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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

A noise assessment and prediction system

A system has been designed to provide an assessment of noise levels that result from testing activities at Aberdeen Proving Ground, Md. The system receives meteorological data from surface stations and an upper air sounding system. The data from these systems are sent to a meteorological model, which provides forecasting conditions for up to three hours from the test time. The meteorological data are then used as input into an acoustic ray trace model which projects sound level contours onto a two-dimensional display of the surrounding area. This information is sent to the meteorological office for verification, as well as the range control office, and the environmental office. To evaluate the noise level predictions, a series of microphones are located off the reservation to receive the sound and transmit this information back to the central display unit. The computer models are modular allowing for a variety of models to be utilized and tested to achieve the best agreement with data. This technique of prediction and model validation will be used to improve the noise assessment system.

Olsen, Robert O.↗
DOE OSTI2024

AEOLUS: Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems

The AEOLUS Center is dedicated to developing a unified optimization-under-uncertainty framework for (1) learning predictive models from data and (2) optimizing experiments, processes, and designs governed by these models, all driven by complex, uncertain energy systems. AEOLUS addressed the critical need for principled, rigorous, scalable, and structure-exploiting capabilities for exploring parameter and decision spaces of complex forward simulation models---the so-called outer loop. This report summarizes the work done under DE-SC0021077 on (1) nonlocal models for solidification problems, (2) a multifidelity method for a nonlocal diffusion model, and (3) multifidelity Monte Carlo methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Hybrid Large Eddy Simulation/Reynolds-Averaged Navier–Stokes Analysis of a Premixed Ethylene-Fueled Dual-Mode Scramjet Combustor

Hydrocarbon fuels offer optimal high energy per volume for scramjet applications for sustained hypersonic flight but require additional residence time due to slower ignition delays (compared with hydrogen fuel). The injection of ethylene at the start of the isolator of a dual-mode scramjet combustor, operating in ramjet mode, allows sufficient mixing to achieve efficient premixed turbulent combustion. A cavity flameholder anchors the flame, supplying sufficient radicals to sustain a stable flame in the high-speed environment. This work investigates flame structure and stabilization limits of a new configuration with a scaled-down cavity embedded in the flow path of the combustor with a strut and insert. The cavity is reduced in size by one-third to enable eventual direct numerical simulations of the flame stabilization process. This work, however, focuses on modeling the full isolator/combustor geometry using a hybrid large eddy simulation/Reynolds-averaged Navier–Stokes simulation strategy. Additionally, particle image velocimetry, planar laser-induced fluorescence, coherent anti-Stokes Raman spectroscopy, and pressure measurements are compared with numerical predictions to analyze and characterize the conditions within the combustor, including flame structure, flow velocities, species composition, and wall pressure. An adjustable air throttle in the extender is able to control the placement of the shock train in the isolator and maintain a stable flame at various equivalence ratios. The simulations show reasonably good agreement with the experimental scalar and velocity data and predict a flame angle consistent with premixed turbulent flame-speed correlations.

42 ENGINEERING↗
NASA NTRS1994

Integrated active and passive control design methodology for the LaRC CSI evolutionary model

A general design methodology to integrate active control with passive damping was demonstrated on the NASA LaRC CSI Evolutionary Model (CEM), a ground testbed for future large, flexible spacecraft. Vibration suppression controllers designed for Line-of Sight (LOS) minimization were successfully implemented on the CEM. A frequency-shaped H2 methodology was developed, allowing the designer to specify the roll-off of the MIMO compensator. A closed loop bandwidth of 4 Hz, including the six rigid body modes and the first three dominant elastic modes of the CEM was achieved. Good agreement was demonstrated between experimental data and analytical predictions for the closed loop frequency response and random tests. Using the Modal Strain Energy (MSE) method, a passive damping treatment consisting of 60 viscoelastically damped struts was designed, fabricated and implemented on the CEM. Damping levels for the targeted modes were more than an order of magnitude larger than for the undamped structure. Using measured loss and stiffness data for the individual damped struts, analytical predictions of the damping levels were very close to the experimental values in the (1-10) Hz frequency range where the open loop model matched the experimental data. An integrated active/passive controller was successfully implemented on the CEM and was evaluated against an active-only controller. A two-fold increase in the effective control bandwidth and further reductions of 30 percent to 50 percent in the LOS RMS outputs were achieved compared to an active-only controller. Superior performance was also obtained compared to a High-Authority/Low-Authority (HAC/LAC) controller.

