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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Applications of remote sensing to water resources

Analyses were made of selected long-term (1985 and beyond) objectives, with the intent of determining if significant data-related problems would be encountered and to develop alternative solutions to any potential problems. One long-term objective selected for analysis was Water Availability Forecasting. A brief overview was scheduled in FY-77 of the objective -- primarily a fact-finding study to allow Data Management personnel to gain adequate background information to perform subsequent data system analyses. This report, includes discussions on some of the larger problems currently encountered in water measurement, the potential users of water availability forecasts, projected demands of users, current sensing accuracies, required parameter monitoring, status of forecasting modeling, and some measurement accuracies likely to be achievable by 1980 and 1990.

Source record↗
DOE OSTI2026

Predictive Model for Starlink Maritime Performance Using Multi-Horizon RandomForest

Low Earth orbit (LEO) satellite systems have become a crucial enabler of broadband access for maritime industries, where traditional networks are unavailable. However, the high mobility of LEO constellations and constantly changing weather conditions result in unpredictable link fluctuations, limiting the ability of maritime platforms to plan bandwidth usage proactively. To the best of our knowledge, no prior work has developed a short-term predictive model for maritime LEO connectivity using real experimental field measurements. This paper proposes a data-driven forecasting model that predicts future downlink throughput using multi-horizon RandomForest regression. The model is trained using real experimental coastal measurement data incorporating recent throughput history, network-layer indicators, and environmental variables. The proposed approach reduces mean absolute error by approximately 31% compared to a persistence baseline for 15-minute horizons. It maintains a measurable improvement at 30 minutes, despite increased stochasticity. These findings confirm that proactive bandwidth awareness is feasible on maritime platforms and can effectively support operational decisions such as adaptive streaming, routing, and resource scheduling. The performance gap between forecasting horizons also highlights the need for expanded offshore datasets to improve prediction robustness under harsher maritime environments.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

Forecasting Lightning at Kennedy Space Center/Cape Canaveral Air Force Station, Florida

The Applied Meteorology Unit (AMU) developed a set of statistical forecast equations that provide a probability of lightning occurrence on Kennedy Space Center (KSC) I Cape Canaveral Air Force Station (CCAFS) for the day during the warm season (May September). The 45th Weather Squadron (45 WS) forecasters at CCAFS in Florida include a probability of lightning occurrence in their daily 24-hour and weekly planning forecasts, which are briefed at 1100 UTC (0700 EDT). This information is used for general scheduling of operations at CCAFS and KSC. Forecasters at the Spaceflight Meteorology Group also make thunderstorm forecasts for the KSC/CCAFS area during Shuttle flight operations. Much of the current lightning probability forecast at both groups is based on a subjective analysis of model and observational data. The objective tool currently available is the Neumann-Pfeffer Thunderstorm Index (NPTI, Neumann 1971), developed specifically for the KSCICCAFS area over 30 years ago. However, recent studies have shown that 1-day persistence provides a better forecast than the NPTI, indicating that the NPTI needed to be upgraded or replaced. Because they require a tool that provides a reliable estimate of the daily thunderstorm probability forecast, the 45 WS forecasters requested that the AMU develop a new lightning probability forecast tool using recent data and more sophisticated techniques now possible through more computing power than that available over 30 years ago. The equation development incorporated results from two research projects that investigated causes of lightning occurrence near KSCICCAFS and over the Florida peninsula. One proved that logistic regression outperformed the linear regression method used in NPTI, even when the same predictors were used. The other study found relationships between large scale flow regimes and spatial lightning distributions over Florida. Lightning, probabilities based on these flow regimes were used as candidate predictors in the equation development. Fifteen years (1 989-2003) of warm season data were used to develop the forecast equations. The data sources included a local network of cloud-to-ground lightning sensors called the Cloud-to-Ground Lightning Surveillance System (CGLSS), 1200 UTC Florida synoptic soundings, and the 1000 UTC CCAFS sounding. Data from CGLSS were used to determine lightning occurrence for each day. The 1200 UTC soundings were used to calculate the synoptic-scale flow regimes and the 1000 UTC soundings were used to calculate local stability parameters, which were used as candidate predictors of lightning occurrence. Five logistic regression forecast equations were created through careful selection and elimination of the candidate predictors. The resulting equations contain five to six predictors each. Results from four performance tests indicated that the equations showed an increase in skill over several standard forecasting methods, good reliability, an ability to distinguish between non-lightning and lightning days, and good accuracy measures and skill scores. Given the overall good performance the 45 WS requested that the equations be transitioned to operations and added to the current set of tools used to determine the daily lightning probability of occurrence.

