Search NASA⌕ Search

SEARCH · Search NASA

Results for “Physics-based learning”

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.

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

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Using Machine-Learning Methods and Expert Prediction Probabilities to Forecast Solar Flares

It has long been known that studying connection between solar flares and properties of magnetic field in active regions is very important for understanding the flare physics and developing space weather forecasts. The Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) obtains tremendous amounts of magnetic field data products. However the operational NOAA Space Weather Prediction Center (SWPC) forecasts of solar flares still represent prediction probabilities issued by the experts. In this research we investigate the possibilities to enhance the daily operational flare forecasts performed at the SWPC by developing a synergy of the expert predictions and physics-based criteria, and by employing machine-learning methods. Among the physics-based criteria we consider the descriptors of the Polarity Inversion Line (PIL) and Space weather HMI Active Region Patches (SHARP), and derive from them daily characteristics of the entire Sun. We also consider the daily descriptors of the GOES Soft X-Ray (SXR) 1-8 Angstroms flux such as the flare history of the previous days and averaged X-Ray flux. We estimate the effectiveness in separation of flaring and non-flaring cases for each characteristic, as well as for the expert prediction probabilities, and find that some PIL, SHARP and SXR descriptors are as effective as the expert prediction probabilities and should be considered to issue the flare forecast. Finally, we train and test several Machine-Learning classification algorithms (Support Vector Classifiers with various kernel functions, k-Nearest Neighbor Classifier, Random Forest Classifier, and Neural Networks) using the most effective descriptors and expert prediction probabilities, and compare the obtained predictions with the current SWPC forecasts.

Machine-Learning↗
NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗
NASA NTRSDate not supplied

TPSAS-NF1676L-35322-DND

This talk discusses emerging methods that seek to fuse and integrate physics-based modeling with machine learning. With the recent rise of machine learning and artificial intelligence, there has been a huge surge in data-driven approaches to solve computational science and engineering problems. However, neglecting a priori knowledge of established physical laws and relying solely on data-driven methods can yield unreliable, less interpretable, and/or non-physical results, especially when data is sparse or predictions are required outside of the training data domain. This two part talk presents two distinct approaches for accelerating predictions with machine learning that are grounded and constrained by relevant physics and their application to problems at NASA.

Julian Cuevas Paniagua↗
NASA NTRS2009

Active Learning for Directed Exploration of Complex Systems

Physics-based simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. Such codes provide the highest-fidelity representation of system behavior, but are often so slow to run that insight into the system is limited. For example, conducting an exhaustive sweep over a d-dimensional input parameter space with k-steps along each dimension requires k(sup d) simulation trials (translating into k(sup d) CPU-days for one of our current simulations). An alternative is directed exploration in which the next simulation trials are cleverly chosen at each step. Given the results of previous trials, supervised learning techniques (SVM, KDE, GP) are applied to build up simplified predictive models of system behavior. These models are then used within an active learning framework to identify the most valuable trials to run next. Several active learning strategies are examined including a recently-proposed information-theoretic approach. Performance is evaluated on a set of thirteen synthetic oracles, which serve as surrogates for the more expensive simulations and enable the experiments to be replicated by other researchers.

oracles↗
NASA NTRS2016

Decision Manifold Approximation for Physics-Based Simulations

With the recent surge of success in big-data driven deep learning problems, many of these frameworks focus on the notion of architecture design and utilizing massive databases. However, in some scenarios massive sets of data may be difficult, and in some cases infeasible, to acquire. In this paper we discuss a trajectory-based framework that quickly learns the underlying decision manifold of binary simulation classifications while judiciously selecting exploratory target states to minimize the number of required simulations. Furthermore, we draw particular attention to the simulation prediction application idealized to the case where failures in simulations can be predicted and avoided, providing machine intelligence to novice analysts. We demonstrate this framework in various forms of simulations and discuss its efficacy.

