Search NASA⌕ Search

SEARCH · Search NASA

Results for “Control Account Manager”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Livewire Data Platform-A Solution for Energy Efficient Mobility Systems (EEMS) Data Sharing

This presentation is an overview of progress made on the Livewire Data Platform since June 2022. Livewire is a publicly accessible platform for sharing energy efficiency and mobility research data funded by DOE's Vehicle Technologies Office. Core services and platform capabilities of Livewire include: Free, secure data storage; Access management that allows data owners to control who sees their data; Data collection and preservation; Quality characterization; Detailed access and download metrics; and Increased visibility of projects and data. Anyone can create an account and access data at https://livewire.energy.gov/.

energy efficiency↗
DOE OSTI2016

A modeling framework for optimal energy management of a residential building

Residential buildings are currently equipped with energy production facilities, e.g., solar rooftops and batteries, which in conjunction with smart meters, can function as smart energy hubs coordinating the loads and the resources in an optimal manner. This paper presents a mathematical model for the optimal energy management of a residential building and proposes a centralized energy management system (CEMS) framework for off-grid operation. The model of each component of the hub is integrated within the CEMS. The optimal decisions are determined in real-time by considering these models with realistic parameter settings and customer preferences. Model predictive control (MPC) is used to adapt the optimal decisions on a receding horizon to account for the deviations in the system inputs. Simulation results are presented to demonstrate the feasibility and effectiveness of the proposed CEMS framework. Results show that the proposed CEMS can reduce the energy cost and energy consumption of the customers by approximately 17% and 8%, respectively, over a day. In conclusion, using the proposed CEMS, the total charging cycles of the ESS were reduced by more than 50% in a day.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Data from Assessing Precipitation, Evapotranspiration, and NDVI as Controls of U.S. Great Plains Plant Production

Productivity throughout the North American Great Plains grasslands is generally considered to be water limited, with the strength of this limitation increasing as precipitation decreases. We hypothesize that cumulative actual evapotranspiration water loss (AET) from April to July is the precipitation‐related variable most correlated to aboveground net primary production (ANPP) in the U.S. Great Plains (GP). We tested this by evaluating the relationship of ANPP to AET, precipitation, and plant transpiration (Tr). We used multi‐year ANPP data from five sites ranging from semiarid grasslands in Colorado and Wyoming to mesic grasslands in Nebraska and Kansas, mean annual NRCS ANPP, and satellite‐derived normalized difference vegetation index (NDVI) data. Results from the five sites showed that cumulative April‐to‐July AET, precipitation, and Tr were well correlated (R2: 0.54–0.70) to annual changes in ANPP for all but the wettest site. AET and Tr were better correlated to annual changes in ANPP compared to precipitation for the drier sites, and precipitation in August and September had little impact on productivity in drier sites. April‐to‐July cumulative precipitation was best correlated (R2 = 0.63) with interannual variability in ANPP in the most mesic site, while AET and Tr were poorly correlated with ANPP at this site. Cumulative growing season (May‐to‐September) NDVI (iNDVI) was strongly correlated with annual ANPP at the five sites (R2 = 0.90). Using iNDVI as a surrogate for ANPP, we found that county‐level cumulative April–July AET was more strongly correlated to ANPP than precipitation for more than 80% of the GP counties, with precipitation tending to perform better in the eastern more mesic portion of the GP. Including the ratio of AET to potential evapotranspiration (PET) improved the correlation of AET to both iNDVI and mean county‐level NRCS ANPP. Accounting for how different precipitation‐related variables control ANPP (AET in drier portion, precipitation in wetter portion) provides opportunity to develop spatially explicit forecasting of ANPP across the GP for enhancing decision‐making by land managers and use of grassland ANPP for biofuels.