Lambert, Winfred↗
DOE OSTI2026

Unsupervised Detection of SOC Spoofing in OCPP 2.0.1 EV Charging Communication Protocol Using One-Class SVM

The electric vehicles (EVs) market keeps growing globally; thus, it is critical to secure the EV charging communication protocols in order to guarantee reliable and fair charging operations among the customers. The Open Charge Point Protocol (OCPP) 2.0.1 supports the communication between the Electric Vehicle Supply Equipment (EVSE) and Charging Station Management Systems (CSMSs); therefore, it becomes vulnerable to several types of attacks, which aim to jeopardize smart charging, billing, and energy management. Specifically, OCPP 2.0.1 allows the self-reporting of the State of Charge (SOC) values, which makes it vulnerable to spoofing-based cyberattacks, which target manipulating the scheduling priorities, distorting the load forecasts, and extending the charging sessions in an unfair manner. In this paper, we try to address this type of attack by providing a comprehensive analysis of the SOC spoofing attacks and introducing a novel unsupervised detection framework based on the One-Class Support Vector Machine (OCSVM) algorithm. Specifically, two types of attack scenarios are analyzed (i.e., priority manipulation and session extension) by deriving engineered features that capture the nonlinear relationships under normal charging behavior. Detailed simulation-based results are derived by utilizing the DESL-EPFL Level 3 EV charging dataset. Our results demonstrate high F1-score and recall in identifying spoofed SOC values and that the proposed OCSVM model demonstrates superior performance compared to alternative clustering and deep-learning based detectors.

EV charging↗
NASA NTRSDate not supplied

Enhancing Autonomous Satellite Communication Systems with Weather-Aware Scheduling and Reconfiguration

NASA currently provides communication support to over 100 satellite missions, and the agency is driving developments in Ka-band communications and network management automation to support additional future missions. At Ka-band frequencies, rain can degrade a communication link by more than 10 dB, which may be mitigated by agile scheduling and data rate control. We present a weather forecasting module for Ka-band communications that is intended to be used in an autonomous network management service that employs machine-to-machine scheduling systems for dynamic user access opportunities. Our forecasting module (NIMBUS) runs on AWS Cloud, consumes the freely and publicly available NOAA MRMS precipitation rate dataset (1km x 1km x 2-min), produces 30-minute Nowcasts using the pySTEPS algorithm, and publishes high level ground station specific link quality predictions. We evaluate two potential NIMBUS outputs, a binary classifier that predicts rain attenuation greater than 3 dB and a rain attenuation estimator, and we backtest these outputs using one year of power measurement data collected from observations of the geostationary ANIK F2 satellite’s Ka-band beacon. We report, with a 30-minute lead time, a binary classifier accuracy of 84% and an estimator RMSE of 1.67 dB. Additionally, we discuss how the NIMBUS module could be incorporated into a user-initiated service framework to enable weather-aware scheduling and reconfiguration. Operating a static link budget with minimal link margin can increase network operation costs by creating additional scheduling tasks due to failed packets, which may require human intervention and/or lead to inefficient asset utilization. The proposed system aims to increase throughput by reducing link margin, while mitigating increases in network operations costs by leveraging autonomous machine-to-machine scheduling, shortened prediction lead times, and advances in precipitation Nowcasting.

satellite communications↗
DOE OSTI2021

Comparison of Deterministic and Statistical Models for Water Quality Compliance Forecasting in the San Joaquin River Basin, California

Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood of agricultural communities. In the agriculture-dominated San Joaquin River Basin of California, real-time salinity management (RTSM) is a state-sanctioned program that helps to maximize allowable salt export while protecting existing basin beneficial uses of water supply. The RTSM strategy supplants the federal total maximum daily load (TMDL) approach that could impose fines associated with exceedances of monthly and annual salt load allocations of up to $1 million per year based on average year hydrology and salt load export limits. The essential components of the current program include the establishment of telemetered sensor networks, a web-based information system for sharing data, a basin-scale salt load assimilative capacity forecasting model and institutional entities tasked with performing weekly forecasts of river salt assimilative capacity and scheduling west-side drainage export of salt loads. Web-based information portals have been developed to share model input data and salt assimilative capacity forecasts together with increasing stakeholder awareness and involvement in water quality resource management activities in the river basin. Two modeling approaches have been developed simultaneously. The first relies on a statistical analysis of the relationship between flow and salt concentration at three compliance monitoring sites and the use of these regression relationships for forecasting. The second salt load forecasting approach is a customized application of the Watershed Analysis Risk Management Framework (WARMF), a watershed water quality simulation model that has been configured to estimate daily river salt assimilative capacity and to provide decision support for real-time salinity management at the watershed level. Analysis of the results from both model-based forecasting approaches over a period of five years shows that the regression-based forecasting model, run daily Monday to Friday each week, provided marginally better performance. However, the regression-based forecasting model assumes the same general relationship between flow and salinity which breaks down during extreme weather events such as droughts when water allocation cutbacks among stakeholders are not evenly distributed across the basin. A recent test case shows the utility of both models in dealing with an exceedance event at one compliance monitoring site recently introduced in 2020.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Day-Ahead Forecasting with Federated LSTM to Plan Energy Sharing in a Community Microgrid

Energy balancing in microgrids is a key enabler of resilience. Community microgrids located close to each other have the added benefit of networking and sharing surplus energy, if available. Such complex decision-making runs on optimization that requires reliable short-term (up to very-short-term) forecasts of energy generation and consumption for scheduling or trading. Each microgrid may also opt to not expose their sensitive data such as consumption patterns of individual businesses or residences. This paper investigates a federated approach to dayahead forecasting that trains naive long short-term memory (LSTM) at each business in a microgrid and aggregates weights at the microgrid controller using proximal regularization. This approach ensures that the controller has access only to energy surplus/deficit and not the actual generation or consumption values, avoiding unwanted exposure of sensitive data. A community microgrid in Adjuntas, Puerto Rico with 3 businesses is selected as a case study with a laboratory-scale computing setup. A central LSTM forecaster, where sensitive data from businesses are aggregated at the controller, is implemented as a baseline for qualifying the results. This work serves as a proof-of-concept for scaling the approach to networked and nested microgrids with more complex control options.

Sundararajan, Aditya [ORNL] (ORCID:000000033577854↗
NASA NTRS1976

Cost/benefit tradeoffs for reducing the energy consumption of the commercial air transportation system. Volume 2: Market and economic analyses

The impact of the most promising fuel conserving options on fuel consumption, passenger demand, operating costs, and airline profits when implemented into the U.S. domestic and international airline fleets is assessed. The potential fuel savings achievable in the U.S. scheduled air transportation system over the forecast period, 1973-1990, are estimated.

Vanabkoude, J. C.↗
NASA NTRS2007

Collaborative Resource Allocation

Collaborative Resource Allocation Networking Environment (CRANE) Version 0.5 is a prototype created to prove the newest concept of using a distributed environment to schedule Deep Space Network (DSN) antenna times in a collaborative fashion. This program is for all space-flight and terrestrial science project users and DSN schedulers to perform scheduling activities and conflict resolution, both synchronously and asynchronously. Project schedulers can, for the first time, participate directly in scheduling their tracking times into the official DSN schedule, and negotiate directly with other projects in an integrated scheduling system. A master schedule covers long-range, mid-range, near-real-time, and real-time scheduling time frames all in one, rather than the current method of separate functions that are supported by different processes and tools. CRANE also provides private workspaces (both dynamic and static), data sharing, scenario management, user control, rapid messaging (based on Java Message Service), data/time synchronization, workflow management, notification (including emails), conflict checking, and a linkage to a schedule generation engine. The data structure with corresponding database design combines object trees with multiple associated mortal instances and relational database to provide unprecedented traceability and simplify the existing DSN XML schedule representation. These technologies are used to provide traceability, schedule negotiation, conflict resolution, and load forecasting from real-time operations to long-range loading analysis up to 20 years in the future. CRANE includes a database, a stored procedure layer, an agent-based middle tier, a Web service wrapper, a Windows Integrated Analysis Environment (IAE), a Java application, and a Web page interface.

