SEARCH · Search NASA

Results for “model accuracy”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19

All sources NASA NTRS DOE OSTI

NASA NTRS2011

Development of FIAT-based Thermal Protection System Mass Estimating Relationships for NASA's Multi-Mission Earth Entry Concep

Mass Estimating Relationships (MERs) have been developed for use in the Program to Optimize Simulated Trajectories II (POST2) as part of NASA's multi-mission Earth Entry Vehicle (MMEEV) concept. MERs have been developed for the thermal protection systems of PICA and of Carbon Phenolic atop Advanced Carbon-Carbon on the forebody and for SIRCA and Acusil II on the backshell. How these MERs were developed, the resulting equations, model limitations, and model accuracy are discussed herein.

MER↗

NASA NTRS2015

Thermal Protection System Mass Estimating Relationships for Blunt-Body, Earth Entry Spacecraft

System analysis and design of any entry system must balance the level fidelity for each discipline against the project timeline. One way to inject high fidelity analysis earlier in the design effort is to develop surrogate models for the high-fidelity disciplines. Surrogate models for the Thermal Protection System (TPS) are formulated as Mass Estimating Relationships (MERs). The TPS MERs are presented that predict the amount of TPS necessary for safe Earth entry for blunt-body spacecraft using simple correlations that closely match estimates from NASA's high-fidelity ablation modeling tool, the Fully Implicit Ablation and Thermal Analysis Program (FIAT). These MERs provide a first order estimate for rapid feasibility studies. There are 840 different trajectories considered in this study, and each TPS MER has a peak heating limit. MERs for the vehicle forebody include the ablators Phenolic Impregnated Carbon Ablator (PICA) and Carbon Phenolic atop Advanced Carbon-Carbon. For the aftbody, the materials are Silicone Impregnated Reusable Ceramic Ablator (SIRCA), Acusil II, SLA-561V, and LI-900. The MERs are accurate to within 14% (at one standard deviation) of FIAT prediction, and the most any MER under predicts FIAT TPS thickness is 18.7%. This work focuses on the development of these MERs, the resulting equations, model limitations, and model accuracy.

mass estimating relationship (MER)↗

NASA NTRS2015

Thermal Protection System Mass Estimating Relationships For Blunt-Body, Earth Entry Spacecraft

Mass estimating relationships (MERs) are developed to predict the amount of thermal protection system (TPS) necessary for safe Earth entry for blunt-body spacecraft using simple correlations that are non-ITAR and closely match estimates from NASA's highfidelity ablation modeling tool, the Fully Implicit Ablation and Thermal Analysis Program (FIAT). These MERs provide a first order estimate for rapid feasibility studies. There are 840 different trajectories considered in this study, and each TPS MER has a peak heating limit. MERs for the vehicle forebody include the ablators Phenolic Impregnated Carbon Ablator (PICA) and Carbon Phenolic atop Advanced Carbon-Carbon. For the aftbody, the materials are Silicone Impregnated Reusable Ceramic Ablator (SIRCA), Acusil II, SLA- 561V, and LI-900. The MERs are accurate to within 14% (at one standard deviation) of FIAT prediction, and the most any MER can under predict FIAT TPS thickness is 18.7%. This work focuses on the development of these MERs, the resulting equations, model limitations, and model accuracy.

Spacecraft↗

NASA NTRS2018

A Case for High-Fidelity Material Response Modeling

Material response modeling of heatshields for planetary entry vehicles has remained largely unchanged since Aerotherm Corp. introduced the CMA program in 1967. Modern models, like FIAT, have tread the same path, introducing efficiencies and better material property data along the way, but otherwise following the same underlying model paradigm. The CMA approach has worked well for heatshield design up to this point. However, there are three motivations for the material response community to pursue higher fidelity beyond simplified, CMA-derived models. The first motivation is that missions are becoming increasingly demanding and complex and, as they do, confidence in simplified models naturally decreases. Second, reliability of materials is now as much or more of a driving concern for mission designers than thermal response. Third, NASA and other agencies are increasingly interested in flight instrumentation for engineering science. This latter motivation places far stricter requirements on model accuracy in order to meet requirements for flight environment reconstruction.

