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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Performance of Heterogeneous Algorithm Scheduling in CMSSW

The CMS experiment started to utilize Graphics Processing Units (GPU) to accelerate the online reconstruction and event selection running on its High Level Trigger (HLT) farm in the 2022 data taking period. The projections of the HLT farm to the High-Luminosity LHC foresee a significant use of compute accelerators in the LHC Run 4 and onwards in order to keep the cost, size, and power budget of the farm under control. This direction of leveraging compute accelerators has synergies with the increasing use of HPC resources in HEP computing, as HPC machines are employing more and more compute accelerators that are predominantly GPUs today. In this work we review the features developed for the CMS data processing framework, CMSSW, to support the effective utilization of both compute accelerators and many-core CPUs within a highly concurrent task-based framework. We measure the impact of various design choices for the scheduling of heterogeneous algorithms on the event processing throughput, using the Run-3 HLT application as a realistic use case.

Bocci, Andrea↗
DOE OSTI2022

In-Kind Contributions: The PIP-II Project at Fermilab

The Proton Improvement Plan II (PIP-II) Project is the first U.S. accelerator project that has significant contributions from international partners. A project management framework was created to fully integrate and make consistent across all partners the design, development, and delivery of In-Kind Contributions (IKC) into PIP-II. This framework consists of planning documentation, procedures, and communication and assessment processes to control schedule, risk, quality, and technical integration over the lifetime of the project. The purpose of this paper is to present the PIP-II IKC model put in place to properly integrate the IKC deliverables into the PIP-II Linac and share experience and lessons learned from its early implementation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

ART Advance Graphite Creep (AGC) Irradiation Experiment

ART Advance Graphite Creep (AGC) Irradiation Experiment topics of discussion include: Schedule, AGC Experiment Update, AGC-4 Status, Anticipated areas data will be used, and Vendor specific irradiation capsule. This includes NRC/Licensing questions on irradiation behavior, behavior model development, and other collaborations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

RL-HEMS: Reinforcement Learning Based Home Energy Management System for HVAC Energy Optimization

Heating, ventilation, and air conditioning (HVAC) is one of the major energy consumers in the residential sector. It is important to be able to monitor and control the energy consumed to provide utility services such as load shaping while satisfying the comfort and economic constraints of the homeowner. The objective of this work is to create the optimal schedule for HVAC operation to reduce the cost while satisfying the home-owner and equipment’s constraints using a model-free Reinforcement Learning (RL)-based optimization. The specific goal is to find the right balance between reducing energy cost, consumption, and customer comfort level. Our research effort addresses this optimization problem using multiple components: the development of initial learning test-bed and implementation of RL techniques on a real home. This will enable the rapid evaluation of the RL techniques and provide an early baseline to train before implementation on site. The RL algorithm is designed to learn the energy use patterns and generate the optimized schedule for HVAC within an acceptable time-interval to satisfy the homeowner’s comfort and minimize the energy usage. Our preliminary results are promising and we have observed a 17% reduction in the total cost and a 15% reduction in the power utilization using our RL-based HVAC model—RL-HEMS.

Kotevska, Olivera↗
DOE OSTI2020

SunDial – An Integrated SHINES System to Enable High-penetration Feeder-level PV

The Project Team of Fraunhofer USA, National Grid, and IPKeys developed and conducted a pilot deployment of the SunDial system, a virtual power plant platform that enables high-penetrations of solar PV to be integrated into the distribution grid. The pilot was conducted over a 15-month period from August 2018 through October 2019 on a National Grid distribution feeder in Shirley, MA. A vendor-agnostic control platform (the “Global Scheduler”) optimally shaped the net load for a virtual portfolio of non-co-located DERs based on user-defined policy objectives. The goal of the SunDial project was to simplify and reduce the risk associated with the deployment of solar in high-penetration environments by: (1) Developing an open-source, vendor-agnostic dispatch platform that can be readily adapted to optimize control of DERs over a variety of use cases; (2) Developing auto-calibrating load and solar prediction methodologies that can be readily implemented and scaled to new deployments; (3) Developing a methodology to use demand-side management with traditional electrochemical energy storage to provide “load shaping” services in high solar penetration environments; (4) Using grid-scale storage to minimize short-term intermittency association with PV production; and (5) Deploying on the National Grid distribution system to gain experience on the potential for (and limits of) integrated storage with demand-side management.

