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DOE OSTI2025

Design-to-Deployment Continuum Platform for Microscopes and Computing Ecosystems

Science ecosystems with networked computing systems and physical instruments are increasingly being deployed with a goal to achieve the productivity promised by AI-supported remote automation. In support of these efforts, the virtual infrastructure twins (VITs) have been successfully utilized to develop the orchestration codes for these ecosystems without requiring physical access to expensive instruments, such as electron microscopes. Currently, the utility of such a VIT is severely limited by the computing capacity and capability of the computing system used as its host. Furthermore, codes developed on the VIT typically need to be transferred and refactored for production use, particularly, on high-performance systems with accelerators. In response, we develop a design-to-deployment continuum platform wherein a VIT runs natively on the ecosystem's own computing system, and thereby facilitates the continual in-situ testing and transition of codes for production use. Here, we describe the development and testing of software for remote microscope steering and GPU-based image reconstruction using this platform on a multi-GPU computing system networked to Nion microscopes. We demonstrate a continual transition of steering and reconstruction codes developed under VIT platform to production ecosystem deployment.

Al-Najjar, Anees [Oak Ridge National Laboratory (O↗

DOE OSTI2021

On Secured Spinning Reserve Deployment of Energy-Limited Resources Against Contingencies

Featured with fast response abilities and high ramp rates, energy storage systems (ESS), such as pumped-storage hydropower (PSH) plants and battery storage systems (BSS), are considered as key first-responders to provide spinning reserve in response to system contingencies. However, ESSs are energy-limited resources, and their sustained spinning reserve deployment is restricted by the stored energy and available capacity. Indeed, the spinning reserve deployment against contingencies may deviate actual state-of-charge (SOC) from the scheduled value, inducing potential SOC boundary violations and non-dispatchability in later hours. This paper proposes the post-contingency operation model and the spinning reserve secure constraints to address these issues. The post-contingency operation model describes that spinning reserve from ESSs is promptly deployed when contingency occurs, and then gradually substituted by quick-start units when they are switched online. By leveraging this operation strategy, spinning reserve secure constraints in terms of SOC headrooms are adopted to guarantee its deplorability. Furthermore, a modified IEEE 118-bus system with multiple PSHs and BSSs is used to verify the proposed approach.

13 HYDRO ENERGY↗

DOE OSTI2024

From Sim to Real: A Pipeline for Training and Deploying Traffic Smoothing Cruise Controllers

Designing and validating controllers for connected and automated vehicles to enhance traffic flow presents significant challenges, from the complexity of replicating real-world stop-and-go traffic dynamics in simulation, to the intricacies involved in transitioning from simulation to actual deployment. In this work, we present a full pipeline from data collection to controller deployment. Specifically, we collect 772 km of driving data from the I-24 in Tennessee, and use it to build a one-lane simulator, placing simulated vehicles behind real-world trajectories. Using policy-gradient methods with an asymmetric critic, we improve fuel efficiency by over 10% when simulating congested scenarios. Our comprehensive approach includes reinforcement learning for controller training, software verification, hardware validation and setup, and navigating various sim-to-real challenges. Furthermore, we analyze the controller's behavior and wave-smoothing properties, and deploy it on four Toyota Rav4’s in a real-world validation experiment on the I-24. Lastly, we release the driving dataset, the simulator and the trained controller, to enable future benchmarking and controller design.

42 ENGINEERING↗

DOE OSTI2023

A Stochastic Charging Station Deployment Model for Electrified Taxi Fleets in Coupled Urban Transportation and Power Distribution Networks

Metropolitansworldwide are increasingly adopting electric taxis (ET) to address concerns about transportation-related emissions. However, the widespread deployment of electric taxis presents challenges in terms of increased electricity demand and changing demand profiles. This transition impacts both the urban transportation network (TN) and the electricity power distribution network (PDN), highlighting the interdependence between these two systems. Here, in this paper, we propose a two-stage stochastic programming planning model that aims to optimize both the TN and PDN, enabling efficient deployment of charging stations and grid upgrades. Our model seeks to strike a balance between meeting ET drivers' charging preferences, minimizing the costs associated with infrastructure deployment and grid expansion, and harmonizing the coordination between the TN and PDN. Additionally, we explore the potential benefits of utilizing an autonomous ET fleet to enhance overall system performance.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2018

