Search NASA⌕ Search

SEARCH · Search NASA

Results for “resource allocation”

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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

TxDOT Road Elevation Model Dataset

This dataset provides three formats of Road Elevation Model (REM) data: 3D road line/polygon GeoPackage (GPKG), road lidar LAZ and COPC LAZ, and road digital surface model (DSM) GeoTIFF. Data are produced from the ~50TB TxGIO (formerly TNRIS) state lidar collections. This dataset is currently organized by maintenance section in each TxDOT district. Computation is done on GPU computing resources at Oak Ridge National Laboratory (ORNL), through a Strategic Partnership Project with UT Austin and an NSF ACCESS computing allocation award that enables fast massive data movement between TACC Corral and ORNL CADES/OLCF using Globus. In addition to this release from ORNL, a copy of this dataset can also be downloaded at https://web.corral.tacc.utexas.edu/nfiedata/road3d/.

13 HYDRO ENERGY↗
NASA NTRS1993

Technical Characteristics and Accuracy Capabilities of Delta Differential One-Way Ranging (DeltaDOR) as a Spacecraft Navigation Tool

The technique of determining spacecraft angular position by Delta Differential One-Way Ranging (DeltaDOR) is described. The dependence of measurement accuracy on parameters which define the spacecraft signal spectrum is shown. An illustration of DeltaDOR measurement accuracy, using the Mars Observer spacecraft, is presented. Guidelines for specifying spacecraft tone spectra are presented based on typical accuracy and ambiguity resolution needs, on frequency allocation for deep space tracking, on the efficient use of spacecraft signal power, and on the efficient use of ground tracking resources.

VLBI Very Long Baseline Interferometry DOR tones D↗
NASA NTRS2006

Conflict-Aware Scheduling Algorithm

conflict-aware scheduling algorithm is being developed to help automate the allocation of NASA s Deep Space Network (DSN) antennas and equipment that are used to communicate with interplanetary scientific spacecraft. The current approach for scheduling DSN ground resources seeks to provide an equitable distribution of tracking services among the multiple scientific missions and is very labor intensive. Due to the large (and increasing) number of mission requests for DSN services, combined with technical and geometric constraints, the DSN is highly oversubscribed. To help automate the process, and reduce the DSN and spaceflight project labor effort required for initiating, maintaining, and negotiating schedules, a new scheduling algorithm is being developed. The scheduling algorithm generates a "conflict-aware" schedule, where all requests are scheduled based on a dynamic priority scheme. The conflict-aware scheduling algorithm allocates all requests for DSN tracking services while identifying and maintaining the conflicts to facilitate collaboration and negotiation between spaceflight missions. These contrast with traditional "conflict-free" scheduling algorithms that assign tracks that are not in conflict and mark the remainder as unscheduled. In the case where full schedule automation is desired (based on mission/event priorities, fairness, allocation rules, geometric constraints, and ground system capabilities/ constraints), a conflict-free schedule can easily be created from the conflict-aware schedule by removing lower priority items that are in conflict.

Wang, Yeou-Fang↗
NASA NTRS1970

Explanation of random experiment sheduling and its application to space station analysis

The capability of the McDonnell-Douglas Phase B space station concept to complete the Blue Book Experiment program is analyzed and the Random experiment program with Resource Impact (REPRI) which was used to generate the data is described. The results indicate that station manpower and electrical power are the two resources which will constrain the amount of the Blue Book program that the station can complete. The station experiment program and its resource requirements are sensitive to levels of manpower and electrical power 13.5 men and 11 kilowatts. Continuous artificial gravity experiments have much less impact on the experiment program than experiments using separate artificial gravity periods. Station storage volume presently allocated for the FPE's and their supplies (1600 cu ft) is more than adequate. The REPRI program uses the Monte Carlo technique to generate a set of feasible experiment schedules for a space station. The schedules are statistically analyzed to determine the impact of the station experiment program resource requirements on the station concept. Also, the sensitivity of the station concept to one or more resources is assessed.

Moore, J. E.↗
NASA NTRS1975

STS Payloads Mission Control Study

Basic study tasks are described which produce documentation to meet the following objectives: (1) flight control functions, (2) NASA flight control capabilities, (3) function allocations, (4) operational communications and information processing plans, (5) alternative system concepts for STS payload flight control support, and (6) estimated additional resources for selected system concept(s).