Voth, Christopher T.↗
NASA NTRS1997

Performance Comparison Between NiH2 Dry Sinter and Slurry Electrode Cells

The electrical and thermal performance of dry sinter and slurry process electrode cells manufactured for the Hubble Space Telescope (HST) batteries have been characterized for a matrix of operating conditions over the temperature range from 14 to 86 F at various charge control levels. The dry sinter process electrode cells tested are similar to the onboard HST NiH2 cells. The slurry process electrode cells were developed to be less susceptible to electrode expansion and impedance changes with life. Both cell types were impregnated by the aqueous electrochemical process. Test conditions included standard capacity tests and electrical cycling using 96-minute cycling regimens incorporating gr depth-of-discharge (DOD) cycles. The dry sinter process electrodes have higher operating capacities to 1.20V/cell, but both electrode types have similar heat dissipation for the conditions tested. The results of the testing included cyclic heat generation during a typical 96-minute cycle, operating capacity data vs. cutoff voltage to generate a temperature-compensated voltage curve, and voltage characteristics suitable to develop a voltage prediction model. Analysis of data shows differences in the discharge voltage plateaus operating conditions evaluated. This is the basis for recommended changes in the battery charge control.

Armantrout, J. D.↗
NASA NTRS2005

Capillary Flow Experiments Began on the International Space Station

The Capillary Flow Experiments (CFEs) are a suite of fluid physics flight experiments designed to investigate capillary flows and phenomena in low gravity. Data obtained from the CFEs will be crucial to NASA s Space Exploration Initiative, particularly pertaining to fluids management systems such as fuels and cryogen storage systems, thermal control systems (e.g., water recycling), and materials processing in the liquid state. NASA s current plans for exploration missions assume the use of larger liquid propellant masses than have ever flown on interplanetary missions. Under low-gravity conditions, capillary forces can be exploited to control fluid orientation so that such large mission-critical systems perform predictably. The first of the CFE experiments has been conducted on the International Space Station, and the data are being analyzed. The experiment suite is described briefly.

J. Mark Hickman↗
DOE OSTI2022

Predicting defects in laser powder bed fusion using in-situ thermal imaging data and machine learning

Variation in the local thermal history during the Laser Powder Bed Fusion (LPBF) process in Additive Manufacturing (AM) can cause micropore defects, which add to the uncertainty of the mechanical properties (e.g., fatigue life, tensile strength) of the built materials. In-situ sensing has been proposed for monitoring the AM process to minimize defects, but successful minimization requires establishing a quantitative relationship between the sensing data and the porosity, which is particularly challenging with a large number of variables (e.g., laser speed, power, scan path, powder property). Physics-based modeling can simulate such an in-situ sensing-porosity relationship, but it is computationally costly. In this work, we develop Machine Learning (ML) models that can use in-situ thermographic data to predict the micropore of LPBF stainless steel materials. This work considers two identified key features from the thermal histories: the time above the apparent melting threshold ($\tau$) and the maximum radiance ( T max ). These features are computed, stored for each voxel in the built material, and then used as inputs. The binary state of each voxel, either defective or normal, is the output. Different ML models are trained and tested for the binary classification task. In addition to using the thermal features of each voxel to predict its own state, the thermal features of neighboring voxels are also included as inputs. This is shown to improve the prediction accuracy, which is consistent with thermal transport physics around each voxel contributing to its final state. Among the models trained, the F1 scores on test sets reach above 0.96 for Random Forests. Feature importance analysis based on the ML models shows that T max is more important to the voxel state than $\tau$ . The analysis also finds that the thermal history of the voxels above the present voxel is more influential than those beneath it. Our study significantly extends the capability of using in-situ thermographic data to predict porosity in LPBF materials. Finally, since ML models are fast, they may play integral roles in the optimization and control of such AM technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Control And Optimization Modular Modeling Application For Nuclear Deployment

