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NASA NTRS2021

Conjugate Photoelectron Energy Spectra Derived From Coincident FUV and Radio Measurements

We present a method for estimating incident photoelectrons' energy spectra as a function of altitude by combining global scale far-ultraviolet (FUV) and radio-occultation (RO) measurements. This characterization provides timely insights important for accurate interpretation of ionospheric parameters inferred from the recently launched Ionospheric Connection Explorer (ICON) observations. Quantification of photoelectron impact is enabled by the fact that conjugate photoelectrons (CPEs) directly affect FUV airglow emissions but not RO measurements. We demonstrate a technique for estimation of photoelectron fluxes and their spectra by combining coincident ICON and COSMIC2 measurements and show that a significant fraction of ICON-FUV measurements is affected by CPEs during the winter solstice. A comparison of estimated photoelectron fluxes with measured photoelectron spectra is used to gain further insights into the estimation method and reveals consistent values within 10–60 eV. Plain Language Summary The impact of solar radiation on the atmosphere produces highly energetic electrons, which travel freely along the magnetic Earth's field lines from one hemisphere to the other. When these electrons flow from the sunlit side into the nightside hemisphere, they interact with the neutral species and produce noticeable effects in the ionosphere such as an increase in electron temperature and enhancement of airglow emissions. This study presents a method to quantify the amount of precipitating electrons and their energy on a global scale using two recent satellite missions, ICON and COSMIC2. Our results demonstrate that coincident far-ultraviolet (ICON) and radio-occultation (COSMIC2) measurements from space are valuable resources to study precipitating electrons in the ionosphere and their impact on inferring ionospheric plasma parameters.

J M Urco↗

NASA NTRS2014

Test and Recommendation of Flight-forward Resistive Temperature Detector for Resource Prospector Mission

The Resource Prospector Mission (RPM) is an in-situ resource utilization (ISRU) technology demonstration mission planned to launch in 2018. The mission will use the Regolith and Environment Science & Oxygen and Lunar Volatile Extraction (RESOLVE) Payload to prospect for lunar volatiles such as water, oxygen, and carbon dioxide. These compounds will validate ISRU capability. The payload, particularly the Lunar Advanced Volatile Analysis (LAVA) subsystem, requires numerous temperature measurements to accurately control on-board heaters that keep the volatiles in the vapor phase to allow quantification and prevent the clogging of delivery lines. Previous spaceflight missions have proven that Resistive Temperature Detector (RTD) failure impedes mission success. The research resulted in a recommendation for a flight-forward RTD. The recommendation was based on accuracy, consistency, and ease of installation of RTDs procured from IST, QTI, and Honeywell.

LAVA↗

NASA NTRS2012

Test and Recommendation of Flight-Forward Resistive Temperature Detector for Resource Prospector Mission

LAVA↗

NASA NTRS2024

Quantification of Uncertainty and Risk Sensitivity for Safety of Emerging Operations

