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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

In-Situ Measurement of Hall Thruster Erosion Using a Fiber Optic Regression Probe

One potential life-limiting mechanism in a Hall thruster is the erosion of the ceramic material comprising the discharge channel. This is especially true for missions that require long thrusting periods and can be problematic for lifetime qualification, especially when attempting to qualify a thruster by analysis rather than a test lasting the full duration of the mission. In addition to lifetime, several analytical and numerical models include electrode erosion as a mechanism contributing to enhanced transport properties. However, there is still a great deal of dispute over the importance of erosion to transport in Hall thrusters. The capability to perform an in-situ measurement of discharge channel erosion is useful in addressing both the lifetime and transport concerns. An in-situ measurement would allow for real-time data regarding the erosion rates at different operating points, providing a quick method for empirically anchoring any analysis geared towards lifetime qualification. Erosion rate data over a thruster's operating envelope would also be useful in the modeling of the detailed physics inside the discharge chamber. A recent fundamental sensor development effort has led to a novel regression, erosion, and ablation sensor technology (REAST). The REAST sensor allows for measurement of real-time surface erosion rates at a discrete surface location. The sensor was tested using a linear Hall thruster geometry, which served as a means of producing plasma erosion of a ceramic discharge chamber. The mass flow rate, discharge voltage, and applied magnetic field strength could be varied, allowing for erosion measurements over a broad thruster operating envelope. Results are presented demonstrating the ability of the REAST sensor to capture not only the insulator erosion rates but also changes in these rates as a function of the discharge parameters.

Polzink, Kurt A.↗
NASA NTRS1993

Estimation of crown closure from AVIRIS data using regression analysis

Crown closure is one of the input parameters used for forest growth and yield modelling. Preliminary work by Staenz et al. indicates that imaging spectrometer data acquired with sensors such as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) have some potential for estimating crown closure on a stand level. The objectives of this paper are: (1) to establish a relationship between AVIRIS data and the crown closure derived from aerial photography of a forested test site within the Interior Douglas Fir biogeoclimatic zone in British Columbia, Canada; (2) to investigate the impact of atmospheric effects and the forest background on the correlation between AVIRIS data and crown closure estimates; and (3) to improve this relationship using multiple regression analysis.

Staenz, K.↗
NASA NTRS2016

Hybrid Propulsion In-Situ Resource Utilization Test Facility Results for Performance Characterization

Hybrid propulsion presents a promising alternative to conventional systems for in-space propulsion applications using In-Situ Propellant Production (ISPP). A hybrid propulsion test facility has been built at the NASA Jet Propulsion Laboratory (JPL) with the capability of evaluating ISPP oxidizers (gaseous mixtures of O2 and CO2) and modifying fuel type and chamber geometry. This work is the result of a continuing e ort at JPL to characterize the burn characteristics and performance of different propellant combinations and motor configurations in order to re ne and support preliminary designs. Twelve tests using gaseous oxygen and two paraffin based fuels (Black Paraffin and SP1X) from the 2015 campaign are presented. Different reduction techniques are compared in an e ort to determine the uncertainty associated with deriving a regression rate law and evaluating c* efficiency with the collected data.Hybrid motors are mechanically and chemically simple propulsion systems that have the potential forlong-term storability with low gross mass. The oxidizer (such as gaseous O2) can be produced in-situ,signicantly reducing the landed mass. The solid, inert fuel is stored in the combustion chamber in its most dense state, further reducing the total structural mass and volume. Considerable eorts have been focusedon ISPP for Environmental Control and Life Support Systems (ECLSS) and human exploration of Mars.At optimum oxidizer to fuel ratio (O/F), about 70% of the total propellant mass could be produced in-situ.Paran based fuels exhibit a fast regression rate behavior due to the formation of a thin liquid layer on thefuel surface.1 A high burn rate is necessary in order to reach a medium to high thrust range. In order toachieve an O/F ratio close to optimum performance (between 2.1 and 2.7 for the O2/Paran combination),the fuel mass ow needs to be suciently large compared to the oxidizer ow. The slow regression rateof classical hybrid rocket fuels leads to long fuel grain designs or complicated multi-port structures. Singleport, high regression rate fuels have the potential to reduce residual propellant mass and have a grainlength to diameter ratio (L/D) more favorable for packaging. However, hybrid motors remain at a fairly lowTechnology Readiness Level (TRL), and solutions to accurately measure and increase performance are stilllacking, especially for small-scale motors that are constrained to tight packaging.A total of 12 hotres were performed and analyzed in this campaign. All of these tests were conductedat pressures above the critical pressure of paran wax, approximately 6.7 bar (97 psi).2 Three dierentmotor congurations were explored while conserving a single cylindrical port fuel grain. Additionally, sincethrottling is a crucial advantage of hybrid rocket propulsion systems, low oxidizer mass ows (60 - 100 g/s)and uxes (5 - 30 g/cm2/s) were investigated to determine the performance impact in this regime.

