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NASA NTRS2008

Superposition of Cohesive Elements to Account for R-Curve Toughening in the Fracture of Composites

The relationships between a resistance curve (R-curve), the corresponding fracture process zone length, the shape of the traction/displacement softening law, and the propagation of fracture are examined in the context of the through-the-thickness fracture of composite laminates. A procedure that accounts for R-curve toughening mechanisms by superposing bilinear cohesive elements is proposed. Simple equations are developed for determining the separation of the critical energy release rates and the strengths that define the independent contributions of each bilinear softening law in the superposition. It is shown that the R-curve measured with a Compact Tension specimen test can be reproduced by superposing two bilinear softening laws. It is also shown that an accurate representation of the R-curve is essential for predicting the initiation and propagation of fracture in composite laminates.

Davila, Carlos G.↗

NASA NTRS2017

Accounting for the Effects of Surface BRDF on Satellite Cloud and Trace-Gas Retrievals: A New Approach Based on Geometry-Dependent Lambertian-Equivalent Reflectivity Applied to OMI Algorithms

Most satellite nadir ultraviolet and visible cloud, aerosol, and trace-gas algorithms make use of climatological surface reflectivity databases. For example, cloud and NO2 retrievals for the Ozone Monitoring Instrument (OMI) use monthly gridded surface reflectivity climatologies that do not depend upon the observation geometry. In reality, reflection of incoming direct and diffuse solar light from land or ocean surfaces is sensitive to the sun-sensor geometry. This dependence is described by the bidirectional reflectance distribution function (BRDF). To account for the BRDF, we propose to use a new concept of geometry-dependent Lambertian equivalent reflectivity (LER). Implementation within the existing OMI cloud and NO2 retrieval infrastructure requires changes only to the input surface reflectivity database. The geometry-dependent LER is calculated using a vector radiative transfer model with high spatial resolution BRDF information from the Moderate Resolution Imaging Spectroradiometer (MODIS) over land and the Cox-Munk slope distribution over ocean with a contribution from water-leaving radiance. We compare the geometry-dependent and climatological LERs for two wavelengths, 354 and 466 nm, that are used in OMI cloud algorithms to derive cloud fractions. A detailed comparison of the cloud fractions and pressures derived with climatological and geometry-dependent LERs is carried out. Geometry-dependent LER and corresponding retrieved cloud products are then used as inputs to our OMI NO2 algorithm. We find that replacing the climatological OMI-based LERs with geometry-dependent LERs can increase NO2 vertical columns by up to 50% in highly polluted areas; the differences include both BRDF effects and biases between the MODIS and OMI-based surface reflectance data sets. Only minor changes to NO2 columns (within 5 %) are found over unpolluted and overcast areas.

Lambert-equivalent reflectivity↗

NASA NTRS2017

Accounting for Epistemic Uncertainty in Mission Supportability Assessment: A Necessary Step in Understanding Risk and Logistics Requirements

Future crewed missions to Mars present a maintenance logistics challenge that is unprecedented in human spaceflight. Mission endurance – defined as the time between resupply opportunities – will be significantly longer than previous missions, and therefore logistics planning horizons are longer and the impact of uncertainty is magnified. Maintenance logistics forecasting typically assumes that component failure rates are deterministically known and uses them to represent aleatory uncertainty, or uncertainty that is inherent to the process being examined. However, failure rates cannot be directly measured; rather, they are estimated based on similarity to other components or statistical analysis of observed failures. As a result, epistemic uncertainty – that is, uncertainty in knowledge of the process – exists in failure rate estimates that must be accounted for. Analyses that neglect epistemic uncertainty tend to significantly underestimate risk. Epistemic uncertainty can be reduced via operational experience; for example, the International Space Station (ISS) failure rate estimates are refined using a Bayesian update process. However, design changes may re-introduce epistemic uncertainty. Thus, there is a tradeoff between changing a design to reduce failure rates and operating a fixed design to reduce uncertainty. This paper examines the impact of epistemic uncertainty on maintenance logistics requirements for future Mars missions, using data from the ISS Environmental Control and Life Support System (ECLS) as a baseline for a case study. Sensitivity analyses are performed to investigate the impact of variations in failure rate estimates and epistemic uncertainty on spares mass. The results of these analyses and their implications for future system design and mission planning are discussed.

