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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Cybersecurity Anomaly Detection in SCADA-Assisted OT Networks Using Ensemble-Based State Prediction Model

The cybersecurity threats of power system gradually grow due to the increased sophisticated interactions between Information Technology (IT) and Operational Technology (OT) networks. False data injection attack (FDIA) that aims to compromise the Supervisory Control and Data Acquisition (SCADA) measurement and disturb the system operation is one of such cyber threats. Such attacks can potentially lead to significant operational issues at the control centers and substations, and hence, result in severe physical consequences. To avoid catastrophic failure across the power grid resulting from these attacks, it is essential to arm the OT network with real-time vulnerability assessment tools. To this end, this paper outlines various drawbacks of the Purdue architecture model to defend against cyberattacks in the OT network. Furthermore, a novel ensemble-based state prediction model is proposed to detect cybersecurity anomalies in SCADA assisted OT networks. The proposed model uses control center level generation and load forecasts, scheduled, and forced outages, power flow solutions, and the substation level historical data. The hypothesis of the proposed scheme relies on the fact that additional control center and substation data can hardly be accessed and compromised by attackers. One of the vital features of the proposed scheme is an hour-ahead prediction of the operational feasibility of the SCADA measurement range at the control center and substation in real time helps in detecting anomalies in measurements across both substation and the control center.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Lawrence Berkley National Laboratory Building 59 (Raw)

The building management system in Building 59 is monitoring and archiving building-level electricity usage, HVAC and lighting system states (e.g., setpoint, temperature, flow rate, pressure), indoor environmental conditions (air temperature, relative humidity, CO2), on-site weather (air temperature, relative humidity), and especially occupant counts as well as other metrics such as Wi-Fi signal. This dataset could support multiple use cases, such as model predictive control and occupant related demand management. Raw data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1991

Micromechanics analysis of space simulated thermal stresses in composites. I - Theory and unidirectional laminates. II - Multidirectional laminates and failure predictions

A micromechanics analysis is used to study the effects of constituent properties on thermally induced stresses in continuous fiber reinforced composites. A finite element formulation is described, and results are presented for unidirectional carbon/epoxy laminates. It is shown that significant stresses develop in composites exposed to thermal excursions typical of spacecraft operating environments and that the fiber thermoelastic properties have a minimal effect on the magnitude of these stresses. The finite element micromechanics analysis is then extended to the study of multidirectional laminates using a simple global/local formulation. Damage initiation predictions are compared with experimental data, and factors controlling the initiation of damage are identified. Ways of improving the durability of composites are discussed.

Bowles, David E.↗
DOE OSTI2024

Digital Twin + AI: Control Room of the Future

A digital twin enhances power grid control room operations by providing real-time monitoring, predictive insights, simulation capabilities, remote control, training opportunities, data integration, and decision support. This technology empowers control room operators to effectively manage the grid, optimize performance, and ensure reliable and efficient energy distribution.

control room of the future↗
DOE OSTI2022

Control flow guided lock address prefetch and filtering

A method of prefetching target data includes, in response to detecting a lock-prefixed instruction for execution in a processor, determining a predicted target memory location for the lock-prefixed instruction based on control flow information associating the lock-prefixed instruction with the predicted target memory location. Target data is prefetched from the predicted target memory location to a cache coupled with the processor, and after completion of the prefetching, the lock-prefixed instruction is executed in the processor using the prefetched target data.

Mashimo, Susumu↗
DOE OSTI2021

Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize

High-resolution point cloud data acquired with a laser scanner from any platform contain random noise and outliers. Therefore, outlier detection in LiDAR data is often necessary prior to analysis. Applications in agriculture are particularly challenging, as there is typically no prior knowledge of the statistical distribution of points, plant complexity, and local point densities, which are crop-dependent. The goals of this study were first to investigate approaches to minimize the impact of outliers on LiDAR acquired over agricultural row crops, and specifically for sorghum and maize breeding experiments, by an unmanned aerial vehicle (UAV) and a wheel-based ground platform; second, to evaluate the impact of existing outliers in the datasets on leaf area index (LAI) prediction using LiDAR data. Two methods were investigated to detect and remove the outliers from the plant datasets. The first was based on surface fitting to noisy point cloud data via normal and curvature estimation in a local neighborhood. The second utilized the PointCleanNet deep learning framework. Both methods were applied to individual plants and field-based datasets. To evaluate the method, an F-score was calculated for synthetic data in the controlled conditions, and LAI, the variable being predicted, was computed both before and after outlier removal for both scenarios. Results indicate that the deep learning method for outlier detection is more robust than the geometric approach to changes in point densities, level of noise, and shapes. The prediction of LAI was also improved for the wheel-based vehicle data based on the coefficient of determination (R2) and the root mean squared error (RMSE) of the residuals before and after the removal of outliers.