Mechentel, F. S.↗
DOE OSTI2025

Uncertainty Quantification Enabled by Automatic Differentiation for Hydrodynamic Simulation of Shock‐to‐Detonation Transition in High Explosives

Quantifying the effects of uncertainty in a reactive burn model on the run-to-detonation time in high explosives (HEs) provides a robust methodology for assessing the probability of an HE failing the IHE qualification standard. Moreover, uncertainty quantification helps evaluate whether the model calibration accurately represents data outside the calibration set. This study uses a specialized hydrodynamic simulation code for modeling detonation to determine the run-to-detonation time of the HE PBX 9502 for various impact velocities. To quickly approximate uncertainties in the model, a surrogate was constructed using a Taylor series expansion centered at the mean of the input parameters. To obtain the sensitivities required for constructing the Taylor series, HYP-percomplex Automatic Differentiation (HYPAD) was implemented. HYPAD is a methodology for infusing existing codes with automatic differentiation capabilities by augmenting variables with one or more imaginary units to compute step-size independent partial derivatives. These derivatives are accurate to machine precision with respect to the implemented numerical algorithm, meaning their accuracy reflects that of the underlying method (e.g., integration or discretization schemes). Using reduced order modeling techniques, the mean and standard deviation of the run-to-detonation time of a shock within PBX 9502 were computed for a number of initial impact velocities. A weighted least squares regression was then performed to obtain a best fit curve and prediction interval for the computed statistics. Historical data points from explosively driven wedge tests were utilized to validate the prediction interval, ensuring its reliability in predicting future outcomes. With this prediction interval and a known safety constraint curve, the most probable point of failure and the probability of failure for the HE PBX 9502 were determined.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information

A Dynamic Bayesian network (DBN) model for solar power generation forecasting in solar plants is proposed in this paper. The key idea is to fuse sensor data, operational indicators, meteorological data, lagged output power information, and model errors for more accurate short-term (e.g., hours) and mid-term (e.g., days to weeks) power generation forecasting. The proposed DBN augments automated data-driven structure learning with expert knowledge encoding using continuous and categorical data given constraints to represent causal relationships within a solar inverter system. Additionally, an error compensation mechanism is proposed to capture temporal fluctuation. The effectiveness of the DBN on solar power generation forecasting was evaluated by rolling window analysis with one-year testing data collected from a local solar plant. The proposed DBN is compared with four state-of-art methods including support-vector regression (SVR), k-nearest neighbors (kNN), artificial neural network (ANN), and long short-term memory (LSTM) models. The result show that the proposed DBN achieves better accuracy in general, and it is not as data-hungry as some neural network-based models. The proposed DBN is also shown to have robust and consistent forecasting power with different forecasting horizons. The accuracy is 92% - 95% from one hour to one week ahead forecasting.

14 SOLAR ENERGY↗
DOE OSTI2025

Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes

Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys (HEAs), especially when integrating computational predictions with sparse experimental observations. This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes (cGP), Deep Gaussian processes (DGP), encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting (XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V. We specifically assess their capabilities in predicting correlated material properties, including yield strength, hardness, modulus, ultimate tensile strength, elongation, and average hardness under dynamic/quasi-static conditions, alongside auxiliary computational properties. The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic, heterotopic, and incomplete data commonly encountered in materials science. Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperform other surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge. This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and data-efficient materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Calibration and Rapid-Adoption Forecasting Techniques

CRAFT (Calibration and Rapid-Adoption Forecasting Techniques) CRAFT is a Python-based project for processing, analyzing, and modeling atmospheric or environmental data. It uses machine learning techniques, specifically Random Forest Regression, to create emulators for various environmental variables such as gross primary production and soil water content. It then uses these emulators to robustly test the parameter space of mechanistic models to provide posterior estimations of the free parameters.