Barnhardt, Michael D.↗

NASA NTRS2014

Development Of FIAT-Based Parametric Thermal Protection System Mass Estimating Relationships For NASA's Multi-Mission Earth Entry Concept

An integrated tool called the Multi Mission System Analysis for Planetary Entry Descent and Landing (M-SAPE) is being developed as part of NASAs In-Space Propulsion Technology (ISPT) program. Part of M-SAPEs development requires the formulation of mass estimating relationships (MERs) to determine the vehicle's Thermal Protection System (TPS) material and required thickness for safe Earth entry. The objective of this study was to develop MERs using simple correlations that were non-ITAR and matched as accurately as possible NASAs high-fidelity ablation modeling tool, the Fully Implicit Ablation and Thermal Analysis Program (FIAT ). These MERs would be a first-estimate for feasibility studies; it is understood that higher-fidelity modeling like FIAT would be necessary once a proposed trajectory was down-selected. The trajectory space for these MERS consisted of 840 different trajectories, and a materials heating limit was the main constraint for an allowable trajectory. MERs for the vehicle fore body included the ablating materials Phenolic Impregnated Carbon Ablator (PICA ) and Carbon Phenolic atop Advanced Carbon-Carbon. For the backshell the materials were Silicone Impregnated Reusable Ceramic Ablator (SIRCA ), Acusil II, SLA-561V, and LI-900. The MERFIAT ratio indicates MERs are accurate to within 14 percent (at one standard deviation) of FIAT prediction, and the most any MER can under-predict TPS thickness is 18.7 percent of FIAT prediction. This poster focuses on the development of these MERs, the resulting equations, model limitations, and model accuracy.

thermal protection system↗

NASA NTRS2019

Using Machine-Learning to Dynamically Generate Operationally Acceptable Strategic Reroute Options

The newly developed Trajectory Option Set (TOS), a preference-weighted set of alternative routes submitted by flight operators, is a capability in the U.S. traffic flow management system that enables automated trajectory negotiation between flight operators and Air Navigation Service Providers. The objective of this paper is to describe and demonstrate an approach for automatically generating pre-departure and airborne TOSs that have a high probability of operational acceptance. The approach uses hierarchical clustering of historical route data to identify route candidates. The probability of operational acceptance is then estimated using predictors trained on historical flight plan amendment data using supervised machine learning algorithms, allowing the routes with highest probability of operational acceptance to be selected for the TOS. Features used describe historical route usage, difference in flight time and downstream demand to capacity imbalance. A random forest was found to be the best performing algorithm for learning operational acceptability, with a model accuracy of 0.96. The approach is demonstrated for an historical pre-departure flight from Dallas/Fort Worth International Airport to Newark Liberty International Airport.

Evans, Antony↗

NASA NTRS2019

Using Machine-Learning to Dynamically Generate Operationally Acceptable Strategic Reroute Options

Evans, Antony↗

NASA NTRS2020

Lower Omo Food Security & Agriculture: Mapping Land Cover Change in Unprotected and Protected Areas in the Lower Omo River Valley, Ethiopia

Ethiopia is home to unique wildlife, biodiversity, and ecosystem services and, like much of the world, is undergoing population growth, development, and land use change. As a result, some biodiverse regions may be at risk of being urbanized, cultivated as agricultural plots, or losing access to water bodies that are essential for maintaining both terrestrial and aquatic life. The DEVELOP team partnered with the Ethiopian Wildlife Conservation Authority to quantify the land cover change between the years 1994, 2010, and 2018. The team utilized Ethiopia’s dry season (January to May) for training point development which was crucial in differentiating the level of greenness between the four land cover classes: water, natural vegetation, cultivated land, and bare ground. The study area covered 62,000 km2 of the Lower Omo River Valley and includes eight protected areas. Data from Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper Plus, Landsat 8 Operational Land Imager, and Shuttle Radar Topography Mission were used to employ a Random Forest classifier and identify the four classes within Google Earth Engine. For each of the supervised classifications, overall model accuracy was between 83% (2018) and 89% (1994). Between 1994 and 2018, the Lower Omo Valley experienced an overall increase of 258% (919 km2) in water and 291% (7,188 km2) in cultivated areas, while experiencing a 9% (2,761 km2) decrease in natural vegetation and 19% (5,346 km2) bare ground. There was an increase in water and cultivated land and a decrease in natural vegetation and bare ground in unprotected areas and all protected areas. However, protected areas maintained natural vegetation better than unprotected areas and only experienced a 1% (59 km2) loss compared to a 10% (2,701 km2) loss in unprotected areas.

