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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 505 records · Page 28

In-Situ Reaction Monitoring for Lunar Applications Utilizing a Single Quadrupole Residual Gas Analyzer

With a revived focus to create a permanent presence on the moon and in preparation for future Mars exploration, it is imperative that all resources are utilized to their fullest potential.[1] In-Situ Resource Utilization (ISRU) will be critical for future mission success as it would enable independent operation of missions while minimally relying on the complex supply chain created between the Earth, moon and Mars. One of the most critical resources that have been identified for ISRU is the creation of liquid oxygen(LO2) for not only breathable air, but also for rocket propellant. On the moon and potentially Mars, one of the most O2 rich resources is in the presence of regolith.[2],[3] Although there are numerous minerals within regolith and various processes to extract O2,this is not the scope of this paper. Since the O2 extraction method can vary, a critical standardized analytical method is needed to verify O2(or oxygenated precursor compounds) for extraction efficiency and purity. Our group utilizes a modified consumer off-the-shelf (COTS), Residual Gas Analyzer (RGA) Single Quadrupole Mass Spectrometer (QMS). Due to the current field mission requirements, the gas analysis module will need to rely only on mass-to-charge ratios (m/z) and peak intensities to differentiate and quantitate targeted gas-phase reactants or products, i.e. carbon monoxide(CO), carbon dioxide (CO2), methane (CH4) and hydrogen (H2). However, due to solar wind implanted volatiles and various other compounds present in lunar regolith, such as nitrogen (N2), helium (He), and deuterium (D2), deconvolution and quantification of isobaric compounds becomes quite difficult. Specifically, the presence of N2 makes quantification of CO, an oxygenated precursor, difficult due to both compounds having a peak at m/z 28.

Mass Spectrometry↗

NASA Engineering and Safety Center Technical Bulletin No. 21-03-1: Best Practices for the Elemental Profiling of High-Purity Hydrazine

Trace contaminants in high-purity hydrazine (HPH) propellant impact a wide variety of commercial, Department of Defense (DoD), and NASA missions. Depending on thruster design, elemental contaminants must be kept at extremely low levels and are verified as such by routine analysis. A number of these contaminants have recently undergone an assessment to shed light on their quantities present following changes in the HPH supply chain. A round robin analysis utilizing four separate laboratories resulted in unacceptably high variability in the quantification of these contaminants. The principal objective of this technical bulletin is to signal the availability of a new analysis methodology which yields accurate and repeatable quantification by providing best practices for both quantitation methodology and strategies for avoiding sample contamination during analysis.

High-Purity Hydrazine↗

J. E. Nichols and D. M. Peteet reply

The quantification of northern peatland carbon stocks is vital to our understanding of the global carbon cycle, and we welcome the opportunity to respond to the comments submitted in the preceding “Matters Arising” (Yu et al., Ratcliffe et al.). In response to Yu et al., we provide additional clarification of how data from the Neotoma Paleoecological Database (NPD) are stored and used. We also repeated our quantification of northern peat carbon stocks using only radiocarbon data vetted by other publications. However, our conclusions are unchanged by the elimination of the additional data. We respond to Ratcliffe et al. by addressing the conflict between the “time-history” and “inventory methods.” We calculated northern peatland carbon stocks by the inventory method using our dataset and carefully quantify the uncertainties. We find that while the mean values for carbon stock differ by twofold, the uncertainties around those means are larger than previously acknowledged and are overlapping. This result emphasizes the need for continued work by the carbon cycle science community to understand and quantify uncertainty in terrestrial carbon stock estimates.