Wang, Yeou-Fang↗
NASA NTRS1995

Re-scheduling as a tool for the power management on board a spacecraft

The scheduling of events on board a spacecraft is based on forecast energy levels. The real time values of energy may not coincide with the forecast values; consequently, a dynamic revising to the allocation of power is needed. The re-scheduling is also needed for other reasons on board a spacecraft like the addition of new event which must be scheduled, or a failure of an event due to many different contingencies. This need of rescheduling is very important to the survivability of the spacecraft. In this presentation, a re-scheduling tool will be presented as a part of an overall scheme for the power management on board a spacecraft from the allocation of energy point of view. The overall scheme is based on the optimal use of energy available on board a spacecraft using expert systems combined with linear optimization techniques. The system will be able to schedule maximum number of events utilizing most energy available. The outcome is more events scheduled to share the operation cost of that spacecraft. The system will also be able to re-schedule in case of a contingency with minimal time and minimal disturbance of the original schedule. The end product is a fully integrated planning system capable of producing the right decisions in short time with less human error. The overall system will be presented with the re-scheduling algorithm discussed in detail, then the tests and results will be presented for validations.

Albasheer, Omar↗
DOE OSTI2024

Adaptive Framework for Maintenance Scheduling Based on Dynamic Preventive Intervals and Remaining Useful Life Estimation

Data-based prognostic methods exploit sensor data to forecast the remaining useful life (RUL) of industrial settings to optimize the scheduling of maintenance actions. However, implementing sensors may not be cost-effective or practical for all components. Traditional preventive approaches are not based on sensor data; however, they schedule maintenance at equally spaced intervals, which is not a cost-effective approach since the distribution of the time between failures changes with the degradation state of other parts or changes in working conditions. This study introduces a novel framework comprising two maintenance scheduling strategies. In the absence of sensor data, we propose a novel dynamic preventive policy that adjusts intervention intervals based on the most recent failure data. When sensor data are available, a method for RUL prediction, designated k-LSTM-GFT, is enhanced to dynamically account for RUL prediction uncertainty. The results demonstrate that dynamic preventive maintenance can yield cost reductions of up to 51.8% compared to conventional approaches. The predictive approach optimizes the exploitation of RUL, achieving costs that are only 3–5% higher than the minimum cost achievable while ensuring the safety of critical systems since all of the failures are avoided.

Nunes, Pedro (ORCID:0000000180012172)↗
NASA NTRS2007

The Challenges of Integrating NASA's Human, Budget, and Data Capital within the Constellation Program's Exploration Launch Projects Office

The U.S. Vision for Space Exploration directs NASA to retire the Space Shuttle in 2010 and replace it with safe, reliable, and cost-effective space transportation systems for crew and cargo travel to the Moon, Mars, and beyond. Such emerging space transportation initiatives face massive organizational challenges, including building and nurturing an experienced, dedicated team with the right skills for the required tasks; allocating and tracking the fiscal capital invested in achieving technical progress against an integrated master schedule; and turning generated data into useful knowledge that equips the team to design and develop superior products for customers and stakeholders. It has been more than 30 years since the Space Shuttle was designed; therefore, the current aerospace workforce has limited experience with developing new designs for human-rated spaceflight hardware. To accomplish these activities, NASA is using a wide range of state-of-the-art information technology tools that connect its diverse, decentralized teams and provide timely, accurate information for decision makers. In addition, business professionals are assisting technical managers with planning, tracking, and forecasting resource use against an integrated master schedule that horizontally and vertically interlinks hardware elements and milestone events. Furthermore, NASA is employing a wide variety of strategies to ensure that it has the motivated and qualified staff it needs for the tasks ahead. This paper discusses how NASA's Exploration Launch Projects Office, which is responsible for delivering these new launch vehicles, integrates its resources to create an engineering business environment that promotes mission success, which is defined by replacing the Space Shuttle by 2014 and returning to the Moon by 2020.

