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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Spatiotemporal Downscaling Model for Solar Irradiance Forecast Using Nearest-Neighbor Random Forest and Gaussian Process

Accurate solar photovoltaic (PV) capacity estimation requires high-resolution, site-specific solar irradiance data to account for localized variability. However, global datasets, such as the National Solar Radiation Database (NSRDB), provide regional averages that fail to capture the fine-scale fluctuations critical for large-scale grid integration. This limitation is particularly relevant in the context of increasing distributed energy resources (DERs) penetration, such as rooftop PV. Additionally, it is critical to the implementation of the U.S. Federal Energy Regulatory Commission (FERC) Order 2222, which facilitates DER participation in U.S. bulk power markets. To address this challenge, this study evaluates Nearest-Neighbor Random Forest (NNRF) and Nearest-Neighbor Gaussian Process (NNGP) models for spatiotemporal downscaling of global solar irradiance data. By leveraging historical irradiance and meteorological data, these models incorporate spatial, temporal, and feature-based correlations to enhance local irradiance predictions. The NNRF model, a machine-learning approach, prioritizes computational efficiency and predictive accuracy, while the NNGP model offers a level of interpretability and prediction uncertainty by numerically quantifying correlations and dependencies in the data. Model validation was conducted using day-ahead predictions. The results showed that the average Goodness of Fit (GoF) of the NNRF model of 90.61% across all eight sites outperformed the GoF of the NNGP of 85.88%. Additionally, the computational speed of NNRF was 2.5 times faster than the NNGP. Finally, the NNGP displayed polynomial scaling while the NNRF scaled linearly with increasing number of nearest neighbors. Additional validation of the model on five sites in Puerto Rico further confirmed the superiority of the NNRF model over the NNGP model. These findings highlight the robustness and computational efficiency of NNRF for large-scale solar irradiance downscaling, making it a strong candidate for improving PV capacity estimation and real-time electricity market integration for DERs.

Asiedu, Shadrack (ORCID:0009000646004826)↗
DOE OSTI2025

Prediction of Silicon Content in a Blast Furnace via Machine Learning: A Comprehensive Processing and Modeling Pipeline

Silicon content plays an important role in determining the operational efficiency of blast furnaces (BFs) and their downstream processes in integrated steelmaking; however, existing sampling methods and first-principles models are somewhat limited in their capability and flexibility. Current data-based prediction models primarily rely on a limited set of manually selected furnace parameters. Additionally, different BFs present a diverse set of operating parameters and state variables that are known to directly influence the hot metal’s silicon content, such as fuel injection, blast temperature, and raw material charge composition, among other process variables that have their own impacts. The expansiveness of the parameter set adds complexity to parameter selection and processing. This highlights the need for a comprehensive methodology to integrate and select from all relevant parameters for accurate silicon content prediction. Providing accurate silicon content predictions would enable operators to adjust furnace conditions dynamically, improving safety and reducing economic risk. To address these issues, a two-stage approach is proposed. First, a generalized data processing scheme is proposed to accommodate diverse furnace parameters. Second, a robust modeling pipeline is used to establish a machine learning (ML) model capable of predicting hot metal silicon content with reasonable accuracy. The method employed herein predicted the average Si content of the upcoming furnace cast with an accuracy of 91% among 200 target predictions for a specific furnace provisioned by the XGBoost model. This prediction is achieved using only the past shift’s operating conditions, which should be available in real time. This performance provides a strong baseline for the modeling approach with potential for further improvement through provision of real-time features.

Chemistry↗
DOE OSTI2026

Synthesis of ARM User Facility Surface Rainfall Datasets to Construct a Best Estimate Value Added Product (PrecipBE)