Wong, Jay Ming↗
NASA NTRSDate not supplied

In-Situ Scanning Electron Microscope Experiments for Microscale Mechanical Testing and Validated Modeling of Fiber Reinforced Thermoplastics

A novel, in-situ, scanning electron microscope (SEM) mechanical testing capability for materials at the microscale which provides experimental validation to a machine learning (ML) toolset for full-field validation of physics-based micromechanics models is being developed by researchers at NASA Glenn Research Center. These are enabling technologies for the integration of multiscale digital twins for materials into system level models which will result in the improved performance, material discovery, reduced production cost and time, rapid characterization, and prognostic structural health monitoring (SHM) for materials and structures for extreme environments in support of NASA space exploration missions. In order to bridge the material structure-to-system gap for digital twins, physics-based models must be experimentally validated at multiple length scales. Seminal microscale experiments, conducted at the Air Force Research Laboratory (AFRL), were limited to transverse compression of single-layer, unidirectional thermoset polymer matrix composite (PMC) micropillar specimens [1]. The early phases of the current project followed those initial results and setup to reproduce the compression testing of PMC material on the custom-built piezoelectric actuated micromechanical testing rig built by MicroTesting Solutions LLC. In this work, samples of thermoplastic PMC material were first machined into 3 mm cubes, and then further machining and final milling was done using a Focused Ion Beam (FIB). The initial experiment was done on a pillar roughly 20 µm x 20 µm x 40 µm tall. Additional pillars were milled with final sizes ranging from 20 µm x 20 µm x 40 µm tall to 40 µm x 40 µm x 65 µm tall. A speckle pattern for in-situ full-field measurements using Digital Image Correlation (DIC) was applied with platinum, which was coated on the surface, and then the FIB was used to mill away some of the coating to produce an irregular pattern of Pt on the pillar surface. The samples were loaded into the custom testing rig and placed into the SEM and loaded under compression until failure. Images were collected in the SEM during testing. Post-processing of the images was conducted using DIC to obtain full-field displacement and strain measurements elucidating the role of the matrix as well as fiber-fiber interaction at the microscale within the composite subjected to compression loading well into the non-linear regime of the material. Moreover, the evolution of fiber-matrix debonding and matrix cracking is observed in-situ at the microscale. This data, along with images segmented with a newly developed ML toolset [2], was used to create and validate physics-based micromechanics models. An image of the failed micropillar is shown in Figure 1. The techniques developed in the initial compression experiment was tailored to the validation needs of the models and expanded to include different sized samples as well as possibly tension and fatigue.

Laura Wilson↗
NASA NTRS2014

MDO and Cross-Disciplinary Practice in R&D: A Portrait of Principles and Current Practice

For several decades, Multidisciplinary Design Optimization (MDO) has served an important role in aerospace engineering by incorporating physics based disciplinary models into integrated system or sub-system models for use in research, development, (R&D) and design. This paper examines MDO's role in facilitating the integration of the researchers from different single disciplines during R&D and early design of large-scale complex engineered systems (LaCES) such as aerospace systems. The findings in this paper are summarized from a larger study on interdisciplinary practices and perspectives that included considerable empirical data from surveys, interviews, and ethnography. The synthesized findings were derived by integrating the data with theories from organization science and engineering. The over-arching finding is that issues related to cognition, organization, and social interrelations mostly dominate interactions across disciplines. Engineering issues, such as the integration of hardware or physics-based models, are not as significant. Correspondingly, the data showed that MDO is not the primary integrator of researchers working across disciplines during R&D and early design of LaCES. Cognitive focus such as analysis versus design, organizational challenges such as incentives, and social opportunities such as personal networks often drove the human interactive practices among researchers from different disciplines. Facilitation of the inherent confusion, argument, and learning in crossdisciplinary research was identified as one of several needed elements of enabling successful research across disciplines.

