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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 OSTI2021

Reachability Analysis for FollowerStopper: Safety Analysis and Experimental Results

Motivated by earlier work and the developer of a new algorithm, the FollowerStopper, this article uses reachability analysis to verify the safety of the FollowerStopper algorithm, which is a controller designed for dampening stop-and-go traffic waves. With more than 1100 miles of driving data collected by our physical platform, we validate our analysis results by comparing it to human driving behaviors. The FollowerStopper controller has been demonstrated to dampen stop-and-go traffic waves at low speed, but previous analysis on its relative safety has been limited to upper and lower bounds of acceleration. To expand upon previous analysis, reachability analysis is used to investigate the safety at the speeds it was originally tested and also at higher speeds. Two formulations of safety analysis with different criteria are shown: distance-based and time headway-based. The FollowerStopper is considered safe with distance-based criterion. However, simulation results demonstrate that the FollowerStopper is not representative of human drivers - it follows too closely behind vehicles, specifically at a distance human would deem as unsafe. On the other hand, under the time headway-based safety analysis, the FollowerStopper is not considered safe anymore. A modified FollowerStopper is proposed to satisfy time-based safety criterion. Simulation results of the proposed FollowerStopper shows that its response represents human driver behavior better.

Chou, Fang-Chieh↗
DOE OSTI2024

A Bayesian Learning Approach to Wireless Outdoor Heatmap Construction using Deep Gaussian Process

We present a novel Bayesian learning approach to outdoor radio heatmap construction utilizing deep Gaussian process (GP). The proposed approach employs a two-layer hierarchy which consists of two cascaded Gaussian processes that are capable of modeling more complex input-output relations than standard single-layer Gaussian processes. Since deriving the exact model likelihood is challenging, a lower bound is optimized instead so that gradient descent-based methods can be performed to find out the optimal model parameters. Typically, inducing points are used in GPs to facilitate low-rank approximation of covariance (kernel) matrices for computation speedup. However, the inaccuracy induced by inducing points can accumulate when stacking multiple layers of GP which may hinder the performance of deep GP. Moreover, since inducing points need to be learned, having them at all layers of deep GP also incurs computational burden. To overcome the above challenges, in contrast to the canonical deep GP model, we use a modified architecture where a full standard GP resides in the first layer and inducing points are only introduced for the second layer. This modified architecture strikes a balance between model accuracy and training complexity. In the proposed model, the noise parameter of the first GP layer is also eliminated to improve the training efficiency as the noise parameter at the output of the second layer suffices to model the uncertainty in the output. The proposed approach is evaluated on real-world datasets, in the form of location-Received Signal Strength (RSS) pairs, collected from the Platform for Open Wireless Data-driven Experimental Research (POWDER) located at the campus of the University of Utah. Experiment results show that the proposed approach can achieve smaller prediction errors on various training and testing data configurations than DNN-based and GP-based methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

A Tutorial Set to Prepare for Science with the Vera C. Rubin Observatory

In this poster the Rubin Observatory's Community Science team (CST) presents its current suite of tutorials, which are designed to help people make use of simulated data sets in preparation for the upcoming Legacy Survey of Space and Time (LSST). We will show examples of the tutorial contents, provide custom learning modules for different astronomical fields, and describe the online environment for data analysis (the Rubin Science Platform; RSP). We will also supply a checklist for how to obtain an RSP account and access the tutorials. All are welcome to drop by the poster or the Rubin booth in the exhibit hall with questions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Bridging Cloud and Edge Computing at NREL Using CONNECT: Cloud Optimized Networking for Next-Gen Edge Computing Technologies [Slides]

CONNECT is an innovative on-premise hardware and software solution that integrates edge and cloud computing infrastructure at NREL. Built on the AWS Greengrass middleware and leveraging the MQTT protocol, CONNECT enables real-time data streaming from IoT devices and gateways to both cloud and local services, empowering researchers to rapidly capture, analyze, and act upon edge-generated data while leveraging cloud capabilities. The platform addresses research infrastructure challenges by providing a pre-approved platform which is already configured with the correct networking and cybersecurity baselines thus eliminating procurement delays and enabling on-demand availability. CONNECT's hybrid architecture efficiently manages burstable workloads, allowing research teams to dynamically scale computational capacity, handle peak data loads, and reduce operational bottlenecks. Advanced capabilities include built-in GPU support for executing machine learning models which enables low-latency inference at the edge from models trained in the cloud. This architecture supports real-time analytics and filtering, providing a mechanism to allow only transmitting and processing high-value data. Cloud-based configuration management permits engineers to manage on-premise systems remotely, optimizing operational efficiency. By bridging edge and cloud computing, CONNECT provides NREL researchers with a flexible, scalable platform that accelerates scientific discovery while maintaining robust security and performance standards.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Open Reproducible Electron Microscopy Data Analysis

