grand challenge in microbiome science is to characterize and quantify the chemical currencies of organic matter (i.e., metabolites) that microbes respond to and alter. Critical to this has been the development of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), which has drastically increased molecular characterization of complex organic matter samples, but challenges users with hundreds of millions of data points where readily available, user-friendly, and customizable software tools are lacking. Results: Here, we build on years of analytical experience with diverse sample types to develop MetaboDirect, an open-source, command-line-based pipeline for the analysis (e.g., chemodiversity analysis, multivariate statistics), visualization (e.g., Van Krevelen diagrams, elemental and molecular class composition plots), and presentation of direct injection high-resolution FT-ICR MS data sets after molecular formula assignment has been performed. When compared to other available FT-ICR MS software, MetaboDirect is superior in that it requires a single line of code to launch a fully automated framework for the generation and visualization of a wide range of plots, with minimal coding experience required. Among the tools evaluated, MetaboDirect is also uniquely able to automatically generate biochemical transformation networks (ab initio) based on mass differences (mass difference network-based approach) that provide an experimental assessment of metabolite connections within a given sample or a complex metabolic system, thereby providing important information about the nature of the samples and the set of microbial reactions or pathways that gave rise to them. Finally, for more experienced users, MetaboDirect allows users to customize plots, outputs, and analyses. Conclusion: Application of MetaboDirect to FT-ICR MS-based metabolomic data sets from a marine phage-bacterial infection experiment and a Sphagnum leachate microbiome incubation experiment showcase the exploration capabilities of the pipeline that will enable the research community to evaluate and interpret their data in greater depth and in less time. It will further advance our knowledge of how microbial communities influence and are influenced by the chemical makeup of the surrounding system. The source code and User’s guide of MetaboDirect are freely available through (https://github.com/Coayala/MetaboDirect) and (https://metabodirect.readthedocs.io/en/latest/), respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Accelerating network layouts using graph neural networks

Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret “hairball” layouts. Here we use Graph Neural Networks (GNN) to accelerate FDL, showing that deep learning can address both limitations of FDL: it offers a 10 to 100 fold improvement in speed while also yielding layouts which are more informative. We analytically derive the speedup offered by GNN, relating it to the number of outliers in the eigenspectrum of the adjacency matrix, predicting that GNNs are particularly effective for networks with communities and local regularities. Finally, we use GNN to generate a three-dimensional layout of the Internet, and introduce additional measures to assess the layout quality and its interpretability, exploring the algorithm’s ability to separate communities and the link-length distribution. The novel use of deep neural networks can help accelerate other network-based optimization problems as well, with applications from reaction-diffusion systems to epidemics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Approach for energy efficient building design during early phase of design process

Energy consumption in the building sector is about 40% of total energy consumed globally and is trending upwards, along with its contribution to greenhouse gas (GHG) emissions. Given the adverse impacts of GHG emissions, it is crucial to integrate energy efficiency into building designs. The most significant opportunities for enhancing energy performance are present during the initial phases of building design, when there is less impact of other design constraints. Various tools exist for simulating different design options and providing feedback in terms of energy consumption and comfort parameters. These simulation outputs must then be analyzed to derive design solutions. This paper presents an innovative approach that utilizes user input parameters, processes them through cloud computing, and outputs easily understandable strategies for energy-efficient building design. The methodology employs Asynchronous Distributed Task Queues (DTQ) - a more scalable and reliable alternative to conventional speedup techniques-for conducting parametric energy simulations in the cloud. The goal of this approach is to assist design teams in identifying, visualizing, and prioritizing energy-saving design strategies from a range of possible solutions for each project. Furthermore, a tool ‘eDOT’ has been developed utilizing the discussed methodology. Unlike existing tools, eDOT leverages artificial intelligence to dynamically generate and provide design strategies during the early phases of design process. By simplifying the simulation process, eDOT enables design teams to make informed, data-driven decisions without needing to interpret complex simulation outputs. A case study simulated for two locations is provided in this paper to demonstrate the effectiveness of eDOT, further underscoring its practical impact on energy-efficient building design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

BinaryCIF and CIFTools—Lightweight, efficient and extensible macromolecular data management

