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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
NASA NTRS2017

Physics-of-Failure Approach to Prognostics

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of the electrical components present in the system. In case of electric vehicles, computing remaining battery charge is safety-critical. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. In this presentation our approach to develop a system level health monitoring safety indicator for different electronic components is presented which runs estimation and prediction algorithms to determine state-of-charge and estimate remaining useful life of respective components. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Kulkarni, Chetan S.↗
NASA NTRS1990

Modeling pilot interaction with automated digital avionics systems: Guidance and control algorithms for contour and nap-of-the-Earth flight

A collection of technical papers are presented that cover modeling pilot interaction with automated digital avionics systems and guidance and control algorithms for contour and nap-of-the-earth flight. The titles of the papers presented are as follows: (1) Automation effects in a multiloop manual control system; (2) A qualitative model of human interaction with complex dynamic systems; (3) Generalized predictive control of dynamic systems; (4) An application of generalized predictive control to rotorcraft terrain-following flight; (5) Self-tuning generalized predictive control applied to terrain-following flight; and (6) Precise flight path control using a predictive algorithm.

Hess, Ronald A.↗
DOE OSTI2020

Wave Prediction using X-Band Radar (Final Summary Report)

Model Predictive Control (MPC) is the best controls framework available to wave energy today. It allows for the maximization of energy yield from a WEC system while respecting various constraints and losses in the system. This type of constrained optimization is a critical capability for the techno-economic optimization of WEC devices and is difficult to do effectively with causal controls frameworks. MPC requires a future prediction of wave excitation forces, requiring wave prediction on a future time horizon of up to 30 seconds. The future horizon required is highly dependent on the WEC and PTO type available. To our knowledge no-one has been able to implement MPC on a WEC device at sea, due to the fact that phase-resolved wave-prediction is not a capability that has been sufficiently developed to date. The key objectives of this project are focused on developing “industry-ready” wave prediction technology building blocks that can be applied to a wide range of different wave energy conversion topologies. Our collaborative work with device developers has demonstrated that the level of improvement attainable for any particular device is heavily dependent on the device and PTO topology chosen. The range of annual average power capture improvements is on the order of 10% to over 100%. However, the important aspect is that we can get within about 10% of theoretical upper limits even when considering errors in the wave prediction and more importantly, we can optimize performance while respecting various constraints such as motion amplitude or peak structural loads. The combination of improved performance while limiting structural loads has a net effect of reducing the levelized cost of energy from these emerging technologies and allows us to design control laws that meet an economic optimum. Efforts under a previous project on controls has focused on developing a set of wave-prediction algorithms that work by leveraging a network of wave sensing measurement buoys that are equipped with real-time telemetry. The present work is focusing primarily on using X-band radar to measure the wave-field around the WEC to predict the future excitation forces on the structure and combining the measurements of these different sources to create improvements in the wave-prediction accuracy.

16 TIDAL AND WAVE POWER↗
NASA NTRS2015

Improved Regression Analysis of Temperature-Dependent Strain-Gage Balance Calibration Data

An improved approach is discussed that may be used to directly include first and second order temperature effects in the load prediction algorithm of a wind tunnel strain-gage balance. The improved approach was designed for the Iterative Method that fits strain-gage outputs as a function of calibration loads and uses a load iteration scheme during the wind tunnel test to predict loads from measured gage outputs. The improved approach assumes that the strain-gage balance is at a constant uniform temperature when it is calibrated and used. First, the method introduces a new independent variable for the regression analysis of the balance calibration data. The new variable is designed as the difference between the uniform temperature of the balance and a global reference temperature. This reference temperature should be the primary calibration temperature of the balance so that, if needed, a tare load iteration can be performed. Then, two temperature{dependent terms are included in the regression models of the gage outputs. They are the temperature difference itself and the square of the temperature difference. Simulated temperature{dependent data obtained from Triumph Aerospace's 2013 calibration of NASA's ARC-30K five component semi{span balance is used to illustrate the application of the improved approach.

