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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 NTRS2015

Benefit Assessment for Metroplex Tactical Runway Configuration Management (mTRCM) in a Simulated Environment

The System-Oriented Runway Management (SORM) concept is a collection of capabilities focused on a more efficient use of runways while considering all of the factors that affect runway use. Tactical Runway Configuration Management (TRCM), one of the SORM capabilities, provides runway configuration and runway usage recommendations, and monitoring the active runway configuration for suitability given existing factors. This report focuses on the metroplex environment, with two or more proximate airports having arrival and departure operations that are highly interdependent. The myriad of factors that affect metroplex opeations require consideration in arriving at runway configurations that collectively best serve the system as a whole. To assess the metroplex TRCM (mTRCM) benefit, the performance metrics must be compared with the actual historical operations. The historical configuration schedules can be viewed as the schedules produced by subject matter experts (SMEs), and therefore are referred to as the SMEs' schedules. These schedules were obtained from the FAA's Aviation System Performance Metrics (ASPM) database; this is the most representative information regarding runway configuration selection by SMEs. This report focused on a benefit assessment of total delay, transit time, and throughput efficiency (TE) benefits using the mTRCM algorithm at representative volumes for today's traffic at the New York metroplex (N90).

Phojanamongkolkij, Nipa↗
NASA NTRS2019

Development of Large-Scale Magnetic Calorimeter Arrays

We describe performance of large-scale arrays of metallic magnetic calorimeters (MMCs) we are developing to meet requirements of the Lynx X-ray Microcalorimeter (LXM) instrument in the astrophysics mission concept Lynx. We have fabricated prototypes with 55,800 x-ray pixels thermally connected to 5,688 MMC sensors. Subarrays demonstrate three types of pixels, which have different energy and spatial resolution goals in LXM. Pixel pitch is 50 or 25 µm. For two subarrays, use of position sensitive detectors, in a "hydra" configuration with 25 x-ray absorbers per sensor, helps achieve a large focal plane by increasing pixel count relative to readout channels. Since each absorber has a different thermal link to its sensor, it generates a different pulse shape and enables discrimination of pixel position. Superconducting wiring from all sensors was brought out to the perimeter of the overall array using multiple buried metal layers planarized by Chemical Mechanical Planarization to achieve high critical current, low inductance, and high fabrication yield. An automated, algorithmic approach was used to layout the complex wiring pattern. For readout with existing small arrays of Superconducting Quantum Interference (SQUID) amplifiers, 112 selected pixels were connected to wire bond pads. In hydras, we successfully identified 25 pulse shapes using rise-time and pulse height. Pulse shapes were similar to simulations. We measured noise and responsivity to substrate temperature and applied x-ray pulses. While we designed sensors to have high inductance to relative to stray inductance of wiring, we had only mismatched (lower input inductance) SQUIDs available in our current apparatus, giving non-optimized resolution. We compared measured and calculated energy resolutions. We have the required sensitivity to reach < 3 eV FWHM resolution for 6 keV x-rays, required for the LXM Main Array, and the ability to discriminate the 25 different absorber pixels down to energies < 300 eV.

microcalorimeters↗
NASA NTRS2018

Optimal Control Prediction Method for Control Allocation

This paper proposes a novel prediction method for online optimal control allocation that extends the volume of moments achievable with the Moore-Penrose generalized inverse to the entire Attainable Moment Set. This method formulates the control allocation problem using selected basis vectors and associated gains which reduces the optimization problem dimensions and provides physical insight into the resulting optimal solutions. The proposed algorithm finds the entire family of unique optimal control solutions along the desired moment vector from the origin to the boundary of the Attainable Moment Set. Numerical results for the Moore-Penrose prediction method show that the unique minimal controls obtained yield the desired moment with near machine precision accuracy while maintaining control effectors within specified position limits. This method has been fully validated against the unique solution obtained on the boundary of the Attainable Moment Set using the Durham Direct Allocation method. Minimal control solutions obtained for moments in the interior of the Attainable Moment Set, similarly yield the desired moment to near machine precision while providing control solutions that are smaller (i.e. 2-norm) than solutions found with traditional control allocation algorithms (e.g. interior point methods) applied to the minimal control problem. Numerical simulations using a Matlab® autocoded executable (MEX) for the representative real world problem of 3-moments with 20 individual control effectors and prescribed control position limits show a mean computation speed of approximately 125 Hz which is sufficient to enable real-time flight allocation.