The growing need to develop and deploy small unmanned aerial vehicles (sUAVs) for various applications in the airspace necessitates reliable tools to accurately predict the flight trajectories of the sUAVs. The knowledge of the predicted trajectories help decision makers anticipate potential conflict, assess the risk, and take appropriate risk mitigation actions. In addition, uncertainties in vehicle models, weather, and controller action further highlights the need for reliable prediction tools. In this project, the application of mixed sparse grid-based quadrature and generalized polynomial chaos(gPC) expansion method for uncertainty quantification and collision assessment in air traffic consisting of fixed-wing small unmanned aerial vehicles (sUAV) was studied. From the results obtained, it can be concluded that this provides a reliable framework to carry out quantitative conflict assessment in an unmanned air traffic, which when employed, can improve the functionalities of the unmanned traffic management system. It was observed that the results from the gPC expansion framework developed in the project can be utilized to conduct rapid probabilistic collision assessment for near real-time unmanned traffic management in the airspace. From the vehicle models, position updates, and wind-field data, a priori gPC based 3-σcon-fidence ellipses can provide estimates of potential conflict at some future instants. The computational costs scaled linearly when the uncertain inputs were fewer. Further, the largest allowable distribution of para-metric uncertainties that leads to the smallest risk of collision in traffic of small unmanned aerial vehicles could be calculated. The time of closest approach between two sUAVs can be established paving way for development of proactive mitigation strategies. The separation between the sUAVs was found to be most significantly affected by uncertainties in the maximum available thrusts, zero-lift drag coefficients, and wing planform areas of the sUAVs. The study of uncertain wind-fields indicated that a heterogeneous traffic mix resulted in an increased probability of conflict. Increased measurement update rate reduced the uncertain-ties in the trajectories of the vehicles, further reducing the probability of conflict but rapid updates of all vehicles in the airspace poses a stringent communication limitation. The gPC framework also provided the means to analyze vehicle impact (crash region) due to loss of control resulting from actuator failure in sUAS traffic, essentially to predict impact and crash zones for representative vehicles. The predicted regions when compared with non-participant density, provides a means to develop an early mitigation strategy, should the sUAV detect an imminent actuator failure.

Rajnish Bhusal↗

NASA NTRS2006

Accuracy Quantification of the Loci-CHEM Code for Chamber Wall Heat Transfer in a GO2/GH2 Single Element Injector Model Problem

A robust rocket engine combustor design and development process must include tools which can accurately predict the multi-dimensional thermal environments imposed on solid surfaces by the hot combustion products. Currently, empirical methods used in the design process are typically one dimensional and do not adequately account for the heat flux rise rate in the near-injector region of the chamber. Computational Fluid Dynamics holds promise to meet the design tool requirement, but requires accuracy quantification, or validation, before it can be confidently applied in the design process. This effort presents the beginning of such a validation process for the Loci-CHEM CFD code. The model problem examined here is a gaseous oxygen (GO2)/gaseous hydrogen (GH2) shear coaxial single element injector operating at a chamber pressure of 5.42 MPa. The GO2/GH2 propellant combination in this geometry represents one the simplest rocket model problems and is thus foundational to subsequent validation efforts for more complex injectors. Multiple steady state solutions have been produced with Loci-CHEM employing different hybrid grids and two-equation turbulence models. Iterative convergence for each solution is demonstrated via mass conservation, flow variable monitoring at discrete flow field locations as a function of solution iteration and overall residual performance. A baseline hybrid was used and then locally refined to demonstrate grid convergence. Solutions were obtained with three variations of the k-omega turbulence model.

West, Jeff↗

NASA NTRS2006

Accuracy Quantification of the Loci-CHEM Code for Chamber Wall Heat Fluxes in a G02/GH2 Single Element Injector Model Problem

A robust rocket engine combustor design and development process must include tools which can accurately predict the multi-dimensional thermal environments imposed on solid surfaces by the hot combustion products. Currently, empirical methods used in the design process are typically one dimensional and do not adequately account for the heat flux rise rate in the near-injector region of the chamber. Computational Fluid Dynamics holds promise to meet the design tool requirement, but requires accuracy quantification, or validation, before it can be confidently applied in the design process. This effort presents the beginning of such a validation process for the Loci- CHEM CPD code. The model problem examined here is a gaseous oxygen (GO2)/gaseous hydrogen (GH2) shear coaxial single element injector operating at a chamber pressure of 5.42 MPa. The GO2/GH2 propellant combination in this geometry represents one the simplest rocket model problems and is thus foundational to subsequent validation efforts for more complex injectors. Multiple steady state solutions have been produced with Loci-CHEM employing different hybrid grids and two-equation turbulence models. Iterative convergence for each solution is demonstrated via mass conservation, flow variable monitoring at discrete flow field locations as a function of solution iteration and overall residual performance. A baseline hybrid grid was used and then locally refined to demonstrate grid convergence. Solutions were also obtained with three variations of the k-omega turbulence model.