Considerations of Alloy N Code Extension for Commercial Molten Salt Reactor Development and Deployment

For commercial development and deployment of the molten salt reactor, a structural alloy that provides both strength at high temperature and resistance to very corrosive molten salt environment is required. To meet this requirement, a survey is conducted on domestic and international candidate alloys. Alloy N turns out to be the sole frontrunner in readiness for qualification to enable the desired deployment within an estimated 10 years. A review of the qualification for commercial nuclear applications indicates that Alloy N has met a large portion of the requirements. Gaps in the qualification are also identified. A search for historical data is underway to retrieve information needed for filling the gaps and upgrading the qualification. Scope of the discovered historical data is briefly discussed and strategic planning for research and development pathway is suggested to ensure successful evolution in commercial deployment of the molten salt reactor system.

Ren, Weiju↗

DOE OSTI2026

Resilience Measurement Framework For Post-deployment Artificial Intelligence (ai) Integrated Systems

Resilience is largely defined as the ability to adapt or recover from adverse conditions, stresses, attacks, or compromises on systems that use or are enabled by digital resources. In Artificial Intelligence Management and Research for Advanced Networked Testbed Hub (AMARANTH), resilience is measured in the amount of time it took from the beginning of a testing period for the model to reach predictions outside of the original 95% confidence interval or using the Kullback-Leibler (KL) divergence theorem, the Population Stability Index (PSI), and traditional methods such as root mean squared error (RMSE) threshold. Artificial Intelligence (AI) model drift is of significant concern when deploying AI-integrated systems into critical and/or secure environments. Drift can impact resilience of the AI-integrated system post-deployment and requires consistent maintenance and upkeep to ensure the model is accurate and precise. To quantify model drift and predict the point when a model's drift becomes unacceptable, we describe using Kullback-Leibler (KL) divergence, Population Stability Index (PSI) and/or confidence interval width estimations to determine the point of failure and time to failure of a model post-deployment. Through simple code functions, the KL-divergence, PSI, confidence interval, and root mean squared (RMSE) point of failures can be used to derive when a model needs to be maintained as well as the impact of adversarial action through statistical means.

Yockey, Patience [Idaho National Laboratory (INL),↗

DOE OSTI2025

If We Build Them, They Will Run: Automated HPC Apps Deployment and Profiling with eBPF in Cloud

The high performance computing (HPC) community is in a period of transition. The rise of AI/ML coupled with a changing landscape of resources deems portability a new metric of performance, and methods to move between on-premises and cloud environments and assess compatibility are paramount. Here we design and test a strategy for bridging the gap between traditional HPC and Kubernetes environments – first containerizing applications, providing automated orchestration to run studies, and packaging the setup with automated means to assess performance using low overhead eXtended Berkeley Packet Filter (eBPF) programs. We first assess different designs for eBPF collection, demonstrating a tradeoff between number of programs deployed on a node and overhead added. We develop 5 low overhead eBPF programs that combine with streaming ML models to assess CPU, futex, TCP, shared memory, and file access across four different builds of an HPC application for CPU and GPU. We use eBPF data to generate insights into the possible underlying etiology of scaling issues. We then assess compatibility of a well-known benchmark, HPCG, across matrices of micro-architectures and optimization levels (217 containers across 24 instance types and over 7500 runs). We provide to the community 30 applications to deploy in our automated setup and perform a scaling study from 4 to a maximum of 256 nodes for both CPU and GPU applications. Finally, we use our gained knowledge about performance to generate compatibility artifacts that are used by a newly developed Kubernetes controller to intelligently select instance type based on optimizing a figure of merit. Along with insights to scaling in this environment with a collection of applications and templates to work from, we provide an overall strategy for approaching HPC application deployment and image selection based on compatibility in cloud.