Owens, Andrew↗

NASA NTRS2018

Accounting for Static and Dynamic Open Water in the Modeling of SMAP Brightness Temperatures over Peatlands

Hydrological change in peatlands due to anthropogenic disturbance and global warming can release enormous amounts of greenhouse gas emissions. Passive microwave satellite observations are an opportunity to globally monitor these changes. Abundant static and dynamic open water surfaces in peatlands strongly affect observed brightness temperatures (Tb). Here, we account for these contributions in radiative transfer modeling using NASA's Goddard Earth Observing System Model version 5 (GEOS-5) static open water mask and, for the dynamic open water fraction, the simulated inundated area using a version of the GEOS-5 Catchment land surface model that has been modified for peatland areas (PEAT-CLSM). Modeled Tb is compared against two years of SMAP L-band Tb. Preliminary results indicate: (i) a bias reduction when including the static open water fraction in a simple RTM mixing model, and ii) significantly improved correlation between modeled and observed Tb when using land surface output from PEAT-CLSM instead of the operational CLSM.

passive microwave↗

NASA NTRS2020

Data Accountability and Uncertainty Analysis for the Mars Science Laboratory

This paper presents machine learning-based approaches to automate and optimize the detection of volume loss for the downlink process of telemetry data from the Mars Curiosity Rover. The Curiosity observes volume loss and data corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the data flow. To resolve this issue, we created a data pipeline to accumulate data from various data sources in the downlink process and detect where the data is missed. In this paper, we benchmarked different methodologies based on the accuracy and excitability of them to identify whether a downlink data that is received to the ground system is complete or incomplete. Our results show that machine learning methods can improve the performance of the GDSA by 55% while the user can diagnose why data is missed and provide an explanation for the data accountability problem.

Chowdhury, Ameera↗

NASA NTRS2021

Automated Data Accountability for Missions in Mars Rover Data

As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The GDSA team can significantly benefit from the automation and optimization of the downlink process of telemetry data. In fact, there is a need for a better understanding of why the data is corrupted, so that the GDSA team can best determine the root cause of the issues in the GDS. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implement various machine learning and deep learning based models, including support vector machines, ensemble methods, and deep neural networks and evaluate their accuracies in identifying whether a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in real time. This hyperparameter optimization also allows our methods to be quickly integrated into other JPL missions. Our results show that our best-performing machine learning and deep learning based models outperform the existing GDSA detection software by 6 accuracy points and can aid analysts by providing insights into the data accountability problem. Since these various machine learning and deep learning approaches vary significantly in interpretability, we provide a discussion on the tradeoffs between their performance and trustworthiness in helping detect issues in data transmission.

Divsalar, Dariush↗

NASA NTRS2022

Nickel Accounting for the Psyche Spacecraft

(16) Psyche is a metallic asteroid located in the asteroid belt between Mars and Jupiter. The composition of Psyche is unknown, but the scientific community believes it is composed primarily of iron, nickel, and silicates. The Psyche mission will be the first space mission to study this asteroid in close proximity, and as such, determining the elemental composition of the asteroid is one of the primary objectives of this mission. The spacecraft measures this elemental composition using the Gamma Ray Spectrometer (GRS), which detects cosmic-ray induced gamma rays from Psyche’s surface. In addition to gamma rays from Psyche, the GRS will also detect background gamma rays from spacecraft material nearby the GRS. Because Ni is such a key element for achieving the mission science objectives, a special effort has been made to minimize background gamma rays from Ni, thus maximizing sensitivity to the Ni gamma rays from Psyche. The Psyche mission approached this potential Ni background issue with two methods, locating the GRS away from the spacecraft/source of Ni and setting a threshold requirement against the total Ni mass of the spacecraft. Verifying that the total amount of Ni on the spacecraft is within this threshold is thus a key part of ensuring the GRS can meet its science objectives. Ni alloys are commonly used on spacecraft, but the mass and location of elemental Ni is not typically accounted for. Ni can be found in many materials all over the spacecraft – in fasteners, optical assemblies, magnetic shields, electromagnetic interference (EMI) tape, under gold plating, in connectors, and so on. The task proved further challenging because of the distributed nature of the build of this spacecraft and the use of vendors external to JPL. While the analysis was atypical and source information difficult to cull, the approach described in this paper used tools and data available to the project in some form to achieve this goal. For Psyche, current analysis shows there is margin in meeting the Ni mass requirement. This paper explains the systematic approach used to accurately and precisely record Ni content on the Psyche spacecraft. While this approach was defined for Ni on the Psyche mission, it can be used for any future project with the need to accurately estimate the total mass of a single element or material on a spacecraft.