14 SOLAR ENERGY↗
DOE OSTI2020

Downwind coning concept rotor for a 25 MW offshore wind turbine

The size of offshore wind turbines over the next decade is expected to continually increase due to reduced balance of station costs per MW and also the higher wind energy at increased altitudes that can lead to higher capacity factors. However, there are challenges that may limit the degree of upscaling which is possible. In this paper, a two-bladed downwind turbine system is upscaled from 13.2 MW to 25 MW, by redesigning aerodynamics, structures, and controls. In particular, three 25 MW rotors have been developed: V1 is the upscaled model, V2 is a partial redesigned model, and V3 is a fully redesigned model. Despite their radically large sizes, it is found that these 25 MW turbine rotors satisfy this limited set of design drivers at the rated condition and that larger blade lengths are possible with cone-wise load-alignment. In addition, flapwise morphing (varying the cone angle with a wind-speed schedule) is investigated in terms of minimizing mean and fluctuating root bending loads using steady inflow proxies for the maximum and damage equivalent load moments. The resulting series of 25 MW rotors, which are the largest ever designed, can be a useful baseline for additional development and assessment.

17 WIND ENERGY↗
DOE OSTI2020

An Approximation Algorithm for a Task Allocation, Sequencing and Scheduling Problem Involving a Human-Robot Team

Here we present an approximation algorithm for a Task Allocation, Sequencing and Scheduling Problem (TASSP) involving a team of human operators and robots. The robots have to travel to a given set of targets and collaboratively work on the tasks at the targets with the human operators. The problem aims to find a sequence of targets for each robot to visit and schedule the tasks at the targets with the human operators such that each target is visited exactly once by some robot, the scheduling constraints are satisfied and the maximum mission time of any robot is minimum. This problem is a generalization of the single Traveling Salesman Problem and is NP-Hard. Given k robots and m human operators, an algorithm is developed for solving the TASSP with an approximation ratio equal to 5/2- 1/k when m ≥ k and equal to 7/2 -1/k otherwise. Computational results are also presented to corroborate the performance of the proposed algorithm.

42 ENGINEERING↗
DOE OSTI2024

Preventive Power Outage Estimation Based on a Novel Scenario Clustering Strategy

The increasing occurrence of extreme weather events is challenging power grid operation. For extreme weather events, the system operator is responsible for estimating the power outages and scheduling the restoration resources. This paper proposes an outage evaluation framework to identify the possible unserved load profiles, vulnerable areas, and mobile energy adequacy. The outputs of an outage prediction model tool are used to generate numerous faulted line scenarios. Next, each scenario's nodal unserved load profile is obtained by solving a three-phase restoration model that considers repair crews and mobile energy resources (MERs). Then, a novel scenario clustering strategy is developed to cluster the unserved load profiles into multiple representative profiles which the system operator can focus on. Finally, case studies on a distribution system evaluate the damage caused by an extreme weather event and verify the effectiveness of the proposed scenario clustering strategy.

