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NASA NTRSDate not supplied

SBIR Space Weather R2O2R Technology Development and Commercial Applications

The Small Business Innovation Research (SBIR) Program provides U.S. small businesses of 500 or fewer employees with the opportunity for early-stage funding for research and development. Through NASA's Science Mission Directorate, the SBIR subtopic S14.01 Space Weather Research-to-Operations-to-Research (R2O2R) Technology Development and Commercial Applications supports high-priority space weather needs as outlined in the National Space Weather Strategy and Action Plan (NSWSAP). There are four focus areas for which solutions are sought; these will be discussed (in no priority order): 1) space-weather forecasting technologies, techniques, and applications, 2) commercial and decision-making applications for space weather technologies, 3) space weather advanced data-driven discovery techniques, and 4) space-weather instrumentation.

Space weather↗

NASA NTRS2023

Development of Level of Detail System and First-Person Camera for the GCAS Visualization Suite

The use of data-driven simulations has become standard practice as part of planning for future space missions. These simulations allow visualizing the data interactively to show what the data represents, as well as the importance of the data in the context of the mission. Using this visualized data can enhance users’ understanding of it and accelerate analysis efforts related to missions planned around it. Three-dimensional (3D) visualization software was developed to allow creating 3D representations of various communication systems, as well as the physical terrain of the Moon, for upcoming missions. The goal of this software development effort was to create interactive visualization capabilities in the Glenn Research Center Communication Analysis Suite (GCAS) using data exported from MATLAB® (MathWorks, Inc.) scripts. This software had the functionality to visualize the line of sight and dynamic link margins of the communication satellites orbiting the Earth and the Moon. One important addition to this was the visualization of the terrain data located within the GeoTIFF files, which were produced in an effort to understand the Moon’s terrain. Proper displacement values of this data have to be visualized to showcase where craters are located and how the shadow casting works with said craters at different points of the day, as well as analysis of possible landing sites for future lunar expeditions. The graphics library coded in JavaScript, three.js, had been previously selected for developing this visualization software. The software was revised to conform to modern standards, then further developed to convert the MATLAB® data into JavaScript 3D objects and Blender GL Transmission Format Binary file (GLB) objects, which were to be imported into the scene. In the process, a variety of other testing projects were created to be combined with this project at a later point; these included the first-person camera movements around spherical objects to portray human movement around the Moon, GeoTIFF loading methods, data transfer methods for incorporating the elevation data into the scene, and level of detail (LOD) capabilities to decrease memory usage and rendering time.

Visualization↗

NASA NTRSDate not supplied

A Survey of ISS and Visiting Vehicle Returned Surfaces for Environmental Characterization and Computer Model Development

The Orbital Debris Engineering Model (ORDEM) developed by the NASA Orbital Debris Program Office (ODPO) is a data-driven model — extensive radar, optical, laboratory, and in situ measurement data sets have been used to build the model since its earliest versions. A salient aspect of professional software development is the verification and validation (V&V) process. Verification answers the question “Is the model built correctly?” while validation addresses the question “Did we build the correct model?” Less extensive, reserved, or independent data sets serve the validation requirement. Due to the dynamic nature of the orbital debris environment, it is critical to use contemporaneous data sources that represent the current environment to support ORDEM development and validation. ORDEM has utilized in situ data collected from Space Shuttle and Hubble Space Telescope surface inspections, now over a decade old. This historical dataset is fundamental for providing baseline in situ measurement data for sizes between 10 to 300 microns, but new data sources are being evaluated using returned surfaces from or near the International Space Station (ISS). This paper reviews a general microscopic survey of ISS soft goods, the Pressurized Mating Adapter 2 (PMA-2) blanket, and a limited-scope feasibility study conducted on the Space Exploration Technologies Corporation (SpaceX) Dragon capsule’s Thermal Protection System (TPS) material. The PMA-2 blanket, exposed to the space environment between 09 July 2013 and 25 February 2015, is an approximately 3.7 m2-area blanket composed of a betacloth outer layer and multiple ballistic fabric inner layers. The SpaceX Cargo Dragon capsule regularly visited the ISS from 2012 through 2020 and potentially provides a timely and well-characterized source of data for modeling purposes. The capsule’s lateral surfaces use SpaceX Proprietary Ablative Material (SPAM) TPS material, a syntactic foam, for thermal management during all mission phases. Seven SPAM extracted samples have been analyzed to date. This paper will provide an overview of the characterization completed for impact features by size, depth, impactor diameter, and the impactor residues chemical analyses, allowing a differentiation between micrometeoroids and orbital debris and a categorization by mass density and density class. Impactor diameter is estimated using damage equations generated from ground-based hypervelocity impact testing. The orbital debris impactors are compared to the current ORDEM 3.2 model of the environment at ISS altitudes. We briefly discuss the meteoroid impactors, including constituents and mass densities, in the general context of current models.