Computer science↗

DOE OSTI2023

Seismoacoustic Signatures Observed During a Long-Term Deployment of Infrasound Sensors at the Nevada National Security Site

Earthquakes have repeatedly been shown to produce inaudible acoustic signals (<20 Hz), otherwise known as infrasound. These signals can propagate hundreds to thousands of kilometers and still be detected by ground-based infrasound arrays depending on the source strength, distance between source and receiver, and atmospheric conditions. Another type of signal arrival at infrasound arrays is the seismic induced motion of the sensor itself, or ground-motion-induced sensor noise. Measured acoustic and seismic waves produced by earthquakes can provide insight into properties of the earthquake such as magnitude, depth, and focal mechanism, as well as information about the local lithology and atmospheric conditions. Large earthquakes that produce strong acoustic signals detected at distances greater than 100 km are the most commonly studied; however, more recent studies have found that smaller magnitude earthquakes ($M$ w <2.0) can be detected at short ranges. In that vein, this study will investigate the ability for a long-term deployment of infrasound sensors (deployed as part of the Source Physics Experiments [SPE] from 2014 to 2020) to detect both seismic and infrasonic signals from earthquakes at local ranges (<50 km). Methods used include a combination of spectral analysis and automated array processing, supported by U.S. Geological Survey earthquake bulletins. Further, this investigation revealed no clear acoustic detections for short range earthquakes. However, secondary infrasound from an $M$ w 7.1 earthquake over 200 km away was detected. Important insights were also made regarding the performance of the SPE networks including detections of other acoustic sources such as bolides and rocket launches. Finally, evaluation of the infrasound arrays is performed to provide insight into optimal deployments for targeting earthquake infrasound.

58 GEOSCIENCES↗

DOE OSTI2025

Rapid and Temporary Deployment of Fiber-Optic Arrays to Measure Surface Chemical Explosion Series with Distributed Acoustic Sensing

Rapid and temporary distributed acoustic sensing (DAS) deployments are crucial for accurately capturing the seismic wavefield following major events, such as earthquakes, or in anticipation of known events of interest, such as chemical explosions. We provide an overview of two DAS campaigns conducted in May and October 2024 to record the seismoacoustic waves generated by two series of surface chemical explosions in New Mexico, United States, involving 1- and 10-ton trinitrotoluene-equivalent charges. In both campaigns, we deployed approximately 2 km of fiber-optic cables in a dry riverbed, about 12 km east of the explosion sites. For the October campaign, seven geophones and two anemometers were collocated with the fiber. We describe the field deployments and present preliminary results from the recorded signals. Specifically, we cross analyze the data recorded in May and October, validate the DAS data with geophone measurements, and highlight the benefits of burying fibers to reduce wind noise and improve signal-to-noise ratios. This study demonstrates the potential of DAS to record the seismoacoustic waves generated by surface chemical explosions of varying sizes at distances of approximately 10 km from the source.

58 GEOSCIENCES↗

DOE OSTI2023

EPICS Deployment at Fermilab

Fermilab has traditionally not been an EPICS house, as such expertise in EPICS is limited and scattered. However, PIP-II will be using EPICS for its control system. Furthermore, when PIP-II is operating, it must to interface with the existing, though modernized (see ACORN) legacy control system. We have developed and deployed a software pipeline that addresses these needs and presents to developers a tested and robust software framework, including template IOCs from which new developers can quickly gain experience. In this presentation, we will discuss the motivation for this work, the implementation of a continuous integration/continuous deployment pipeline, testing, template IOCs, and the deployment of user applications. We will also discuss how this is used with the current PIP-II teststand and lessons learned.

43 PARTICLE ACCELERATORS↗

DOE OSTI2013

Sensitivity of Utility-Scale Solar Deployment Projections in the SunShot Vision Study to Market and Performance Assumptions

The SunShot Vision Study explored the potential growth of solar markets if solar prices decreased by about 75% from 2010 to 2020. The ReEDS model was used to simulate utility PV and CSP deployment for this present study, based on several market and performance assumptions - electricity demand, natural gas prices, coal retirements, cost and performance of non-solar renewable technologies, PVresource variability, distributed PV deployment, and solar market supply growth - in addition to the SunShot solar price projections. This study finds that utility-scale solar deployment is highly sensitive to solar prices. Other factors can have significant impacts, particularly electricity demand and natural gas prices.