Rivas McGowan, Anna-Maria↗
NASA NTRS2014

Training Knowledge Bots for Physics-Based Simulations Using Artificial Neural Networks

Millions of complex physics-based simulations are required for design of an aerospace vehicle. These simulations are usually performed by highly trained and skilled analysts, who execute, monitor, and steer each simulation. Analysts rely heavily on their broad experience that may have taken 20-30 years to accumulate. In addition, the simulation software is complex in nature, requiring significant computational resources. Simulations of system of systems become even more complex and are beyond human capacity to effectively learn their behavior. IBM has developed machines that can learn and compete successfully with a chess grandmaster and most successful jeopardy contestants. These machines are capable of learning some complex problems much faster than humans can learn. In this paper, we propose using artificial neural network to train knowledge bots to identify the idiosyncrasies of simulation software and recognize patterns that can lead to successful simulations. We examine the use of knowledge bots for applications of computational fluid dynamics (CFD), trajectory analysis, commercial finite-element analysis software, and slosh propellant dynamics. We will show that machine learning algorithms can be used to learn the idiosyncrasies of computational simulations and identify regions of instability without including any additional information about their mathematical form or applied discretization approaches.

Samareh, Jamshid A.↗
NASA NTRS2014

Interdisciplinary Interactions During R&D and Early Design of Large Engineered Systems

Designing Large-Scale Complex Engineered Systems (LaCES) such as aircraft and submarines requires the input of thousands of engineers and scientists whose work is proximate in neither time nor space. Comprehensive knowledge of the system is dispersed among specialists whose expertise is in typically one system component or discipline. This study examined the interactive work practices among such specialists seeking to improve engineering practice through a rigorous and theoretical understanding of current practice. This research explored current interdisciplinary practices and perspectives during R&D and early LaCES design and identified why these practices and perspectives prevail and persist. The research design consisted of a three-fold, integrative approach that combined an open-ended survey, semi-structured interviews, and ethnography. Significant empirical data from experienced engineers and scientists in a large engineering organization were obtained and integrated with theories from organization science and engineering. Qualitative analysis was used to obtain a holistic, contextualized understanding. The over-arching finding is that issues related to cognition, organization, and social interrelations mostly dominate interactions across disciplines. Engineering issues, such as the integration of hardware or physics-based models, are not as significant. For example, organization culture is an important underlying factor that guided researchers more toward individual sovereignty over cross-disciplinarity. The organization structure and the engineered system architecture also serve as constraints to the engineering work. Many differences in work practices were observed, including frequency and depth of interactions, definition or co-construction of requirements, clarity or creation of the system architecture, work group proximity, and cognitive challenges. Practitioners are often unaware of these differences resulting in confusion and incorrect assumptions regarding work expectations. Cognitively, the enactment and coconstruction of knowledge are the fundamental tasks of the interdisciplinary interactions. Distributed and collective cognition represent most of the efforts. Argument, ignorance, learning, and creativity are interrelated aspects of the interactions that cause discomfort but yield benefits such as problem mitigation, broader understanding, and improved system design and performance. The quality and quantity of social interrelations are central to all work across disciplines with reciprocity, respectful engagement, and heedful interrelations being significant to the effectiveness of the engineering and scientific work.

McGowan, Anna-Maria Rivas↗
NASA NTRS1999

Aerospace Engineering Systems and the Advanced Design Technologies Testbed Experience

Continuous improvement of aerospace product development processes is a driving requirement across much of the aerospace community. As up to 90% of the cost of an aerospace product is committed during the first 10% of the development cycle, there is a strong emphasis on capturing, creating, and communicating better information (both requirements and performance) early in the product development process. The community has responded by pursuing the development of computer-based systems designed to enhance the decision-making capabilities of product development individuals and teams. Recently, the historical foci on sharing the geometrical representation and on configuration management are being augmented: 1) Physics-based analysis tools for filling the design space database; 2) Distributed computational resources to reduce response time and cost; 3) Web-based technologies to relieve machine-dependence; and 4) Artificial intelligence technologies to accelerate processes and reduce process variability. The Advanced Design Technologies Testbed (ADTT) activity at NASA Ames Research Center was initiated to study the strengths and weaknesses of the technologies supporting each of these trends, as well as the overall impact of the combination of these trends on a product development event. Lessons learned and recommendations for future activities are reported.