Electron microscopy (EM) is a cornerstone technique in the materials and biological sciences capable of imaging structures at nano- to atomic-scale resolution. Advances in technologies mean that one acquires datasets at increasing data rates and sizes. These advancements present enormous opportunities for researchers to understand complex systems. However, processing the resulting large-scale, complex data in a reproducible and shareable way is a real challenge for researchers. The building, managing, and maintaining complex workflows in a reproducible manner requires extensive knowledge in several areas outside the researchers’ core skill sets, such as software engineering, data science, and high-performance computing (HPC). Our work demonstrates an innovative approach to solving these problems, enabling reproducible EM data analysis through container encapsulated pipelines. Using modern container technologies, we encapsulate processing elements and connect them using shared memory. We expose user-friendly, advanced algorithms and tools to allow end users to utilize without expert programming skills. The platform enables reproducible, scalable, shareable pipelines for the analysis and visualization of EM data. Focusing on interoperability, we leverage the DOE and other agencies’ existing investments to provide a powerful software platform for EM data analysis.

Harris, Christopher↗
DOE OSTI2019

Mechanistic and Data-Driven Agent-Based Models to Explain Human Behavior in Online Networked Group Anagram Games

In anagram games, players are provided with letters for forming as many words as possible over a specified time duration. Anagram games have been used in controlled experiments to study problems such as collective identity, effects of goalsetting, internal-external attributions, test anxiety, and others. The majority of work on anagram games involves individual players. Recently, work has expanded to group anagram games where players cooperate by sharing letters. In this work, we analyze experimental data from online social networked experiments of group anagram games. We develop mechanistic and data-driven models of human reasoning to predict detailed game player actions (e.g., what word to form next). With these results, we develop a composite agent-based modeling and simulation platform that incorporates the models from data analysis. We compare model predictions against experimental data, which enables us to provide explanations of human reasoning and behavior. Finally, we provide illustrative case studies using agent-based simulations to demonstrate the efficacy of models to provide insights that are beyond those from experiments alone.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗
DOE OSTI2024

UAS remote sensing (Osprey platform): Red-green-blue (RGB) imagery, thermal infrared (TIR) imagery, and canopy reflectance, Seward Peninsula, Alaska, 2017

Airborne remote sensing data collected using the Brookhaven National Laboratory's (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform - the Osprey - operated by the Terrestrial Ecosystem Science and Technology (TEST) group (https://www.bnl.gov/testgroup). This package includes raw (L0) data from 14 flights flown over the NGEE-Arctic Council Mile Marker 71 (MM71), Kougarok MM64, and Teller MM 27 sites in August, 2017. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface "skin" temperature imagery, as well as near-surface surface reflectance at 1 nm intervals in the visible to near-infrared spectral range from ~350-1000 nm measured at regular intervals along each flight path. This package provides the Level 0 (raw, unprocessed) data collected by the Osprey platform. Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Data and metadata are provided as text (*.txt, *.json), tabular (*.dat, *.csv, *.waypoint), and image (*.jpg) formats. This metadata documents contain flight campaign, platform, sensor, flight and file metadata, along with a description of the L0 data and the file naming scheme. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Tunable White Light for Elders (TWLITE): A Protocol Demonstrating Feasibility and Acceptability for Deployment, Remote Data Collection, and Analysis of a Home-Based Lighting Intervention in Older Adults

Sleep disturbances are common in older adults and may contribute to disease progression in certain populations (e.g., Alzheimer’s disease). Light therapy is a simple and cost-effective intervention to improve sleep. Primary barriers to light therapy are: (1) poor acceptability of the use of devices, and (2) inflexibility of current devices to deliver beyond a fixed light spectrum and throughout the entirety of the day. However, dynamic, tunable lighting integrated into the native home lighting system can potentially overcome these limitations. Herein, we describe our protocol to implement a whole-home tunable lighting system installed throughout the homes of healthy older adults already enrolled in an existing study with embedded home assessment platforms (Oregon Center for Aging & Technology—ORCATECH). Within ORCATECH, continuous data on room location, activity, sleep, and general health parameters are collected at a minute-to-minute resolution over years of participation. This single-arm longitudinal protocol collected participants’ light usage in addition to ORCATECH outcome measures over a several month period before and after light installation. The protocol was implemented with four subjects living in three ORCATECH homes. Technical/usability challenges and feasibility/acceptability outcomes were explored. The successful implementation of our protocol supports the feasibility of implementing and integrating tunable whole-home lighting systems into an automated home-based assessment platform for continuous data collection of outcome variables, including long-term sleep measures. Challenges and iterative approaches are discussed. This protocol will inform the implementation of future clinical intervention trials using light therapy in patients at risk for developing Alzheimer’s disease and related conditions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-186: Cloud-Based Quality Assurance Application for Linear Accelerator Commissioning

