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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Deploying a Model Predictive Traffic Signal Control Algorithm - A Field Deployment Experiment Case Study

This paper presents a field deployment experiment of a real-time traffic signal control algorithm. We implemented the model predictive control (MPC) algorithm based on the virtual phase-link (VPL) model. We selected the deployment locations and times based on an energy saving potential concept. We developed a set of experiment systems, which included sensing, processing, and actuating components, to enable field deployment. We tested the systems rigorously before the experiment days. We reported the key procedures on the experiment days, including the steps taken, the real-time control procedure, and the monitoring of the experiment. We evaluated the impact of the deployment by looking at the changes in delay and energy consumption.

deployment↗
DOE OSTI2024

A Framework to Assess Advanced Reactor Spent Fuel Management Facility Deployment

Advanced nuclear reactors offer various operational advantages over existing light water reactors but could produce types of spent nuclear fuel (SNF) with a wide variety of forms and characteristics depending on how many different concepts are deployed. Each advanced reactor SNF type potentially poses unique management challenges. New planning efforts will be necessary to anticipate how the management requirements of advanced reactor SNF will affect the deployment of an integrated waste management system. This paper applies a framework of high-level facility deployment milestones to a generic SNF management system, reviewing them together with the advanced reactor SNF characteristics and management requirements. This allows for the investigation of factors that influence facility and system deployment, and ultimately, the identification of challenges facing the deployment of different kinds of SNF management facilities. Here, the back end of the once-through fuel cycle is examined for four advanced reactor system technology types: sodium-cooled fast reactors, high-temperature gas-cooled reactors, liquid-fuel molten salt reactors, and lead-cooled fast reactors. It is observed that milestones earlier in the facility deployment process (e.g., siting and facility design) are more impacted by the uniqueness of advanced reactor SNF characteristics than others (e.g., construction and testing). Ultimately, none of the differences are seen as fundamentally disqualifying in a technical sense; however, they should be considered early, potentially as part of reactor design, to avoid issues in the future.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Assessing the PACE of California residential solar deployment: Impacts of Property Assessed Clean Energy programs on residential solar photovoltaic deployment in California, 2010-2015

A new study by Berkeley Lab found that residential Property Assessed Clean Energy (R-PACE) programs increased deployment of residential solar photovoltaic (PV) systems in California, raising it by about 7-12% in cities that adopt these programs. R-PACE is a financing mechanism that uses a voluntary property tax assessment, paid off over time, to facilitate energy improvements and, in some jurisdictions, water and resilience measures. While previous studies demonstrated that early, regional R-PACE programs increased solar PV deployment, this new analysis is the first to demonstrate these impacts from the large, statewide R-PACE programs dominating the California market today, which use private capital to fund the upfront costs of the improvements. Berkeley Lab estimated the impacts using econometric techniques on two samples: -Large cities only, allowing annual demographic and economic data as control variables -All California cities, without these annual data Analysis of both samples controls for several factors other than R-PACE that would be expected to drive solar PV deployment. We infer that on average, cities with R-PACE programs were associated with greater solar PV deployment in our study period (2010-2015). In the large cities sample, solar PV deployment in jurisdictions with R-PACE programs was higher by 1.1 watts per owner-occupied household per month, or 12%. Across all cities, solar PV deployment in jurisdictions with R-PACE programs was higher by 0.6 watts per owner-occupied household per month, or 7%. The large cities results are statistically significant at conventional levels; the all-cities results are not. The estimates imply that the majority of solar PV deployment financed by R-PACE programs would likely not have occurred in their absence. Results suggest that R-PACE programs have increased PV deployment in California even in relatively recent years, as R-PACE programs have grown in market share and as alternate approaches for financing solar PV have developed. The U.S. Department of Energy’s Building Technologies Office supported this research.

14 SOLAR ENERGY↗
DOE OSTI2024

The Urban Deployment Model: A Toolset for the Simulation and Performance Characterization of Radiation Detector Deployments in Urban Environments

Static and mobile radiation detectors can be deployed in urban environments for a range of nuclear security applications, including radiological source search-and-tracking scenarios. Modeling detector performance for such applications is challenging, as it does not depend solely on the detector capabilities themselves. Many factors must be taken into consideration, including specific source and background signatures, the topology and constraints of the deployment environment, the presence of nuisance sources, and whether detectors are mobile or static. When considering the simultaneous deployment of multiple, heterogeneous detectors, assessment of the system-wide performance requires the simulation of the individual detectors, and a system-level analysis of the detection performance. In radiological source search-and-tracking scenarios, performance is mostly dominated by the probability of encounter, which depends on the specifics of a given deployment, e.g., static vs. mobile detectors or a combination of both modalities, the number of detectors deployed, the dynamic vs. static setting of false alarm rates, and individual vs. networked operation. The Urban Deployment Model (UDM) toolset was specifically developed to cover the gap in the available generic frameworks for the simulation of radiation detector deployments at city scales. UDM provides a unified and modular framework to support the simulation and performance characterization of heterogeneous detector deployments in urban environments. This paper presents the key components along the UDM workflow.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Status Quo of Heliostat Field Deployment Processes