Acheson, Michael J.↗
NASA NTRSDate not supplied

Joint Spectrum Access and Power Control in Air-Air Communications - A Deep Reinforcement Learning Based Approach

This paper considers the dynamic spectrum access and power control problem in a single-hop point-to-point Air-Air Communication Network (AACN). Due to spectrum scarcity, we assume the number of Aircraft-to-Aircraft (A2A) communication links is greater than that of the available channels, such that some communication links need to share the same channel, causing co-channel interference. We formulate the joint channel selection and power control optimization problem to maximize the Weighted Sum Spectral Efficiency (WSSE). A distributed and dynamic deep Q learning-based algorithm is proposed to find the optimal solution. Specifically, we design two different policies that are trained by conducting a trial-and-error scheme. Each communication link can achieve the optimal policy by exploiting the local information from its neighbors, and this distributive approach make it scalable to large networks. Finally, our experimental results demonstrate the effectiveness of the proposed solution in various AACN scenarios.

Zhe Wang↗
NASA NTRSDate not supplied

Joint Spectrum Access and Power Control in Air-Air Communications - A Deep Reinforcement Learning Based Approach

This paper considers the dynamic spectrum access and power control problem in a single-hop point-to-point Air-Air Communication Network (AACN). Due to spectrum scarcity, we assume the number of Aircraft-to-Aircraft (A2A) communication links is greater than that of the available channels, such that some communication links need to share the same channel, causing co-channel interference. We formulate the joint channel selection and power control optimization problem to maximize the Weighted Sum Spectral Efficiency (WSSE). A distributed and dynamic deep Q learning-based algorithm is proposed to find the optimal solution. Specifically, we design two different policies that are trained by conducting a trial-and-error scheme. Each communication link can achieve the optimal policy by exploiting the local information from its neighbors, and this distributive approach make it scalable to large networks. Finally, our experimental results demonstrate the effectiveness of the proposed solution in various AACN scenarios.

Zhe Wang↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Deep Reinforcement Learning

To support sustainable infrastructure on the Moon, NASA needs to leverage lunar resources for in-situ processing and construction. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for these tasks. To reliably perform these operations on the lunar surface, RASSOR's sensors and control systems need to be robust and maximize information extracted from a reduced sensor payload. Herein, we present our findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments in which we applied reinforcement learning algorithms to learn autonomous trenching controllers and produced state estimation architectures. We developed two simulations: a 2D excavation simulation used to facilitate parameter selection, and a 3D simulation developed using a game physics engine to simulate simplified soil interactions and incorporate robotic agents parameterized by dynamic models. Within these simulations, we learned autonomous excavation routines that exceed excavation efficiency measures as compared against RASSOR's existing control and teleoperation-based methods.

RASSOR↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Deep Reinforcement Learning

To support sustainable infrastructure on the Moon, NASA needs to leverage lunar resources for in-situ processing and construction. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for these tasks. To reliably perform these operations on the lunar surface, RASSOR's sensors and control systems need to be robust and maximize information extracted from a reduced sensor payload. Herein, we present our findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments in which we applied reinforcement learning algorithms to learn autonomous trenching controllers and produced state estimation architectures. We developed two simulations: a 2D excavation simulation used to facilitate parameter selection, and a 3D simulation developed using a game physics engine to simulate simplified soil interactions and incorporate robotic agents parameterized by dynamic models. Within these simulations, we learned autonomous excavation routines that exceed excavation efficiency measures as compared against RASSOR's existing control and teleoperation-based methods.

