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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRSDate not supplied

Modeling of Multiscale Solar Dynamics for Understanding Drivers of Space Weather

Understanding the solar dynamics is critical for improving our capabilities to forecast the evolution of space weather conditions. We take advantage of currently available computational capabilities to model solar dynamics from the deep interior to the corona and investigate mechanisms that may drive space weather conditions. The simulations are performed using the 3D radiative MHD code StellarBox. Comparison of synthetic spectroscopic observables obtained from numerical simulations and actual observations allows us to uncover physical processes associated with observed phenomena. To facilitate a transition from modeling short-term physical phenomena to developing a reliable forecast-oriented model, we suggest using the data assimilation approach. It allows us to cross-analyze dynamo model solutions and observations and to consider possible uncertainties and errors. In this presentation, we briefly summarize current multi-scale modeling capabilities and results and discuss ongoing developments to build a reliable physics-based forecast-oriented model of solar activity

Irina N. Kitiashvili↗
NASA NTRSDate not supplied

Data Science and the Knowledge Discovery Adventure

This talk will cover the important steps involved in the data science and knowledge discovery process: • Initial fact gathering (interview domain experts, review reports, articles, state-of-the-art) • Identify the problem (prediction, classification, statistical analysis, etc.) • Survey supporting data sources • Understand the data (numerical, categorical, text, sampling rate, data quality issues, etc.) • Selecting relevant features and sources • Acquire the data (set up agreements with the data stewards, APIs to download, etc.) • Merge data sources (temporal, spatial, common key, other ontologies...) • Feature Engineering (non linear domain knowledge or physics-based relationships) • Build data processing pipeline (may need to tap into data stream, develop parallel processing algorithm, federated learning etc.) • Build model and test (tune hyper-parameters, cross validation.) • Analyze/Validate results (do the results make sense. Does it answer the original question). • Deploy/Publish (Monitor and assess benefits)

Data science↗
NASA NTRSDate not supplied

NAIRAS Model transition to the CCMC: real-time dosimetric output and low-Earth orbit applications

NASA’s Nowcast of Atmospheric Ionizing Radiation for Aviation Safety (NAIRAS) model is areal-time, global, physics-based model originally developed to predict exposure to cosmic radiation to air travelers from both galactic and solar sources. A prototype operational NAIRAS model has provided tabular and graphical data products via its public web site for a number of years. We present the advances in the transition of NAIRAS to operations at the Community Coordinated Modeling Center (CCMC), located at NASA Goddard Space Flight Center, where global NAIRAS products are available for a real-time space weather version of the model. In addition, a runs-on-request version provides model output along user selected flight trajectories. We show the global dosimetric output from the model that are available in real-time as well as the new products developed to support commercial spaceflight missions to low-Earth orbit(LEO) and the International Space Station (ISS).

Guillaume Gronof↗
NASA NTRSDate not supplied

Laminar-Turbulent Transition in High Mach-Number Flows

Accurate prediction of laminar-turbulent transition is critical for the design and characterization of high-speed flight systems. Presently, laminar-turbulent transition prediction in computational fluid dynamics (CFD) is accomplished using PDE-based models coupled to the governing equations of the flow system. Current industrial models suffer significant performance issues when analyzing high-speed systems because they were developed for lower speed flows. Current research focuses on multiple solutions to this problem. The coupling of physics-based stability analysis tools which produce quality transition predictions for high-speed flows to NASA’s OVERFLOW CFD solver is being pursued, as well as the implementation and improvement of emerging models developed specifically for high-speed flow.