Field Data↗
DOE OSTI2022

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management

Abstract Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO $$_2$$ 2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. The heterogeneity typically requires high-fidelity physics-based models to make predictions on CO $$_2$$ 2 fate. Furthermore, characterizing the heterogeneity accurately is fraught with parametric uncertainty. Accounting for both, heterogeneity and uncertainty, makes this a computationally-intensive problem challenging for current reservoir simulators. To tackle this, we use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization at critical reservoir locations. We use DPFEHM framework, which has trustworthy physics based on the standard two-point flux finite volume discretization and is also automatically differentiable like machine learning models. Our physics-informed machine learning framework uses convolutional neural networks to learn an appropriate extraction rate based on the permeability field. We also perform a hyperparameter search to improve the model’s accuracy. Training and testing scenarios are executed to evaluate the feasibility of using physics-informed machine learning to manage reservoir pressures. We constructed and tested a sufficiently accurate simulator that is 400 000 times faster than the underlying physics-based simulator, allowing for near real-time analysis and robust uncertainty quantification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Ten-Year ALARA Review of Dosimetry Results 1 January 2008 through 31 December 2017

Dosimetry results from 1 January 2008 through 31 December 2017 were reviewed to demonstrate that radiation protection methods used at Sandia National Laboratories are compliant with regulatory limits and consistent with the philosophy to keep exposures to radiation As Low As is Reasonably Achievable (ALARA). Personnel dosimetry (external and internal) and environmental thermoluminescent dosimeter results were reviewed for the Sandia National Laboratories in New Mexico, California, and Nevada. ALARA is a philosophical approach to radiation protection by managing and controlling radiation exposures (individual and collective) to the work force and to the public to levels that are As Low As is Reasonably Achievable taking social, technical, economic, practical, and public policy considerations into account. ALARA is not a dose limit but a planning tool with the objective to keep doses below applicable dose limits As Low As is Reasonably Achievable. In the case of Sandia National Laboratories, formal ALARA goals are not needed since Collective and individual doses are well below applicable limits through operational ALARA practices implemented during Work Planning and Control activities.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2019

Robust Decentralized State Estimation

The health of a distribution grid is maintained by a distribution management system (DMS). Before the rise of distributed energy resource (DER), the DMS primarily managed the unidirectional power flow through the distribution grid from external sources (e.g. power brought from a large generating plant to the distribution grid by a transmission grid). Now, a typical DMS will utilize distributed energy resource manage systems (DERMS) to account for DER such as rooftop solar, community solar, and wind turbines. Each DERMS is responsible for an area of the distribution grid, issuing commands to the associated real-time automation controller (RTAC) to regulate power flow and maintain health of that section of the grid.

42 ENGINEERING↗
DOE OSTI2023

Ten questions concerning reinforcement learning for building energy management

As buildings account for approximately 40% of global energy consumption and associated greenhouse gas emissions, their role in decarbonizing the power grid is crucial. The increased integration of variable energy sources, such as renewables, introduces uncertainties and unprecedented flexibilities, necessitating buildings to adapt their energy demand to enhance grid resiliency. Consequently, buildings must transition from passive energy consumers to active grid assets, providing demand flexibility and energy elasticity while maintaining occupant comfort and health. This fundamental shift demands advanced optimal control methods to manage escalating energy demand and avert power outages. Reinforcement learning (RL) emerges as a promising method to address these challenges. Here, in this paper, we explore ten questions related to the application of RL in buildings, specifically targeting flexible energy management. We consider the growing availability of data, advancements in machine learning algorithms, open-source tools, and the practical deployment aspects associated with software and hardware requirements. Our objective is to deliver a comprehensive introduction to RL, present an overview of existing research and accomplishments, underscore the challenges and opportunities, and propose potential future research directions to expedite the adoption of RL for building energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Assessing precipitation, evapotranspiration, and NDVI as controls of U.S. Great Plains plant production