Source record↗
DOE OSTI2025

LandScan HD: a high-resolution gridded ambient population methodology for the world

Unwarned population distributions accounting for routine human activities are needed to address many global human security challenges, including disasters, conflict, and infrastructure demand. LandScan High Definition (LSHD) supports this need through gridded ambient population estimates that measure average human presence between daytime and nighttime at a high spatial resolution of 3 arcseconds (approximately 90 m). Although LSHD has traditionally been produced on a country-specific basis, advances in global foundational data and computational resources now enable scaling its methodology to the world. Combining aspects of top-down and bottom-up gridded population methods, LSHD allocates subnational population totals from authoritative statistics to built-up areas based on occupancy estimates for multiple facility types (e.g., residential, commercial) and then reaggregates these estimates to a global population grid. We scale this approach by organizing the LSHD data stack into a 1° resolution tileset of vector analytic features, enabling an efficient and repeatable workflow for all countries worldwide. Examining the Philippines as an output of the global LSHD baseline dataset, we contrast unwarned and residential (WorldPop) population distributions by (1) exploring a practical application of flood risk assessment and (2) evaluating their congruence with outcomes of collective human activities (subnational CO 2 emissions). Finally, we discuss plans to address current LSHD limitations through data/modeling and uncertainty quantification improvements and provide outlook for workflow automation and extending the model to social, demographic and economic population characteristics.

Building morphology↗
NASA NTRS2001

National Environmental Change Information System Case Study

The Global Hydrology and Climate Center and NASA's Marshall Space Flight Center conducted a fact-finding case study for the Data Management Working Group (DMWG), now referred to as the Data and Information Working Group (DIWG), of the U.S. Global Change Research Program (USGCRP) to determine the feasibility of an interagency National Environmental Change Information System (NECIS). In order to better understand the data and information needs of policy and decision makers at the national, state, and local level, the DIWG asked the case study team to choose a regional water resources issue in the southeastern United States that had an impact on a diverse group of stakeholders. The southeastern United States was also of interest because the region experiences interannual climatic variations and impacts due to El Nino and La Nina. Jointly, with input from the DIWG, a focus on future water resources planning in the Apalachicola-Chattahoochee-Flint (ACF) River basins of Alabama, Georgia, and Florida was selected. A tristate compact and water allocation formula is currently being negotiated between the states and U.S. Army Corps of Engineers (COE) that will affect the availability of water among competing uses within the ACF River basin. All major reservoirs on the ACF are federally owned and operated by the U.S. Army COE. A similar two-state negotiation is ongoing that addresses the water allocations in the adjacent Alabama-Coosa-Tallapoosa (ACT) River basin, which extends from northwest Georgia to Mobile Bay. The ACF and ACT basins are the subject of a comprehensive river basin study involving many stakeholders. The key objectives of this case study were to identify specific data and information needs of key stakeholders in the ACF region, determine what capabilities are needed to provide the most practical response to these user requests, and to identify any limitations in the use of federal data and information. The NECIS case study followed the terms of reference developed by the interagency DIWG. The case study "lessons learned" and "key findings" offer guidelines and considerations to the DMWG for the development and implementation of a NECIS that would support the data and information needs of policy and decision makers at the national, state, and local level.

Goodman, S. J.↗
DOE OSTI2024

FORCE Regression Testing

Via programs including the Light Water Reactor Sustainability and Integrated Energy Systems, the U.S. Department of Energy has invested in the Framework for Optimization of ResourCes and Economics (FORCE) software framework (Idaho National Laboratory 2024a) for the technical and economic analysis of nuclear-integrated energy systems (IES). Nuclear IES expand the use of nuclear from traditional baseload electricity generation to a flexible and adaptive source of combined heat and power. Nuclear heat can be used in the production of a variety of energy currencies such as hydrogen and ammonia as well as other heat applications including water desalination and district heating. FORCE is designed with the intent to provide interconnected analysis tools that enable the accurate technical and economic assessment of specific nuclear IES configurations for individual energy markets. FORCE consists of three main analysis pathways: HYBRID (Idaho National Laboratory 2024b), which contains high-resolution physical models for IES; Holistic Energy Resource Optimization Network (HERON) (Idaho National Laboratory 2024c), which analyzes IES long-term economic viability; and Optimization of Real-time Capacity Allocation (ORCA) (Idaho National Laboratory 2024d), designed for real-time control of IES via digital twins and optimal decision making, including autonomous and remote operation research. Development of the FORCE ecosystem is guided by three pillars: capability, which assures that the computational requirements of IES analysis are met by the software tools; reliability, which provides for consistent code performance and expected behaviors; and accessibility, which lowers the barrier to entry for using the software and accelerates analysis by users beyond the FORCE primary developers. Reliability of the FORCE ecosystem is established according to the American Nuclear Society?s Nuclear Quality Assurance (NQA-1) program [American Society of Mechanical Engineers 1982], with specific levels of software quality assurance (SQA) within NQA-1 applied to each software tool in FORCE. As the tools within FORCE have matured, some integration algorithms to accurately connect the software tools for holistic analysis have been developed and deployed within the FORCE software repository. In accordance with NQA-1 standards, regression tests are required to guarantee the software performs consistently even when new capabilities are added to the software. In this report, we document the deployment of both unit tests, which test the consistent behavior of small pieces of the FORCE code base, as well as integration tests, which test the consistent performance of full use cases for the FORCE integration algorithms. We further document the encapsulation of these tests within a test harness, which collectively checks for each successful test completion on demand. Finally, we document the automation of the test harness using GitHub Actions [GitHub 2024], which require all tests succeed before any new capability or other changes can be added to the FORCE integration software