14 SOLAR ENERGY↗

DOE OSTI2013

Economic Impact of Fuel Cell Deployment in Forklifts and for Backup Power under the American Recovery and Reinvestment Act

Through the American Recovery and Reinvestment Act (the Recovery Act or ARRA), the United States Department of Energy (DOE) funded several projects to deploy polymer electrolyte membrane (PEM) fuel cells in forklift and backup power applications. DOE invested $\$$9.7 million to deploy fuel cells in forklifts and an additional $\$$11.8 million was contributed through industry cost shares, for a total investment of $\$$21.5 million in forklift projects. DOE invested $\$$18.5 million to deploy fuel cells in backup power projects and an additional $\$$30.8 million was contributed through industry cost shares, for a total investment of $\$$49.3 million in backup power projects.

Mintz, M.↗

DOE OSTI2014

Hydraulic Hybrid Parcel Delivery Truck Deployment, Testing & Demonstration

Although hydraulic hybrid systems have shown promise over the last few years, commercial deployment of these systems has primarily been limited to Class 8 refuse trucks. In 2005, the Hybrid Truck Users Forum initiated the Parcel Delivery Working Group including the largest parcel delivery fleets in North America. The goal of the working group was to evaluate and accelerate commercialization of hydraulic hybrid technology for parcel delivery vehicles. FedEx Ground, Purolator and United Parcel Service (UPS) took delivery of the world’s first commercially available hydraulic hybrid parcel delivery trucks in early 2012. The vehicle chassis includes a Parker Hannifin hydraulic hybrid drive system, integrated and assembled by Freightliner Custom Chassis Corp., with a body installed by Morgan Olson. With funding from the U.S. Department of Energy, CALSTART and its project partners assessed the performance, reliability, maintainability and fleet acceptance of three pre-production Class 6 hydraulic hybrid parcel delivery vehicles using information and data from in-use data collection and on-road testing. This document reports on the deployment of these vehicles operated by FedEx Ground, Purolator and UPS. The results presented provide a comprehensive overview of the performance of commercial hydraulic hybrid vehicles in parcel delivery applications. This project also informs fleets and manufacturers on the overall performance of hydraulic hybrid vehicles, provides insights on how the technology can be both improved and more effectively used. The key findings and recommendations of this project fall into four major categories: Performance, Fleet deployment, Maintenance, and Business case. Hydraulic hybrid technology is relatively new to the market, as commercial vehicles have been introduced only in the past few years in refuse and parcel delivery applications. Successful demonstration could pave the way for additional purchases of hydraulic hybrid vehicles throughout the trucking industry. By providing unbiased, third-party assessment of this “hybrid without batteries” technology, this report offers relevant, timely and valuable information to the industry.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2015

GrayQb TM Single-Faced Version 2 (SF2) Hanford Plutonium Reclamation Facility (PRF) deployment report

The Savannah River National Laboratory (SRNL) in partnership with CH2M Plateau Remediation Company (CHPRC) deployed the GrayQb TM SF2 radiation imaging device at the Hanford Plutonium Reclamation Facility (PRF) to assist in the radiological characterization of the canyon. The deployment goal was to locate radiological contamination hot spots in the PRF canyon, where pencil tanks were removed and decontamination/debris removal operations are on-going, to support the CHPRC facility decontamination and decommissioning (D&D) effort. The PRF canyon D&D effort supports completion of the CHPRC Plutonium Finishing Plant Decommissioning Project. The GrayQb TM SF2 (Single Faced Version 2) is a non-destructive examination device developed by SRNL to generate radiation contour maps showing source locations and relative radiological levels present in the area under examination. The Hanford PRF GrayQbTM Deployment was sponsored by CH2M Plateau Remediation Company (CHPRC) through the DOE Richland Operations Office, Inter-Entity Work Order (IEWO), DOE-RL IEWO- M0SR900210.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

Impacts of Federal Tax Credit Extensions on Renewable Deployment and Power Sector Emissions