Phillip Anz-Meador↗

NASA NTRSDate not supplied

The Unintended Consequences of Focusing on Human Error (And How You Can Help)

The literature on human performance is rich with findings of cognitive failures and methods to identify, label, and measure them. In many real-world contexts, however, outcomes are driven far more by successful than failed cognition. Designers of systems intended for human use, in an effort to be “data driven,” rely upon findings from the cognitive performance literature to inform their system designs. When most available data are about human error, data-driven designs focus on the human primarily as a source of failure. Designs intended to support or replace humans often fail to acknowledge or understand the capabilities that humans routinely contribute to successful performance. Consequently, designs intended to “protect” the system from “error-prone” humans can design-out the capability for the human to effectively intervene or adapt. The development of paradigms to study successful human performance represents a significant and largely untapped opportunity for research in cognition.

Jon Holbrook↗

NASA NTRSDate not supplied

Enhancements to Linear Stability-Based, CFD-integrated Transition Prediction for High-Speed Flows

Combining linear stability calculations with computational fluid dynamics (CFD) simulations has great potential for the automated modeling of high-speed flows, especially when adequate information about the configuration and the disturbance environment is available. However, a significant impediment to the applicability of this technique is the lack of an efficient method to calculate the crucial amplification ratio corresponding to the onset of transition in hypersonic flows. This ratio, also known as the "transition N-factor," is dependent upon the freestream disturbance environment as well as the surface properties of the test article. In response to the need for an engineering solution to predict the transition N-factor within conventional hypersonic wind tunnels, this paper presents a data-driven correlation that expands the existing correlations from straight circular cones with a narrow range of half angles to a broader array of axisymmetric configurations. Furthermore, when tested against a chosen dataset that was not used in its calibration, the suggested correlation shows good predictive accuracy with an RMS error of only 6.9%. Although similar accuracy may also be achieved via existing correlations based on similar datasets, predictions based on the proposed correlation have the advantage of not requiring an extensive amount of configuration-specific data. Practical applications often have access to the input parameters needed for this correlation, such as the freestream disturbance intensity, Mach number, and body-based slenderness Reynolds number. Additionally, this correlation outperforms the traditional assumption of a constant N-factor, particularly for configurations with blunted nose geometries. The development of this correlation is grounded in an extensive dataset encompassing conical models with body half-angles varying between 5 degrees and 16 degrees, Mach numbers ranging from 5 to 14, and nosetip-based Reynolds numbers approaching the transition reversal limit for blunt-nosed cones.