High-Speed Flow↗
NASA NTRSDate not supplied

An ODE-Fitting Approach to Estimate Critical Aircraft Performance Parameters for Trajectory Prediction

Ground-based decision support tools (DST) in air traffic management (ATM) typically perform trajectory prediction based on aircraft performance model (APM) parameters, but some or all of these parameters might not be readily available. In particular, the three critical parameters required for trajectory prediction are the thrust setting, drag coefficients, and takeoff weight of the aircraft. Unfortunately, these parameters are coupled and appear together in physics-based kinetic models. Past approaches utilize data from a specific phase of flight (climb, level flight or descent), where one or more of the parameters are assumed to be known and estimate the remaining unknown parameters. This approach introduces bias/errors and also does not extend to scenarios where all of the above parameters are not known with sufficient accuracy. This paper is the first of its kind to propose a generalized framework for simultaneous estimation of all three critical APM parameters (thrust, drag, and mass). The proposed approach utilizes data from both the climb and descent phases and fits the ordinary differential equation for altitude in each phase using historical trajectory data available from radar tracks or ADS-B. The approach yields a set of optimized APM parameters that are best suited to fit each historical flight record. The methodology is applied on on sample flights from three different aircraft types, and the results demonstrate low fit error and consequently will yield a high level of prediction accuracy.

trajectory prediction↗
NASA NTRS2022

Sun Chaser - A Mission to the Earth-Sun Lagrangian Point 4

Placed at L4, Sun Chaser is a mission concept that will follow (or chase) high-energy processes around the west limb, combining solar remote sensing & in situ observations, and overseeing the entire solar radiation hemisphere. Sun Chaser’s remote sensing is essential for ~90% of current physics-based and empirical solar energetic particle (SEP) event forecasting techniques. Without Sun Chaser, there cannot be a basis for SEP event all-clear forecasting. It establishes and maintains a space weather (SWx) radiation safe zone that supports all near-term human missions to the Moon and Mars. Sun Chaser latitude in-situ coverage also provides a unique opportunity for solar wind-, interplanetary- and interstellar-dust science. In combination with existing and planned observatories at L1 and L5, the three locations provide 240° longitude coverage of resolving photospheric magnetic field structure and safe Earth-directed CME viewing. A ~14°-inclination of both L4 and L5 out of the ecliptic guarantees continuous viewing of both solar poles and continuous in-situ presence on both sides of the heliographic equator, with >3.6° elevation. Extended observations in both longitude and latitude will revolutionize global solar wind modeling and immediate validation, and enables the development of local helioseismology, with potential for long-term solar activity forecasting.

space weather↗
NASA NTRSDate not supplied

A Framework for Optimization-Based ISRU Tool Design Using Discrete Element Modeling

Novel robotic excavation technologies are needed to perform in-situ resource utilization (ISRU) tasks at levels required to sustain a long-term presence on the lunar surface. Developing and testing multiple iterations of functional hardware is time and cost prohibitive, thus slowing down the pace of progress and delaying humanity’s settlement of the Moon. High-fidelity, physics-based simulation can reduce the time and effort required to develop and deploy robotic systems [1]. We have adopted this approach to create high-fidelity models of robotic test hardware to enable rapid virtual design and optimization of excavation technologies [2]. Such models can leverage modern computational tools like Discrete Element Method (DEM) simulations that can be coupled with automated design approaches like topology optimization to reduce the amount of prototyping and physical testing needed to realize useful tools.

ISRU↗
NASA NTRS2022

Flows in Enthalpy-based Thermal Evolution of Loops

Plasma-filled loop structures are common in the solar corona. Because detailed modeling of the dynamical evolution of these structures is computationally costly, an efficient method for computing approximate but quick physics-based solutions is to rely on space-integrated 0D simulations. The enthalpy-based thermal evolution of loops (EBTEL) framework is a commonly used method to study the exchange of mass and energy between the corona and transition region. EBTEL solves for density, temperature, and pressure, averaged over the coronal part of the loop, velocity at coronal base, and the instantaneous differential emission measure distribution in the transition region. The current single-fluid version of the code, EBTEL2, assumes that at all stages the flows are subsonic. However, sometimes the solutions show the presence of supersonic flows during the impulsive phase of heat input. It is thus necessary to account for this effect. Here, we upgrade EBTEL2 to EBTEL3 by including the kinetic energy term in the Navier–Stokes equation. We compare the solutions from EBTEL3 with those obtained using EBTEL2, as well as the state-of-the-art field-aligned hydrodynamics code HYDRAD. We find that the match in pressure between EBTEL3 and HYDRAD is better than that between EBTEL2 and HYDRAD. Additionally, the velocities predicted by EBTEL3 are in close agreement with those obtained with HYDRAD when the flows are subsonic. However, EBTEL3 solutions deviate substantially from HYDRAD’s when the latter predicts supersonic flows. Using the mismatches in the solution, we propose a criterion to determine the conditions under which EBTEL can be used to study flows in the system.