Deployment of the solar field of a concentrating solar power plant is one of many factors that are integral to the success of a project. Knowledge transfer from outside the industry is limited due to the unique nature of heliostats, which redirect sunlight to a receiver with high precision while maintaining a high level of reflectivity. Moreover, learning from project to project can be limited due to the site-specific nature of projects, as the market includes several developers, each with their own unique design. In this paper, we discuss the state of the art in heliostat field deployment. We cover all the key aspects of deployment from project assessment to a fully functioning system, which include site selection, layout development, supply chain, assembly, site preparation and construction, calibration, and operations and maintenance.

concentrating solar power↗
DOE OSTI2025

Hong-Ou-Mandel interference with a coexisting clock using transceivers for synchronization over deployed fiber

Interference between independently generated photons is a key step towards distributing entanglement over long distances, but it requires synchronization between the distantly-located photon sources. Synchronizing the clocks of such photon sources using coexisting two-way classical optical communications over the same fiber that transports the quantum photonic signals is a promising approach for achieving photon-photon interference over long distances, enabling entanglement distribution for quantum networking using the deployed fiber infrastructure. Here, we demonstrate photon-photon interference by observing the Hong-Ou-Mandel dip between two distantly-located sources: a weak coherent-state source obtained by attenuating the output of a laser and a heralded single-photon source. We achieve a maximum dip visibility of 0.58 +/- 0.04 when the two sources are connected via 4.3 km of deployed fiber. Dip visibilities > 0.5 are nonclassical and a first step towards achieving teleportation over the deployed fiber infrastructure. In our experiment, the classical optical communication is achieved with - 21 dBm of optical signal launch power, which is used to synchronize the clocks in the two independent, distantly-located photon sources. The impact of spontaneous Raman scattering from the classical optical signals is mitigated by appropriate choice of the quantum- and classical-channel wavelengths. All equipment used in our experiment (the photon sources and the synchronization setup) is commercially available. Finally, our experiment represents a scalable approach to enabling practical quantum networking with commercial equipment and coexistence with classical communications in optical fiber.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Clouds, Aerosols, and Precipitation in the Marine Boundary Layer: An Arm Mobile Facility Deployment

The Clouds, Aerosol, and Precipitation in the Marine Boundary Layer (CAP-MBL) deployment at Graciosa Island in the Azores generated a 21 month (April 2009-December 2010) comprehensive dataset documenting clouds, aerosols and precipitation using the Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF). The scientific aim of the deployment is to gain improved understanding of the interactions of clouds, aerosols and precipitation in the marine boundary layer. Graciosa Island straddles the boundary between the subtropics and midlatitudes in the Northeast Atlantic Ocean, and consequently experiences a great diversity of meteorological and cloudiness conditions. Low clouds are the dominant cloud type, with stratocumulus and cumulus occurring regularly. Approximately half of all clouds contained precipitation detectable as radar echoes below the cloud base. Radar and satellite observations show that clouds with tops from 1- 11 km contribute more or less equally to surface-measured precipitation at Graciosa. A wide range of aerosol conditions was sampled during the deployment consistent with the diversity of sources as indicated by back trajectory analysis. Preliminary findings suggest important two-way interactions between aerosols and clouds at Graciosa, with aerosols affecting light precipitation and cloud radiative properties while being controlled in part by precipitation scavenging. The data from at Graciosa are being compared with short-range forecasts made a variety of models. A pilot analysis with two climate and two weather forecast models shows that they reproduce the observed time-varying vertical structure of lower-tropospheric cloud fairly well, but the cloud-nucleating aerosol concentrations less well. The Graciosa site has been chosen to be a permanent fixed ARM site that became operational in October 2013.

clouds↗
DOE OSTI2024

Guidance for Integrating Energy Justice and Equity in Building Technology Deployment Programs: Tracking, Reporting, and Maximizing the Flow of Benefits from Building System Technology Deployment Activities to Disadvantaged Communities and Target Sectors