Surface precipitation measurements are essential for Earth system model (ESM) evaluation and understanding cloud processes. An ever-growing need for robust, temporally evolving, and easy-to-use statistical datasets provides motivation for a baseline ground-based precipitation properties data product. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility operates an extensive suite of precipitation instruments with various sensitivities and operating mechanisms, which render the decision of which instrument to use based on one or more fixed thresholds challenging and prone to errors and bias. Using a long-term instrument inter-comparison from a unique per-precipitation event perspective, rather than instantaneous sample comparison, we demonstrate that ARM rainfall-measuring instruments are generally consistent with each other at the statistical level. Inter-instrument deviations at the single event level can be large, especially for specific rainfall event properties such as maximum precipitation rates. A machine-learning (ML) analysis using a random forest regressor indicates that in some cases, depending on instrument, local site climatology, and/or specific deployment configuration, certain atmospheric state variables influence the measured quantities in an unpredictable manner. Thus, a-priori weighting of different instruments does not necessarily lead to more accurate and less biased synthesis of instrument data. These results motivate the design of the ARM precipitation best-estimate (PrecipBE) value-added product, which incorporates all valid precipitation data while considering data quality and other instrument limitations. PrecipBE consists of time series and tabular statistics datasets in an easy-to-use and insightful per-precipitation event format. It provides a large set of precipitation event properties supplemented with ancillary data from ARM datasets that correspond to the detected precipitation events. We describe the PrecipBE algorithm and demonstrate its use via the examination of a single-day output as well as a long-term trend analysis of precipitation events at the ARM Southern Great Plains (SGP) site, covering more than 30 years of data. The trend analysis tentatively suggests a long-term temporal tendency for mainly shorter and less intense precipitation events at the SGP site, but a long-term increase in annual rainfall by more than 36 mm (5 %) per decade. This rainfall trend is catalyzed primarily by more extreme event properties of relatively rare, intense precipitation events, with event total and 1 min maximum precipitation rate at a 1 year timeframe increasing up to 5 mm and 9 mm h −1 (several percent) per decade, respectively. While the currently available PrecipBE datasets (at https://adc.arm.gov/discovery/, last access: 8 December 2025) cover rainfall from multiple ARM deployments up to March 2025, PrecipBE is planned to be expanded to include solid-phase precipitation and will soon become an operational product with a several-day lag from real-time. We invite the ARM user community to leverage this new product and welcome user feedback to further enhance the dataset.

Silber, Israel [Pacific Northwest National Laborat↗
NASA NTRS1971

ALSEP data management.

Nine million measurements a day are transmitted from each functional ALSEP on the moon and continuously collected on earth for use in long-term scientific analysis. Periodically, this massive flow of intricate data is examined on a real-time basis to evaluate ALSEP performance and to study the need for new commands to any of the ALSEP systems. A worldwide complex of facilities and personnel has the challenging task of efficient and timely data processing and dissemination. This paper describes facility characteristics and support activities and details certain constraints imposed on ALSEP hardware design by data handling requirements. The highlights of the first two years of lunar data management are reviewed.

Murtaugh, C. R.↗
NASA NTRS1972

Design of a high-speed real-time symbiont

The problems involved, approach taken, and solution arrived at are described in a software study to design a high speed, real time symbiont for analog telemetry processing. The symbiont design is based on the need for transferring data from one I/O device to another without significant use of core space or central processor time. The queues, programmed wait states, and teletype commands incorporated in the symbiont design are discussed.

Grunby, E. I.↗
NASA NTRS1975

Real-time computer data system for the 40- by 80-foot wind tunnel facility at Ames Research Center

The background material and operational concepts of a computer-based system for an operating wind tunnel are described. An on-line real-time computer system was installed in a wind tunnel facility to gather static and dynamic data. The computer system monitored aerodynamic forces and moments of periodic and quasi-periodic functions, and displayed and plotted computed results in real time. The total system is comprised of several off-the-shelf, interconnected subsystems that are linked to a large data processing center. The system includes a central processor unit with 32,000 24-bit words of core memory, a number of standard peripherals, and several special processors; namely, a dynamic analysis subsystem, a 256-channel PCM-data subsystem and ground station, a 60-channel high-speed data acquisition subsystem, a communication link, and static force and pressure subsystems. The role of the test engineer as a vital link in the system is also described.