RASSOR↗
NASA NTRS2020

A Machine Learning-Based Cloud Detection and Thermodynamic Phase Classification Algorithm using Passive Spectral Observations

We trained two Random Forest (RF) machine-learning models for cloud mask and cloud thermodynamic phase detection using spectral observations from VIIRS on Suomi NPP (SNPP). Observations from CALIOP were carefully selected to provide reference labels. The two RF models were trained for all-day and daytime-only conditions using a 4-year collocated VIIRS/CALIOP dataset from 2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPP VIIRS training samples cover a broad viewing zenith angle range, which is a great benefit to overall model performance. The all-day model uses 3 VIIRS infrared (IR) bands (8.6,11, and 12 μm) and the daytime model uses 5 Near-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64 and 2.25 μm) together with the 3 IR bands to detect clear, liquid water, and ice cloud pixels. Up to 7 surface types, namely, ocean/water, forest, cropland, grassland, snow/ice, barren/desert, and shrubland, were considered separately to enhance performance for both models. Detection of cloudy pixels and thermodynamic phase with the two RF models were compared against collocated CALIOP products from 2017. It is shown that, with a conservative screening process that excludes the most challenging cloudy pixels for passive remote sensing, the two RF models have high accuracy rates in comparison with the CALIOP reference for both cloud detection and thermodynamic phase. Other existing SNPP VIIRS and Aqua MODIS cloud mask and phase products are also evaluated, with results showing that the two RF models and the MODIS MYD06 optical property phase product are the top 3 algorithms with respect to lidar observations during the daytime. During the nighttime, the RF all-day model works best for both cloud detection and phase, in particular for pixels over snow/ice surfaces. The present RF models can be extended to other similar passive instruments if training samples can be collected from CALIOP or other lidars. However, the quality of reference labels and potential sampling issues that may impact model performance would need further attention.

cloud detection↗
NASA NTRS2023

Machine Learning to Increase the Quality and Repeatability of 3D Printing - Workflow

The imprecise nature of three-dimensional (3D) printing limits the technology’s use beyond prototyping. For production of end-use parts, such as those for aerospace applications, improvements are needed to enhance quality and repeatability. Much of the difficulty in obtaining high quality printed parts lies in finding optimum printing parameters. Currently, this requires trial and error performed by an expert. Finding the optimum printing parameters is also obfuscated by the variation in optimum parameters throughout the part due to part geometry and printer effects. To allow for locally optimized printing parameters, one can envision a machine learning algorithm that could take in an object, predict the best printing parameters, and communicate these parameters to a printer. With this scenario in mind, we developed a tool that can predict and implement locally optimized printing parameters in 3D printing. This tool consists of elements designed to detect errors in a printed part, predict the probability of local flaws occurring at each point in the part, and select the optimal local parameters for the highest quality part given hardware limitations. The results of this work were highlighted in Advanced Materials Technologies. In this paper, we will discuss in greater depth the workflow and algorithms involved with this tool that were not detailed in the journal publication.

additive manufacturing↗
NASA NTRS2010

Regression Model Term Selection for the Analysis of Strain-Gage Balance Calibration Data

The paper discusses the selection of regression model terms for the analysis of wind tunnel strain-gage balance calibration data. Different function class combinations are presented that may be used to analyze calibration data using either a non-iterative or an iterative method. The role of the intercept term in a regression model of calibration data is reviewed. In addition, useful algorithms and metrics originating from linear algebra and statistics are recommended that will help an analyst (i) to identify and avoid both linear and near-linear dependencies between regression model terms and (ii) to make sure that the selected regression model of the calibration data uses only statistically significant terms. Three different tests are suggested that may be used to objectively assess the predictive capability of the final regression model of the calibration data. These tests use both the original data points and regression model independent confirmation points. Finally, data from a simplified manual calibration of the Ames MK40 balance is used to illustrate the application of some of the metrics and tests to a realistic calibration data set.

Ulbrich, Norbert Manfred↗
NASA NTRS2022

A New Machine Learning Based Analysis for Improving Satellite Retrieved Atmospheric Composition Data: OMI SO2 as an Example