Robins, Zachary↗
NASA NTRS1977

Active microwave measurement from space of sea-surface winds

Radar backscatter measurements from the ocean were made at 13.9 GHz from Skylab. The radar signal increased rapidly with wind speed over the entire range of winds encountered, and for angles of incidence of 30 deg larger. Signals observed were normalized to a nominal incidence angle and to a nominal upwind observation direction, using a theoretical model that has been verified as approximately true with aircraft experiments. Observations during the summer and winter Skylab missions were treated separately because of possible differences caused by an accident to the antenna between the two sets of observations. The results are in general agreement with the theory in all cases. The objective analysis method used for determining surface-truth winds in the Skylab experiment was tested by comparing results obtained at weather ships with the observations made by the weather ships themselves. In most cases, the variance about the regression line between objective analysis and weather-ship data actually exceeded that about the regression line between objective analysis and backscatter data

Young, J. D.↗
NASA NTRS1984

The Effects of Seven Alloying Elements on the Microstructure and Stress-Rupture Behavior of Nickel-Base Superalloys

Seven alloying elements: Al, Cr, Ti, Nb, Ta, Mo, and W were added at two levels of concentration to produce a series of experimental nickel-base superalloys. Fifty alloys, representing a fraction of a 2 to the 7th power factorial design, were cast, tested, and analyzed. Each alloy's microstructure was characterized by phase extractions, X-ray diffraction, metallography and energy dispersive X-ray spectroscopy. Regression analysis was used to determine the effect of alloying element content on microstructure and stress-rupture life.

Hull, D. R.↗
NASA NTRS1985

Statistical analysis of astronomical data containing upper bounds - General methods and examples drawn from X-ray astronomy

Statistical procedures taken from the field of survival analysis have been adapted to astronomical usage and have been applied to a sample of stars in the B-V color range between 0.1 and 0.5 with measured soft X-ray luminosities and projected equatorial velocities. The two-sample problem and linear regression problem with arbitrarily censored data were studied. A new method for determining the linear regression between two random variables in the presence of arbitrary censoring has been developed which can also be used for a likelihood-ratio test for the independence of two random variables and for principal-component analysis in the presence of arbitrary censoring. The required numerical computations can be carried out straightforwardly and rapidly.

Schmitt, J. H. M. M.↗
NASA NTRS1987

Propulsion estimation techniques

A brief overview of statistical tools needed to perform post flight/test reconstruction of state variables is given. Linear regression, recursive linear regression, and the exact connection between the Kalman filter and linear regression are discussed. The regression connection is expected to serve as an aid in the application of a recently developed analytical method of flight reconstruction to single engine test firing data.

Temple, Enoch C.↗
NASA NTRS2019

A Single Stage to Orbit Design for a Hybrid Mars Ascent

A Single Stage to Orbit (SSTO) hybrid propulsion system has been previously studied as an option for a conceptual Mars Ascent Vehicle (MAV). The hybrid motor uses a wax-based fuel developed specifically for this application, so it can take advantage of a single port design. The oxidizer is Mixed Oxides of Nitrogen (MON-25). Higher Nitric Oxide concentrations have been discussed in the past, however, the lower temperature capability is no longer needed. The MAV Payload Assembly (MPA), which would house the Orbiting Sample (OS) has changed substantially from previous iterations and has become more compact. Benefits of the hybrid option include its predicted low temperature behavior, high performance and ability to restart (enabling the SSTO). However, the hybrid technology remained at a relatively low Technology Readiness Level (TRL). In an attempt to increase the TRL, a technology development program has been underway for the past four years. The results of the technology development program are now being incorporated to an updated concept for a hybrid Mars Ascent Vehicle, with the eventual goal of informing a hybrid propulsion design that closes under the guidelines currently envisioned for a potential Mars Sample Return campaign. This paper focuses on the hybrid propulsion system design and the preliminary results from the first part of the FY19 technology development program (October 2018 to July 2019) and includes some results from a Preliminary Architecture Assessment (PAA) study. In the PAA, experts from all relevant subsystems (propulsion, avionics, GN&C, structures, thermal, etc.) are brought together to determine an updated vehicle design. The PAA is being run out of Marshall Space Flight Center (MSFC) in coordination with the Mars Sample Return study lead by the Jet Propulsion Laboratory (JPL). Currently, it is thought that the Mars Ascent Vehicle would be housed in a Sample Retrieval Lander (SRL), along with a fetch rover. The SRL would be responsible for several crucial functions on the MAV including heating, erection and providing the ignition signal. This paper will also outline the future testing and path forward through the rest of the fiscal year. This includes full scale testing at Whittinghill Aerospace, hypergolic additive testing at Purdue, evaluation of adding hypergolic additives to a full-scale grain. A hybrid fuel formulation has been updated with a reduced regression rate, which again was developed by Space Propulsion Group. This design will be used to determine the benefits of a hybrid versus solid propulsion system for a MAV, as they fit into the larger vision for a potential Mars Sample Return campaign.