Solar coronal heating↗
NASA NTRSDate not supplied

Effects of Dose Error and Sample Size on Sonic Boom Dose-response Curves

NASA will soon be collecting noise-annoyance community survey data as the X-59 aircraft flies supersonically over several communities in the USA. Sparse measurements of the X-59 sonic thumps will be used together with physics-based simulations to estimate noise doses at survey participant locations. These dose estimates have associated error that affects the accuracy of modeled dose-response curves, which can result in misestimation of annoyance. The precision in dose-response curves is also a consideration in selecting the number of survey participants. To enable pretest studies of dose error and precision, simulated dose-response data were generated based on NASA’s Quiet Supersonic Flights 2018 test. The data included various degrees of dose error and sample size. Frequentist multilevel logistic regression models were fit to the true and perturbed dose-response data. Simple proportional relationships were identified between the model parameters and the perturbation standard deviation. The summary dose-response curves illustrate the impact on accuracy if dose error is not accounted for in the model. The precision in the dose-response curves is also shown as the number of participants and degree of participation is varied. Finally, sampling variability is illustrated by showing the dose-response curves for several replicates with random draws of participants and errors.

X-59↗
NASA NTRS2022

Investigation of the Drivers and Atmospheric Impacts of Energetic Electron Precipitation

The drivers and atmospheric impacts of energetic electron precipitation are not yet well understood. Further, electron precipitation is often poorly represented in atmospheric modeling. Additional investigations of the drivers and impacts of electron precipitation are needed to improve models and space weather forecasting requirements. To accurately represent the troposphere through the ionosphere in model simulations, it is vital to account for the chemistry accurately. Electron precipitation is a frequent, yet often ignored middle to high latitude forcing that can have dramatic effects on the middle and upper atmosphere. Over the past decade, several electron precipitation data sets have been developed, however, validation has been difficult due to the lack of independent observations of electron fluxes. Additionally, the limited number of satellites making measurements of global magnetospheric wave activity in concert with the resulting electron precipitation restricts our ability to accurately capture the drivers simultaneously with the precipitation. Accurate characterization of the drivers is needed for physics-based magnetosphere modeling. Likewise, accurate precipitating electron fluxes and relative energies are needed to improve our atmospheric modeling studies. Finally, in order to properly validate and improve our current modeling efforts, observations of atmospheric composition are necessary.

Joshua Pettit↗
NASA NTRSDate not supplied

NAIRAS Model Updates and Improvements to the Prediction of the Ionizing Radiation Environment from the Earth's Surface to Geospace

- NAIRAS Model Description - Nowcast of Aerospace Ionizing RAdiation System (NAIRAS) Model - Running in real-time on LaRC computer cluster since 2011, results hosted on Space Environment Technologies server/website - Running in real-time at CCMC since 2020 - Key Model Features - Global ionizing radiation environment model - Physics-based HZETRN (High Charge (Z) and Energy TRaNsport) code - Real-time inclusion of solar energetic particle (SEP) radiation - Real-time solar-magnetospheric effects on radiation (cutoff model by Kress et al. [2004, 2010]) - New/Current Model Development - Improved SEP dose nowcast and forecast - Extend to low-Earth orbit (LEO) environment - Single-Event Effects (SEE) radiation risk assessment quantities - Run-on-Request (RoR) @ CCMC

Christopher J. Mertens↗
NASA NTRSDate not supplied

Characterization of Radiation Exposure at Aviation Flight Altitudes Using the Nowcast of Aerospace Ionizing Radiation System (NAIRAS)

Exposure to ionizing radiation from galactic cosmic rays (GCRs) and solar energetic particles (SEPs) at aircraft flight altitudes can have an adverse effect on human health. Although airline crews are classified as radiation workers by the International Commission on Radiological Protection (ICRP), in most countries, their level of exposure is unquantified and undocumented throughout the duration of their career. As such, there is a need to assess pilot ionizing radiation exposure. The Nowcast of Aerospace Ionizing Radiation System (NAIRAS), a real-time global, physics-based model is used to assess such exposure. The Automated Radiation Measurements for Aerospace Safety (ARMAS) measurement dataset consists of high latitude, high altitude, and long-duration aircraft flights between 2013-2021. Here, we characterize radiation exposure at aviation flight altitudes using the NAIRAS model and compare with over 800 flight trajectories from the ARMAS flight measurement inventory.