Despite recent progress, satellite retrievals of anthropogenic SO2 still suffer from relatively low signal-tonoise ratios. In this study, we demonstrate a new machine learning data analysis method to improve the quality of satellite SO2 products. In the absence of large ground-truth datasets for SO2, we start from SO2 slant column densities (SCDs) retrieved from the Ozone Monitoring Instrument (OMI) using a data-driven, physically based algorithm and calculate the ratio between the SCD and the root mean square (rms) of the fitting residuals for each pixel. To build the training data, we select presumably clean pixels with small SCD / rms ratios (SRRs) and set their target SCDs to zero. For polluted pixels with relatively large SRRs, we set the target to the original retrieved SCDs. We then train neural networks (NNs) to reproduce the target SCDs using predictors including SRRs for individual pixels, solar zenith, viewing zenith and phase angles, scene reflectivity, and O3 column amounts, as well as the monthly mean SRRs. For data analysis, we employ two NNs: (1) one trained daily to produce analyzed SO2 SCDs for polluted pixels each day and (2) the other trained once every month to produce analyzed SCDs for less polluted pixels for the entire month. Test results for 2005 show that our method can significantly reduce noise and artifacts over background regions. Over polluted areas, the monthly mean NN-analyzed and original SCDs generally agree to within ±15 %, indicating that our method can retain SO2 signals in the original retrievals except for large volcanic eruptions. This is further confirmed by running both the NN-analyzed and original SCDs through a topdown emission algorithm to estimate the annual SO2 emissions for ∼ 500 anthropogenic sources, with the two datasets yielding similar results. We also explore two alternative approaches to the NN-based analysis method. In one, we employ a simple linear interpolation model to analyze the original SCD retrievals. In the other, we develop a PCA–NN algorithm that uses OMI measured radiances, transformed and dimension-reduced with a principal component analysis (PCA) technique, as inputs to NNs for SO2 SCD retrievals. While the linear model and the PCA–NN algorithm can reduce retrieval noise, they both underestimate SO2 over polluted areas. Overall, the results presented here demonstrate that our new data analysis method can significantly improve the quality of existing OMI SO2 retrievals. The method can potentially be adapted for other sensors and/or species and enhance the value of satellite data in air quality research and applications.

Can Li↗
NASA NTRS2009

Guaranteeing Failsafe Operation of Extended-Scene Shack-Hartmann Wavefront Sensor Algorithm

A Shack-Hartmann sensor (SHS) is an optical instrument consisting of a lenslet array and a camera. It is widely used for wavefront sensing in optical testing and astronomical adaptive optics. The camera is placed at the focal point of the lenslet array and points at a star or any other point source. The image captured is an array of spot images. When the wavefront error at the lenslet array changes, the position of each spot measurably shifts from its original position. Determining the shifts of the spot images from their reference points shows the extent of the wavefront error. An adaptive cross-correlation (ACC) algorithm has been developed to use scenes as well as point sources for wavefront error detection. Qualifying an extended scene image is often not an easy task due to changing conditions in scene content, illumination level, background, Poisson noise, read-out noise, dark current, sampling format, and field of view. The proposed new technique based on ACC algorithm analyzes the effects of these conditions on the performance of the ACC algorithm and determines the viability of an extended scene image. If it is viable, then it can be used for error correction; if it is not, the image fails and will not be further processed. By potentially testing for a wide variety of conditions, the algorithm s accuracy can be virtually guaranteed. In a typical application, the ACC algorithm finds image shifts of more than 500 Shack-Hartmann camera sub-images relative to a reference sub -image or cell when performing one wavefront sensing iteration. In the proposed new technique, a pair of test and reference cells is selected from the same frame, preferably from two well-separated locations. The test cell is shifted by an integer number of pixels, say, for example, from m= -5 to 5 along the x-direction by choosing a different area on the same sub-image, and the shifts are estimated using the ACC algorithm. The same is done in the y-direction. If the resulting shift estimate errors are less than a pre-determined threshold (e.g., 0.03 pixel), the image is accepted. Otherwise, it is rejected.

Sidick, Erikin↗
NASA NTRS1973

Diagnostic device for monitoring the technical condition of mechanical assemblies

An automatic diagnostic device for monitoring the condition of tractor transmission gears is described. The structural noise spectrum of the gearshift box and rear axle of the tractor were analyzed in a digital computer, by an algorithm based on the multiple correlation method. The optimum assembly of operating frequencies, by use of which the errors in measurement were minimized, was selected from the entire frequency spectrum. Selected frequencies are necessary for choosing the measurement range of the diagnostic device. It turned out that, to obtain a relative error of no more than 2%, it was sufficient to use two filters, vibrating only at the frequencies carrying the maximum data of the mechanical parameter being investigated. The measurement system consists of frequency-selection filters, amplifiers and quadratic detectors, at the outlets of which constant voltages are created, which are proportional to the signal level at the frequencies selected.