MATHEMATICS AND COMPUTING,POWER TRANSMISSION AND D↗
DOE OSTI2024

Digital Twin Enabling Technologies for Online Condition Monitoring of Nuclear Power Plant Components

Online condition monitoring is an area of active research that may enable improved scheduling, maintenance, and safety of nuclear power plant components, reducing unnecessary derate while simultaneously improving operational capacity. Digital twins are one avenue to conduct online condition monitoring and are currently being explored by national laboratories and universities alike. Digital twins for online condition monitoring are, in essence, state concurrent models that emulate a physical process which predicts a parameter and compares it against a measured value. However, digital twins may also provide additional insights by combining and interpreting various sources of information. These insights may be used for preventative maintenance scheduling optimization or early fault detection and are projected to be valuable for meeting requirements under 10 CFR 50.55a. However, digital twin technologies are still under significant development; quantifying model uncertainties, improving unique fault identification, and multimodal sensor fusion are some areas under investigation. Therefore, in this work, we discuss and review the various enabling technologies, in the form of advanced sensors, instrumentation, and modelling methods, that may be used to implement and enhance digital twins for online condition monitoring. A potential use case for pump-motors is presented to demonstrate how these various pieces of enabling digital twin technologies may integrated together for online condition monitoring. Challenges and opportunities associated with the pump-motor digital twin enabling technologies are also identified and discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Digital-Twin-Enabling Technologies for Online Condition Monitoring of Nuclear Power Plant Components

Online condition monitoring is an area of active research that may enable improved scheduling, maintenance, and safety of nuclear power plant components, reducing unnecessary derates while simultaneously improving operational capacity. Digital twins are one avenue to conduct online condition monitoring and are currently being explored by national laboratories and universities alike. Digital twins for online condition monitoring are, in essence, state concurrent models that emulate a physical process which predicts a parameter and compares it against a measured value. However, digital twins may also provide additional insights by combining and interpreting various sources of information. These insights may be used for preventative maintenance scheduling optimization or early fault detection and are projected to be valuable for meeting requirements under 10 CFR 50.55a. However, digital twin technologies are still under significant development; quantifying model uncertainties, improving unique fault identification, and multimodal sensor fusion are some areas under investigation. Therefore, in this work, we discuss and review the various enabling technologies, in the form of advanced sensors, instrumentation, and modelling methods, that may be used to implement and enhance digital twins for online condition monitoring. A potential use case for pump-motors is presented to demonstrate how these various pieces of enabling digital twin technologies may integrated together for online condition monitoring. Challenges and opportunities associated with the pump-motor digital twin enabling technologies are also identified and discussed.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Optimal pumping schedule with high-viscosity gel for uniform distribution of proppant in unconventional reservoirs

Hydrocarbon recovery from a hydraulic-fractured unconventional reservoir can be enhanced by replacing slickwater with non-Newtonian high-viscosity gel as a fracturing fluid. However, in the literature, the role of gel rheology on hydrocarbon production from a fractured reservoir is not very well understood. This lack of understanding makes it difficult for the industry to choose appropriate viscosity parameters of fracturing gels. Motivated by these limitations, first, we have developed a high-fidelity model of non-Newtonian fluid flow to simulate fracture propagation, fluid leak-off, and gel flowback processes to predict the oil production from a hydraulic fractured reservoir. Simulation results of the high-fidelity model show how fracture geometry, formation damage, and gel cleanup process varies with the fracturing fluid rheology. Therefore, the developed model is used to understand the effect of gel rheology on hydrocarbon production via a sensitivity analysis, which provides a set of rheological parameters that maximize the cumulative amount of oil production. Finally, utilizing this optimal high-viscosity gel, a model predictive controller is designed to obtain an optimal pumping schedule necessary for producing a target fracture geometry and proppant concentration. The closed-loop simulation results demonstrate that the obtained pumping schedule successfully achieved the desired fracture geometry and proppant concentration at the end of pumping, leading to the maximum oil production from an unconventional reservoir.