The purpose of the COMMAND code is to provide a flexible, scalable tool for use in developing, integrating, and testing the technologies necessary for achieving autonomous operations of advanced nuclear reactors. The code enables users to efficiently implement custom simulations and experiments by combining key methods from different software modules. These modules are focused on: modeling and simulation tools, such as nuclear simulation tools used for high-fidelity modeling (e.g., Reactor Excursion and Leak Analysis Program [RELAP5-3D] and Monte Carlo N-Particle [MCNP]); machine learning and optimization tools (e.g., anomaly detection and data-driven modeling techniques); advanced control in its digital, high-performance, and supervisory control forms (e.g., proportional integral derivative (PID) control and model predictive control (MPC); and integration with hardware through industrial communication protocols. To ensure flexibility and scalability, COMMAND was designed to be both modular—the software “pieces” all inherit from generic building blocks and can be combined and connected to create complicated simulations—and high performing—designed for parallel processing, enabling simulations and experiments to take advantage of multi-core computers, servers, and nodes. The code is written in the Python programming language due to the language's popularity, active community, and open-source and cross-platform nature. Maintaining consistency with other simulation tools used within the nuclear energy community, users implement simulations and experiments through text input files, which define components, parameters, connections, etc., through lines of text. Given that COMMAND is written in Python, these input files are native Python scripts, and so use the standard Python structure and formatting. This also enables users to take advantage of Python's extensive package library to develop custom capabilities for their specific use cases.

Faber, Jacob [Idaho National Laboratory (INL), Ida↗
DOE OSTI2024

Exploring 2D X-ray diffraction phase fraction analysis with convolutional neural networks: Insights from kinematic-diffraction simulations

Abstract Deep-learning models are effective for analyzing the complex information in 2D X-ray diffraction (XRD) patterns. Accurately collecting parameters of the material sample is crucial during model training, significantly impacting model performance. In this study, we employ a kinematic-diffraction simulator to generate simulated 2D XRD patterns for Ti–6Al–4V alloy, allowing precise control of sample parameters. These simulated patterns are used to train convolutional neural networks, predicting $$\upbeta$$ β -phase volume fractions. The training data set consists exclusively of 2D XRD patterns with pure $$\upalpha$$ α - or pure $$\upbeta$$ β -phase, while the testing set incorporates patterns with intermediate phase volume fraction. In particular, we investigate how the architectures of the model influence prediction reliability and computational performance. Experimental results reveal that, with appropriate training, the convolutional neural network accurately detects intermediate phase volume fractions even trained with only pure-phase patterns, achieving a mean square error accuracy of $$9.4 \times 10^{-4}$$ 9.4 × 10 - 4 . Graphical abstract

Yue, Weiqi↗
DOE OSTI2024

The influence of burn severity on dissolved organic carbon concentrations across a stream network differs based on seasonal wetness conditions