Cambra, J. M.↗
NASA NTRS1975

Sensor development in the Shuttle era

The use of the Space Shuttle in the development of earth observation sensors is examined. Two sensor classes are selected for case histories: infrared temperature sounders and microwave radiometers. The most significant finding in each of the developmental studies of these two sensor classes is considered to be the feasibility and value of using the Shuttle/Spacelab as a test vehicle for the operation in space of a versatile multimode experimental sensor. The Shuttle Electrically Scanned Microwave Radiometer and the Shuttle Infrared Interferometer are found to be the most effective instruments in this context. The Shuttle/Spacelab Sortie mission characteristics provide opportunities for new approaches to the development of sensors, using the Shuttle as a test vehicle to improve the efficiency of the process with respect to time, cost, and/or quality of the final product. As for crew functions, the short-term Spacelab mission requires some near real-time evaluation of data quality and sensor function in order to insure efficient data collection.

Gerding, R. B.↗
NASA NTRS1976

Study report on laser storage and retrieval of image data

The theoretical foundation is presented for a system of real-time nonphotographic and nonmagnetic digital laser storage and retrieval of image data. The system utilizes diffraction-limited laser focusing upon thin metal films, melting elementary holes in the metal films in laser focus. The metal films are encapsulated in rotating flexible mylar discs which act as the permanent storage carries. Equal sized holes encompass two dimensional digital ensembles of information bits which are time-sequentially (bit by bit) stored and retrieved. The bits possess the smallest possible size, defined by the Rayleigh criterion of coherent physical optics. Space and time invariant reflective read-out of laser discs with a small laser, provides access to the stored digital information. By eliminating photographic and magnetic data processing, which characterize the previous state of the art, photographic grain, diffusion, and gamma-distortion do not exist. Similarly, magnetic domain structures, magnetic gaps, and magnetic read-out are absent with a digital laser disc system.

Becker, C. H.↗
NASA NTRS1977

Feasibility study of microprocessor systems suitable for use in developing a real-time for the 4.75 GHz scatterometer

A class of signal processors suitable for the reduction of radar scatterometer data in real time was developed. The systems were applied to the reduction of single polarized 13.3 GHz scatterometer data and provided a real time output of radar scattering coefficient as a function of incident angle. It was proposed that a system for processing of C band radar data be constructed to support scatterometer system currently under development. The establishment of a feasible design approach to the development of this processor system utilizing microprocessor technology was emphasized.

Source record↗
NASA NTRS1977

Use of TV in space science activities - Some considerations

Advantages in the use of TV on board satellites as the primary data-recording system in a manned space laboratory when certain types of experiments are flown are indicated. Real-time or near-real-time validation, elimination of film weight, improved depth of field and low-light sensitivity, and better adaptability to computer and electronic processing of data are spelled out as advantages of TV over photographic techniques, say, in fluid dynamics experiments, and weightlessness studies.

Bannister, T. C.↗
NASA NTRS1979

Real-time onboard geometric image correction

A system to perform real time onboard geometric connection of LANDSAT D resolution satellite imagery is described. System requirements, algorithms, sensors, and other hardware components are defined. Feasibility of implementing the correction process is demonstrated using Kalman filter techniques to incorporate information from onboard ephemeris, attitude control, and ground control points. Random access sensor systems, such as charge injected devices and charge coupled devices are used to obtain pixel values at desired ground location, thus greatly reducing the data processing requirements.

Discenza, W.↗
NASA NTRS1980

Mark 3 real-time fringe detection system

A RAM memory built into the Mark 3 decoder module allows the capture of 1 Megabit of data. Data may be collected either in real time or from a pre-recorded tape. Once collected, the data may be retrieved using a standard EIA serial data link. The data may be transmitted to a remote computer for cross correlation processing with similar data from other stations to verify fringes in real time. The data may also be analyzed by a local computer to verify phase calibration, bandpass, format, etc., during a Mark 3 observing session.

Levine, J. I.↗
NASA NTRS1980

NEEDS - Information Adaptive System

The Information Adaptive System (IAS) is an element of the NASA End-to-End Data System (NEEDS) Phase II and is focused toward onboard image processing. The IAS is a data preprocessing system which is closely coupled to the sensor system. Some of the functions planned for the IAS include sensor response nonuniformity correction, geometric correction, data set selection, data formatting, packetization, and adaptive system control. The inclusion of these sensor data preprocessing functions onboard the spacecraft will significantly improve the extraction of information from the sensor data in a timely and cost effective manner, and provide the opportunity to design sensor systems which can be reconfigured in near real-time for optimum performance. The purpose of this paper is to present the preliminary design of the IAS and the plans for its development.