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Small-Scale Irrigation: Improving Food Security under Changing Climate and Water Resource Conditions in Ethiopia

We develop a new systems modeling tool that integrates knowledge from hydrology, agriculture, and economics to understand the effect of small-scale irrigation on food security and groundwater sustainability in Ethiopia. Irrigation is an effective tool to mitigate climate impacts and improve agricultural yields. Small-scale irrigation, such as decentralized groundwater irrigation, is well suited for developing countries where smallholder farming communities are widely dispersed and can only afford small infrastructure investment. We study the underlying interdependencies between food and water systems in Ethiopia, where small-holder agriculture is the foundation of the nation’s economy and climate variability has led to great challenges to its food security. Our coupled market and crop model with groundwater module captures the interdependencies of climate, water availability (including irrigation), crop yield, farmland allocation, crop production, transport and consumption based on a system approach across multiple spatial scales. We study the implication of small-scale irrigation to Ethiopia’s food security and water resource conditions as a “what-if” question by comparing an irrigation scenario to the calibrated baseline in 2015, a year of significant drought and crop failure over a large portion of Ethiopia. Our model offers fresh insights into geographic disparities in outcomes that are driven by baseline climate variability, soil fertility, and market conditions. In general, we find that small-scale irrigation can potentially improve food security through increases in food consumption, but it requires policy support to direct the increases of production to domestic consumption while maintaining a sustainable groundwater condition. By using Ethiopia as an example, we show the strength of our model to study how water infrastructure resources support critical functions and service in water and food systems.

Zhang, Ying↗
NASA NTRS1976

Frequency band justifications for passive sensors, 1 to 10 GHz

Remote sensor systems operating in the microwave region of the frequency spectrum provide information unobtainable with basic imaging techniques such as photography, television, or multispectral imaging. The frequency allocation requirements for passive microwave sensors used in the earth exploration satellite and space research services are presented for: (1) agriculture, forestry, and range resources; (2) land use survey and mapping: (3) water resources; (4) weather and climate; (5) environmental quality; and (6) marine resources, estuarine and oceans. Because measurements are required simultaneously in multiple frequency bands to adequately determine values of some phenomena, the relationships between frequency bands are discussed. The various measurement accuracies, dynamic range, resolutions and frequency needs are examined. A band-by-band summary of requirements, unique aspects, and sharing analyses of the required frequency bands is included.

Source record↗
NASA NTRS1989

Efficient scheduling algorithm for demand-assigned TDMA satellite systems

An efficient scheduling algorithm (stations burst plan) for demand-assigned time-division multiple-access (TDMA) satellite network systems is introduced. The total demand for transmitting data through a transponder may exceed the available bit-rate capacity, and a scheduler of the system wishes to utilize the system with minimum changes of slot allocations while maximizing throughputs. By implementing such a burst-plan algorithm, transmission of all demanded data traffic can be completed with minimum unused resources (idle slots). The underlying ideas adopted for the algorithm are that jobs with shorter remaining processing times should have higher priorities and that as many jobs are processed at a time as possible. The algorithm is particularly useful for deriving smooth burst plans for a satellite system with a large number of ground stations.