Peatlands↗

Automated Noise Calibration System (VT-1000)

This paper details an automated Noise Source calibration system in development at Jet Propulsion Laboratory, California Institute of Technology (JPL). The paper begins with a discussion on Noise Figure and Excess Noise Ratio (ENR) theory, fundamentals and governing equations. As part of the fundamentals is a discussion of the system’s use of the Y-factor method to obtain accurate measurements of the Unit Under Test (UUT), and how these measurements are compared against a known ENR standard to obtain the UUT’s ENR values. There is also an in-depth discussion on uncertainty quantification for Noise Source system calibrations. The architecture of the automated calibration system is provided, which includes both the system’s hardware and software configuration. The software is written in Python 3, and provides the user detailed instruction on how to proceed, including step-by-step connection requirements. This system automates much of the measurement process, including real-time uncertainty quantification and report generation, as well as real-time feedback to the user to allow intervention if necessary. The system takes advantage of a database of results from previous measurements to compare calibration history of the ENR measurements. The automated system presented here operates over a frequency range from 10 MHz to 50 GHz, and has shown substantial time savings over traditional manual methods of performing this calibration.

Timpe, Scott↗

Toward Comprehensive Uncertainty Predictions for Remote Imaging Spectroscopy

Remote imaging spectroscopy’s role in Earth science will grow in the coming decade as a series of globe-spanning spectroscopy missions launch from NASA, ESA, and other agencies. The nature of remote imaging spectroscopy will change, advancing from short regional studies to address global multi-year questions. The diversity of data will also grow with exposure to a wider range of biomes and atmospheric conditions. To execute these new investigations we must reconcile diverse observing conditions to derive consistent global maps. To this end, rig- orous uncertainty quantification and propagation enables an optimal synthesis of data accounting for observing conditions and data quality. Understanding data uncertainties is also important for principled hypothesis testing, information content assessment, and informed decision making by end users. We survey prior efforts in uncer- tainty quantification for imaging spectroscopy, and describe methods for validating the accuracy of uncertainty predictions. We conclude with a discussion of remaining challenges and promising avenues for future research.

Susiluoto, Jouni↗

Quantifying and Characterizing Striping of Microwave Humidity Sounder with Observation and Simulation

Striping has been observed in the MetOp-A Microwave Humidity Sounder (MHS) data since its degradation in November 2018. However, accurate striping quantification and characterization remain challenging due to the large scene dynamics observed at W/G bands of MHS. Here we have developed a set of novel algorithms for striping quantification, decomposition, characterization, and simulation. Our algorithm extracts striping from the warm-load and cold-space scenes that are relatively stable. We break down the striping into two parts of thermal and 1/f noise and quantify their absolute magnitude and relative ratio. We found a significant increase in striping at 157 GHz that has more than quadrupled by October 2019 relative to its normal level. Regardless of the degradation, the ratio of thermal and 1/f noise remains the same. Our simulation reproduces all the characteristics of striping against observation. It is shown that 1/f noise generates sharp, non-periodic stripes, while thermal noise also generates stripes but with smoother band features. The latter is due to the periodic calibration that has a chopping effect. The striping percentage, defined as the ratio of 1/f to total noise, shows no dependence on the scene temperature. Striping is pronounced not only in 157 GHz but also in 89 and190 GHz with the striping percentage over 50%, while lower in183 GHz of 20%. The results provide insights for quantifying and understanding striping. Our algorithm can be applied to other radiometers and to simulate striping for evaluating its impact in data assimilation and science products.

John Xun Yang↗

Exploring Carbon Mineral Systems: Recent Advances in C Mineral Evolution, Mineral Ecology, and Network Analysis

Large and growing data resources on the spatial and temporal diversity and distribution of the more than 400 carbon-bearing mineral species reveal patterns of mineral evolution and ecology. Recent advances in analytical and visualization techniques leverage these data and are propelling mineralogy from a largely descriptive field into one of prediction within complex, integrated, multidimensional systems. These discoveries include: (1) systematic changes in the character of carbon minerals and their networks of coexisting species through deep time; (2) improved statistical predictions of the number and types of carbon minerals that occur on Earth but are yet to be discovered and described; and (3) a range of proposed and ongoing studies related to the quantification of network structures and trends, relation of mineral “natural kinds” to their genetic environments, prediction of the location of mineral species across the globe, examination of the tectonic drivers of mineralization through deep time, quantification of preservational and sampling bias in the mineralogical record, and characterization of feedback relationships between minerals and geochemical environments with microbial populations. These aspects of Earth’s carbon mineralogy underscore the complex coevolution of the geosphere and biosphere and highlight the possibility for scientific discovery in Earth and planetary systems.