Kelly, W. L.↗
NASA NTRS1978

Bank Terminals

In the photo, employees of the UAB Bank, Knoxville, Tennessee, are using Teller Transaction Terminals manufactured by SCI Systems, Inc., Huntsville, Alabama, an electronics firm which has worked on a number of space projects under contract with NASA. The terminals are part of an advanced, computerized financial transaction system that offers high efficiency in bank operations. The key to the system's efficiency is a "multiplexing" technique developed for NASA's Space Shuttle. Multiplexing is simultaneous transmission of large amounts of data over a single transmission link at very high rates of speed. In the banking application, a small multiplex "data bus" interconnects all the terminals and a central computer which stores information on clients' accounts. The data bus replaces the maze-of wiring that would be needed to connect each terminal separately and it affords greater speed in recording transactions. The SCI system offers banks real-time data management through constant updating of the central computer. For example, a check is immediately cancelled at the teller's terminal and the computer is simultaneously advised of the transaction; under other methods, the check would be cancelled and the transaction recorded at the close of business. Teller checkout at the end of the day, conventionally a time-consuming matter of processing paper, can be accomplished in minutes by calling up a summary of the day's transactions. SCI manufactures other types of terminals for use in the system, such as an administrative terminal that provides an immediate printout of a client's account, and another for printing and recording savings account deposits and withdrawals. SCI systems have been installed in several banks in Tennessee, Arizona, and Oregon and additional installations are scheduled this year.

Source record↗
DOE OSTI2025

Fusion of Experiments and Simulations for Real-Time Identification of Pipeline Defects

In this study, we explored fusion of experiments and simulations for real time identification of pipeline defects across physical and non-physical domains. The challenges associated to data processing were addressed and a combined classification models was presented via CNN models. In addition, regression model based on XGBOOST is built to determine the defect location and defect dimension from data-driven features of guided wave signals captured by SMS fiber optic sensor.

deep learning↗
DOE OSTI2025

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Reverse Osmosis (RO) are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in (ultra-filtration) UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square error (RMSE) metric. To evaluate how different classes of process variables contribute to TMP dynamics over time, we examine the feature importance of independent covariates across multiple forecast horizons. This analysis provides insight into the temporal relevance of operational and sensor-derived features, guiding control and monitoring strategies. Additionally, the impact of hyperparameter tuning on TMP prediction performance is studied for both direct and recursive RF modelling approaches across increasing forecast horizons. Accurate prediction of initial TMP is critical for optimizing RO operations, as it enables the development of robust modelling frameworks by accurately estimating membrane fouling trends, thereby enhancing process efficiency and long-term reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2025

Insights Into On-Demand Transit: A Case Study of Houston METRO's curb2curb Transit Services: Preprint

As modern on-demand transit (ODT) deployments increase, the insights gained from studying real-world systems prove more useful. In this study, a shared ODT system called "METRO curb2curb" in the suburbs of Houston, Texas was studied, with a particular focus on the Missouri City zone, which has the highest ridership out of the four total zones in which the system operates. The Missouri City zone spans 18 square miles with an average daily ridership of nearly 400 as of November 2024. In order to gain a passenger perspective, data collection was performed through using the ODT system first-hand, completing 50 rides in March and April of 2023. These rides included a mix of three different types of on-demand travel accommodations - 1.) advance bookings (30 min - 2 hours ahead), 2.) as-soon-as-possible / real-time dispatch bookings, and 3.) no-reservation trips at two designated "anchor points." Wait and travel time were collected for each ride. Anecdotal findings related to ease of locating the driver/vehicle, the process of booking a ride, and popular trip purposes were also documented. This study provides an overview of the METRO curb2curb system, including its history, service provider, service area, ridership, booking options, wait time, and travel time. Intricacies related to different definitions of wait time are included. Finally, relevant considerations for automating the on-demand transit system and discussed.

33 ADVANCED PROPULSION SYSTEMS↗