West, Jeff↗

NASA NTRS2002

Quantification of Surface Suspended Sediments along a River Dominated Coast with NOAA AVHRR and SeaWiFS Measurements: Louisiana, USA

The ability to quantify suspended sediment concentrations accurately over both time and space using satellite data has been a goal of many environmental researchers over the past few decades This study utilizes data acquired by the NOAA Advanced Very High Resolution Radiometer (AVHRR) and the Orbview-2 Sea-viewing wide field-of-view (SeaWiFS) ocean colour sensor, coupled with field measurements to develop statistical models for the estimation of near-surface suspended sediment and suspended solids "Ground truth" water samples were obtained via helicopter, small boat and automatic water sampler within a few hours of satellite overpasses The NOAA AVHRR atmospheric correction was modified for the high levels of turbidity along the Louisiana coast. Models were developed based on the field measurements and reflectance/radiance measurements in the visible and near infrared Channels of NOAA-14 and Orbview-2 SeaWiFS. The best models for predicting surface suspended sediment concentrations were obtained with a NOAA AVHRR Channel 1 (580-680nm) cubic model, Channel 2 (725-1100 nm) linear mod$ and SeaWiFs Channel 6 (660-68Onm) power modeL The suspended sediment models developed using SeaWiFS Channel 5 (545-565 nm) were inferior, a result that we attribute mainly to the atmospheric correction technique, the shallow depth of the water samples and absorption effects from non-sediment water constituents.

Myint, S. W.↗

NASA NTRS2021

Improved representation of the global dust cycle using observational constraints on dust properties and abundance

Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, atmospheric models struggle to accurately represent its spatial and temporal distribution. These model errors are partially caused by fundamental difficulties in simulating dust emission in coarse-resolution models and in accurately representing dust microphysical properties. Here we mitigate these problems by developing a new methodology that yields an improved representation of the global dust cycle. We present an analytical framework that uses inverse modeling to integrate an ensemble of global model simulations with observational constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth. We then compare the inverse model results against independent measurements of dust surface concentration and deposition flux and find that errors are reduced by approximately a factor of 2 relative to current model simulations of the Northern Hemisphere dust cycle. The inverse model results show smaller improvements in the less dusty Southern Hemisphere, most likely because both the model simulations and the observational constraints used in the inverse model are less accurate. On a global basis, we find that the emission flux of dust with a geometric diameter up to 20 µm (PM20) is approximately 5000 Tg yr−1, which is greater than most models account for. This larger PM20 dust flux is needed to match observational constraints showing a large atmospheric loading of coarse dust. We obtain gridded datasets of dust emission, vertically integrated loading, dust aerosol optical depth, (surface) concentration, and wet and dry deposition fluxes that are resolved by season and particle size. As our results indicate that this dataset is more accurate than current model simulations and the MERRA-2 dust reanalysis product, it can be used to improve quantifications of dust impacts on the Earth system.

Dust cycle↗

NASA NTRS1993

Quantification of processing artifacts in textile composites

One of the greatest difficulties in developing detailed models of the mechanical response of textile reinforced composites is an accurate model of the reinforcing elements. In the case of elastic property prediction, the variation of fiber position may not have a critical role in performance. However, when considering highly localized stress events, such as those associated with cracks and holes, the exact position of the reinforcement probably dominates the failure mode. Models were developed for idealized reinforcements which provide an insight into the local behavior. However, even casual observations of micrographical images reveals that the actual material deviates strongly from the idealized models. Some of the deviations and causes are presented for triaxially braided and three dimensionally woven textile composites. The necessary modeling steps to accommodate these variations are presented with some examples. Some of the ramifications of not accounting for these discrepencies are also addressed.