Carbon↗

GC/MS Method Development for Separating Lunar Volatile Ice Simulant Headspace Gases

Various investigators propose the lunar surface contains widely distributed volatiles, especially water- like species, i.e. OH and H2O. Surface volatiles are theorized to exist as a hydrated regolith layer, concentrated in extremely cold polar permanently shadowed regions (PSR), and/or solar wind implantation reservoirs in lunar glasses. The proposed sources of lunar surface volatiles range from cometary impacts, solar wind, or a supply present during moon formation. Future Artemis missions aim to collect and return the samples containing volatiles collected near lunar polar craters or PSRs. We, as advanced curation scientists, are responsible for developing techniques and methodologies for preserving returned sample integrity as much as possible. Pristine volatile-bearing samples are invaluable to the scientific community seeking to unravel the history of the solar system. Realistically, a sample will experience alteration during collection, transportation back to earth, and storage. The Planetary Exploration and Astromaterials Research Lab (PEARL) seeks to understand temperature and pressure effects on high-fidelity volatile-containing regolith simulants, the foundation for the future of cold curation. This abstract outlines the separation, identification, and quantification of headspace gases over volatile ice feed stock material using gas chromatography/mass spectrometry (GC/MS). Preliminary objectives concentrated on sample handling, reproducibility, and understanding the elution characteristics for each analyte. Initial GC/MS method development experiments utilized diluted static headspace sample preparation. Diluted samples were used because sampling headspace gases directly from a vial containing liquid analyte resulted in overloading of the column and detector. Overloading is evident based on chromatogram peak shapes and instrument contamination, or carry over, between experiments. A mixture of three alcohols were used for a majority of the sample handling and reproducibility studies. Reproducibility was tested via multiple users, calibration curves, and check standards. Stock solutions of condensed lunar volatile analytes included methanol, ammonia in methanol, hydrogen sulfide in water, and an equal volume mixture of methanol, ethanol, and isopropanol. Current samples use room air as the headspace sample matrix, however future experiments will incorporate an inert purge gas, such as argon or nitrogen. Three mL of each analyte solution were capped in separate 20 mL crimp top GC vials. Dilutions were carried out by removing an aliquot of headspace gases with a calibrated 1 mL gastight syringe and immediately transferring to a 20 mL capped crimp top vial. The GC/MS is a Thermo Fisher Trace 1310/ISQ 7000 with a TriPlus RSH autosampler and split/splitless injector module. The experiments outlined in this abstract use the following hardware: a 2.5 mL gastight headspace syringe tool, 1 mm ID x 78.5 mm length ultra-inert straight injection liner, and a TG-BondQ 30 m × 0.32 mm × 10 μm column. Various parameters, such as hardware selection and the temperature, pressure, and split ratio set points, continue to evolve as the overall experiment is refined. Diluted headspace chromatograms were collected for the individual stock solutions. Retention times, peak shapes, and mass spectra were evaluated and added to the data processing method for each molecule of interest. Figure 1 shows the total ion chromatograms for the three major lunar volatile simulant stock solutions: methanol, 7 N ammonia in methanol, and 0.4% hydrogen sulfide in water. Tailing peak shapes for ammonia (2.98 min rt) and water (4.06 min rt) indicate the molecules are not properly eluting from the selected column with the current separation method. Additionally, hydrogen sulfide and ammonia have overlapping peak windows, which could impact quantification. Ongoing experiments aim to address the peak shape and overlapping via the separation method and hardware selection. Sample preparation reproducibility experiments used stock solution containing equal volumes of a non- interactive mixture of methanol, ethanol, and isopropanol. Mass spectrum ion traces were used to identify and quantify all three alcohols. Peaks were automatically detected, identified, and integrated through the mass spectra detection and processing parameters. Calibration response curves and check standards were used to evaluate the validity of the sample preparation procedure. Figure 2 shows the methanol chromatogram peak area versus total headspace dilution volume transferred from the alcohol mixture vial. The calibration response curves and check standards validate sample preparation procedure. Continuing data analysis efforts are working towards correlating the peak area and instrument response factor to the headspace analyte concentration and condensed phase composition. Static headspace gas chromatography theory relies on Dalton’s law, Raoult’s law, Henry’s Law, and the Kolb and Ettre equation to associate peak area to the analyte composition in a non-ideal solution. Equation 1 is a simplified expression derived from the aforementioned theories. Future experiments involve liquid injections of the individual stock solutions, liquid and headspace analysis of various stock solution combinations, and the addition of regolith simulants to the mixtures. Temperature is another variable expected to affect reaction rates and will be explored.