Osovskiy, V. I.↗
NASA NTRS1979

Investigation of the Multiple Method Adaptive Control (MMAC) method for flight control systems

The stochastic adaptive control of the NASA F-8C digital-fly-by-wire aircraft using the multiple model adaptive control (MMAC) method is presented. The selection of the performance criteria for the lateral and the longitudinal dynamics, the design of the Kalman filters for different operating conditions, the identification algorithm associated with the MMAC method, the control system design, and simulation results obtained using the real time simulator of the F-8 aircraft at the NASA Langley Research Center are discussed.

Athans, M.↗
NASA NTRS1979

Identification and dual adaptive control of a turbojet engine

The objective of this paper is to utilize the design methods of modern control theory to realize a dual-adaptive feedback control unit for a highly nonlinear single spool airbreathing turbojet engine. Using a very detailed and accurate simulation of the nonlinear engine as the data source, linear operating point models of unspecified dimension are identified. Feedback control laws are designed at each operating point for a prespecified set of sampling rates using sampled-data output regulator theory. The control system sampling rate is determined by an adaptive sampling algorithm in correspondence with turbojet engine performance. The result is a dual-adaptive control law that is functionally dependent upon the sampling rate selected and environmental operating conditions. Simulation transients demonstrate the utility of the dual-adaptive design to improve on-board computer utilization while maintaining acceptable levels of engine performance.

Merrill, W.↗
NASA NTRS1979

Identification and dual adaptive control of a turbojet engine

The objective of this paper is to utilize the design methods of modern control theory to realize a 'dual-adaptive' feedback control unit for a highly non-linear single spool airbreathing turbojet engine. Using a very detailed and accurate simulation of the non-linear engine as the data source, linear operating point models of unspecified dimension are identified. Feedback control laws are designed at each operating point for a prespecified set of sampling rates using sampled-data output regulator theory. The control system sampling rate is determined by an adaptive sampling algorithm in correspondence with turbojet engine performance. The result is a 'dual-adpative' control law that is functionally dependent upon the sampling rate selected and environmental operating conditions. Simulation transients demonstrate the utility of the dual-adaptive design to improve on-board computer utilization while maintaining acceptable levels of engine performance.

Merrill, W.↗
NASA NTRS1983

Evaluation of the effect of vibration nonlinearity on convergence behavior of adaptive higher harmonic controllers

Effect of nonlinearity on convergence of the local linear and global linear adaptive controllers is evaluated. A nonlinear helicopter vibration model is selected for the evaluation which has sufficient nonlinearity, including multiple minimum, to assess the vibration reduction capability of the adaptive controllers. The adaptive control algorithms are based upon a linear transfer matrix assumption and the presence of nonlinearity has a significant effect on algorithm behavior. Simulation results are presented which demonstrate the importance of the caution property in the global linear controller. Caution is represented by a time varying rate weighting term in the local linear controller and this improves the algorithm convergence. Nonlinearity in some cases causes Kalman filter divergence. Two forms of the Kalman filter covariance equation are investigated.

Molusis, J. A.↗
NASA NTRS1984

Reusable rocket engine turbopump condition monitoring

Significant improvements in engine readiness with reductions in maintenance costs and turn-around times can be achieved with an engine condition monitoring systems (CMS). The CMS provides health status of critical engine components, without disassembly, through monitoring with advanced sensors. Engine failure reports over 35 years were categorized into 20 different modes of failure. Rotor bearings and turbine blades were determined to be the most critical in limiting turbopump life. Measurement technologies were matched to each of the failure modes identified. Three were selected to monitor the rotor bearings and turbine blades: the isotope wear detector and fiberoptic deflectometer (bearings), and the fiberoptic pyrometer (blades). Signal processing algorithms were evaluated for their ability to provide useful health data to maintenance personnel. Design modifications to the Space Shuttle Main Engine (SSME) high pressure turbopumps were developed to incorporate the sensors. Laboratory test fixtures have been designed for monitoring the rotor bearings and turbine blades in simulated turbopump operating conditions.

Hampson, M. E.↗