Pastore, Christopher M.↗

NASA NTRS2021

Improved Representation of the Global Dust Cycle Using Observational Constraints on Dust Properties and Abundance

Even though desert dust is the most abundant aerosol by mass in Earth’s atmosphere, atmospheric models 25 struggle to accurately represent its spatial and temporal distribution. These model errors are partially caused byfundamental difficulties in simulating dust emission in coarse-resolution models and in accurately representing dust microphysical properties. Here we mitigate these problems by developing a new methodology that yields an improved representation of the global dust cycle. We present an analytical framework that uses inverse modeling to integrate an ensemble of global model simulations with observational constraints on the dust size distribution, 30 extinction efficiency, and regional dust aerosol optical depth. We then compare the inverse model results against independent measurements of dust surface concentration and deposition flux and find that errors are reduced by approximately a factor of two relative to current model simulations of the Northern Hemisphere dust cycle. The inverse model results show smaller improvements in the less dusty Southern Hemisphere, most likely because both the model simulations and the observational constraints used in the inverse model are less accurate. On a global 35 basis, we find that the emission flux of dust with geometric diameter up to 20 μm (PM20) is approximately 5,000 Tg/year, which is greater than most models account for. This larger PM20 dust flux is needed to match observational constraints showing a large atmospheric loading of coarse dust. We obtain gridded data sets of dust emission, vertically integrated loading, dust aerosol optical depth, (surface) concentration, and wet and dry deposition fluxes that are resolved by season and particle size. As our results indicate that this data set is more accurate than current 40 model simulations and the MERRA-2 dust reanalysis product, it can be used to improve quantifications of dust impacts on the Earth system.

aerosols, dust↗

NASA NTRS1993

A computer-controlled apparatus for Seebeck inhomogeneity testing of sheathed thermocouples

Mineral-insulated metal-sheathed (MIMS) thermocouple assemblies are used throughout industry and research facilities as a method of temperature measurement where requirements for either harsh environmental conditions exist, or where rigidity of the measurement probe is required. Seebeck inhomogeneity is the abnormal variation of the Seebeck coefficient from point to point in a material. It is not disclosed in conventional calibration. A standardized method of measuring thermoelectric inhomogeneity along the thermocouple probe length is not available. Therefore, calibration for sheathed probes normally does not include testing of probe inhomogeneity. The measurement accuracy would be severely impacted if significant inhomogeneity and a temperature gradient were present in the same region of the probe. A computer-controlled system for determining inhomogeneities was designed, fabricated, and tested. This system provides an accurate method for the identification of the location of inhomogeneity along the length of a sheathed thermocouple and for the quantification of the inhomogeneity. This paper will discuss the apparatus and procedure used to perform these tests and will present data showing tests performed on sheathed thermocouple probes.

Burkett, Cecil G., Jr.↗

NASA NTRS1987

Quantification of observed flare parameters in relation to a shear-index and verification of MHD models for flare prediction

The goal for the SAMEX magnetograph's optical system is to accurately measure the polarization state of sunlight in a narrow spectral bandwidth over the field of view of an active region to make an accurate determination of the magnetic field in that region. The instrumental polarization is characterized. The optics and coatings were designed to minimize this spurious polarization introduced by foreoptics. The method developed to calculate the instrumental polarization of the SAMEX optics is described.

Wu, S. T.↗

NASA NTRS1990

A quantification of errors in surface albedo due to common assumptions

A study comparing the performance of three approaches to estimating the spectral albedo of a typical land surface is presented. The most accurate albedo estimates under all atmospheric situations are those for which the scattering properties of the atmosphere can be used. Simply utilizing the direct-to-total ratio as a weight between direct and Lambertian albedos reduced the errors in broadband albedo to less than one percent for almost all simulated atmospheric conditions.