Productivity throughout the North American Great Plains grasslands is generally considered to be water limited, with the strength of this limitation increasing as precipitation decreases. We hypothesize that cumulative actual evapotranspiration water loss (AET) from April to July is the precipitation-related variable most correlated to aboveground net primary production (ANPP) in the U.S. Great Plains (GP). We tested this by evaluating the relationship of ANPP to AET, precipitation, and plant transpiration (Tr). We used multi-year ANPP data from five sites ranging from semiarid grasslands in Colorado and Wyoming to mesic grasslands in Nebraska and Kansas, mean annual NRCS ANPP, and satellite-derived normalized difference vegetation index (NDVI) data. Results from the five sites showed that cumulative April-to-July AET, precipitation, and Tr were well correlated (R 2 : 0.54–0.70) to annual changes in ANPP for all but the wettest site. AET and Tr were better correlated to annual changes in ANPP compared to precipitation for the drier sites, and precipitation in August and September had little impact on productivity in drier sites. April-to-July cumulative precipitation was best correlated (R 2 = 0.63) with interannual variability in ANPP in the most mesic site, while AET and Tr were poorly correlated with ANPP at this site. Cumulative growing season (May-to-September) NDVI (iNDVI) was strongly correlated with annual ANPP at the five sites (R 2 = 0.90). Using iNDVI as a surrogate for ANPP, we found that county-level cumulative April–July AET was more strongly correlated to ANPP than precipitation for more than 80% of the GP counties, with precipitation tending to perform better in the eastern more mesic portion of the GP. Including the ratio of AET to potential evapotranspiration (PET) improved the correlation of AET to both iNDVI and mean county-level NRCS ANPP. Accounting for how different precipitation-related variables control ANPP (AET in drier portion, precipitation in wetter portion) provides opportunity to develop spatially explicit forecasting of ANPP across the GP for enhancing decision-making by land managers and use of grassland ANPP for biofuels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Five-year ALARA review of dosimetry results :

A review of personnel dosimetry (external and internal) and environmental monitoring results from 1 January 2008 through 31 December 2012 performed at Sandia National Laboratories, New Mexico was conducted to demonstrate that radiation protection methods used are compliant with regulatory limits and conform with the ALARA philosophy. ALARA is the philosophical approach to radiation protection by managing and controlling radiation exposures (individual and collective) to the work force and to the general public to levels that are As Low As is Reasonably Achievable taking social, technical, economic, practical, and public policy considerations into account. ALARA is not a dose limit but a process which has the objective of attaining doses as far below applicable dose limits As Low As is Reasonably Achievable.

Paulus, Luke R.↗
DOE OSTI2014

Five-Year ALARA Review of Dosimetry Results: 1 January 2009 through 31 December 2013

A review of dosimetry results from 1 January 2009 through 31 December 2013 was conducted to demonstrate that radiation protection methods used are compliant with regulatory limits and conform to the ALARA philosophy. This included a review and evaluation of personnel dosimetry (external and internal) results at Sandia National Laboratories, New Mexico as well as at Sandia National Laboratories, California. Additionally, results of environmental monitoring efforts at Sandia National Laboratories, New Mexico were reviewed. ALARA is a philosophical approach to radiation protection by managing and controlling radiation exposures (individual and collective) to the work force and to the general public to levels that are As Low As is Reasonably Achievable taking social, technical, economic, practical, and public policy considerations into account. ALARA is not a dose limit but a process which has the objective of attaining doses as far below applicable dose limits As Low As is Reasonably Achievable.

Paulus, Luke R.↗
DOE OSTI2015

Five-Year ALARA Review of Dosimetry Results 1 January 2010 through 31 December 2014

A review of dosimetry results from 1 January 2010 through 31 December 2014 was conducted to demonstrate that radiation protection methods used are compliant with regulatory limits and conform to the philosophy to keep exposures to radiation As Low As is Reasonably Achievable (ALARA). This included a review and evaluation of personnel dosimetry (external and internal) results at Sandia National Laboratories, New Mexico as well as at Sandia National Laboratories, California. Additionally, results of environmental monitoring efforts at Sandia National Laboratories, New Mexico were reviewed. ALARA is a philosophical approach to radiation protection by managing and controlling radiation exposures (individual and collective) to the work force and to the general public to levels that are As Low As is Reasonably Achievable taking social, technical, economic, practical, and public policy considerations into account. ALARA is not a dose limit but a process which has the objective of attaining doses as far below applicable dose limits As Low As is Reasonably Achievable.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Five-Year ALARA Review of Dosimetry Results (1 Jan 2011 - 31 Dec 2015)