Cullinan, Joe↗

NASA NTRS2022

Nickel Accounting for the Psyche Spacecraft

Cullinan, Joe↗

NASA NTRS2023

Accounting for Dose Uncertainty in Dose-Response Curve Estimation Using Hierarchical Bayes Models

As part of their development of the technology for low-boom supersonic flight, the National Aeronautics and Space Administration (NASA) is planning to conduct a set of supersonic aircraft annoyance surveys in select communities in the United States, to measure public perception of the reduced sonic boom. The relationship between a noise exposure level of a supersonic flight event and the probability of an individual being highly annoyed by the event is quantified by a dose-response curve, which is usually based on logistic regression modeling. Unavoidable amounts of estimation error are expected in the upcoming noise measurements, which can bias the results of the logistic regression if ignored. Hence, the development of an estimation approach that accounts for such error when estimating the dose-response curve is imperative. In this paper, an evaluation study is conducted to assess the impact of measurement error on dose-response curve estimation. For this, hierarchical Bayes models using different specifications are fit to data collected by NASA in 2018, as part of a risk-reduction study of supersonic flights affecting Galveston, Texas. It is observed that the estimated dose-response curve appears sensitive to uncertainty in dose measurements, being subject to attenuation bias.

community response↗

NASA NTRS2024

Accounting for 3D Radiative Effects in MODIS Aerosol Retrievals near Clouds using CALIPSO Observations

Retrievals of aerosol properties near clouds from passive remote sensing is challenging. Sunlight scattered by clouds into nearby clear regions can effectively enhance the clear area reflectance. These cloud 3D radiative effects may lead to large biases in aerosol retrievals if uncorrected, risking an incorrect interpretation of satellite observations for aerosol-cloud interaction in cloudy atmosphere. In earlier studies, we developed a simple two-layer model (2LM) to estimate the cloud-induced clear-sky radiance enhancements in cloud fields. In this study, we take advantage of CALIPSO lidar observations, which should not be affected by the 3D radiative effect, to study passive aerosol retrievals in cloud fields in the Amazon region, specifically those produced by the operational Dark Target algorithm applied to Aqua-MODIS. From two-years’ worth of co-located CALIPSO/MODIS aerosol retrievals, we find a larger increase in operationally retrieved MODIS AOD from clear to cloudy regions (~0.075 or ~40%) than for the CALIPSO AOD (~0.021 or ~20%). The much larger increase in MODIS AOD is mainly due to the 3D radiative effects. After using the 2LM model to account for cloud 3D radiative effects, the clear to cloudy increase in MODIS AOD was reduced to ~0.043 (~23%), much closer to CALIPSO observations. The 3D corrected average MODIS AOD for cloudy scenes is significantly larger than AOD for clear conditions, even for cloud fraction (CF) less than 0.1, suggesting aerosols in cloudy scenes are characteristically different from aerosols in clear conditions. Furthermore, the 3D correction of AOD (i.e., τ_1D-τ_3D) increases linearly with CF for a large range of CF. We have also examined the impact of the 3D effect on aerosol Ångström Exponent (AE) and fine model fraction (FMF) of AOD. We found that the uncorrected average AE and FMF depend strongly on CF, ~25% increase in AE (decrease in particle size) and ~60% increase in FMF as CF increases from 0.05 to 0.45. The 3D correction leads to smaller average AE (bigger particle size) and FMF that are almost independent of CF. Thus, the 3D corrected aerosol properties are expected to provide more accurate information for better understanding aerosol-cloud interactions.