3D macromolecular structural data is growing ever more complex and plentiful in the wake of substantive advances in experimental and computational structure determination methods including macromolecular crystallography, cryo-electron microscopy, and integrative methods. Efficient means of working with 3D macromolecular structural data for archiving, analyses, and visualization are central to facilitating interoperability and reusability in compliance with the FAIR Principles. We address two challenges posed by growth in data size and complexity. First, data size is reduced by bespoke compression techniques. Second, complexity is managed through improved software tooling and fully leveraging available data dictionary schemas. To this end, we introduce BinaryCIF, a serialization of Crystallographic Information File (CIF) format files that maintains full compatibility to related data schemas, such as PDBx/mmCIF, while reducing file sizes by more than a factor of two versus gzip compressed CIF files. Moreover, for the largest structures, BinaryCIF provides even better compression—factor ten and four versus CIF files and gzipped CIF files, respectively. Herein, we describe CIFTools, a set of libraries in Java and TypeScript for generic and typed handling of CIF and BinaryCIF files. Together, BinaryCIF and CIFTools enable lightweight, efficient, and extensible handling of 3D macromolecular structural data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Best Practices for Energy Management Information Systems Metadata Schemas

The Federal Energy Management Program (FEMP) promotes best practices for impactful utilization of Energy Management Information Systems (EMIS) at federal facilities. This best practice document is part of a series of fact sheets created to help accelerate the market adoption and use of EMIS in the federal sector. The use of standard naming conventions and a metadata schema, which may be referred to as 'data tags,' improves the ability of the EMIS to consistently analyze, visualize, and derive value from operational data. To help address these issues, this fact sheet highlights best practices for use of metadata in an EMIS stack, which can significantly reduce the initial time required to deploy an EMIS.

EMIS↗
DOE OSTI2022

Per- and Polyfluoroalkyl Substances: A Preliminary Evaluation of Groundwater Contamination in the Western States

Per- and Polyfluoroalkyl Substances (PFAS) are a manufactured group of chemicals that have been used since the 1940s. PFAS break down very slowly and studies show that exposure to these chemicals may cause negative reproductive effects, developmental delays in children, increased risk of some cancers, and interference with the body’s natural hormones. On June 15, 2022, EPA issued interim updated drinking water health advisories for the two most widely studied PFAS, perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS). The concentrations for the health advisory limits are 0.004 parts per trillion (ppt) and 0.02 ppt respectively. Because data is often reported in combined PFOA and PFOS concentrations, and for ease of data visualization, concentrations of the two chemicals will be discussed as a single, summed value and will be compared to the combined health advisory limits of 0.024 ppt. Due to the recently published near-zero health advisory limits and the known negative health effects, a map of estimated PFAS groundwater contamination was created using ArcGIS Pro and geostatistical tools to inform the public and determine which regions are most at-risk.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Visualizing Light-Induced Microstrain and Phase Transition in Lead-Free Perovskites Using Time-Resolved X-Ray Diffraction

Metal halide perovskites have emerged as promising materials for optoelectronic applications in the last decade. A large amount of effort has been made to investigate the interplay between the crystalline lattice and photoexcited charge carriers as it is vital to their optoelectronic performance. Among them, ultrafast laser spectroscopy has been intensively utilized to explore the charge carrier dynamics of perovskites, from which the local structural information can only be extracted indirectly. Here, we have applied a time-resolved X-ray diffraction technique to investigate the structural dynamics of prototypical two-dimensional lead-free halide perovskite Cs 3 Bi 2 Br 9 nanoparticles across temporal scales from 80 ps to microseconds. We observed a quick recoverable (a few ns) photoinduced microstrain up to 0.15% and a long existing lattice expansion (~a few hundred nanoseconds) at mild laser fluence. Once the laser flux exceeds 1.4 mJ/cm 2 , the microstrain saturates and the crystalline phase partially transfers into a disordered phase. This photoinduced transient structural change can recover within the nanosecond time scale. Furthermore, these results indicate that photoexcitation of charge carriers couples with lattice distortion, which fundamentally affects the dielectric environment and charge carrier transport.