A review of dosimetry results from 1 January 2011 through 31 December 2015 was conducted to demonstrate that radiation protection methods used are compliant with regulatory limits and conform to the philosophy to keep exposures to radiation As Low As is Reasonably Achievable (ALARA). This included a review and evaluation of personnel dosimetry (external and internal) results at Sandia National Laboratories, New Mexico as well as at Sandia National Laboratories, California. Additionally, results of environmental monitoring efforts at Sandia National Laboratories, New Mexico were reviewed. ALARA is a philosophical approach to radiation protection by managing and controlling radiation exposures (individual and collective) to the work force and to the general public to levels that are As Low As is Reasonably Achievable taking social, technical, economic, practical, and public policy considerations into account. ALARA is not a dose limit but a process which has the objective of attaining doses as far below applicable dose limits As Low As is Reasonably Achievable. In the case of Sandia National Laboratories, a need for formal ALARA goals has not been identified. Collective and individual doses are kept at acceptable through Operational ALARA practices implemented during the development of radiological Technical Work Documents.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

Machine learning surrogate of physics-based building-stock simulator for end-use load forecasting

Building energy models are used to simulate heat and mass transfer and estimate end-use load in buildings. With the proliferation of solar photovoltaics on residential and commercial buildings, increasingly, buildings are expected to provide grid services, for which accurate and computationally efficient building energy simulations and end-use load prediction are imperative. Existing building energy simulation tools, however, have significant computational overhead that make them less practical in real-time deployment for optimization, design, uncertainty quantification and control in building energy management systems. Here this article presents a data-driven machine learning model based on light gradient boosting method (LightGBM) as a surrogate for a physics-based simulator for residential buildings to predict end-use load. The machine learning based surrogate model accounts for time-series related variables, seasonality and trend component of end-use load, and history of end-use load. The accuracy of the surrogate model is assessed on the prediction of the load profiles of 100 different houses in Cook County, Illinois, USA. The LightGBM surrogate model is shown to reduce the root-mean-squared error by 53% relative to a reference decision tree (DT) based model reported previously in the literature. Moreover, the model predicts the load spikes and high-ramp rate events throughout the year which are often the Achilles heel of other models in the literature. The machine learning based surrogate model is demonstrated to be computationally efficient, with a ten-fold reduction in the computational time compared to a physics-based building energy simulation, and suitable for uncertainty analysis and real-time control of building characteristics in response to uncertainty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Material Protection, Accounting, and Control Technologies (MPACT): Modeling and Simulation Roadmap

The development of sustainable advanced nuclear fuel cycles is a long-term goal of the Office of Nuclear Energy’s (DOE-NE) Fuel Cycle Technologies program. The Material Protection, Accounting, and Control Technologies (MPACT) campaign is supporting research and development (R&D) of advanced instrumentation, analysis tools, and integration methodologies to meet this goal. This advanced R&D is intended to facilitate safeguards and security by design of fuel cycle facilities. The lab-scale demonstration of a virtual facility, distributed test bed, that connects the individual tools being developed at National Laboratories and university research establishments, is a key program milestone for 2020. These tools will consist of instrumentation and devices as well as computer software for modeling. To aid in framing its long-term goal, during FY16, a modeling and simulation roadmap is being developed for three major areas of investigation: (1) radiation transport and sensors, (2) process and chemical models, and (3) shock physics and assessments. For each area, current modeling approaches are described, and gaps and needs are identified.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Co-Optimization of Velocity and Charge-Depletion for Plug-In Hybrid Electric Vehicles: Accounting for Acceleration and Jerk Constraints

Abstract Recent advances in vehicle connectivity and automation technologies promote advanced control algorithms that co-optimize the longitudinal dynamics and powertrain operation of hybrid electric vehicles. Typically, a sequential optimization with the vehicle dynamics optimized followed by powertrain optimization is adopted to manage a number of complexities such as the inherent mixed-integer nature of the hybrid powertrain, the numerous state and control variables, the differing time scales of vehicle and powertrain subsystems, time-varying state constraints, and large horizon lengths. Instead, we solve the offline optimization problem in a centralize manner assuming exact knowledge of the lead vehicle's position over the entire trip by applying a discrete-time single shooting-based numerical approach, Discrete Mixed-Integer Shooting (DMIS), including a linearly increasing computational complexity to the problem horizon. In particular, the hierarchical problem structure is exploited to decompose the computationally intensive Hamiltonian minimization step into a set of low-dimensional optimizations. DMIS allows us to compute the direct fuel minimization problem including the vehicle and powertrain dynamics in a centralized manner to its full horizon while systematically tuning weighting factors that penalize passenger discomfort. For the first time, this study reveals that practically implemented sequential optimization exhibits similar fuel optimality as co-optimization when a certain level of passenger comfort is required.