Purpose: To identify anomalies and safety issues during data collection and modeling for treatment planning systems Methods: A cloud-based quality assurance system (AQUIRE - Automated QUalIty REassurance) has been developed to allow the uploading and analysis of beam data aquired during the treatment planning system commissioning process. In addition to comparing and aggregating measured data, tools have also been developed to extract dose from the treatment planning system for end-to-end testing. A gamma index is perfomed on the data to give a dose difference and distance-to-agreement for validation that a beam model is generating plans consistent with the beam data collection. Results: Over 20 linear accelerators have been commissioning using this platform, and a variety of errors and potential saftey issues have been caught through the validation process. For example, the gamma index of 2% dose, 2mm DTA is quite sufficient to see curves not corrected for effective point of measurement. Also, data imported into the database is analyzed against an aggregate of similar linear accelerators to show data points that are outliers. The resulting curves in the database exhibit a very small standard deviation and imply that a preconfigured beam model based on aggregated linear accelerators will be sufficient in most cases. Conclusion: With the use of this new platform for beam data commissioning, errors in beam data collection and treatment planning system modeling are greatly reduced. With the reduction in errors during acquisition, the resulting beam models are quite similar, suggesting that a common beam model may be possible in the future. Development is ongoing to create routine quality assurance tools to compare back to the beam data acquired during commissioning. I am a medical physicist for Alzyen Medical Physics, and perform commissioning services.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications

The Modular Autonomous Research System (MARS) was created to address a key challenge in scientific discovery: experiments are often slow, require significant manual labor, and generate data that is not easily integrated across different tools. This limits how quickly scientists can explore new materials, processes, and chemical reactions. Our motivation was to design a system that makes research faster, more reliable, and adaptable by combining automation with artificial intelligence. By doing so, we aimed to reduce human error, accelerate discovery, and allow researchers to quickly test many possibilities that would otherwise take months or years. Our approach was to build a flexible platform that connects laboratory robots, measurement instruments, and a central data system, all guided by artificial intelligence. MARS integrates liquid handling robots, robotic arms, and plate readers with an intelligent decision-making system that chooses the most informative experiments to run next. This creates a closed loop where experiments are performed automatically, the data is analyzed in real time, and new conditions are immediately tested. Through this work, we demonstrated that MARS can carry out multiple experiments with little or no human intervention, adapt to different scientific problems, and handle uncertain or noisy measurements in a robust way. The results show that modular and intelligent automation can significantly accelerate the pace of discovery, providing a model for future self-driving laboratories. This approach addresses the growing scientific need for adaptable, data-driven research platforms that can keep up with the complexity and scale of modern science.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

National Geothermal Data System: Transforming the Discovery, Access, and Analytics of Data for Geothermal Exploration