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

rcsb-api : Python Toolkit for Streamlining Access to RCSB Protein Data Bank APIs

The Protein Data Bank (PDB) was founded in 1971 as the first open-access digital data resource in biology to serve as the single global archive for three-dimensional (3D) macromolecular structure data. Current PDB holdings exceed 230,000 experimentally determined structures of proteins, nucleic acids, viruses, and macromolecular machines. The RCSB Protein Data Bank RCSB.org research-focused web portal facilitates search, analyses, and visualization of every PDB structure along with more than one million Computed Structure Models from AlphaFold DB and the ModelArchive. It is powered by a set of publicly available Application Programming Interfaces (APIs) that both support RCSB.org users and provide programmatic access to PDB data. Given the breadth and levels of granularity encompassed in this rich data collection, efficiently accessing the information programmatically may be challenging for new users. RCSB PDB has developed a Python software package, rcsb-api , that facilitates easy and efficient use of RCSB PDB APIs within a Python environment. This software tool is designed to streamline access to the extensive corpus of data housed within the PDB, enabling researchers to search, retrieve, and analyze 3D biostructure data seamlessly. Its use will accelerate research in structural biology, molecular biology and biochemistry, drug discovery, and bioinformatics by providing more efficient tools for data integration and analysis. The new toolkit is available on GitHub (github.com/rcsb/py-rcsb-api) and published to the public Python package repository (PyPI) to foster wider usage and support basic and applied research in fundamental biology, biomedicine, and the energy sciences.

FAIR principles↗
DOE OSTI2020

The geometry of signal space: a case study of direct mapping between seismic signals and event distribution

SUMMARY Under favourable circumstances, seismic waveforms corresponding to an ensemble of events related by a common, spatially distributed process collectively exhibit a regular, signal-space geometry. When events in the ensemble have a common, or nearly common, source mechanism, this geometry is a distorted image of the distribution of events in the source region. The signal-space image can be visualized using a relatively simple waveform alignment and projection operation. Ensemble waveform correlation measurements can be inverted to estimate the distribution of the events in the source region, up to an arbitrary rotation, reflection and scaling, with residual distortion. We demonstrate these concepts with synthetic waveforms and with observations of long-wall mining induced seismicity for which substantial ground truth information is available. Our experience with these data has implications for location, correlation detection and machine learning and possible application to studies of repeating events in induced, volcanic and glacial seismicity. Our results place limits on the widely held assumption that waveform correlation is a useful measure of event separation. We suggest that the constraints on event separation need to be evaluated in the context of a population of related events, whose waveforms sample the signal space image of the source region. A better indicator of event separation is the length of the shortest path in signal space along the image.

58 GEOSCIENCES↗
DOE OSTI2021

LL18-Signal Analysis-NDD2Ad (Final Report)

Under favorable circumstances, seismic waveforms corresponding to an ensemble of events related by a common, spatially-distributed process collectively exhibit a regular, signal-space geometry. When events in the ensemble have a common, or nearly common, source mechanism, this geometry is a distorted image of the distribution of events in the source region. The signal space image can be visualized using a relatively simple waveform alignment and projection operation. Ensemble waveform correlation measurements can be inverted to estimate the distribution of the events in the source region, up to an arbitrary rotation, reflection, and scaling with residual distortion. We demonstrate these concepts with synthetic waveforms and with observations of long-wall mining induced seismicity for which substantial ground truth information is available. Our experience with these data has implications for location, correlation detection and machine learning, and possible application to studies of repeating events in induced, volcanic, and glacial seismicity. Our results place limits on the widely-held assumption that waveform correlation is a useful measure of event separation. We suggest that the constraints on event separation need to be evaluated in the context of a population of related events, whose waveforms sample the signal space image of the source region. A better indicator of event separation is the length of the shortest path in signal space along the image.