Daniel B Phoenix↗
NASA NTRS2022

Review of Solar Energetic Particle Models

Solar Energetic Particle (SEP) events are interesting from a scientific perspective as they are the product of a broad set of physical processes from the corona out through the extent of the heliosphere, and provide insight into processes of particle acceleration and transport that are widely applicable in astrophysics. From the operations perspective, SEP events pose a radiation hazard for aviation, electronics in space, and human space exploration, in particular for missions outside of the Earth’s protective magnetosphere including to the Moon and Mars. Thus, it is critical to improve the scientific understanding of SEP events and use this understanding to develop and improve SEP forecasting capabilities to support operations. Many SEP models exist or are in development using a wide variety of approaches and with differing goals. These include computationally intensive physics-based models, fast and light empirical models, machine learning-based models, and mixed-model approaches. The aim of this paper is to summarize all of the SEP models currently developed in the scientific community, including a description of model approach, inputs and outputs, free parameters, and any published validations or comparisons with data.

Kathryn Whitman↗
NASA NTRS2022

A Comprehensive Machine Learning Study to Classify Precipitation Type over Land from Global Precipitation Measurement Microwave Imager (GPM-GMI) Measurements

Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud–convection–precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote sensing measurements that help us distinguish them. The Global Precipitation Measurement (GPM) Core Observatory’s microwave imager (GMI) and dual-frequency precipitation radar (DPR) together provide ample information on global precipitation characteristics. As an active sensor, the DPR provides an accurate precipitation type assignment, while passive sensors such as the GMI are traditionally only used for empirical understanding of precipitation regimes. Using collocated precipitation type flags from the DPR as the “truth”, this paper employs machine learning (ML) models to train and test the predictability and accuracy of using passive GMI-only observations together with ancillary information from a reanalysis and GMI surface emissivity retrieval products. Out of six ML models, four simple ones (support vector machine, neural network, random forest, and gradient boosting) and the 1-D convolutional neural network (CNN) model are identified to produce 90–94% prediction accuracy globally for five types of precipitation (convective, stratiform, mixture, no precipitation, and other precipitation), which is much more robust than previous similar effort. One novelty of this work is to introduce data augmentation (subsampling and bootstrapping) to handle extremely unbalanced samples in each category. A careful evaluation of the impact matrices demonstrates that the polarization difference (PD), brightness temperature (Tc) and surface emissivity at high-frequency channels dominate the decision process, which is consistent with the physical understanding of polarized microwave radiative transfer over different surface types, as well as in snow and liquid clouds with different microphysical properties. Furthermore, the view-angle dependency artifact that the DPR’s precipitation flag bears with does not propagate into the conical-viewing GMI retrievals. This work provides a new and promising way for future physics-based ML retrieval algorithm development.

machine learning/artificial intelligence↗
NASA NTRS2022

Beyond Basic Drag in Interplanetary CME Modeling: Effects of Solar Wind Pileup and High-Speed Streams