Cecilia L. Amick↗

The Effect of Fiber-Angle Fidelity on the Linear Response of Tow-steered Composite Plates

Composite laminate tailoring is traditionally performed by uniformly changing the in-plane ply orientation to obtain the desired mechanical performance. The emergence of tow-steered plies, where the fibers follow a prescribed curvilinear path, have increased the tailorability of composite laminates. However, characterizing the behavior of tow-steered laminates using finite element analysis is challenging because additional, and often numerous, orientation definitions may be required. The additional orientation definitions detrimentally increase the computational cost and hinder the use of advanced analysis techniques, such as Monte Carlo or uncertainty quantification, in the design process. To reduce computational cost without adversely affecting mechanics-based performance, the results of a parametric study that was used to investigate the effects of fiber-angle fidelity, element size, and tow-steered radius-of-curvature on various loading scenarios for tow-steered composite plates are presented. The three loading scenarios that were analyzed using finite element analysis included an axial tension load, an applied constant through-thickness-direction pressure load, and an axial compression load. Results for mechanics-based metrics of interest are presented and discussed for each loading scenario. Little sensitivity (less than one percent difference) to the effect of fiber-angle fidelity is observed in the mechanics-based metrics until the coarse-end of the considered range. Sensitivity to element size generally dominates the observed results for the mechanics-based metrics. Notable reductions in the preprocessing time are observed for increasingly coarse element size and fiber-rounding parameters. The preprocessing times decreased up to three orders of magnitude from a few thousand seconds to a few seconds without loss of accuracy in the mechanics-based metrics. Such increased computational performance is of particular interest to the structural design and analysis communities that may be conducting large counts of finite-element analyses, such as in other parametric studies, Monte-Carlo analyses, uncertainty quantification, or tow-steered optimization.

tow-steered composites↗

Dynamic Assurance of Autonomous Systems through Ground Control Software∗

Assurance cases are being increasingly acknowledged as a way to build trust in complex systems with autonomous capabilities [1]. An assurance case is a comprehensive, defensible, and valid justification that a system will function as intended for the specific mission and operating environment. Such justifications for systems with autonomous capabilities are often based on various probabilistic quantifications [2]. Due to the dynamic nature of the environmental conditions in which these systems operate, as well as the changing nature of the autonomous systems themselves, these probabilistic quantifications cannot be simply estimated once during design time. Rather, they need to be continually evaluated during systems operations to ensure that the assurance case justifications are valid. We refer to the assurance case that combines both the static and dynamic elements as a Dynamic Assurance Case (DAC).

dynamic assurance case↗

Carbothermal Reduction Demonstration (CaRD) Gas Analysis Subsystem Development

The Carbothermal Reduction Demonstration (CaRD) project is currently developing a subscale system to demonstrate the operation and performance of the carbothermal reduction process and quantify the production of molar equivalent oxygen (carbon monoxide (CO) and carbon dioxide (CO2)). Traditionally, gas chromatography (GC) systems are utilized to qualify gases due to their ability to separate the volatile compounds within the gas mixture, followed by quantification of the isolated component of interest. Despite these capabilities, GCs require consumables for their operational mobile phase and consequently are not often considered a flight-forward option. An alternative analytical technique known as mass spectrometry measures the presentence of gas phase molecules based on their mass-to-charge ratio. Residual gas analyzers (RGAs) which utilize mass spectrometry are not traditionally used to quantify analytes, but proper design and calibrations can result in a flight-forward analytical instrument capable of direct quantification of volatile gases of interest. Kennedy Space Center's role for the project is to design a gas analyzer system to detect and quantify the CO and CO2 gas produced during the carbothermal reaction utilizing a commercial version of the Mass Spectrometer Observing Lunar Operations (MSolo) instrument. MSolo is a modified commercial off-the-shelf mass spectrometer which consists of quadrupole mass filter and space rated electronics. MSolo is currently a TRL 6 technology and manifested on the PRIME-1 and VIPER missions. This paper describes leveraging MSolo based instrumentation to develop a gas analysis system for the work outlined in the 2023 International Conference on Environmental Systems (ICES) paper number 313. The design and testing of the brassboard phase of the project will be described along with the current work underway which will cover the design and preliminary test results of the prototype phase.