13 HYDRO ENERGY↗
DOE OSTI2022

GradientGraph

Under this SBIR Phase II, Reservoir Labs has developed G2 Analytics, a new technology that allows network operators to analyze bottleneck and flow performance with high precision. G2 delivers a new analytical approach and framework to resolve a variety of key problems found in modern communication networks, including: traffic engineering, routing, flow scheduling, network design, capacity planning, resiliency analysis, network slicing, or service level agreement (SLA) management, among others. G2 leverages the bottleneck structure of congestion-controlled communication networks, a recent mathematical discovery by the Reservoir team [RL19b, RL20a, RL20b, RL21a]. Bottleneck structures reveal how perturbations on flows and links propagate through the network, providing an analytical framework to measure (qualitatively and quantitatively) the ripple effects induced as they traverse the network. Leveraging the mathematics of bottleneck structures, Reservoir Labs is developing the G2 technology to provide network operators with a framework to design, optimize and troubleshoot network performance. This delivery includes the G2 software stack.

Yellamraju, Sruthi↗
DOE OSTI2021

ADDS-EVS: An agent-based deployment decision-support system for electric vehicle services

Rapid and sustainable development of the electric vehicle (EV) industry places the requirement for the plan of EV deployment. For public EV, existing models mainly focus on the charging facility design and fail to capture the multi-modal scenarios. In this work, we develop an agent-based decision-support system for multi-modal electric transits to locate the optimal combinations of key parameters, including the fleet size, the transit schedule, the charging facility design, and the routing strategy. We demonstrate the utilities of our system by simulating public EV services deployed to serve travel needs related to a transportation hub in New York City. To support the decision of the fleet size, we summarize system-level performances including the total satisfied demand, passengers' waiting time, vehicle idling time. The spatial and temporal patterns are extracted to serve a deeper understanding of system dynamics and service quality. Finally, we investigate the interaction between the fleet size design and the routing strategy. The results suggest a necessity of integrating the operation strategies into the planning phase.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Fracture Network Prediction Using Physics-based Machine Learning Algorithms

In recent years, systematic CO2 injection into geological reservoirs across the U.S. has gained traction as a strategy to mitigate greenhouse gas emissions. This approach necessitates precise monitoring to ensure secure containment, minimize risks, and optimize storage management. Our study leverages machine learning (ML) techniques to advance the understanding of CO2 injection processes, focusing on the Illinois Basin. Over a three-year injection period, we analyzed microseismic data, identifying 19 temporal intervals with significant bottom-hole pressure changes. By partitioning microseismic events into these intervals and estimating b-values, we revealed over 100 clusters of events related to fracture initiation or reactivation. Advanced spatial analysis highlighted horizontally-oriented fractures along the NNW-SSE axis. This quantification of fracture networks informs dynamic injection scheduling, work-over strategies, and risk assessments, enhancing carbon capture, utilization, and storage (CCUS) operations. Additionally, our methodology offers valuable insights for oil and gas operations and geothermal development, supporting fracture-based monitoring and risk mitigation.

Kumar, Abhash↗
DOE OSTI2020

The Challenge of Achieving Site Closure of a Complex Groundwater Plume - 20531

Over the last three decades, DOE has made substantial progress in nearly every area of nuclear waste cleanup and has closed and transitioned 91 of the 107 sites in the EM complex. Several of the remaining groundwater plumes at Hanford, Los Alamos, Paducah, Oak Ridge, and Savannah River are complex and tend to have unique site or contaminant characteristics which provide an almost impossible challenge to remediate to regulatory levels. As a result, these complex sites will require a large investment to remediate. In order for DOE to achieve regulatory closure, a technically sound case must be developed and presented to regulators and stakeholders that clearly demonstrates that remedial goals have been or will be irreversibly achieved or that there is an alternate acceptable end-state. Five lines of evidence are required: - A conceptual site model that is as complete as possible, with the knowledge that it may continue to evolve with time; - Demonstration that trends in contaminant concentrations, or flux, versus time indicate that remedial goals will be achieved on schedule or within a reasonable time thereafter and that trends that deviate from this path are explainable by the conceptual model; - Demonstration that remobilization of attenuated contaminants is unlikely to occur in the future; - Explicit consideration of remedial contingencies, should deviations in the path toward remedial goals occur; - Development of an effective long-term monitoring plan to detect any conditions that might alter the path toward remedial goals. SRS has made significant progress in the remediation of contamination associated with the F-area and H-Area Seepage Basins, and it is currently developing a strategic closure path that addresses each of these lines of evidence and identifies both basic and applied scientific issues that will need to be addressed in order to obtain regulatory closure. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Using containers to speed up development, to run integration tests and to teach about distributed systems