58 GEOSCIENCES↗
DOE OSTI2024

The secondary metabolism collaboratory: a database and web discussion portal for secondary metabolite biosynthetic gene clusters

Secondary metabolites are small molecules produced by all corners of life, often with specialized bioactive functions with clinical and environmental relevance. Secondary metabolite biosynthetic gene clusters (BGCs) can often be identified within DNA sequences by various sequence similarity tools, but determining the exact functions of genes in the pathway and predicting their chemical products can often only be done by careful, manual comparative analysis. To facilitate this, we report the first release of the secondary metabolism collaboratory (SMC), which aims to provide a comprehensive, tool-agnostic repository of BGC sequence data drawn from all publicly available and user-submitted bacterial and archaeal genome and contig sources. On the website, users are provided a searchable catalog of putative BGCs identified from each source, along with visualizations of gene and domain annotations derived from multiple sequence analysis tools. SMC’s data is also available through publicly-accessible application programming interface (API) endpoints to facilitate programmatic access. Users are encouraged to share their findings (and search for others’) through comment posts on BGC and source pages. At the time of writing, SMC is the largest repository of BGC information, holding 13.1M BGC regions from 1.3M source sequences and growing, and can be found at https://smc.jgi.doe.gov.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Full Scale HEPA Filter Encapsulation in Ultra-High-Performance Grout - Proof of Concept

The Hanford Tank Waste Treatment and Immobilization Plant (WTP) currently being constructed to treat radioactive waste, includes vitrification facilities for both the high-level waste (HLW) and low activity waste (LAW) fractions. Operation of the WTP will produce contaminated high-efficiency particulate air filters (HEPA), as part of the solid secondary waste (SSW) stream. The HEPA filters receive off-gas from the vessel vent header and primary off-gas treatment system in LAW Facility and remove particulate contaminants including 99 Tc and 129 I salts. The current disposal method for HEPA filters is encapsulation in metal containers using cementitious material (CM) and disposal in the Integrated Disposal Facility (IDF). Results from the 2017 IDF Performance Assessment (PA) WRPS (2018) demonstrated that while compliance is maintained for the 1000 year compliance period mandated by DOE O 435.1 and its accompanying manual, release of constituents from the HEPA filters result in exceedance of the performance objective imposed as the groundwater regulatory limit at later times. For example, at about 1500 years post-closure, solid secondary waste (SSW), including HEPA filters is predicted to become a dominant contributor to 99 Tc release and over the 10,000-year sensitivity analysis period, SSW is the dominant contributor of 129 I release to the groundwater. The estimated release could potentially be reduced if the HEPA filters are not compacted and waste containers could be distributed throughout a large space, thereby diluting the contaminant release. Additionally, a better cementitious material could be used to encapsulate the HEPA filters. One alternative method for disposal of contaminated HEPA filters is encapsulation of the filters in ultra high-performance grout (UHPG). UHPG is a variation of ultra-high-performance concrete (UHPC) is commonly used in the prestressed concrete industry for large structural members. Recent studies of UHPG show it has excellent properties for containing radionuclides such as 99 Tc and 129 I Nichols and Kaplan (2021). This report presents the results of the first attempt to encapsulate a clean, full-size HEPA filter in UHPG and evaluate the effectiveness of the immobilization process and final waste form. A full-scale proof-of-concept simulated waste form was prepared by encapsulating a HEPA filter in a 110-gallon stainless steel (SS) drum using UHPG. A change from Type I/II PC to Type 1L PLC was made after American Rock Products informed the team that they would no longer be using Type I/II by the end of 2024 and the northwest was phasing out Type I/II PC overall. Type I/II PC used in previous studies of UHPG for encapsulation (Nichols and Kaplan 2021). After the UHPG was cured both the scaled mockup and the full-scale simulated waste forms were sectioned for visual examination. UHPG completely encapsulated the filters and bonded to the external surfaces of materials comprising the filters. No cracks were observed in the sectioned waste forms.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Improving Detection of Disease Re-emergence Using a Web-Based Tool (RED Alert): Design and Case Analysis Study

Background: Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This practice may lead to ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence. Objective: Our objective is to bring together a variety of disease-related data and analytics needed to help public health analysts answer the following 3 primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence? Methods: We collected and cleaned disease-related data (eg, case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the World Health Organization (WHO), Pan American Health Organization (PAHO), World Bank, and Gideon. We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for the following 4 diseases: measles, cholera, dengue, and yellow fever. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies. Results: Our supervised learning models were able to identify 82%-90% of the local re-emergence events, although with 18%-31% (except 46% for dengue) false positives. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. The review of the web-based tool through case studies showed that local re-emergence detection was possible and that the tool provided actionable information about potential factors contributing to the local disease re-emergence and trends in global disease re-emergence. Conclusions: To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Model Data Archive Associated with Manuscript "Fire-altered Carbon Pools Create Disturbance Memory in Stream Dissolved Organic Carbon"