36 MATERIALS SCIENCE↗
NASA NTRS1979

Vortex development on slender missiles at supersonic speeds

A theoretical and experimental effort has been made to develop a vortex-prediction capability on circular and noncircular missiles at supersonic speeds. Predicted vortex patterns are computed by two linear-theory computer codes. One calculates the strengths and initial locations of the vortices, and the other calculates their trajectories. A short color motion picture has been produced from the calculations to illustrate the predicted vortex patterns on a typical missile. Experimental vapor-screen photographs are presented to show the longitudinal development of the vortices on a fin-control missile. Comparisons are made between these data and the predicted vortices to assess the accuracy of the theory. The theory appears to be fairly accurate in predicting the number, locations, and relative strengths of individual vortices which develop over the missile, but cannot predict vortex sheets or diffuse vorticity whenever they occur.

Allen, J. M.↗
NASA NTRS1993

Computerized atmospheric trace contaminant control simulation for manned spacecraft

Buildup of atmospheric trace contaminants in enclosed volumes such as a spacecraft may lead to potentially serious health problems for the crew members. For this reason, active control methods must be implemented to minimize the concentration of atmospheric contaminants to levels that are considered safe for prolonged, continuous exposure. Designing hardware to accomplish this has traditionally required extensive testing to characterize and select appropriate control technologies. Data collected since the Apollo project can now be used in a computerized performance simulation to predict the performance and life of contamination control hardware to allow for initial technology screening, performance prediction, and operations and contingency studies to determine the most suitable hardware approach before specific design and testing activities begin. The program, written in FORTRAN 77, provides contaminant removal rate, total mass removed, and per pass efficiency for each control device for discrete time intervals. In addition, projected cabin concentration is provided. Input and output data are manipulated using commercial spreadsheet and data graphing software. These results can then be used in analyzing hardware design parameters such as sizing and flow rate, overall process performance and program economics. Test performance may also be predicted to aid test design.

Perry, J. L.↗
NASA NTRS2013

TSAFE Interface Control Document v 2.0

This document specifies the data interface for TSAFE, the Tactical Separation-Assured Flight Environment. TSAFE is a research prototype of a software application program for alerting air traffic controllers to imminent conflicts in enroute airspace. It is intended for Air Route Traffic Control Centers ("Centers") in the U.S. National Airspace System. It predicts trajectories for approximately 3 minutes into the future, searches for conflicts, and sends data about predicted conflicts to the client, which uses the data to alert an air traffic controller of conflicts. TSAFE itself does not provide a graphical user interface.

Paielli, Russell A.↗
DOE OSTI2023

Two decades of fumigation data from the Soybean Free Air Concentration Enrichment facility

Abstract The Soybean Free Air Concentration Enrichment (SoyFACE) facility is the longest running open-air carbon dioxide and ozone enrichment facility in the world. For over two decades, soybean, maize, and other crops have been exposed to the elevated carbon dioxide and ozone concentrations anticipated for late this century. The facility, located in East Central Illinois, USA, exposes crops to different atmospheric concentrations in replicated octagonal ~280 m 2 Free Air Concentration Enrichment (FACE) treatment plots. Each FACE plot is paired with an untreated control (ambient) plot. The experiment provides important ground truth data for predicting future crop productivity. Fumigation data from SoyFACE were collected every four seconds throughout each growing season for over two decades. Here, we organize, quality control, and collate 20 years of data to facilitate trend analysis and crop modeling efforts. This paper provides the rationale for and a description of the SoyFACE experiments, along with a summary of the fumigation data and collation process, weather and ambient data collection procedures, and explanations of air pollution metrics and calculations.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1977

ANOPP programmer's reference manual for the executive System

Documentation for the Aircraft Noise Prediction Program as of release level 01/00/00 is presented in a manual designed for programmers having a need for understanding the internal design and logical concepts of the executive system software. Emphasis is placed on providing sufficient information to modify the system for enhancements or error correction. The ANOPP executive system includes software related to operating system interface, executive control, and data base management for the Aircraft Noise Prediction Program. It is written in Fortran IV for use on CDC Cyber series of computers.