Kidd, Luanne↗
NASA NTRS2004

Optimizing Mars Airplane Trajectory with the Application Navigation System

Planning complex missions requires a number of programs to be executed in concert. The Application Navigation System (ANS), developed in the NAS Division, can execute many interdependent programs in a distributed environment. We show that the ANS simplifies user effort and reduces time in optimization of the trajectory of a martian airplane. We use a software package, Cart3D, to evaluate trajectories and a shortest path algorithm to determine the optimal trajectory. ANS employs the GridScape to represent the dynamic state of the available computer resources. Then, ANS uses a scheduler to dynamically assign ready task to machine resources and the GridScape for tracking available resources and forecasting completion time of running tasks. We demonstrate system capability to schedule and run the trajectory optimization application with efficiency exceeding 60% on 64 processors.

Frumkin, Michael↗
DOE OSTI2022

Operating Dynamic Reserve Dimensioning Using Probabilistic Forecasts

The rapid integration of variable energy sources (VRES) into power grids increases variability and uncertainty of the net demand, making the power system operation challenging. Operating reserve is used by system operators to manage and hedge against such variability and uncertainty. Traditionally, reserve requirements are determined by rules-of-thumb (static reserve requirements, e.g., NERC Reliability Standards), and more recently, dynamic reserve requirements from tools and methods which are in the adoption process (e.g., DynADOR, DRD, and RESERVE, among others). While these methods/tools significantly improve the static rule-of-thumb approaches, they rely exclusively on deterministic data (i.e., best guess only). Consequently, these methods disregard the probabilistic uncertainty thresholds associated with specific days and their weather conditions (i.e., best guess plus probabilistic uncertainty). This work presents practical approaches to determine the operating reserve requirements leveraging the wealth information from probabilistic forecasts. Proposed approaches are validated and tested using actual data from the CAISO system. Furthermore, results show the benefits in terms of risk reduction of considering the probabilistic forecast information into the dimensioning process of operating reserve requirements.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1977

Study of the impact of cruise speed on scheduling and productivity of commercial transport aircraft

A comparison is made between airplane productivity and utilization levels derived from commercial airline type schedules which were developed for two subsonic and four supersonic cruise speed aircraft. The cruise speed component is the only difference between the schedules which are based on 1995 passenger demand forecasts. Productivity-to-speed relationships were determined for the three discrete route systems: North Atlantic, Trans-Pacific, and North-South America. Selected combinations of these route systems were also studied. Other areas affecting the productivity-to-speed relationship such as aircraft design range and scheduled turn time were examined.

Bond, E. Q.↗
DOE OSTI2023

A Secure and Adaptive Hierarchical Multi-Timescale Framework for Resilient Load Restoration Using a Community Microgrid

Distribution system integrated community microgrids (CMGs) can partake in restoring loads during extended duration outages. At such times, the CMGs are challenged with limited resource availability, absence of robust grid support, and heightened demand-supply uncertainty. Here, this paper proposes a secure and adaptive three-stage hierarchical multi-timescale framework for scheduling and real-time (RT) dispatch of CMGs with hybrid PV systems to address these challenges. The framework enables the CMG to dynamically expand its boundary to support the neighboring grid sections and is adaptive to the changing forecast error impacts. The first stage solves a stochastic extended duration scheduling (EDS) problem to obtain referral plans for optimal resource rationing. The intermediate near-real-time (NRT) scheduling stage updates the EDS schedule closer to the dispatch time using new obtained forecasts, followed by the RT dispatch stage. To make the decisions more secure and robust against forecast errors, a novel concept called delayed recourse is designed. The approach is evaluated via numerical simulations on a modified IEEE 123-bus system and validated using OpenDSS and hardware-in-loop simulations. The results show superior performance in maximizing load supply and continuous secure distribution network operation under different operating scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