This data package supports the publication “Fire-altered Carbon Pools Create Disturbance Memory in Stream Dissolved Organic Carbon” by Li et al. (2026). The package contains processed model inputs, configuration files, restart files, simulation outputs, scripts, and visualization products used to evaluate post-fire dissolved organic carbon (DOC) dynamics in the Naches River Watershed, Washington, USA, following the 2021 Schneider Springs Fire. The modeling workflow couples ELM-BGC, the biogeochemistry-enabled Energy Exascale Earth System Model Land Model; ATS, the Advanced Terrestrial Simulator for integrated surface-subsurface hydrology; and PFLOTRAN, a reactive transport model for multicomponent aqueous geochemistry. Together, these models simulate how wildfire-induced changes in vegetation, litter, coarse woody debris, and soil organic matter influence DOC production, transport, and reaction from burned hillslopes to stream networks. The archive includes preprocessed meteorological, geospatial, hydrologic, and biogeochemical forcing data; ELM-BGC-derived DOC source terms; ATS mesh files; PFLOTRAN reactive-transport inputs; model configuration files; spin-up and transient restart files; watershed-scale diagnostic outputs; stream concentration time series; and figures or visualization files used to inspect and reproduce key results. File types include Hierarchical Data Format 5 (HDF5) files for gridded forcing and model-coupling data, model input and configuration files for ELM-BGC, ATS, and PFLOTRAN, restart and simulation-output files generated by the modeling workflow, tabular or time-series diagnostic outputs, scripts for post-processing and figure generation, and image or visualization products associated with the manuscript. Use of the package depends on the intended task. Re-running the simulations requires the relevant modeling software, including ELM-BGC, ATS, and PFLOTRAN as ATS's geochemical engine. Inspecting outputs and reproducing figures requires Python with scientific plotting libraries such as Matplotlib, and three-dimensional model outputs may be viewed with ParaView. Geographic information system files or maps may be inspected with ArcGIS Pro or comparable GIS software. The data package is intended to enable traceability, reuse, and partial reproduction of the coupled land-to-watershed hydro-biogeochemical modeling workflow used to test how wildfire disturbance affects terrestrial carbon pools and downstream DOC dynamics.

ATS↗
DOE OSTI2022

Scaling Building Energy Audits through Machine Learning Methods on Novel Drone Image Data

Building energy audits are time-consuming and labor-intensive. This paper describes a new method using machine learning (ML) techniques on novel data sources (drone images) to improve the identification of building characteristics and retrofit opportunities, and thereby reduce the effort for audits. The new ML method includes: (1) Building footprint extraction using line extraction, polygonization, and polygon-merging, (2) Building envelope extraction using PIX4d modeling software to reconstruct a building 3D model, (3) Visualization tool for viewing images from the 3D model, (4) Window-to-wall ratio (WWR) using state-of-art deep neural network semantic segmentation, (5) Envelope thermal anomaly detection using an unsupervised machine learning clustering algorithm, and (6) Rooftop energy equipment detection based on an object detection algorithm. The testing of this method involved a comparison of additional ML-generated information overlaid on current ‘state-of-practice’ audit and remote assessment baselines using evaluation metrics: labor time and associated cost, marginal benefits of using ML-generated information in workflows for audits and remote assessments, integration potential with existing processes and tools, and replicability/scalability of the method. In two test buildings in California that had comprehensive drawings and meter data available, the ML method effectively generated a building footprint, envelope, rooftop equipment, WWR, and locations of envelope thermal anomalies. Projected target segments of the ML method are sites with minimal drawings and energy data, and underserved sectors such as multistoried housing, disadvantaged communities, and schools for which the ML method can enable identification of building asset characteristics and prioritization of envelope retrofits and decentralized energy equipment retrofits.

Singh, Reshma↗