Federal tax credits for renewable energy (RE) have served as one of the primary financial incentives for RE deployment over the last two decades in the United States. In December 2015, the wind power production tax credit and solar investment tax credits were extended for five years as part of the Consolidated Appropriations Act of 2016. This report explores the impact that these tax credit extensions might have on future RE capacity deployment and power sector carbon dioxide (CO2) emissions. The analysis examines the impacts of the tax credit extensions under two distinct natural gas price futures as natural gas prices have been key factors in influencing the economic competitiveness of new RE development. The analysis finds that, in both natural gas price futures, RE tax credit extensions can spur RE capacity investments at least through the early 2020s and can help lower emissions from the U.S. electricity system. More specifically, the RE tax credit extensions are estimated to drive a net peak increase of 48-53 GW in installed RE capacity in the early 2020s -- longer term impacts are less certain. In the longer term after the tax credits ramp down, greater RE capacity is driven by a combination of assumed RE cost declines, rising fossil fuel prices, and other clean energy policies such as the Clean Power Plan. The tax credit extension-driven acceleration in RE capacity development can reduce fossil fuel-based generation and lower electric sector CO2 emissions. Cumulative emissions reductions over a 15-year period (spanning 2016-2030) as a result of the tax credit extensions are estimated to range from 540 to 1420 million metric tonnes CO2. These findings suggest that tax credit extensions can have a measurable impact on future RE deployment and electric sector CO2 emissions under a range of natural gas price futures.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2016

How well could existing sensors detect the deployment of a solar radiation management (SRM) geoengineering effort?

While the stated reason for asking this question is “to understand better our ability to warn policy makers in the unlikely event of an unanticipated SRM geoengineering deployment or large-scale field experiment”, my colleagues and I felt that motives would be important context because the scale of any meaningful SRM deployment would be so large that covert deployment seems impossible. However, several motives emerged that suggest a less-than-global effort might be important.

47 OTHER INSTRUMENTATION↗

DOE OSTI2017

Installed Cost Benchmarks and Deployment Barriers for Residential Solar Photovoltaics with Energy Storage: Q1 2016

In this report, we fill a gap in the existing knowledge about PV-plus-storage system costs and value by providing detailed component- and system-level installed cost benchmarks for residential systems. We also examine other barriers to increased deployment of PV-plus-storage systems in the residential sector. The results are meant to help technology manufacturers, installers, and other stakeholders identify cost-reduction opportunities and inform decision makers about regulatory, policy, and market characteristics that impede solar plus storage deployment. In addition, our periodic cost benchmarks will document progress in cost reductions over time. To analyze costs for PV-plus-storage systems deployed in the first quarter of 2016, we adapt the National Renewable Energy Laboratory's component- and system-level cost-modeling methods for standalone PV. In general, we attempt to model best-in-class installation techniques and business operations from an installed-cost perspective. In addition to our original analysis, model development, and review of published literature, we derive inputs for our model and validate our draft results via interviews with industry and subject-matter experts. One challenge to analyzing the costs of PV-plus-storage systems is choosing an appropriate cost metric. Unlike standalone PV, energy storage lacks universally accepted cost metrics, such as dollars per watt of installed capacity and lifetime levelized cost of energy. We explain the difficulty of arriving at a standard approach for reporting storage costs and then provide the rationale for using the total installed costs of a standard PV-plus-storage system as our primary metric, rather than using a system-size-normalized metric.

14 SOLAR ENERGY↗

DOE OSTI2017

Level-2 Milestone 6007: Sierra Early Delivery System Deployed to Secret Restricted Network

This report documents the delivery and installation of Shark, a CORAL Sierra early delivery system deployed on the LLNL SRD network. Early ASC program users have run codes on the machine in support of application porting for the final Sierra system which will be deployed at LLNL in CY2018. In addition to the SRD resource, Shark, unclassified resources, Rzmanta and Ray, have been deployed on the LLNL Restricted Zone and Collaboration Zone networks in support of application readiness for the Sierra platform.

97 MATHEMATICS AND COMPUTING↗