GlideinWMS is a workload manager provisioning resources for many experiments including CMS and DUNE. The software is distributed both as native packages and specialized production containers. Following an approach used in other communities like web development we built our workspaces, system-like containers to ease development and testing. Developers can change the source tree or check out a different branch and quickly reconfigure the services to see the effect of their changes. In this paper, we’ll talk about what differentiates workspaces from other containers. We’ll describe our base system composed of three containers. A one-node cluster including a compute element and a batch system. A GlideinWMS Factory controlling pilot jobs. And a scheduler and Frontend, to submit jobs and provision resources. Additional containers can be used for optional components. This system can easily run on a laptop and we’ll share our evaluation of different container runtimes, with an eye for ease of use and performance. Finally, we’ll talk about our experience as developers and with students. The GlideinWMS workspaces are easily integrated with IDEs like VS Code, simplifying debugging and allowing development and testing of the system also when offline. They simplified the training and onboarding of new team members and Summer interns. And they were useful in workshops where students could have first-hand experience with the mechanisms and components that, in production, run millions of jobs.

Mambelli, Marco↗
DOE OSTI2025

Using Containers to Speed Up Development, to Run Integration Tests and to Teach About Distributed Systems

GlideinWMS is a workload manager provisioning resources for many experiments, including CMS and DUNE. The software is distributed both as native packages and specialized production containers. Following an approach used in other communities like web development, we built our workspaces, system-like containers to ease development and testing. Developers can change the source tree or check out a different branch and quickly reconfigure the services to see the effect of their changes. In this paper, we will talk about what differentiates workspaces from other containers. We will describe our base system, composed of three containers: a one-node cluster including a compute element and a batch system, a GlideinWMS Factory controlling pilot jobs, and a scheduler and Frontend to submit jobs and provision resources. Additional containers can be used for optional components. This system can easily run on a laptop, and we will share our evaluation of different container runtimes, with an eye for ease of use and performance. Finally, we will talk about our experience as developers and with students. The GlideinWMS workspaces are easily integrated with IDEs like VS Code, simplifying debugging and allowing development and testing of the system even when offline. They simplified the training and onboarding of new team members and summer interns. And they were useful in workshops where students could have first-hand experience with the mechanisms and components that, in production, run millions of jobs.

Mambelli, Marco [Fermilab] (ORCID:0000000294892681↗
DOE OSTI2021

Data-driven optimization of mixed-integer bi-level multi-follower integrated planning and scheduling problems under demand uncertainty

The coordination of interconnected elements across the different layers of the supply chain is essential for all industrial processes and the key to optimal decision-making. Yet, the modeling and optimization of such interdependent systems are still burdensome. Here we address the simultaneous modeling and optimization of medium-term planning and short-term scheduling problems under demand uncertainty using mixed-integer bi-level multi-follower programming and data-driven optimization. Bi-level multi-follower programs model the natural hierarchy between different layers of supply chain management holistically, while scenario analysis and data-driven optimization allow us to retrieve the guaranteed feasible solutions of the integrated formulation under various demand considerations. We address the data-driven optimization of this challenging class of problems using the DOMINO framework, which was initially developed to solve single-leader single-follower bi-level optimization problems to guaranteed feasibility. This framework is extended to solve single-leader multi-follower stochastic formulations and its performance is characterized by well-known single and multi-product process scheduling case studies. Through our data-driven algorithmic approach, we present guaranteed feasible solutions to linear and nonlinear mixed-integer bi-level formulations of simultaneous planning and scheduling problems and further characterize the effects of the scheduling level complexity on the solution performance, which spans over several hundred continuous and binary variables, and thousands of constraints.

42 ENGINEERING↗