Ryan Patrick Gott↗

Bayesian Symbolic Regression: Addressing Challenges in Estimating Fractional Bayes Factors and Application to Fatigue Crack Growth Modeling

This research pioneers advancements in computational mechanics by integrating Bayesian-based uncertainty quantification into symbolic regression, specifically focusing on the critical task of accurately estimating the fractional Bayes factor for selecting arbitrary equations. In our exploration, we rigorously study two prominent methods—sequential Monte Carlo and the Laplace approximation—employed for computing the fractional Bayes factor. Our findings underscore the limitations of the Laplace approximation, revealing its diminished accuracy in nonlinear and multimodal scenarios. Specifically, the Laplace approximation is shown to underpredict fractional Bayes factor on a wide set of equations associated with a symbolic regression benchmark. This comparative analysis sheds light on the nuanced performance of these techniques, guiding researchers toward more informed choices in uncertainty quantification within symbolic regression. Furthermore, we showcase the practical utility of these enhanced symbolic regression tools through their application to a real-world problem in fatigue crack growth modeling, emphasizing their efficacy in capturing the complexities of mechanical systems.

Geoffrey Bomarito↗

Dynamics and Control of Quadcopter in Uncertain Environment

We consider problem of dynamics, control, and uncertainty quantification for quadcopter. We use the 6DOF model of quadcopter dynamics, linear quadratic regulator and linear quadratic Gaussian control of quadcopter in the presence of dynamical disturbances, measurement noise, hidden dynamical variables, dashing GPS signal, and wind gusts to predict quadcopter trajectory. We identify key sources of uncertainties and report on progress in development of a system that estimates the probability of safety-critical events using a set of algorithms based on the trajectory predictions.

uncertainty quantification↗

Variational Coupled Loads Analysis using the Hybrid Parametric Variation Method

Time-domain coupled loads analysis (CLA)is used to determine the response of a launch vehicle and payload system to transient forces, such as liftoff, engine ignitions and shutdowns, jettison events, and atmospheric flight loads, such as buffet. CLA, using Hurty/Craig-Bampton (HCB)component models, is the accepted method for the establishment of design-level loads for launch systems. However, uncertainty in the component models flows into uncertainty in predicted system results. Uncertainty in the structural responses during launch is a significant concern because small variations in launch vehicle and payload mode shapes and their interactions can result in significant variations in system loads. Uncertainty quantification (UQ)is used to determine statistical bounds on prediction accuracy based on model uncertainty. In this paper uncertainty is treated at the HCB component-model level. In an effort to account for model uncertainties and statistically bound their effect on CLA predictions, this work combines CLA with UQ in a process termed variational coupled loads analysis (VCLA). The modeling of uncertainty using a parametric approach, in which input parameters are represented by random variables, is common, but its major drawback is the resulting uncertainty is limited to the form of the nominal model. Uncertainty in model form is one of the biggest contributors to uncertainty in complex built-up structures. Model-form uncertainty can be represented using a nonparametric approach based on random matrix theory (RMT). In this work, UQ is performed using the hybrid parametric variation (HPV)method, which combines parametric with nonparametric uncertainty at the HCB component model level. The HPV method requires the selection of dispersion values for the HCB fixed-interface (FI)eigenvalues, and the HCB mass and stiffness matrices. The dispersions are based upon component test-analysis modal correlation results. During VCLA, random component models are assembled into an ensemble of random systems using a Monte Carlo (MC)approach. CLA is applied to each of the ensemble members to produce an ensemble of system-level responses for statistical analysis. The proposed methodology is demonstrated through its application to a buffet loads analysis of NASA’s Space Launch System (SLS)during the transonic regime fifty seconds after liftoff. Core stage (CS)section shears and moments are recovered, and statistics are computed.