wind tunnel testing↗
NASA NTRS2017

Prognostics and Health Monitoring: Application to Electric Vehicles

As more and more autonomous electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of remaining useful life of the systemssubsystems, specifically the electrical powertrain. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle.Our research approach is to develop a system level health monitoring safety indicator either to the pilotautopilot for the electric vehicles which runs estimation and prediction algorithms to estimate remaining useful life of the vehicle e.g. determine state-of-charge in batteries. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Prognostics↗
DOE OSTI2024

Exploring the fragmentation efficiency of proteins analyzed by MALDI-TOF-TOF tandem mass spectrometry using computational and statistical analyses

Matrix-assisted laser desorption/ionization time-of-flight-time-of-flight (MALDI-TOF-TOF) tandem mass spectrometry (MS/MS) is a rapid technique for identifying intact proteins from unfractionated mixtures by top-down proteomic analysis. MS/MS allows isolation of specific intact protein ions prior to fragmentation, allowing fragment ion attribution to a specific precursor ion. However, the fragmentation efficiency of mature, intact protein ions by MS/MS post-source decay (PSD) varies widely, and the biochemical and structural factors of the protein that contribute to it are poorly understood. With the advent of protein structure prediction algorithms such as Alphafold2, we have wider access to protein structures for which no crystal structure exists. In this work, we use a statistical approach to explore the properties of bacterial proteins that can affect their gas phase dissociation via PSD. We extract various protein properties from Alphafold2 predictions and analyze their effect on fragmentation efficiency. Our results show that the fragmentation efficiency from cleavage of the polypeptide backbone on the C-terminal side of glutamic acid (E) and asparagine (N) residues were nearly equal. In addition, we found that the rearrangement and cleavage on the C-terminal side of aspartic acid (D) residues that result from the aspartic acid effect (AAE) were higher than for E- and N-residues. From residue interaction network analysis, we identified several local centrality measures and discussed their implications regarding the AAE. We also confirmed the selective cleavage of the backbone at D-proline bonds in proteins and further extend it to N-proline bonds. Finally, we note an enhancement of the AAE mechanism when the residue on the C-terminal side of D-, E- and N-residues is glycine. To the best of our knowledge, this is the first report of this phenomenon. Our study demonstrates the value of using statistical analyses of protein sequences and their predicted structures to better understand the fragmentation of the intact protein ions in the gas phase.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2004

Design and Testing of a Low Noise Flight Guidance Concept

A flight guidance concept was developed to assist in flying continuous descent approach (CDA) procedures designed to lower the noise under the flight path of jet transport aircraft during arrival operations at an airport. The guidance consists of a trajectory prediction algorithm that was tuned to produce a high-efficiency, low noise flight profile with accompanying autopilot and flight display elements needed by the flight control system and pilot to fly the approach. A key component of the flight guidance was a real-time display of energy error relative to the predicted flight path. The guidance was integrated with the conventional Flight Management System (FMS) guidance of a modern jet transport airplane and tested in a high fidelity flight simulation. A charted arrival procedure, which allowed flying conventional arrivals, CDA arrivals with standard guidance, and CDA arrivals with the new low noise guidance, was developed to assist in the testing and evaluation of the low noise guidance concept. Results of the simulation testing showed the low noise guidance was easy to use by airline pilot test subjects and effective in achieving the desired noise reduction. Noise under the flight path was reduced by at least 2 decibels in Sound Exposure Level (SEL) at distances from about 3 nautical miles out to about 17.5 nautical miles from the runway, with a peak reduction of 8.5 decibels at about 10.5 nautical miles. Fuel consumption was also reduced by about 17% for the LNG conditions compared to baseline runs for the same flight distance. Pilot acceptance and understanding of the guidance was quite high with favorable comments and ratings received from all test subjects.

Williams, David H.↗
NASA NTRS1990

Predictive sensor method and apparatus

A predictive algorithm is used to determine, in near real time, the steady state response of a slow responding sensor such as hydrogen gas sensor of the type which produces an output current proportional to the partial pressure of the hydrogen present. A microprocessor connected to the sensor samples the sensor output at small regular time intervals and predicts the steady state response of the sensor in response to a perturbation in the parameter being sensed, based on the beginning and end samples of the sensor output for the current sample time interval.