Gillian, R. E.↗
DOE OSTI2026

Data-Driven Modeling and Correction of Vehicle Dynamics

We develop a data-driven framework for learning and correcting nonautonomous vehicle dynamics. Physics-based vehicle models are often simplified for tractability and therefore exhibit inherent model-form uncertainty, motivating the need for data-driven correction. Moreover, nonautonomous dynamics are governed by time-dependent control inputs, which pose challenges in learning predictive models directly from temporal snapshot data. To address these, we reformulate the vehicle dynamics via a local parameterization of the time-dependent inputs, yielding a modified system composed ofa sequence of local parametric dynamical systems. Here, we approximate these parametric systems using two complementary approaches. First, we employ the dimension reduction and interpolation in parameter space (DRIPS) methodology to construct efficient linear surrogate models, equipped with lifted observable spaces and manifold-based operator interpolation. This enables data-efficient learning of vehicle models whose dynamics admit accurate linear representations in the lifted spaces. Second, for more strongly nonlinear systems, we employ flow map learning (FML), a deep neural network (DNN) approach that approximates the parametric evolution map without requiring special treatment of nonlinearities. We further extend FML with a transfer-learning-based model correction procedure, enabling the correction of misspecified prior models using only a sparse set of high-fidelity or experimental measurements, without assuming a prescribed form for the correction term. Through a suite of numerical experiments on unicycle, simplified bicycle, and slip-based bicycle models, we demonstrate that DRIPS offers robust and highly data-efficient learning of nonautonomous vehicle dynamics, while FML provides expressive nonlinear modeling and effective correction of model-form errors under severe data scarcity.

data-driven modeling↗
NASA NTRS1989

Prediction of forces and moments for flight vehicle control effectors: Workplan

Two research activities directed at hypersonic vehicle configurations are currently underway. The first involves the validation of a number of classical local surface inclination methods commonly employed in preliminary design studies of hypersonic flight vehicles. Unlike studies aimed at validating such methods for predicting overall vehicle aerodynamics, this effort emphasizes validating the prediction of forces and moments for flight control studies. Specifically, several vehicle configurations for which experimental or flight-test data are available are being examined. By comparing the theoretical predictions with these data, the strengths and weaknesses of the local surface inclination methods can be ascertained and possible improvements suggested. The second research effort, of significance to control during take-off and landing of most proposed hypersonic vehicle configurations, is aimed at determining the change due to ground effect in control effectiveness of highly swept delta planforms. Central to this research is the development of a vortex-lattice computer program which incorporates an unforced trailing vortex sheet and an image ground plane. With this program, the change in pitching moment of the basic vehicle due to ground proximity, and whether or not there is sufficient control power available to trim, can be determined. In addition to the current work, two different research directions are suggested for future study. The first is aimed at developing an interactive computer program to assist the flight controls engineer in determining the forces and moments generated by different types of control effectors that might be used on hypersonic vehicles. The first phase of this work would deal in the subsonic portion of the flight envelope, while later efforts would explore the supersonic/hypersonic flight regimes. The second proposed research direction would explore methods for determining the aerodynamic trim drag of a generic hypersonic flight vehicle and ways in which it can be minimized through vehicle design and trajectory optimization.

Maughmer, Mark D.↗
NASA NTRS2001

Real-Time Surface Traffic Adviser

A real-time data management system which uses data generated at different rates by multiple heterogeneous incompatible data sources are presented. In one embodiment, the invention is as an airport surface traffic data management system (traffic adviser) that electronically interconnects air traffic control, airline, and airport operations user communities to facilitate information sharing and improve taxi queuing. The system uses an expert system to fuse dam from a variety of airline, airport operations, ramp control, and air traffic control sources, in order to establish, predict, and update reference data values for every aircraft surface operation.

Brian J Glass↗
NASA NTRS1972

Longitudinal aerodynamic characteristics of light, twin-engine, propeller-driven airplanes

Representative state-of-the-art analytical procedures and design data for predicting the longitudinal static and dynamic stability and control characteristics of light, propeller-driven airplanes are presented. Procedures for predicting drag characteristics are also included. The procedures are applied to a twin-engine, propeller-driven airplane in the clean configuration from zero lift to stall conditions. The calculated characteristics are compared with wind-tunnel and flight data. Included in the comparisons are level-flight trim characteristics, period and damping of the short-period oscillatory mode, and windup-turn characteristics. All calculations are documented.

Wolowicz, C. H.↗
NASA NTRS1984

Rotary balance data for a typical single-engine general aviation design for an angle-of-attack range of 20 to 90 deg. 3: Influence of control deflection on predicted model D spin modes

The influence of control deflections on the rotational flow aerodynamics and on predicted spin modes is discussed for a 1/6-scale general aviation airplane model. The model was tested for various control settings at both zero and ten degree sideslip angles. Data were measured, using a rotary balance, over an angle-of-attack range of 30 deg to 90 deg, and for clockwise and counter-clockwise rotations covering an omegab/2V range of 0 to 0.5.

Ralston, J. N.↗