The Federal Justice40 Initiative directs at least 40% of the overall benefits of certain clean energy investments to flow to disadvantaged communities and requires that all federal programs covered by Justice40 consult stakeholders to determine the program benefits, and that the flow of benefits to disadvantaged communities is tracked and reported. Unequal distribution of benefits, in terms of access to clean energy research, design, development, and deployment, can disproportionately benefit or burden certain communities. This can result in higher rates of pollution, negative health effects, and increased energy burdens and insecurities in disadvantaged communities. Programs focused on decarbonizing the built environment can enhance health, quality of life, and economic opportunities for impacted communities. This guidance document, developed by Pacific Northwest National Laboratory and funded by the U.S. Department of Energy’s Building Technologies Office, provides best practices and approaches for incorporating energy justice and equity principles into building technology deployment activities. It provides best practices and recommendations for communicating with and involving disadvantaged communities and target building sectors in program activities, along with methods to monitor and report the distribution of benefits to these sectors. This document lays out a set of strategies, metrics, and best practices that can be implemented over time to apply energy justice and equity approaches, whether the program is just starting out or ongoing. Although this guidance was designed for Building Technologies Office technology deployment programs, the best practices, recommendations, and methods can be valuable to any program concerned with the equitable deployment of clean energy technologies. The goal of this project is to enable the equitable development, deployment, and adoption of clean energy technologies and practices.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Deploying a Model Predictive Traffic Signal Control Algorithm - A Field Deployment Experiment Case Study: Preprint

This paper presents a field deployment experiment of a real-time traffic signal control algorithm. We implemented the Model Predictive Control (MPC) algorithm based on Virtual Phase-Link (VPL) model. We selected the deployment locations and times based on an energy saving potential concept. We developed a set of experiment systems to enable the deployment including sensing, processing, and actuating components. We tested the systems rigorously before the experiment days. We reported the key procedures on the experiment days including the steps took, real-time control procedure, and the monitoring of the experiment. We evaluated the impact of the deployment with the changes in delay and energy consumption.

deployment↗
DOE OSTI2014

Early opportunities of CO₂ geological storage deployment in coal chemical industry in China

Carbon dioxide capture and geological storage (CCS) is regarded as a promising option for climate change mitigation; however, the high capture cost is the major barrier to large-scale deployment of CCS technologies. High-purity CO₂ emission sources can reduce or even avoid the capture requirements and costs. Among these high-purity CO₂ sources, certain coal chemical industry processes are very important, especially in China. In this paper, the basic characteristics of coal chemical industries in China is investigated and analyzed. As of 2013 there were more than 100 coal chemical plants in operation. These emission sources together emit 430 million tons CO₂ per year, of which about 30% are emit high-purity and pure CO₂ (CO₂ concentration >80% and >98.5% respectively). Four typical source-sink pairs are chosen for techno-economic evaluation, including site screening and selection, source-sink matching, concept design, and economic evaluation. The technical-economic evaluation shows that the levelized cost of a CO₂ capture and aquifer storage project in the coal chemistry industry ranges from 14 USD/t to 17 USD/t CO₂. When a 15USD/t CO₂ tax and 20USD/t for CO₂ sold to EOR are considered, the levelized cost of CCS project are negative, which suggests a benefit from some of these CCS projects. This might provide China early opportunities to deploy and scale-up CCS projects in the near future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Reinforcement-Learning-Based Smart Water Heater Control: An Actual Deployment

Utilizing smart control algorithms for electric water heaters (EWHs) is essential for fully harnessing the demand response (DR) potential of EWHs. For this reason, the use of reinforcement learning (RL) algorithms for EWHs has received increasing attention in recent years. However, existing RL approaches are either simulation-based or use pretrained RL agents. To this end, this paper presents the real-world deployment of a set of model-free RL approaches that aim to minimize the electricity cost of a EWH under a time-of-use electricity pricing policy using standard DR commands (e.g., shed, load up). The experiment results showed that the RL agents can help save electricity cost in the range of 11% to 14% compared to the baseline operation. This study demonstrated that RL-based EWH controllers can be deployed in real world without any prior training and can still save electricity cost.

deep learning↗
DOE OSTI2026

CROCUS Tipping Bucket Rain Gauge Data from Argonne Deployable Mast Deployed at Argonne National Laboratory During Urban Flooding Campaign

The Tipping Bucket Rain Gauge (TBRG) dataset contains data from a non-heated Met One 12-inch tipping bucket rain gauge that was mounted on the Argonne Deployable Mast (ADM). The ADM is a rapid deployable meteorological trailer that can be outfitted with instrumentation to measure urban heat island effects, urban flooding or urban flux measurements. During the urban flooding field campaign, the ADM was outfitted with multiple precipitation measurement systems, including the TBRG. This dataset contains one minute measurements for precipitation accumulation during the ADM's deployment at the Argonne Testbed for Multiscale Observational Science (ATMOS) site. These data are helpful for identifying periods of precipitation, leading to potential flooding. TBRGs can be used to validate optical rain gauge data and disdrometer data collected during the CROCUS urban flooding campaign. Data were collected at ATMOS, a 20-acre prairie site at Argonne National Laboratory in Lemont, Illinois. The data is presented as daily NetCDF (.nc) files, each containing approximately 24 hours of observations. Files follow the naming convention of: the project (CROCUS), location (ADM-atmos), instrument name (tbrg), data level (raw, a1), and date (year, month, day). The NetCDF format can be accessed using common scientific software such as Python using xarray, netCDF4 or act-doe.