Compendium of Papers from the 38th Workshop on Geothermal Reservoir Engineering Stanford University, Stanford, California February 11-13, 2013 The National Geothermal Data System (NGDS) is a distributed, interoperable network of data collected from state geological surveys across all fifty states and the nation’s leading academic geothermal centers. The system serves as a platform for sharing consistent, reliable, geothermal-relevant technical data with users of all types, while supplying tools relevant for their work. As aggregated data supports new scientific findings, this content-rich linked data ultimately broadens the pool of knowledge available to promote discovery and development of commercial-scale geothermal energy production. Most of the up-front risks associated with geothermal development stem from exploration and characterization of subsurface resources. Wider access to distributed data will, therefore, result in lower costs for geothermal development. NGDS is on track to become fully operational by 2014 and will provide a platform for custom applications for accessing geothermal relevant data in the U.S. and abroad. It is being built on the U.S. Geoscience Information Network (USGIN) data integration framework to promote interoperability across the Earth sciences community. The basic structure of the NGDS employs state-of-the art informatics to advance geothermal knowledge. The following four papers comprising this Open-File Report are a compendium of presentations, from the 38th Annual Workshop on Geothermal Reservoir Engineering, taking place February 11-13, 2013 at Stanford University, Stanford, California. “NGDS Geothermal Data Domain: Assessment of Geothermal Community Data Needs,” outlines the efforts of a set of nationwide data providers to supply data for the NGDS. In particular, data acquisition, delivery, and methodology are discussed. The paper addresses the various types of data and metadata required and why simple links to existing data are insufficient for promoting geothermal exploration. Authors of this paper are Arlene Anderson, US DOE Geothermal Technologies Office, David Blackwell, Southern Methodist University (SMU), Cathy Chickering (SMU), Toni Boyd, Oregon Institute of Technology’s GeoHeat Center, Roland Horne, Stanford University, Matthew MacKenzie, Uberity, Joe Moore, University of Utah, Duane Nickull, Uberity, Stephen Richard, Arizona Geological Survey, and Lisa Shevenell, University of Nevada, Reno. “NGDS User Centered Design: Meeting the Needs of the Geothermal Community,” discusses the user- centered design approach taken in the development of a user interface solution for the NGDS. The development process is research based, highly collaborative, and incorporates state-of-the-art practices to ensure a quality user interface for the widest and greatest utility. Authors of this paper are Harold Blackman, Boise State University, Suzanne Boyd, Anthro-Tech, Kim Patten, Arizona Geological Survey, and Sam Zheng, Siemens Corporate Research. “Fueling Innovation and Adoption by Sharing Data on the DOE Geothermal Data Repository Node on the National Geothermal Data System,” describes the motivation behind the development of the Geothermal Data Repository (GDR) and its role in the NGDS. This includes the benefits of using the GDR to share geothermal data of all types and DOE’s data submission process. Authors of this paper are Jon Weers, National Renewable Energy Laboratory and Arlene Anderson, US DOE Geothermal Technologies Office. Finally, “Developing the NGDS Adoption of CKAN for Domestic & International Data Deployment,” provides an overview of the “Node-In-A-Box” software package designed to provide data consumers with a highly functional interface to access the system, and to ease the burden on data providers who wish to publish data in the system. It is important to note that this software package constitutes a reference implementation and that the NGDS architecture is based on open standards, which means other server software can make resources available, and other client applications can utilize NGDS data. Authors of this paper are Ryan Clark, Arizona Geological Survey (AZGS), Christoph Kuhmuench, Siemens Corporate Research, and Stephen Richard, AZGS.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Pilot Study for Determining HVAC Fault Prevalence from Fault Monitoring Data

HVAC faults in U.S. commercial buildings are estimated to waste 0.7 quads of energy annually and many companies have deployed software at scale for fault detection and diagnosis (FDD). Yet the lack of comprehensive published data on fault prevalence makes it difficult for researchers, software developers, and building operators to target the most important types of faults and accelerate the widespread deployment of this technology. A multi-lab effort is underway to quantify the prevalence of commercial building HVAC faults. This paper describes initial analysis conducted on over 2-years of FDD data from 12 campus buildings to inform the methodology and data collection strategy of the full study. The analysis found a large variability in fault prevalence across different buildings, pieces of equipment, and fault types, many duplicate fault alarms, and challenges in validating faults with secondary data sources such as manually entered work order data. This paper also discusses the requirements for a successful full study, which necessitates the acquisition of a large, diverse dataset, from buildings that vary in end-use, climate region, and installed FDD software platform, as well as robust validation data easily linked to the FDD data. It is especially important to perform this analysis on data from multiple FDD providers and building owners to ensure that the results are agnostic to the software methodology used and building-specific settings. Future work will present the results of the full study, which will contain data from several different partner organizations, spanning a large proportion of U.S. climate zones.

Newman, Sarah F.↗
DOE OSTI2024

UAS remote sensing (Osprey platform): Red-green-blue (RGB) imagery, thermal infrared (TIR) imagery, and canopy reflectance, Seward Peninsula, Alaska, 2018

Airborne remote sensing data collected using the Brookhaven National Laboratory's (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform - the Osprey - operated by the Terrestrial Ecosystem Science and Technology (TEST) group. This package includes data from 34 flights flown over the NGEE-Arctic Council Mile Maker 72 (MM72), Kougarok MM64, Kougarok MM80, and Teller MM27 sites in July, 2018. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface "skin" temperature imagery, as well as surface reflectance at 1 nm intervals in the visible to near-infrared spectral range from ~350-1000 nm measured at regular intervals along each flight path. This package provide the Level 0 (raw, unprocessed) data collected by the Osprey platform. Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Data and metadata are provided as text (*.txt, *.json), tabular (*.dat, *.csv, *.waypoint), and image (*.jpg) formats. This metadata document contains flight campaign, instrument and file metadata, along with a description of the L0 data, and file naming scheme. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