Large, high-severity wildfires in many regions across the globe have increased concerns about their impacts on carbon cycling in watersheds. Altered sources of carbon and changes in catchment hydrology after wildfire can lead to shifts in dissolved organic carbon (DOC) concentrations in streams, which can have negative impacts on aquatic ecosystem health and downstream drinking-water treatment. Despite its importance, post-fire DOC responses remain relatively unconstrained in the literature, and we lack critical knowledge of how burn severity, landscape elements, and climate interact to affect DOC concentrations. To improve our understanding of the impact of burn severity on DOC concentrations, we measured DOC at 129 sites across a stream network extending upstream, within, and downstream of a large, high-severity wildfire in Oregon, USA. We collected samples across the study sub-basin during four distinct seasonal wetness conditions. We used our high-spatial-resolution data to develop spatial stream network (SSN) models to predict DOC across the stream network and to improve our understanding of the controls on DOC concentrations. Spatially, we found no obvious wildfire signal – instead, we observed a pattern of increasing DOC concentrations from the high-elevation headwaters to the sub-basin outlet, while the mainstem maintained consistently low DOC concentrations. This suggests that effects from large wildfires may be “averaged” out at higher stream orders and larger spatial scales. When we grouped DOC concentrations by burn severity group, we observed a significant decrease in the variability of DOC concentrations in the moderate and high burn severity sub-catchments. However, our SSN models were able to predict decreases in DOC concentrations with increases in burn severity across the stream network. Decreases in DOC concentrations were also highly variable across seasonal wetness conditions, with the greatest (–1.40 to –1.64 mg L –1 ) decrease occurring in the high-severity group during the wetting season. Additionally, our models indicated that in all seasons, baseflow index was more influential in predicting DOC concentrations than burn severity was, indicating that groundwater discharge can obscure the impacts of wildfire in a stream network. Overall, our results suggested that landscape characteristics can regulate the DOC response to wildfire. Moreover, our results also indicated that the seasonal timing of sampling can influence the observed response of DOC concentrations to wildfire.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

Correlation of Predicted and Flight Derived Stability and Control Derivatives with Particular Application to Tailless Delta Wing Configurations

Flight derived longitudinal and lateral-directional stability and control derivatives were compared to wind-tunnel derived values. As a result of these comparisons, boundaries representing the uncertainties that could be expected from wind-tunnel predictions were established. These boundaries provide a useful guide for control system sensitivity studies prior to flight. The primary application for this data was the space shuttle, and as a result the configurations included in the study were those most applicable to the space shuttle. The configurations included conventional delta wing aircraft as well as the X-15 and lifting body vehicles.

Weil, J.↗
NASA NTRS1986

Lateral stability and control derivatives extracted from five early flights of the space shuttle Columbia

Flight data taken from the first five flights (STS-2, 3, 4, 5 and 9) of the Space Transportation System Shuttle Columbia during entry are analyzed to determine the Shuttle lateral aerodynamic characteristics. Maximum likelihood estimation is applied to data derived from accelerometer and rate gyro measurements and trajectory, meteorological and control surface data to estimate lateral-directional stability and control derivatives. The estimated parameters are compared across the five flights and to preflight predicted values.

Schiess, J. R.↗
NASA NTRS1988

ARGES: an Expert System for Fault Diagnosis Within Space-Based ECLS Systems

ARGES (Atmospheric Revitalization Group Expert System) is a demonstration prototype expert system for fault management for the Solid Amine, Water Desorbed (SAWD) CO2 removal assembly, associated with the Environmental Control and Life Support (ECLS) System. ARGES monitors and reduces data in real time from either the SAWD controller or a simulation of the SAWD assembly. It can detect gradual degradations or predict failures. This allows graceful shutdown and scheduled maintenance, which reduces crew maintenance overhead. Status and fault information is presented in a user interface that simulates what would be seen by a crewperson. The user interface employs animated color graphics and an object oriented approach to provide detailed status information, fault identification, and explanation of reasoning in a rapidly assimulated manner. In addition, ARGES recommends possible courses of action for predicted and actual faults. ARGES is seen as a forerunner of AI-based fault management systems for manned space systems.