1-min Precipitation Accumulation↗
DOE OSTI2026

CROCUS Tipping Bucket Rain Gauge Data from Argonne Deployable Mast Deployed at NEIU Carruthers Center for Inner City Studies (CCICS)

The Tipping Bucket Rain Gauge (TBRG) dataset contains data from a non-heated Met One 12-inch tipping bucket rain gauge that was mounted on the Argonne Deployable Mast (ADM). The ADM is a rapid deployable meteorological trailer that can be outfitted with instrumentation to measure urban heat island effects, urban flooding or urban flux measurements. During the urban flooding field campaign, the ADM was outfitted with multiple precipitation measurement systems, including the TBRG. This dataset contains one minute measurements for precipitation accumulation during the ADM's deployment at the Northeastern Illinois University (NEIU) Carruthers Center for Inner City Studies (CCICS) campus. These data are helpful for identifying periods of precipitation, leading to potential flooding. TBRGs can be used to validate optical rain gauge data and disdrometer data collected during the CROCUS urban flooding campaign. Data were collected at the CCICS building parking lot, located in the south side of Chicago, IL. The data is presented as daily NetCDF (.nc) files, each containing approximately 24 hours of observations. Files follow the naming convention of: the project (CROCUS), location (ADM-ccics), instrument name (tbrg), data level (raw, a1), and date (year, month, day). The NetCDF format can be accessed using common scientific software such as Python using xarray, netCDF4 or act-doe.

1-minute Precipitation Accumulation↗
DOE OSTI2020

Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment

Training neural networks for neuromorphic deployment is non-trivial. There have been a variety of approaches proposed to adapt back-propagation or back-propagation-like algorithms appropriate for training. Considering that these networks often have very different performance characteristics than traditional neural networks, it is often unclear how to set either the network topology or the hyperparameters to achieve optimal performance. In this work, we introduce a Bayesian approach for optimizing the hyperparameters of an algorithm for training binary communication networks that can be deployed to neuromorphic hardware. We show that by optimizing the hyperparameters on this algorithm for each dataset, we can achieve improvements in accuracy over the previous state-of-the-art for this algorithm on each dataset (by up to 15 percent). This jump in performance continues to emphasize the potential when converting traditional neural networks to binary communication applicable to neuromorphic hardware.

Parsa, Maryam↗
DOE OSTI2016

The Uranium from Seawater Program at the Pacific Northwest National Laboratory: Overview of Marine Testing, Adsorbent Characterization, Adsorbent Durability, Adsorbent Toxicity, and Deployment Studies

The Pacific Northwest National Laboratory’s (PNNL) Marine Science Laboratory (MSL) located along the coast of Washington State is evaluating the performance of uranium adsorption materials being developed for seawater extraction under realistic marine conditions with natural seawater. Two types of exposure systems were employed in this program: flow-through columns for testing of fixed beds of individual fibers and pellets and a recirculating water flume for testing of braided adsorbent material. Testing consists of measurements of the adsorption of uranium and other elements from seawater as a function of time, typically 42 to 56 day exposures, to determine the adsorbent capacity and adsorption rate (kinetics). Analysis of uranium and other trace elements collected by the adsorbents was conducted following strong acid digestion of the adsorbent with 50% aqua regia using either Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) or Inductively Coupled Plasma Mass Spectrometer (ICP-MS). The ORNL 38H adsorbent had a 56 day adsorption capacity of 3.30 ± 0.68 g U/ kg adsorbent (normalized to a salinity of 35 psu), a saturation adsorption capacity of 4.89 ± 0.83 g U/kg of adsorbent material (normalized to a salinity of 35 psu) and a half-saturation time of 28 ± 10 days. The AF1 adsorbent material had a 56 day adsorption capacity of 3.9 ± 0.2 g U/kg adsorbent material (normalized to a salinity of 35 psu), a saturation capacity of 5.4 ± 0.2 g U/kg adsorbent material (normalized to a salinity of 35 psu) and a half saturation time of 23 ± 2 days. The ORNL amidoxime-based adsorbent materials are not specific for uranium, but also adsorb other elements from seawater. The major doubly charged cations in seawater (Ca and Mg) account for a majority of the cations adsorbed (61% by mass and 74% by molar percent). For the ORNL AF1 adsorbent material, U is the 4th most abundant element adsorbed by mass and 7th most abundant by molar percentage

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