UAS remote sensing (Osprey platform): Red-green-blue (RGB) imagery, thermal infrared (TIR) imagery, and canopy reflectance, Seward Peninsula, Alaska, 2019

Airborne remote sensing data collected using the Brookhaven National Laboratory's (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform - the Osprey - operated by the Terrestrial Ecosystem Science and Technology (TEST) group (https://www.bnl.gov/testgroup). This package includes data from 17 flights flown over the NGEE-Arctic Council Mile Maker 64 (MM64) and Teller MM27 sites in July, 2019. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface "skin" temperature imagery, as well as surface reflectance at 1 nm intervals in the visible to near-infrared spectral range from ~350-1000 nm measured at regular intervals along each flight path. This package provide the Level 0 (raw, unprocessed) data collected by the Osprey platform. Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Data and metadata are provided as text (*.txt, *.json), tabular (*.dat, *.csv, *.waypoint), and image (*.jpg) formats. This metadata document contains flight campaign, instrument and file metadata, along with a description of the L0 data, and file naming scheme. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Design and operational experience of a microwave cavity axion detector for the 20 – 100 μ eV range

We describe a dark matter axion detector designed, constructed, and operated both as an innovation platform for new cavity and amplifier technologies and as a data pathfinder in the 5-25 GHz range (~20-100 eV). The platform is small but flexible to facilitate the development of new microwave cavity and amplifier concepts in an operational environment. The experiment has recently completed its first data production; it is the first microwave cavity axion search to deploy a Josephson parametric amplifier and a dilution refrigerator to achieve near-quantum limited performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Determination of a most representative cycle from cylinder pressure ensembles via statistical method using distribution skewness

In internal combustion engine research, cylinder pressure measurements provide valuable information about the underlying thermodynamic and combustion processes, and are typically collected in ensembles of several 100 traces. Although in some particular fields of combustion research all traces are analyzed, in most cases only one trace is studied because analyzing all the traces is impractical due to the large number of collected samples. Instead, an ensemble-averaged pressure trace is commonly calculated and used for analysis. However, this pressure trace is highly smoothed and dynamic information is lost during the averaging process. With the average trace, pressure rise rates are lower and pressure oscillations such as the ones resulting from combustion knock are lost. In this work, a statistical method was developed to determine the “most representative cycle,” which is the cycle from the ensemble that has the pressure trace most representative of the engine operating condition. Eleven characteristic parameters are computed from each pressure trace and probabilistic distributions are obtained for each of the parameters using all the traces in the ensemble. Finally, the most representative cycle is selected by means of a cost function minimization. The benefits of this method are illustrated using experimental data from four very different engine platforms, under four different combustion modes and over a range of operating conditions.

42 ENGINEERING↗
DOE OSTI2020

Platform for efficient large-scale storage and analysis of multi-omics data in plant and microbial systems (Final Technical Report)

Genomic variation at the sequence level fundamentally affects the phenotypic state of all organisms at all stages of development, while dynamic processes such as changes in the epigenome (e.g. DNA methylation state) and transcriptome regulate the specific phenotype expressed at any given state of development based upon that genomic variation. In plants, DNA methylation is a particularly important mechanism for both regulating transcriptomic expression and for management of genomic variations that could be deleterious to the organism due to the presence of active retrotransposons in plant genomes. While DNA methylation is heritable, it is also dynamic through a given plant’s development and life cycle, particularly during the development from seed to mature specimen suggesting variations in DNA methylation could be critical regulators of biologically and commercially important phenotypes such as time to flowering; in addition, plant DNA methylation is more complex than that of animals, with methylation of CHG and CHH trinucleotides evident in addition to the better-known CG methylation. The complexity of plant DNA methylation and its interplay with genomic sequence variation, transcriptomics and other epigenomic factors demand a storage and analysis framework that can cope with the complexity both within a single specimen and with analyses that span many individuals and even many species, such as attempts to extend models from model organisms to commercially relevant species. In addition to complexity, the rapid development and proliferation of sequencing technology has led to an explosion of data volume that conventional storage and analysis solutions will likely be unable to cope with in the long run. We proposed to study these with suitable distributed storage and computation and therefore for the application of cloud computing to biological analyses; integrate with existing data sources and compatible with virtually any interface use case, from fully automated shell scripts to notebooks and do all these at scale in this STTR grant.

60 APPLIED LIFE SCIENCES↗