CFD↗

NASA NTRSDate not supplied

Global SO 2 Data Record from OMPS Instruments on the JPSS Constellation

NASA’s Earth Observing System (EOS) SO 2 climate data record (CDR) started in 2004, with the launch of the Aura/Ozone Monitoring Instrument (OMI) and is now being continued with the SNPP/Ozone Mapping and Profiler Suite (OMPS) launched in 2011. Both OMI and SNPP/OMPS SO 2 CDRs are produced with the Goddard principal component analysis (PCA) spectral fitting algorithm. An advantage of the data-driven PCA retrieval technique is that it enables highly consistent retrievals from different instruments, by inherently accounting for various instrumental factors. To further extend the EOS SO 2 CDR, we are implementing the PCA SO 2 retrieval algorithm with the L1B measurements from OMPS instruments flying on the Joint Polar Satellite System (JPSS) constellation. In this presentation, we will provide an update on our progress in NOAA-20 (launched in 2017) and NOAA-21 (launched in 2022) PCA SO2 retrievals. We will focus on our new NOAA-20/OMPS PCA SO 2 EOS continuity product, to be publicly released in fall of 2023. We will present statistical analyses on the quality of NOAA-20 PCA SO 2 product, including retrieval noise, biases over background areas, and long-term stability. We will compare our PCA SO 2 retrievals from NOAA-20 with those from OMI, SNPP/OMPS, and S5P/TROPOMI (TROPOspheric Monitoring Instrument) for anthropogenic sources as well as large volcanic plumes. We will also discuss the application of a new machine learning technique that helps to further reduce the noise of NOAA-20 SO 2 retrievals. In addition, we will present preliminary PCA SO 2 retrievals from NOAA-21/OMPS, including those from direct readout implementation for aviation disaster avoidance. Finally, we will share some first results applying the PCA algorithm to NASA’s geostationary TEMPO (Tropospheric Emissions: Monitoring of Pollution) instrument to obtain hourly, high resolution SO 2 data over North America.

SO2↗

NASA NTRSDate not supplied

A Machine Learning Approach to Determine Surface Radiative Fluxes based on CERES Observations

The Clouds and Earth’s Radiant Energy System (CERES) projects provides satellite-based observations of the radiative fluxes and clouds systems. CERES climate quality data products typically take several months of calibration and validation before release to the public. An alternative data product, Fast Longwave and Shortwave radiative Flux (FLASHFlux), was created to provide data to the applied sciences and educational users. FLASHFlux provides Top-of-Atmosphere radiative fluxes, Clouds properties, and parameterized surface radiative fluxes within four days for footprint (Level 2) data. We investigate the use of Artificial Neural Network (ANN) using MODerate resolution Imaging Spectroradiometer (MODIS) derived clouds properties and meteorology from the Global Assimilation and Meteorology Office (GMAO) scaled to the CERES footprint from the CERES Clouds Radiative Swath (CRS) data product to compute surface radiative fluxes. We test ANN produce fluxes against surface fluxes produced from the Fu-Liou model used in CRS and the Langley Parameterized Shortwave Algorithm (LPSA) and Langley Parameterized Longwave Algorithm (LPLA) used in FLASHFlux. We also validated each model with ground-based observations. Furthermore, we investigate Leave-One-Feature-Out Importance (LOFO) to evaluate the significance of each feature in our training and provide insight for future models. Advances in machine learning, along with increases in computational capabilities and available data allow us to estimate effects of unresolved processes in our climate without direct modeling. This work evaluates the ability to create accurate data-driven models to supplement or replace current models that estimate surface radiative fluxes.

Climatology↗

NASA NTRSDate not supplied

Enabling Intelligent Data Downlink Prioritization of In-Situ Observations through Generalizable and Computationally Inexpensive Anomaly Detection