Story, George↗
NASA NTRSDate not supplied

Algorithmic Classification of Raman Spectra Biosignatures: Improving Life Detection Confidence

“Agnostic” biosignatures – indicators of life (or the absence of life), independent of a particular biochemistry – are increasingly considered a high standard for life detection. The Ladder of Life Detection (2018) called for investigating how combinations of independent and different potential biosignatures affect confidence. To address this gap, statistical classification of elemental abundances, isotopic fractionation, and reflectance spectroscopy (VNIR) has been implemented. Raman spectroscopy, highly desirable due to its wide availability, has the potential to improve this predictive power. This work implemented biosignature classification algorithms on Raman data alone, in preparation for combination with the other data types. Raman spectroscopy data was collected from published databases and papers as part of a manually curated dataset of “indicative” and “non-indicative of life” samples. These currently include 61 non-indicative samples (meteorites, magnetite); 3 indicative living samples (bacteria); 20 indicative non-living samples (chalk, bone); and 12 indicative mixed (with non-indicative material) samples (soil, microbial mats). Laboratory work is ongoing to characterize additional samples, particularly a greater breadth of mixed systems. Spectra were interpolated, filtered with the Savitzsky-Golay filter, and de-noised. For a preliminary examination, agnostic features were manually extracted including mean intensity, number of peaks, and mean peak width. Different peak prominences and filtering polynomials were used to refine features. Classification algorithms were implemented: k-nearest neighbors (KNN), logistic regression (LR), linear support vector machines (SVM), random forest (RF), Gaussian naïve bayes (GNB). Lastly, Monte Carlo simulations on 1,000 50%-train-test-splits were used to validate classification performance and feature significance. The preliminary feature set achieved its highest AUC of 0.52 with LR, with no strongly discriminatory features. Work to improve feature extraction, such as through deep learning with back propagation, is planned. In future work, the Raman data will be combined with the other data types, and potentially new data types such as enantiomeric excess. This project was partially supported through the NASA Ames Project EXcellence (APEX) incubator program.

Astrobiology↗
NASA NTRS2007

Stepwise Regression Analysis of MDOE Balance Calibration Data Acquired at DNW

This paper reports a comparison of two experiment design methods applied in the calibration of a strain-gage balance. One features a 734-point test matrix in which loads are varied systematically according to a method commonly applied in aerospace research and known in the literature of experiment design as One Factor At a Time (OFAT) testing. Two variations of an alternative experiment design were also executed on the same balance, each with different features of an MDOE experiment design. The Modern Design of Experiments (MDOE) is an integrated process of experiment design, execution, and analysis applied at NASA's Langley Research Center to achieve significant reductions in cycle time, direct operating cost, and experimental uncertainty in aerospace research generally and in balance calibration experiments specifically. Personnel in the Instrumentation and Controls Department of the German Dutch Wind Tunnels (DNW) have applied MDOE methods to evaluate them in the calibration of a balance using an automated calibration machine. The data have been sent to Langley Research Center for analysis and comparison. This paper reports key findings from this analysis. The chief result is that a 100-point calibration exploiting MDOE principles delivered quality comparable to a 700+ point OFAT calibration with significantly reduced cycle time and attendant savings in direct and indirect costs. While the DNW test matrices implemented key MDOE principles and produced excellent results, additional MDOE concepts implemented in balance calibrations at Langley Research Center are also identified and described.