Pachura, David W.↗
NASA NTRS2007

Tailored Excitation for Frequency Response Measurement Applied to the X-43A Flight Vehicle

An important aspect of any flight research project is assessing aircraft stability and flight control performance. In some programs this assessment is accomplished through the estimation of the in-flight vehicle frequency response. This estimation has traditionally been a lengthy task requiring separate swept sine inputs for each control axis at a constant flight condition. Hypersonic vehicles spend little time at any specific flight condition while they are decelerating. Accordingly, it is difficult to use traditional methods to calculate the vehicle frequency response and stability margins for this class of vehicle. A technique has been previously developed to significantly reduce the duration of the excitation input by tailoring the input to excite only the frequency range of interest. Reductions in test time were achieved by simultaneously applying tailored excitation signals to multiple control loops, allowing a quick estimate of the frequency response of a particular aircraft. This report discusses the flight results obtained from applying a tailored excitation input to the X-43A longitudinal and lateral-directional control loops during the second and third flights. The frequency responses and stability margins obtained from flight data are compared with preflight predictions.

Baumann, Ethan↗
DOE OSTI2020

Using predictions of outcomes of cache memory access requests for controlling whether a request generator sends memory access requests to a memory in parallel with cache memory access requests

An electronic device handles memory access requests for data in a memory. The electronic device includes a memory controller for the memory, a last-level cache memory, a request generator, and a predictor. The predictor determines a likelihood that a cache memory access request for data at a given address will hit in the last-level cache memory. Based on the likelihood, the predictor determines: whether a memory access request is to be sent by the request generator to the memory controller for the data in parallel with the cache memory access request being resolved in the last-level cache memory, and, when the memory access request is to be sent, a type of memory access request that is to be sent. When the memory access request is to be sent, the predictor causes the request generator to send a memory request of the type to the memory controller.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Time Delay of Wide Area Damping Control in Urban Power Grid: Model-Based Analysis and Data-Driven Compensation

Due to the rapid development of economies, large urban cities consume an increasing amount of energy and have a higher requirement for power quality. Voltage source converter based high voltage direct current (VSC-HVDC) is a promising device to transmit clean power from remote regions to urban power systems, while also providing wide area damping control (WADC) for frequency stabilization. However, the time-delay naturally existing in the VSC-HVDC system may degrade the performance of WADC and even result in instability. To address this issue, this paper develops a time-delay correction control strategy for VSC-HVDC damping control in urban power grids. First, a small signal model of WADC is built to analyze the negative impacts of time delay. Then, a data-driven approach is proposed to compensate for the inherent time delay in VSC-HVDC damping control. The extensive training data will be generated under various disturbances. After offline training, the long short-term memory network (LSTM) can be implemented online to predict the actual frequency deviation based on real-time measurements. Finally, the proposed method is validated through MATLAB-Simulink in a two-area four-machine system. The results indicate that the data-driven compensation has a strong generalization ability for random delay time constants and can improve the performance of WADC significantly.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1982

Applications of system identification methods to the prediction of helicopter stability, control and handling characteristics

A set of results on rotorcraft system identification is described. Flight measurements collected on an experimental Puma helicopter are reviewed and some notable characteristics highlighted. Following a brief review of previous work in rotorcraft system identification, the results of state estimation and model structure estimation processes applied to the Puma data are presented. The results, which were obtained using NASA developed software, are compared with theoretical predictions of roll, yaw and pitching moment derivatives for a 6 degree of freedom model structure. Anomalies are reported. The theoretical methods used are described. A framework for reduced order modelling is outlined.

Padfield, G. D.↗
NASA NTRS2013

Data Mining for Understanding and Impriving Decision-Making Affecting Ground Delay Programs

The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions. The work reported in this paper is focused on ground delay programs. Data mining algorithms have the potential to develop associations between weather patterns and the corresponding ground delay program responses. If successful, they can be used to improve and standardize TFM decision resulting in better predictability of traffic flows on days with reliable weather forecasts. The approach here seeks to develop a set of data mining and machine learning models and apply them to historical archives of weather observations and forecasts and TFM initiatives to determine the extent to which the theory can predict and explain the observed traffic flow behaviors.

data mining↗