Uncertainty Quantification↗

Monte-Carlo Analysis of Minimum Thermocouple Depths using Icarus

Icarus is a three-dimensional, unstructured, finite-volume material response solver developed at NASA Ames Research Center and has been verified against other NASA material response tools like FIAT, which have a long history of successfully designing thermal protection system (TPS). Icarus solves a set of conservation equations for mass and energy and uses Darcy’s Law in place of momentum conservation. An ecosystem of material response tools has been built around a general-purposed Icarus library that in addition to the typical material response analysis also supports TPS sizing (1-D and multi-dimensional), uncertainty quantification, and has been successfully integrated into a multi-physics architecture built around US3D. In this paper, a brief overview of Icarus and its capabilities will be presented using an illustrative Monte Carlo analysis of the one-dimensional, in-depth material response of a representative Dragonfly trajectory.

Material Response↗

Comparisons of Performance Metrics and Machine Learning Methods on an Entry Descent and Landing Database

This work focuses on evaluating machine learning methods and their applicability to the generation of an aerodynamic database, particularly for trajectory analysis of a capsule during entry, descent, and landing with a focus on uncertainty quantification. The source data to be used is the wind tunnel and computational data for the Integrated Design Assessment Team (IDAT) configuration of the Orion project, which has been publicly released. The methods used to generate the proposed databases are designed to naturally include a prediction interval, which will be evaluated both for their mean response as well as how well the prediction interval performs. These machine learning methods are compared to a traditionally generated database used by the Orion team as a baseline. It is found that while these machine learning methods perform well, the Orion database tends to still outperform them showing that engineering experience is still needed to make the best database possible. However, these methods still provide comparable results with significantly less effort.

Orion↗

Comparisons of Performance Metrics and Machine Learning Methods on an Entry, Descent, and Landing Database

This work focuses on evaluating machine learning methods and their applicability to the generation of an aerodynamic database, particularly for trajectory analysis of a capsule during entry, descent, and landing with a focus on uncertainty quantification. The source data to be used is the wind tunnel and computational data for the Integrated Design Assessment Team (IDAT) configuration of the Orion project, which has been publicly released. The methods used to generate the proposed databases are designed to naturally include a prediction interval, which will be evaluated both for their mean response as well as how well the prediction interval performs. These machine learning methods are compared to a traditionally generated database used by the Orion team as a baseline. It is found that while these machine learning methods perform well, the Orion database tends to still outperform them showing that engineering experience is still needed to make the best database possible. However, these methods still provide comparable results with significantly less effort.

Orion↗

Differential Equation Approximation Using Gradient-Boosted Quantile Regression

The operation of cyber-physical-human (CPH) systems is subject to various epistemic and aleatory uncertainties. Overall trustworthiness of CPH systems relies on the trustworthiness of its components and their interactions. It is important that computational models comprising the cyber component of CPH provide predictions accompanied by a measure of confidence in model outcomes. Uncertainty quantification (UQ) and propagation are especially important in safety critical CPH systems. Gradient-boosted trees is a modeling approach capable both of learning the dynamics of a system and performing UQ. In this paper, we devise a method for using gradient boosting to learn the dynamics of a second order differential equation and estimate uncertainty at the same time. We do this by creating a custom loss function that trains the model to approximate the second derivative of a noisy time series, and to penalize based on a parameter that corresponds to the desired quantile. The resulting gradient boosting model can simulate stochastic trajectories of the system given a single starting point, that is, it can estimate both the expected trajectory and its uncertainty. We show that the uncertainty estimation is well calibrated and that the model can learn the dynamics even in the presence of noise. We demonstrate the approach on a simple cartpole system.

Autonomous systems↗