Food Security & Agriculture↗

NASA NTRS2020

Lower Omo Food Security & Agriculture: Mapping Land Cover Change in Unprotected and Protected Areas in the Lower Omo River Valley, Ethiopia

Ethiopia is home to unique wildlife, biodiversity, and ecosystem services and, like much of the world, is undergoing population growth, development, and land use change. As a result, some biodiverse regions may be at risk of being urbanized, cultivated as agricultural plots, or losing access to water bodies that are essential for maintaining both terrestrial and aquatic life. The DEVELOP team partnered with the Ethiopian Wildlife Conservation Authority to quantify the land cover change between the years 1994, 2010, and 2018. The team utilized Ethiopia’s dry season (January to May) for training point development which was crucial in differentiating the level of greenness between the four land cover classes: water, natural vegetation, cultivated land, and bare ground. The study area covered 62,000 sq.km of the Lower Omo River Valley and includes eight protected areas. We used Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper Plus, Landsat 8 Operational Land Imager, and Shuttle Radar Topography Mission imagery within Google Earth Engine to employ a Random Forest Classifier and identify these four distinct classes. For each of our supervised classifications, overall model accuracy was between 83% (2018) and 89% (1994). Between 1994 and 2018, the Lower Omo Valley experienced an overall increase of 258.57% in water bodies and 291.23% in cultivated areas, while experiencing an 8.91% decrease in natural vegetation and 18.76% bare ground. There was an increase in water bodies and cultivated land and a decrease in natural vegetation and bare ground in unprotected areas and all protected areas. However, protected areas disproportionately experienced an increase in land cover change, including Tama Community Conservation Area which saw a 17,272.33% increase between 1994 and 2018.

Food Security & Agriculture↗

NASA NTRS2004

Mass properties testing of the Mars Exploration Rover missions

Lockheed Martin Space Systems Company (LMSSC) Denver, under contract to the Jet Propulsion Laboratory (JPL) performed the mass properties testing of the Mars Exploration Rover (MER) missions. This testing occurred over a ten-month period at bath the Jet Propulsion Laboratory and Kennedy Space Center (KSC). The tests at JPL were performed to verify model accuracy and determine arty issues that may affect the final mass properties measurements at the KSC launch site. Testing at KSC was performed on the Entry Vehicles and [he fully fueled spacecraft configurations for both the Spirit and Opportunity missions. All tests were completed successfully contributing to both mission's successful launch and subsequent landing on the surface of Mars in January of 2003.

Greene, Jason↗

NASA NTRSDate not supplied

Virtual Fit Assessment: Validation using Historical Spacesuit Fit Data

Virtual fit tests using 3D body scans have provided a cost-effective means to predictively assess spacesuit fit for the current and future astronaut population. However, fit is a complex issue influenced by physical interferences, subjective preference, and many other factors. Fit can substantially differ between the type of hardware, environmental conditions, and tasks being performed. Namely, fit changes across different contexts, such as 3D printed mockup evaluations, pressurized one-g suited test events, neutral buoyancy and other training events, and flight extravehicular activities (EVA). These challenges make it difficult to validate virtual fit frameworks to physical suit sizing. This study proposes a new method of validation using the wealth of historically archived suit fit data. Physically Assessed Fit (PAF) data was assessed from astronauts and test volunteers who wore the legacy Extravehicular Mobility Unit (EMU). The data was collected during the past decades at NASA and considered to be most reliable and dependable. Each participant was 3D scanned for body shape and spacesuit-critical dimensions were measured per NASA guidelines. The participants’ size preference for hard upper torso (HUT) assembly was retrieved from their pressurized suit fit check records. Fit assessments were also updated after one-g EVA training, neutral buoyancy training, and EVA flights. Virtually Assessed Fit (VAF) was done by overlaying the CAD model of a HUT with the 3D body scans of the PAF participants. Each body scan was iteratively adjusted for the position inside the HUT to minimize the suit-to-body contact interference while satisfying a set of prescribed requirements. Then the residual contact area, depth, and volume were quantified as VAF metrics. This process was repeated for the different HUT sizes, including medium, large, and extra-large. Statistical modeling is currently in progress and will be presented at the conference. A statistical classifier will be developed to predict a fit probability for different HUT sizes as a function of the corresponding person’s VAF metrics. The HUT size with the highest fit probability will constitute the most likely size selection for the person, and the prediction will be compared against the corresponding PAF data. The participant data will be randomly pre-grouped into either a model development or validation subset. While the model development subset will be used to build the probability model, the validation subset will be used to assess the model accuracy. Also, the locations and magnitudes of suit-body-contacts will be estimated from VAF. This information can identify the critical suit geometry and body shape features that influence suit fit. The variations found in PAF size selections by subjects with similar VAF metrics will allow for investigations into subjective preferences, for example, tight versus loose fit. Overall, this study is expected to provide a structured validation of a virtual suit fit framework, which has not been possible in the past. The outcome can also provide useful insights and potential limitations for interpreting virtual fit tests for future spacesuit designs and population accommodation.