Coronal mass ejections (CMEs) cause severe space weather effects throughout our solar system. As a fast CME propagates through interplanetary space, it accumulates solar wind materials at its front. This pileup of materials, or CME-driven sheath, can be important in determining the geoeffectiveness of a CME. We take an existing arrival time model that includes expansion and deformation of the CME flux rope (ANTEATR; Kay & Gopalswamy, 2018, https://doi.org/10.1029/2018JA025780; Kay & Nieves-Chinchilla, 2021a, https://doi.org/10.1029/2020JA028911) and add a pileup procedure (PUP) as a physics-based approach to modeling the CME-driven sheath. ANTEATR-PUP solves the Rankine-Hugoniot equations for an oblique shock to determine the shock speed and sheath density, magnetic field, and temperature. The extra sheath mass affects the background drag calculation. Additionally, ANTEATR can now use any 1D profile for the background solar wind as opposed to the simple empirical models it previously relied upon. We present initial results from ANTEATR-PUP and compare with previous ANTEATR findings. Using results from an MHD simulation, we explore the effects of interactions with a static high-speed stream (HSS) on the CME's and sheath's interplanetary evolution. The drag forces essentially disappear while a CME remains within the HSS, but reappear stronger once the CME exits. The HSS-CME interaction produces the largest changes in the CME and sheath properties at 1 au when it occurs either close to the Sun near the inner simulation boundary at 0.1 au or right before the CME reaches 1 au. We estimate that these changes could significantly affect the geoeffectiveness.

C Kay↗
NASA NTRS2022

Mars 2020 Perseverance Rover SHERLOC Instrument Isolation System

The NASA Jet Propulsion Laboratory (JPL) successfully landed the Mars 2020 Perseverance Rover at Jezero Crater on the Martian surface. Perseverance’s main mission objective is to cache Martian rock and regolith samples in hermetically sealed tubes to be brought back to Earth by future missions. To achieve this goal the rover is equipped with a 2-meter-long Robotic Arm (RA) which manipulates the Turret to interact with the surface. The Turret is comprised of science instruments and tools that enable surface sampling and science. The backbone of the Turret hardware is the Rotary Percussive Coring Drill. The science instruments, which are directly mounted to the structural housing of this drill, must be able to withstand not only the launch and entry, descent and landing (EDL) loads of the vehicle but also the dynamic percussive environment, induced by the drill throughout surface operations. This paper discusses the technical hardware design, development and testing of a vibration isolation system to protect the Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) Instrument from these dynamic environments. The hardware design aspect of this problem was limited by both size and mass constraints. The Turret is tightly configured and the physical space in which hardware can reside is restricted due to multiple axes of surface interaction points. Another driving requirement is the extreme range of thermal non-operational environment from -135C to +90C. This, in addition to SHERLOC being a heavier instrument than previously integrated on a turret, made the use of isolation systems employed on previous rover missions problematic. The final design incorporates custom wire mesh springs preloaded within titanium hexapod struts equipped with flexure ends. Custom versions of commercially available wire mesh springs were designed and tested. These custom springs differed in dimension, density and stiffness from the off the shelf options. The newly designed springs went through various stages of flight acceptance testing. Detailed physics-based modeling to understand what loads the isolation system would experience was used to define the test conditions for the springs and entire isolation subsystem. The testing program developed around this was extensive to fully characterize the isolation performance over cleanliness levels, temperature and life. Another major aspect of this work was developing a method of replicating the drill’s percussive environment for performance and life testing of Turret mounted hardware. Attaching the instrument to an operating drill for testing was not a viable option. A method of using a Highly Accelerated Life Test (HALT) table was developed and applied across the project as the most representative and tunable option to physically simulate this new environment. This method, along with launch environment random vibration testing, was used to build an entire dynamic qualification and flight hardware test program. The results of the design, modeling, testing and analysis of the SHERLOC Isolation System is described throughout the following paper.

Krafchak, Todd↗
NASA NTRS2020

Simulation-Based Analysis and Prediction of Thrust Vector Servoelastic Coupling

A method of analysis and prediction of servoelastic coupling in launch vehicles is presented, surveying the discovery and subsequent resolution of a predicted servoelastic resonance phenomenon affecting the NASA Space Launch System launch vehicle at specific flight conditions. A physics-based linearized multibody mechanization of the governing equations is combined with first principles analysis to demonstrate that antisymmetric bending of the solid rocket motors leads to a reduction of equivalent viscous modal damping through coupling with the thrust vector control actuators. The sensitivity to parameters and the effects of the resonance phenomenon on flight control performance and stability are confirmed through extensive simulation verification in the time and frequency domain. A novel enhancement in model fidelity that accounts for Coriolis effects of fluid flow on bending within the solid rocket motor case and nozzle is shown to add sufficient damping to reduce the risk of adverse control-structure interaction.

Jeb S Orr↗