Arduini, Robert F.↗

NASA NTRSDate not supplied

Development Strategies for Uncertainty Quantification to Enable Aircraft Certification by Analysis

Aircraft Certification by Analysis (CbA) is a broad term that describes the process by which engineering analysis tools are used to supplement flight testing to demonstrate compliance with regulatory requirements. For each requirement that is to be met through this process, the analysis tools must be sufficiently accurate to ensure that an equivalent level of safety is obtained as if the aircraft to be certified had been flight tested for the requirement. In recent decades, the ability of analysis tools to accurately predict aircraft performance has improved dramatically, leading to increasing interest and limited early adoption of Certification by Analysis across the aerospace industry. However, rigorously demonstrating the credibility of analysis tools remains a challenge for many types of certification requirements, yet is necessary for widespread adoption of CbA approaches in the future. The field of uncertainty quantification is well-suited to address this problem, but will require substantial advances in order to achieve the ambitious industry goals for Certification by Analysis. The objective of this position paper is to highlight gaps in the current technical and programmatic state of uncertainty quantification. Four main categories – Mindset & Awareness, Tools & Capabilities, Data and Benchmarks for V&V, and Applied Research & Established Processes – will be introduced. Each of these categories contains several shortcomings and impediments which, if overcome, will enable more widespread possibilities for Certification by Analysis in the 2040-2050 timeframe.

Uncertainty Quantification↗

NASA NTRS2022

MLtool: Universal Supervised Machine Learning Tool to Model Tabulated Data

Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

Machine learning↗

NASA NTRS2022

How Well do Multisatellite Products Capture the Space-Time Dynamics of Precipitation? Part II: Building an Error Model Through Spectral System Identification

Satellite precipitation products, as all quantitative estimates, come with some inherent degree of uncertainty. To associate a quantitative value of the uncertainty to each individual estimate, error modeling is necessary. Most of the error models proposed so far compute the uncertainty as a function of precipitation intensity only, and only at one specific spatio-temporal scale. We propose a spectral error model which accounts for the neighboring space-time dynamics of precipitation into the uncertainty quantification. Systematic distortions of the precipitation signal and random errors are characterized distinctively in every frequency-wavenumber band in the Fourier domain, to accurately characterize error across scales. The systematic distortions are represented as a deterministic space-time linear filtering term. The random errors are represented as a non-stationary additive noise. The spectral error model is applied to the IMERG multi satellite precipitation product and its parameters are estimated empirically through a system identification approach using the GV-MRMS gauge-radar measurements as reference (“truth”) over the eastern United States. The filtering term is found to be essentially low-pass. While traditional error models attribute most of the error variance to random errors, it is found here that the systematic filtering term explains 48% of the error variance at the native resolution of IMERG. This fact confirms that, at high resolution, filtering effects in satellite precipitation products cannot be ignored, and that the error cannot be represented as a purely random additive or multiplicative term. An important consequence is that precipitation estimates derived from totally different sources shall not be expected to automatically have statistically independent errors.

Precipitation↗

NASA NTRSDate not supplied

CFD Validation Study of a Hypersonic Cone-Slice-Flap Variable Geometry Configuration

Model validation is the process of determining the degree of accuracy between physical reality and the model. The result of model validation can either be used to improve the model through calibration or quantify the model-form uncertainty. This work focuses on providing the model-form uncertainty through an area metric for a hypersonic cone-slice-flap variable geometry configuration given uncertainty in both the simulation and experimental data. The research here compares two different turbulence models for the simulations. For a variable geometry, performing uncertainty quantification to capture the model-form uncertainty on every configuration is computationally challenging. This work lays out a procedure that can give an accurate representation of the model-form uncertainty using a small number of high-fidelity runs and many low-fidelity runs on multiple configurations. Running this comparison provides a quantifiable measurement for the accuracy of each turbulence model for this type of design. The high-fidelity CFD solver used was VULCAN-CFD and the low-fidelity results came from Cart3D. The experimental data came from the 20-Inch Mach 6 Tunnel located at NASA Langley Research Center. The present work showed that the using both the Spalart and Allamaras and Menter Shear-Stress Transport turbulence models overpredicted the drag and lift coefficient, while underpredicting the pitching moment coefficient. The model-form uncertainty estimate resulted in up to a 13.6% change in the total uncertainty for the drag coefficient, up to a 57.4% change in total uncertainty for the lift coefficient, and up to a 100% change in total uncertainty for the pitching moment coefficient.

Laura M. White↗

NASA NTRS2022

MLtool Python Code

Machine Learning↗