Automation & Control Systems↗
DOE OSTI2013

Quantification of UV-Visible and Laser Spectroscopic Techniques for Materials Accountability and Process Control

Ultraviolet–visible spectroscopy (UV–Visible) and time-resolved laser fluorescence spectroscopy (TRLFS) optical techniques can permit on-line analysis of actinide elements in a solvent extraction process in real time. These techniques have been used for measuring actinide speciation and concentration under laboratory conditions and are easily adaptable to multiple sampling geometries, such as dip probes, fiber-optic sample cells, and flow-through cell geometries. To fully exploit these techniques, researchers must determine the fundamental speciation of target actinides and the resulting influence on spectroscopic properties. Detection limits, process conditions, and speciation of key actinide components can be established and utilized in a range of areas, particularly those related to materials accountability and process control. Through this project, researchers will develop tools and spectroscopic techniques to evaluate solution extraction conditions and concentrations of U, Pu, and Cm in extraction processes, addressing areas of process control and materials accountability. The team will evaluate UV– Visible and TRLFS for use in solvent extraction-based separations. Ongoing research is examining efficacy of UV-Visible spectroscopy to evaluate uranium and plutonium speciation under conditions found in the UREX process and using TRLFS to evaluate Cm speciation and concentration in the TALSPEAK process. A uranyl and plutonium nitrate UV–Visible spectroscopy study met with success, which supports the utility and continued exploration of spectroscopic methods for evaluation of actinide concentrations and solution conditions for other aspects of the UREX+ solvent extraction scheme. This project will examine U and Pu absorbance in TRUEX and TALSPEAK, perform detailed examination of Cm in TRUEX and TALSPEAK, study U laser fluorescence, and apply project data to contactors. The team will also determine peak ratios as a function of solution concentrations for the UV-Visible spectroscopy studies. The use of TRLFS to examine Cm and U will provide data to evaluate lifetime, peak location, and peak ratios (mainly for U). The bases for the spectroscopic techniques have been investigated, providing fundamental evidence for the application’s utility.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Cost-optimized energy storage operation for a grid-connected solar PV system at community and individual scales

This study provides a comparative analysis of grid-connected PV-integrated battery storage at individual and community scales. The paper addresses the challenge of managing energy demand-generation mismatch by using a battery energy storage optimization algorithm, which minimizes operational costs while accounting for battery degradation. Also, this work introduces a broader evaluation basis that includes seasonal variability, grid exchange smoothness, and scalability across different battery capacities. Results show that community-scale storage more effectively dampens grid exchange power fluctuations and reduces system costs, particularly with moderate price differences between electricity buying and selling prices and low battery capacities. The paper also analyzes the impacts of static control versus cost-optimized battery system management. Here, it is shown that the gap in system costs between the cost-optimized and static control scenarios widens as the price difference increases.

25 ENERGY STORAGE↗
DOE OSTI2012

Facility Measurements

Measurements are the basis of accounting for inventories and transfers and provide assurance that no nuclear material is missing. They provide information on quantities, isotopic composition and weight percent determination of nuclear material in given locations which is essential to determining category levels and protection requirements. Los Alamos National Laboratory (LANL) uses confirmatory and verification measurements to meet DOE requirements when moving or processing material on External Receipts, External Shipments, Internal Transfers, Processing, and Physical Inventories. This presentation provides details on what type of measurement is required based on several factors. These factors include but are not limited to Category of the Material, application of Tamper Indicating Devices (TID), application of the measurement, and graded safeguards. These elements are part of the overall MC&A program required to effectively control and account for nuclear materials. LANL depends on multiple organizations to take the measurements and monitor the controls of instruments required to have an effective program. The MC&A group does oversight and control of all standards and instruments. The NDA group manages the day-to-day measurement control activities and does NDA measurements while the operations people use balances in their day-to-day processing. Confirmatory and Verification measurements are used throughout the nuclear material life cycle to verify inventories and detect theft or diversion of nuclear materials.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