High-fidelity measurements of magnetic fields and other observed properties, such as energetic particle fluxes, are a necessary component to our understanding of the highly dynamic near-Earth space environment. As our desire to study smaller-scale phenomena such as shocks and dipolorizations has increased, we have been driven to take and telemeter measurements at higher cadences. Unfortunately, many missions are unable to downlink all their captured data due to the well-known data transmission bottleneck at the DSN. These missions must then prioritize their high-cadence data such that the most scientifically useful intervals are transmitted. One simple prioritization technique uses the spacecraft position to telemeter data from only the region of interest. Although easy to implement, this method does not leverage the available scientific data and can omit intervals of useful scientific data when they lie outside the region of interest. The Magnetospheric Multiscale Mission (MMS) uses mission-specific parameterization of several data products to automatically prioritize scientifically useful intervals. Then, MMS verifies the automatically selected intervals by having a domain expert manually select intervals for downlink. The overall complexity required by this technique make it prohibitive for deployment on low-cost platforms (i.e., CubeSats) or on future missions featuring large constellations of satellites such as the Geospace Dynamics Constellation (GDC). We present preliminary results for a simple, generic, and data-driven method of downlink prioritization for magnetic field (and other) measurements. Specifically, Principal Components Analysis (PCA) and One-Class Support Vector Machines (OC-SVMs) are used to detect intervals containing anomalous activity, which can then be prioritized for subsequent downlink. The computational simplicity of this algorithm makes it an excellent candidate for implementation on spaceflight hardware, as well as provide generalizability to a broad range of missions and data products. Initial analysis of this technique has been performed using magnetic field measurements from the Magnetospheric Multiscale Mission and CASSIOP, where it automatically identified scientifically interesting intervals containing Alfvén waves and EMIC activity.

Matthew G. Finley↗

NASA NTRS2024

Teachers’ Use and Adaptation of A Model-Based Climate Curriculum: A Three-Year Longitudinal Study

Foregrounding climate education in formal science learning environments provides students with opportunities to develop critical climate-related knowledge and skills. However, research has shown many challenges to teaching and learning about Earth’s climate and global climate change (GCC). This longitudinal study aims to establish how secondary science teachers, over time, implement model-based climate curricula in support of students’ climate and GCC education by utilizing EzGCM. The model (EzGCM) is a data-driven, computer-based climate modeling tool use to explore global climate data. Multiple sources of data collection, including teacher interviews, classroom observations, and daily reflections, were employed to address the research question: “How did two teachers’ implementation strategies evolve over the three-year study while utilizing a model-based, climate-focused curriculum?” This study provides insight into how and why these resources [model-based climate education curricula] are utilized in science learning environments, thereby informing ongoing efforts to enhance climate education and, in doing so, preparing the next generation of climate-literate adults prepared to confront this most critical global challenge of our age. The findings showed while both teachers engaged in increasingly model-centric instructional practices, these changes were modest. Furthermore, both teacher’s observed classroom practices were less model-centric than the designed curriculum. Ultimately emphasizing the transition from existing practices to improved ones, rather than seeking the perfect approach, the study offers practical insights that can honestly assist secondary educators in real-world settings by highlighting state of climate education in secondary science classrooms.

Secondary science teaching↗

NASA NTRS2022

Learning-Based State-Dependent Coefficient Form Task Space Tracking Control of Soft Robot

n this paper, a data-driven modeling and control framework is developed for task space control of a soft robot gripper which consists of four individual soft fingers. Each of the four fingers is modeled as a manipulator with high degrees of freedom. The corresponding task space dynamics of the manipulator are derived using a rigid-link approximation of the continuum manipulator. A neural network approach is used to learn the derived dynamics in State Dependent Coefficient (SDC) form. Using the learned SDC matrices, an asymptotically stable optimal closed-loop tracking controller which is based on solving the State Dependent Riccati Equation (SDRE) is derived. The model learning and trajectory tracking controller is implemented on an open source Soft Motion (SoMo) platform simulating the soft gripper motion and corresponding tracking results are presented.

Rounak Bhattacharya↗

NASA NTRSDate not supplied

Planning Bias: Planning as a Source of Sampling Bias

Many data-driven planning methods are trained on data generated by planners. It is well known that many statistical learning methods are sensitive to sampling bias, and yet there has been little or no attention to planning as a sampling method and its role in introducing sampling bias into planner-generated training data. Recently, it has been demonstrated that A**,* in the presence of problems with variable heuristic error, prefers some solutions over other equally cost-optimal solutions. But, as we discuss in this paper, mitigation may not be as simple as resolving arbitrary tie-breaking by sampling from ties uniformly at random. In this paper, we formalize an intuition of planning bias. We focus on problems which output a single solution. Diverse planning only complicates the problem by generalizing it to bias in the set of sets; we show how it is subject to bias in the single solution. We make some useful observations about deterministic algorithms in contrast to non-deterministic algorithms. We explain how information entropy may be a good way to measure planning bias, and discuss some issues in evaluating practical approaches to measurement. We address the intuition that uniform random tiebreaking should mitigate bias; and sketch a novel approach to constructing an appropriate random distribution for duplicate detection during forward search for unbiased A*. Finally, we suggest directions for future work.