Durrani, Sajjad H.↗
DOE OSTI2024

Transforming Regional Transmission Planning: FERC Order 1920 Explained [Slides]

This presentation presents the key topics from FERC Order 1920: Building for the Future Through Electric Regional Transmission Planning and Cost Allocation. It breaks down and summarizes the main reforms from the regulation including comments from diverse perspectives on how the new rules may be implemented. This presentation can serve as a resource for diverse stakeholders including policymakers, utilities, industry, and researchers who seek to understand how the new ruling may impact regional transmission planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRSDate not supplied

Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.

Ruixuan Han↗
NASA NTRSDate not supplied

Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.

Ruixuan Han↗
DOE OSTI2024

Utility-Scale Shared Energy Storage Deployment: Challenges, Research Gaps, and Opportunities

Although community energy storage (CES) and behind-the-meter (BTM) energy storage systems have been widely used to offer homeowners and communities a variety of localized benefits, their scalability and grid support functionalities are limited. On the other hand, utility-scale shared energy storage (USES) systems may offer a number of benefits for grid integration, scalability, and economic viability. When compared to BTM and CES alternatives, these large-scale systems provide more storage capacity, more efficient operations, and more economically viable options. The deployment of USES presents opportunities for optimizing grid performance, integrating renewable energy resources, and improving energy security at the community level. However, significant research gaps exist in optimizing the integration and operation of these systems, especially to allocate energy for consumer use, grid services, and enhancing energy resilience. This paper reviews the literature in this regard, focusing on the opportunities, research gaps, and challenges associated with USES deployment. Firstly, the paper provides an overview of USES systems and emphasizes their benefits. Secondly, the key challenges are identified, and research gaps associated with the operation and integration of these systems are highlighted. Lastly, some potential solutions and opportunities that can be adopted to facilitate the rapid deployment and management of USES are presented. Technological, economic, regulatory, and environmental aspects are also discussed in this paper, providing an overview of the current state and future prospects of this technology.

Gautam, Mukesh [BATTELLE (PACIFIC NW LAB)] (ORCID:↗
NASA NTRS1980

Analysis of eighty-four commercial aviation incidents - Implications for a resource management approach to crew training

A resource management approach to aircrew performance is defined and utilized in structuring an analysis of 84 exemplary incidents from the NASA Aviation Safety Reporting System. The distribution of enabling and associated (evolutionary) and recovery factors between and within five analytic categories suggests that resource management training be concentrated on: (1) interpersonal communications, with air traffic control information of major concern; (2) task management, mainly setting priorities and appropriately allocating tasks under varying workload levels; and (3) planning, coordination, and decisionmaking concerned with preventing and recovering from potentially unsafe situations in certain aircraft maneuvers.

Murphy, M. R.↗
DOE OSTI2025

EVSE DERMS Controls [SWR-26-010]

An MQTT (Message Queuing Telemetry Transport) and OCPP (Open Charge Point Protocol) based remote smart charging controller framework for AC Electric Vehicle Supply Equipments (EVSEs). The code in this repo allows for the National Laboratory of the Rockies (NLR) controls to interface with the real Distributed Energy Resource Management System (DERMS) and EVSEs in NLR's ESIF Optimization and Control Laboratory (OCL). Different charge management algorithms can be tested to determine which power allocation method is most effective with the overall goal of demonstrating clear and well documented test results as well as providing functional control algorithms which could be utilized to provide effective smart charge management (SCM) at EV charging stations. Different power allocation methods are programmed in lab_demo_controller.py and include allocation based on first come first served, equal sharing, state of charge (SOC), priority factors, and behind the meter control methods.

Panossian, Nadia [National Laboratory of the Rocki↗
NASA NTRS1984

PASCAL/48 reference manual

PASCAL/48 is a programming language for the Intel MCS-48 series of microcomputers. In particular, it can be used with the Intel 8748. It is designed to allow the programmer to control most of the instructions being generated and the allocation of storage. The language can be used instead of ASSEMBLY language in most applications while allowing the user the necessary degree of control over hardware resources. Although it is called PASCAL/48, the language differs in many ways from PASCAL. The program structure and statements of the two languages are similar, but the expression mechanism and data types are different. The PASCAL/48 cross-compiler is written in PASCAL and runs on the CDC CYBER NOS system. It generates object code in Intel hexadecimal format that can be used to program the MCS-48 series of microcomputers. This reference manual defines the language, describes the predeclared procedures, lists error messages, illustrates use, and includes language syntax diagrams.

Knight, J. C.↗