Nail, William L.↗
NASA NTRS1990

Telescopic limiting magnitudes

The prediction of the magnitude of the faintest star visible through a telescope by a visual observer is a difficult problem in physiology. Many prediction formulas have been advanced over the years, but most do not even consider the magnification used. Here, the prediction algorithm problem is attacked with two complimentary approaches: (1) First, a theoretical algorithm was developed based on physiological data for the sensitivity of the eye. This algorithm also accounts for the transmission of the atmosphere and the telescope, the brightness of the sky, the color of the star, the age of the observer, the aperture, and the magnification. (2) Second, 314 observed values for the limiting magnitude were collected as a test of the formula. It is found that the formula does accurately predict the average observed limiting magnitudes under all conditions.

Schaefer, Bradley E.↗
NASA NTRS2009

Analysis of Sting Balance Calibration Data Using Optimized Regression Models

Calibration data of a wind tunnel sting balance was processed using a search algorithm that identifies an optimized regression model for the data analysis. The selected sting balance had two moment gages that were mounted forward and aft of the balance moment center. The difference and the sum of the two gage outputs were fitted in the least squares sense using the normal force and the pitching moment at the balance moment center as independent variables. The regression model search algorithm predicted that the difference of the gage outputs should be modeled using the intercept and the normal force. The sum of the two gage outputs, on the other hand, should be modeled using the intercept, the pitching moment, and the square of the pitching moment. Equations of the deflection of a cantilever beam are used to show that the search algorithm s two recommended math models can also be obtained after performing a rigorous theoretical analysis of the deflection of the sting balance under load. The analysis of the sting balance calibration data set is a rare example of a situation when regression models of balance calibration data can directly be derived from first principles of physics and engineering. In addition, it is interesting to see that the search algorithm recommended the same regression models for the data analysis using only a set of statistical quality metrics.

Ulbrich, Norbert↗
NASA NTRS1986

Intelligent information extraction from reflectance spectra Absorption band positions

A multiple high-order derivative analysis algorithm has been developed which can automatically extract absorption band positions from low-quality reflectance spectra with little degredation of accuracy. Overlapping bands with comparable widths and intensities can be resolved whose centers are as close as 0.3-0.5 W, with safer resolution limits of 0.6-1.0 W band center separations suggested for overlapping bands that are dissimilar. The segment length for smoothing is continually adjusted to about 0.5 W to minimize signal distortion, and a spectral pattern recognition algorithm predicts the signal spectrum and calculates approximate W across the spectrum using its second derivative. A single-pass cubic spline is applied to the smoothed data, and a sliding segment sixth-order polynomial is fit to the spectrum, with the length of the segment being continuously locally adjusted to 1.0 W across the spectrum. Good reliability and consistency of the algorithm is demonstrated with application to laboratory and earth-based telescope spectra.

Huguenin, R. L.↗
NASA NTRS2016

Machine-z: Rapid Machine-Learned Redshift Indicator for Swift Gamma-Ray Bursts

Studies of high-redshift gamma-ray bursts (GRBs) provide important information about the early Universe such as the rates of stellar collapsars and mergers, the metallicity content, constraints on the re-ionization period, and probes of the Hubble expansion. Rapid selection of high-z candidates from GRB samples reported in real time by dedicated space missions such as Swift is the key to identifying the most distant bursts before the optical afterglow becomes too dim to warrant a good spectrum. Here, we introduce 'machine-z', a redshift prediction algorithm and a 'high-z' classifier for Swift GRBs based on machine learning. Our method relies exclusively on canonical data commonly available within the first few hours after the GRB trigger. Using a sample of 284 bursts with measured redshifts, we trained a randomized ensemble of decision trees (random forest) to perform both regression and classification. Cross-validated performance studies show that the correlation coefficient between machine-z predictions and the true redshift is nearly 0.6. At the same time, our high-z classifier can achieve 80 per cent recall of true high-redshift bursts, while incurring a false positive rate of 20 per cent. With 40 per cent false positive rate the classifier can achieve approximately 100 per cent recall. The most reliable selection of high-redshift GRBs is obtained by combining predictions from both the high-z classifier and the machine-z regressor.