MODIS↗

NASA NTRS2024

Inverse Modeling of the Initial Stage of the 1991 Pinatubo Volcanic Cloud Accounting for Radiative Feedback of Volcanic Ash

The way volcanic clouds evolve is very sensitive to the initial spatial 3D distributions of volcanic materials, which are often unknown. In this study, we conducted inverse modeling of the Mt. Pinatubo cloud using TOMS 2D mapping of Aerosol Index and SO 2 loading during the first three post-eruption days to estimate the time-dependent emissions profiles and initial 3D spatial distributions of volcanic ash and SO 2 . We account for aerosol radiative feedback and dynamic lofting of volcanic ash in the inversion calculations for the first time. This resulted in a lower ash injection height (by 1.5 km for ash) than without ash radiative feedback. The Pinatubo eruption ejected ≈77% of fine ash at 12 to 23 km, ≈65% of SO 2 at 18–25 km. In contrast with previous studies, which suggested that all volcanic materials were emitted above the tropopause, a significant fraction of SO 2 (5.1 of 15.5 Mt) and fine ash (37.2 of 66.5 Mt) were ejected in the troposphere, where SO 2 quickly oxidized into sulfate aerosol that is short-lived in the troposphere. This explains the early presence of sulfate aerosols in the plume and why the models can reproduce the observed volcanic aerosols' optical depth (AOD), assuming lower-than-observed SO 2 emission in the stratosphere. Despite the quicker than in observations build-up of sulfate AOD, in a month after the eruption, the evolution of the Pinatubo AOD simulated using the obtained ash and SO 2 initial distributions converges with the available stratospheric aerosol and gas experiment (SAGE) observations.

Pinatubo volcanic ash and SO2 emissions↗

NASA NTRSDate not supplied

Accounting for Point Estimate Uncertainty in Space Systems Reliability and Risk Analysis

Understanding and accounting for uncertainty in risk analysis is a critical step in the management and communication of risk in engineered systems. The component and system-level analysis to determine the probability of a negative outcome and its consequence is often quantified by a point estimate. Many Program and Enterprise decisions involving technical concerns and issues rely on reliability engineering activities to produce quantified risk analysis to inform the decision making process. At NASA, it is common to use a Probabilistic Risk Analysis (PRA) to inform the overall risk to Loss of Mission or Loss of Crew that involves integration across all spacecraft subsystem fault trees to produce an overall probability of mission failure. The point estimate is an estimate of this overall probability and is an immediate result of a fault tree model. It is the result of a model where the probability of each event is taken to be equal to its mean. The value provides an approximation of the overall mean without running any uncertainty calculations (e.g., no sampling). Using only the point estimate can lead to a false sense of precision and the point estimate may not match the resulting mean when uncertainty is taken into consideration. This paper will explore five conditions that can cause the PRA model mean to diverge from the point estimate and will provide engineers and managers insight into the importance of understanding uncertainty in the elements of PRA models.

Paul J Collier↗

NASA NTRS2025

Single Integrated Flux Files to Account for Spacecraft Attitude Motion in Meteoroid Risk Assessments Using the NASA MEM3 and Bumper Codes

Spacecraft meteoroid risk assessments can be performed using NASA’s Meteoroid Engineering Model 3 (MEM3) and the Bumper risk assessment code. The MEM3 meteoroid flux is highly directional in heliocentric space. To represent the directionality, MEM3 outputs the flux in commonly used coordinate frames. For example, if a spacecraft attitude is fixed in the orbital tangential frame, a single Bumper run using the MEM3 flux file in the body-fixed frame can be performed. Cases exist in which the spacecraft does not remain fixed in one of these frames during a mission. This could be handled by performing a series of Bumper runs at points along the trajectory. For each trajectory point, the spacecraft has to be rotated into the frame in which the MEM3 flux file is defined. Another option presented here is to generate a single integrated flux (SIF) file. A SIF file is an average over a trajectory of the time varying MEM3 flux files rotated from their native frame into the spacecraft bus frame. A SIF file enables accounting for spacecraft rotation (coupled with flux variation over the trajectory) in a single Bumper run. An example of an application of SIF files is the meteoroid risk assessment for the Mars Sample Return (MSR) campaign Capture Containment and Return System (CCRS). This report discusses how SIF files were generated for the CCRS risk assessment and presents metrics on the accuracy of the impinging fluence that is generated when using the files. The results show a maximum difference per element in a finite element model within 1 percent for seven of the eight examined cases, with the outlier being less than 2 percent. The results also show that the maximum standard deviation of differences across all elements is within 0.275 percent. The maximum difference in the total fluence across all the elements is 0.0463 percent. The maximum difference in the mean fluence across all the elements is 0.0407 percent.

Meteoroids↗

NASA NTRS1964