Planning Scheduling Algorithms↗

NASA NTRS2024

Discrete Event Simulation-Based Timeline Validation Using R2U2

The Gateway Vehicle Systems Manager (VSM), the top-level software control system in a distributed, hierarchical Autonomous System Management Architecture is, like most modern spacecraft software control systems, heavily data-driven. For example, schedules (timelines) will be developed on the ground and, due to the high degree of autonomy, contain complex procedures involving conditional branching, variable timing, and resource contention resolution. In order to verify that an uploaded timeline will function correctly, it is necessary to explore the feasible set of possible executions. While it is possible to test a timeline using a mission simulation, the complexity of the system and duration of a timeline limits the number of trials and therefore the test coverage. To address this problem, the VSM team is using a discrete event system model that can rapidly generate from a timeline sets of event sequences using Monte Carlo techniques. To achieve rapid and trustworthy checking of the event sequences, we use an offline version of the runtime model checking tool R2U2. This presentation describes the approach the VSM team is using to implement the discrete event simulation and evaluate event sequences using R2U2. The presentation will discuss: 1. Description of the timelines by VSM in the context of VSM operations 2. Expansion of a timeline into a sequence of atomic events 3. Adjustment, in the Monte Carlo environment, of an event sequence to account for uncertainty, external events, and failures 4. Definition of R2U2 input and mission-time linear temporal logic files 5. Generation and use of R2U2 verdict sequences 6. Lessons learned and future work

Verification↗

NASA NTRS2023

Damage Detection of a Pressure Vessel with Smart Sensing and Deep Learning

Structural Health Monitoring plays a crucial role in ensuring the safety and reliability of critical infrastructure, including pressure vessels involved in various applications. This research reports the damage detection of a pressure box employed in space habitat that operates in harsh environment where both structural failure and bolt joint loosening may occur. These failure modes are extremely hard to model based on first principles. We explore proper sensing mechanism and the associated inverse analysis algorithm that can elucidate the health condition of the pressure box. It is identified that piezoelectric impedance based active interrogation can provide necessary information for damage detection in such a system. Concurrently, deep learning technique leveraging spatial convolutional neural network is synthesized to analyze the raw data acquired and identify different types of damage. By training the deep learning model on a dataset of healthy and various damage scenarios, we can achieve high accuracy in identifying the presence of damage and its type. This research provides a data-driven methodology for structural damage detection using deep learning and has the potential to be extended to various systems with different failure modes.

Yang Zhang↗

NASA NTRS2026

Benchmarking Bayesian Optimization Frameworks and Acquisition Strategies for Materials Discovery and Autonomous Laboratories

Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.

Bayesian optimization↗

NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗

NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Database↗

NASA NTRS2012

The Role in the Virtual Astronomical Observatory in the Era of Massive Data Sets

The Virtual Observatory (VO) is realizing global electronic integration of astronomy data. One of the long-term goals of the U.S. VO project, the Virtual Astronomical Observatory (VAO), is development of services and protocols that respond to the growing size and complexity of astronomy data sets. This paper describes how VAO staff are active in such development efforts, especially in innovative strategies and techniques that recognize the limited operating budgets likely available to astronomers even as demand increases. The project has a program of professional outreach whereby new services and protocols are evaluated.

data-driven science↗

NASA NTRS2019

Formulative Input into Future NASA Aeronautics Planning

This presentation covers industry input received for future work in NASA Aeronautics over the next 5 years. It is intended to present areas of significant imput and to stimulate further discussion.

future aeronautics planning↗