gamma-ray burst: general↗
DOE OSTI2023

Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data

Abstract Heatwaves lead to catastrophic consequences on public health and the economy. Accurate and timely predictions of regional heatwaves can improve climate preparedness and foster decision‐making to alleviate the burdens due to climate change. In this paper, we propose a heatwave prediction algorithm based on a novel deep learning model, that is, Graph Neural Network (GNN). This new GNN framework can provide real time warnings of the sudden occurrence of regional heatwaves with high accuracy at lower costs of computation and data collection. In addition, its interpretable structure unravels the spatiotemporal patterns of regional heatwaves and helps to enrich our understanding of the general climate dynamics and the causal influences between locations. The proposed GNN framework can be applied for the detection and prediction of other extreme or compound climate events, which calls for future studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Integrating functional scoring and regulatory data to predict the effect of non-coding SNPs in a complex neurological disease

Abstract Most SNPs associated with complex diseases seem to lie in non-coding regions of the genome; however, their contribution to gene expression and disease phenotype remains poorly understood. Here, we established a workflow to provide assistance in prioritising the functional relevance of non-coding SNPs of candidate genes as susceptibility loci in polygenic neurological disorders. To illustrate the applicability of our workflow, we considered the multifactorial disorder migraine as a model to follow our step-by-step approach. We annotated the overlap of selected SNPs with regulatory elements and assessed their potential impact on gene expression based on publicly available prediction algorithms and functional genomics information. Some migraine risk loci have been hypothesised to reside in non-coding regions and to be implicated in the neurotransmission pathway. In this study, we used a set of 22 non-coding SNPs from neurotransmission and synaptic machinery-related genes previously suggested to be involved in migraine susceptibility based on our candidate gene association studies. After prioritising these SNPs, we focused on non-reported ones that demonstrated high regulatory potential: (1) VAMP2_rs1150 (3′ UTR) was predicted as a target of hsa-mir-5010-3p miRNA, possibly disrupting its own gene expression; (2) STX1A_rs6951030 (proximal enhancer) may affect the binding affinity of zinc-finger transcription factors (namely ZNF423) and disturb TBL2 gene expression; and (3) SNAP25_rs2327264 (distal enhancer) expected to be in a binding site of ONECUT2 transcription factor. This study demonstrated the applicability of our practical workflow to facilitate the prioritisation of potentially relevant non-coding SNPs and predict their functional impact in multifactorial neurological diseases.

Felício, Daniela↗
NASA NTRS1998

The Design and Evaluation of the Lighting Imaging Sensor Data Applications Display (LISDAD)

The design and evaluation of the Lightning Imaging Sensor Data Applications Display (LISDAD). The ultimate goal of the LISDAD system is to quantify the utility of total lightning information in short-term, severe-weather forecasting operations. To this end, scientists from NASA, NWS, and MIT organized an effort to study the relationship of lightning and severe-weather on a storm-by-storm, and even cell-by-cell basis for as many storms as possible near Melbourne, Florida. Melbourne was chosen as it offers a unique combination of high probability of severe weather and proximity to major relevant sensors - specifically: NASA's total lightning mapping system at Kennedy Space Center (the LDAR system at KSC); a NWS/NEXRAD radar (at Melbourne); and a prototype Integrated Terminal Weather System (ITWS, at Orlando), which obtains cloud-to-ground lightning Information from the National Lightning Detection Network (NLDN), and also uses NSSL's Severe Storm Algorithm (NSSL/SSAP) to obtain information about various storm-cell parameters. To assist in realizing this project's goal, an interactive, real-time data processing system (the LISDAD system) has been developed that supports both operational short-term weather forecasting and post facto severe-storm research. Suggestions have been drawn from the operational users (NWS/Melbourne) in the design of the data display and its salient behavior. The initial concept for the users Graphical Situation Display (GSD) was simply to overlay radar data with lightning data, but as the association between rapid upward trends in the total lightning rate and severe weather became evident, the display was significantly redesigned. The focus changed to support the display of time series of storm-parameter data and the automatic recognition of cells that display rapid changes in the total-lightning flash rate. The latter is calculated by grouping discrete LDAR radiation sources into lightning flashes using a time-space association algorithm. Specifically, the GSD presents the user with the Composite Maximum Reflectivity obtained from the NWS/NEXRAD. Superimposed upon this background image are placed small black circles indicating the locations of storm cells identified by the NSSL/SSA. The circles become cyan if lightning is detected within the storm-cell; if the cell has lightning rates indicative of a severe-storm, the circle turns red. This paper will: (1) review the design of LISDAD system; (2) present some examples of its data display; and shown results of the lightning based severe-weather prediction algorithm.