Han Kim↗

NASA NTRS2020

Satellite Gravimetry Improves Seasonal Streamflow Forecast Initialization in Africa

West Africa is one of the poorest regions in the world and highly vulnerable to extreme hydrological events due to the lack of reliable monitoring and forecast systems. For the first time, we demonstrate that initial hydrological conditions informed by satellite-based terrestrial water storage (TWS) estimates improve seasonal streamflow forecasts. TWS variability detected by the Gravity Recovery and Climate Experiment (GRACE) satellites is assimilated into a land surface model during 2003–2016 and used to initialize 6-month hindcasts (i.e., forecasts of past events) during West Africa's wet seasons. We find that GRACE data assimilation (DA) generally increases groundwater and soil moisture storage in the region, resulting in increased evapotranspiration and reduced total runoff. Total runoff is particularly lower at the headwaters of the Niger River, positively impacting streamflow simulations and hindcast initializations. Compared to simulations without GRACE-DA, hindcasts are notably improved at locations draining from large basin areas, in particular, over the Niger River basin, which is consistent with GRACE's coarse spatial resolution. The long memory of groundwater and deep soil moisture, two main TWS components updated by GRACE-DA, is reflected in prolonged improvements in the streamflow hindcasts. Model accuracy at Niamey, Niger, the most populated city where streamflow observations are available, improved up to 33% during the flood season. These new findings directly contribute to ongoing developments in food security, flood potential forecast, and water-related disaster warning systems for Africa.

Augusto Getirana↗

NASA NTRS2020

Comparison of Cloud Detection Algorithms for Sentinel-2 Imagery

Accurate, automated cloud and cloud shadow detection is a key component of the processing needed to prepare optical satellite imagery for scientific analysis. Many existing cloud detection algorithms rely on temperature information to identify clouds, making detection difficult for imagers that lack a thermal band, like Sentinel-2. To get maximum benefit from Sentinel-2 products it is critical to understand which algorithms best identify clouds and their shadows in images. We examined the relative performance of five different cloud-masking algorithms (Sen2Cor, MAJA, LaSRC, Fmask and Tmask) in 6 Sentinel-2 scenes (28 total images) distributed across the Eastern Hemisphere. Expanding on these comparisons, we tested ensemble approaches to improve results. We tested three ensemble approaches to cloud and shadow classification based on the outputs of the five initial algorithms using the cloud masks in: (1) a majority prediction model; (2) a random forests model; and (3) a conditional logic model. Accuracy assessments show a trade-off between omission and commission errors in cloud detection for individual algorithms across all sites, and some algorithms are better at detecting either clouds or cloud shadows. No single algorithm outperforms the others for both clouds and shadows. Aggregating the results from multiple algorithms produces fewer undetected clouds and higher overall accuracy than any single algorithm, with as high as 2.7% improvement over the top-performing algorithm, suggesting an ensemble approach may be the most useful for processing of Sentinel-2 data.

Sentinel-2↗

NASA NTRSDate not supplied

ATD-2 Digital Services / DIP

The path to NAS-wide implementation for ATD-2 multi-airport IADS is as a digital service for flight operators that builds on and supplements FAA TBO investments. This session will focus on a suite of Machine Learning (ML) services the ATD-2 team developed to model airport surface operations. The approach used to leverage SWIM data sets will be described and a preliminary analysis of ML model accuracy will be presented. NASA’s plans to build on this work under the ATM-X Digital Information Platform (DIP) sub-project will be discussed.

ATD TIM, ATD-2 Digital Services, DIP↗

NASA NTRSDate not supplied

Design of a Lunar Plume-Surface Interaction Measurement System

Plume-surface interactions between a rocket plume and the lunar surface will be studied in-situ during two of NASA’s upcoming Commercial Lunar Payload Services Program missions. The payload, Stereo Cameras for Lunar Plume-Surface Studies (SCALPSS), will employ a multiple-camera photogrammetry system to obtain stereo images of the lunar regolith before, during, and after significant erosion and subsequent crater formation. The evaluation of measurement system capabilities and design process of the payload is informed by computational fluid dynamics predictions, accuracy modeling based on experimental data, camera simulation, and lander design, all of which are combined in the Virtual Diagnostic Interface. The second SCALPSS mission, traveling to the moon on Firefly Aerospace’s Blue Ghost lander in early 2023, aims to build on the design and complexity of the first payload, which is launching in early 2022 on Intuitive Machines’ Nova-C lander. The Blue Ghost SCALPSS mission will include two additional cameras and a total of three different lens focal lengths which will target specific points in the vehicle’s descent to obtain quantitative and accurate 3D reconstruction of the lunar surface both prior to and after crater formation.