DeLoach, RIchard↗
NASA NTRS2007

Aeroelastic Model Structure Computation for Envelope Expansion

Structure detection is a procedure for selecting a subset of candidate terms, from a full model description, that best describes the observed output. This is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modelling may be of critical importance in the development of robust, parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion which may save significant development time and costs. In this study, a least absolute shrinkage and selection operator (LASSO) technique is investigated for computing efficient model descriptions of nonlinear aeroelastic systems. The LASSO minimises the residual sum of squares by the addition of an l(sub 1) penalty term on the parameter vector of the traditional 2 minimisation problem. Its use for structure detection is a natural extension of this constrained minimisation approach to pseudolinear regression problems which produces some model parameters that are exactly zero and, therefore, yields a parsimonious system description. Applicability of this technique for model structure computation for the F/A-18 Active Aeroelastic Wing using flight test data is shown for several flight conditions (Mach numbers) by identifying a parsimonious system description with a high percent fit for cross-validated data.

Kukreja, Sunil L.↗
NASA NTRS2007

An Update on the Lithium-Ion Cell Low-Earth-Orbit Verification Test Program

A Lithium-Ion Cell Low-Earth-Orbit Verification Test Program is being conducted by NASA Glenn Research Center to assess the performance of lithium-ion (Li-ion) cells over a wide range of low-Earth-orbit (LEO) conditions. The data generated will be used to build an empirical model for Li-ion batteries. The goal of the modeling will be to develop a tool to predict the performance and cycle life of Li-ion batteries operating at a specified set of mission conditions. Using this tool, mission planners will be able to design operation points of the battery system while factoring in mission requirements and the expected life and performance of the batteries. Test conditions for the program were selected via a statistical design of experiments to span a range of feasible operational conditions for LEO aerospace applications. The variables under evaluation are temperature, depth-of-discharge (DOD), and end-of-charge voltage (EOCV). The baseline matrix was formed by generating combinations from a set of three values for each variable. Temperature values are 10 C, 20 C and 30 C. Depth-of-discharge values are 20%, 30% and 40%. EOCV values are 3.85 V, 3.95 V, and 4.05 V. Test conditions for individual cells may vary slightly from the baseline test matrix depending upon the cell manufacturer s recommended operating conditions. Cells from each vendor are being evaluated at each of ten sets of test conditions. Cells from four cell manufacturers are undergoing life cycle tests. Life cycling on the first sets of cells began in September 2004. These cells consist of Saft 40 ampere-hour (Ah) cells and Lith ion 30 Ah cells. These cells have achieved over 10,000 cycles each, equivalent to about 20 months in LEO. In the past year, the test program has expanded to include the evaluation of Mine Safety Appliances (MSA) 50 Ah cells and ABSL battery modules. The MSA cells will begin life cycling in October 2006. The ABSL battery modules consist of commercial Sony hard carbon 18650 lithium-ion cells configured in series and parallel combinations to create nominal 14.4 volt, 3 Ah packs (4s-2p). These modules have accumulated approximately 3000 cycles. Results on the performance of the cells and modules will be presented in this paper. The life prediction and performance model for Li-ion cells in LEO will be built by analyzing the data statistically and performing regression analysis. Cells are being cycled to failure so that differences in performance trends that occur at different stages in the life of the cell can be observed and accurately modeled. Cell testing is being performed at the Naval Surface Warfare Center in Crane, IN.

Reid, Concha M.↗
DOE OSTI2026

Iterative HOMER with uncertainties

We present iHOMER, an iterative version of the HOMER method to extract Lund fragmentation functions from experimental data. Through iterations, we address the information gap between latent and observable phase spaces and systematically remove bias. To quantify uncertainties on the inferred weights, we use a combination of Bayesian neural networks and uncertainty-aware regression. We find that the combination of iterations and uncertainty quantification produces well-calibrated weights that accurately reproduce the data distribution. A parametric closure test shows that the iteratively learned fragmentation function is compatible with the true fragmentation function.

Butter, Anja [Heidelberg Univ. (Germany); Sorbonne↗
NASA NTRS1982

Performance degradation and cleaning of photovoltaic arrays

NASA tests results from an 18 mo program of cleaning silicone-encapsulated and glass fronted solar cell panels in urban and desert environments to examine the effects of cleaning on module performance are reported. The panels were cleaned on weekly, monthly, quarterly, or semi-annual basis, while other panels of the same construction were not cleaned and served as controls. Commercially-available detergents and city water were employed for the tests, and the measurements were maintained of the modules' continuing short-circuit current output. The decay of the output was determined by least square regression analyses. Performance degradation was noticeably less in glass covered, rather than silicone-encapsulated modules which decayed faster in urban than in desert environments. Lower frequency cleanings are recommended where labor costs are high.

Sheskin, T. J.↗