Boldi, B.↗
NASA NTRS2011

Exploring the Model Design Space for Battery Health Management

Battery Health Management (BHM) is a core enabling technology for the success and widespread adoption of the emerging electric vehicles of today. Although battery chemistries have been studied in detail in literature, an accurate run-time battery life prediction algorithm has eluded us. Current reliability-based techniques are insufficient to manage the use of such batteries when they are an active power source with frequently varying loads in uncertain environments. The amount of usable charge of a battery for a given discharge profile is not only dependent on the starting state-of-charge (SOC), but also other factors like battery health and the discharge or load profile imposed. This paper presents a Particle Filter (PF) based BHM framework with plug-and-play modules for battery models and uncertainty management. The batteries are modeled at three different levels of granularity with associated uncertainty distributions, encoding the basic electrochemical processes of a Lithium-polymer battery. The effects of different choices in the model design space are explored in the context of prediction performance in an electric unmanned aerial vehicle (UAV) application with emulated flight profiles.

Saha, Bhaskar↗
NASA NTRS1984

Evaluation of a real-time predictive guidance law for landing VTOL aircraft at sea

A piloted simulation evaluation was conducted to assess the merits of a predictive lull/swell guidance law for landing a vertical takeoff and landing aircraft at sea. Two cases were evaluated. The first was performed by the pilot without the aid of the lull/swell guidance indicator, which indicates a landing opportunity. The second was performed in a similar manner with the aid of the guidance indicator. The pilot was instructed to use the guidance indicator only as a landing aid. The results indicated the pilots were able to visually determine ship lulls and swells prior to landing. However, use of the lull/swell guidance resulted in increased pilot confidence in the existence of a landing opportunity, which resulted in significantly shorter hover waiting times prior to landing. The performance of the lull/swell guidance was conservative, in that it forecast the onset of deck lulls and swells later than the pilot could detect them, but with greater reliability and therefore greater pilot confidence. In no instances did the guidance algorithm predict a false ship lull with which the pilot did not agree.

Paulk, C. H., Jr.↗
DOE OSTI2025

Knowledge graph-aided Bayesian active learning for top- K genetic interaction discovery

In silico methods for predicting the effects of multi-gene perturbations hold great promise for advancing functional genomics, computational drug discovery, and disease modeling. However, the development of these predictive algorithms for mammalian systems has been hampered by limited datasets and high experimental costs. In this study, we present a Bayesian active learning framework designed to discover pairwise host gene knockdowns that effectively inhibit viral proliferation in an in vitro HIV-1 infection model. Our method leverages a biological knowledge graph as side information and employs a computationally efficient batch diversification approach. We evaluated this framework using a dataset of viral load measurements obtained from multi-day dual-gene depletion experiments, encompassing all possible pairwise knockdowns of over 350 host genes associated with HIV infection. We demonstrate that our framework rapidly identifies the most effective gene knockdown pairs for reducing viral load. Furthermore, we show that incorporating side information enhances performance during the early stages of active learning (low data regime), while our batch diversification strategy significantly boosts performance in later stages (high data regime). This framework is general and can be adapted to explore gene interactions in other contexts, such as synthetic lethality prediction and mapping epistatic effects across quantitative trait loci.

Computational biology and bioinformatics↗