photogrammetry↗

NASA NTRSDate not supplied

Statistical Classification of Biosignature Information using Multiple Instrument Observations

The accurate identification of biosignatures (indications of life) from data taken from remote or in situ planetary exploration is one of the most important challenges in astrobiology, the interdisciplinary field examining habitability and the potential for extraterrestrial life. This study employs machine learning algorithms to optimize the identification of biosignatures, with an emphasis on those which are agnostic to a specific biochemical basis. We exploit the wealth of terrestrial data available from biogenic and abiogenic systems to enhance efficient feature prioritization. Our dataset, pulled from public databases and laboratory recorded measurements, includes elemental abundance, isotopic fractionation, and VNIR/Raman spectra The data curation process included standardization for detection limits and ranges. Subsequent feature extraction yielded detailed inputs for machine learning, including combinations of elemental content, isotopic ratios, and parameters of spectral peaks and troughs. Feature significance was evaluated across diverse machine learning methodologies, such as k-nearest neighbors, logistic regression, Random Forest, support vector machines, and Gaussian Naïve Bayes, along with a combined voting classifier. We utilized Receiver Operating Characteristic Area Under the Curve (ROC AUC) across 2,000 50% test-train splits as a robust metric of model performance. Results revealed a promising ROC AUC of 0.853 for the combined voting classifier. Removing elemental abundance data notably reduced model accuracy (13% decrease in AUC), highlighting its critical role in biosignature detection. Several other individual data features exhibited significance within their respective data types, offering additional granularity. This research fortifies the relevance of machine learning to astrobiology, potentially enhancing life detection missions by allowing algorithmic prioritization of high-interest samples for further investigation. Future work will refine data standardization, expand the dataset to include more terrestrial systems, and incorporate convolutional neural networks for spectral feature extraction. The potential for public data sharing is also under exploration, reinforcing our commitment to collective scientific advancement.

Statistical↗

NASA NTRSDate not supplied

Improving Quantification of Medical Evacuation Risk for Human Spaceflight in the IMPACT Model

Traditionally, mission planners have used a heuristic and qualitative approach to design medical systems based on prior experience however this approach may result in high variability and implicit bias to design and could put missions and crewmembers at risk. The Informing Mission Planning through Analysis of Complex Tradespaces (IMPACT) tool is a probabilistic risk assessment tool designed by NASA to model medical risk in long duration missions outside of low earth orbit (LEO). This tool can quantify risk metrics such as risk of crewmember death, loss of crew task time, and the need for medical evacuation to better inform and augment the more traditional approach to medical system design. The risk metric of Return to Definitive Care (RTDC) represents the likelihood of requiring medical evacuation and has been difficult to quantify in a reliable and standardized manner. Initial development of the RTDC metric was constrained due to the complexity of a medical evacuation decision and the lack of prior spaceflight data and this presentation will discuss the review and improvement process used to increase model accuracy and fidelity.

Prashant Parmar↗

NASA NTRSDate not supplied

Numerical Simulation of No-Vent Chill and Fill of a Large Liquid Hydrogen Tank

To enable the design of future in-space cryogenic propellant transfer vehicles, high accuracy models of the cryogenic propellant transfer process are desired. For in-space refueling applications, accurate simulations are desired of the propellant tank chilldown and no-vent fill (NVF) process in order to estimate the amount of propellant required, to determine the maximum sustainable flow rate to fill the tank, and to establish a timeline for the filling process. NVF is a complex process that involves numerous two-phase heat and mass transfer phenomena, such as boiling at the wall and evaporation and condensation at the liquid/vapor interface. In recent years, progress has been made in developing a numerical modeling technique using the Generalized Fluid System Simulation Program (GFSSP) that has been validated against several small-scale liquid nitrogen NVF tests. This paper presents GFSSP numerical model validation against three liquid hydrogen NVF tests on a large-scale propellant tank. Comparing the GFSSP model to the data for pressure, fill level, and wall temperature, the mean absolute percentage error is below 20% across the range of simulated conditions, indicating very good predictive agreement for this large-scale hydrogen NVF test series.

Alok Majumdar↗