VanDalsem, William R.↗
NASA NTRS1999

Aerospace Engineering Systems

Continuous improvement of aerospace product development processes is a driving requirement across much of the aerospace community. As up to 90% of the cost of an aerospace product is committed during the first 10% of the development cycle, there is a strong emphasis on capturing, creating, and communicating better information (both requirements and performance) early in the product development process. The community has responded by pursuing the development of computer-based systems designed to enhance the decision-making capabilities of product development individuals and teams. Recently, the historical foci on sharing the geometrical representation and on configuration management are being augmented: Physics-based analysis tools for filling the design space database; Distributed computational resources to reduce response time and cost; Web-based technologies to relieve machine-dependence; and Artificial intelligence technologies to accelerate processes and reduce process variability. Activities such as the Advanced Design Technologies Testbed (ADTT) project at NASA Ames Research Center study the strengths and weaknesses of the technologies supporting each of these trends, as well as the overall impact of the combination of these trends on a product development event. Lessons learned and recommendations for future activities will be reported.

VanDalsem, William R.↗
NASA NTRS2004

Land Surface Temperature Measurements from EOS MODIS Data

This report summarizes the accomplishments made by the MODIS LST (Land-Surface Temperature) group at University of California, Santa Barbara, under NASA Contract. Version 1 of the MODIS Land-Surface Temperature Algorithm Theoretical Basis Document (ATBD) was reviewed in June 1994, version 2 reviewed in November 1994, version 3.1 in August 1996, and version 3.3 updated in April 1999. Based on the ATBD, two LST algorithms were developed, one is the generalized split-window algorithm and another is the physics-based day/night LST algorithm. These two LST algorithms were implemented into the production generation executive code (PGE 16) for the daily standard MODIS LST products at level-2 (MODII-L2) and level-3 (MODIIA1 at 1 km resolution and MODIIB1 at 5km resolution). PGE codes for 8-day 1 km LST product (MODIIA2) and the daily, 8-day and monthly LST products at 0.05 degree latitude/longitude climate model grids (CMG) were also delivered. Four to six field campaigns were conducted each year since 2000 to validate the daily LST products generated by PGE16 and the calibration accuracies of the MODIS TIR bands used for the LST/emissivity retrieval from versions 2-4 of Terra MODIS data and versions 3-4 of Aqua MODIS data. Validation results from temperature-based and radiance-based methods indicate that the MODIS LST accuracy is better than 1 C in most clear-sky cases in the range from -10 to 58 C. One of the major lessons learn from multi- year temporal analysis of the consistent V4 daily Terra MODIS LST products in 2000-2003 over some selected target areas including lakes, snow/ice fields, and semi-arid sites is that there are variable numbers of cloud-contaminated LSTs in the MODIS LST products depending on surface elevation, land cover types, and atmospheric conditions. A cloud-screen scheme with constraints on spatial and temporal variations in LSTs was developed to remove cloud-contaminated LSTs. The 5km LST product was indirectly validated through comparisons to the 1 km LST product. Twenty three papers related to the LST research work were published in journals over the last decade.

Wan, Zheng-Ming↗
NASA NTRS2005

Physics-Based Spectra of Accretion Disks around Black Holes

The purpose of this grant was to begin the process of deriving the light output of accretion disks around black holes directly from the actual processes that inject heat into the accreting matter, rather than from guessed dependences of heating rate on physical parameters. At JHU, the effort has focussed so far on models of accretion onto "intermediate mass black holes", a possible class of black holes, examples of which may have recently been discovered in nearby galaxies. There, Krolik and his student (Yawei Hui) have computed stellar atmospheres for uniformly-heated disks around this class of black holes. Their models serve two purposes: they are the very first serious attempts to compute the spectrum from accreting black holes in this mass range; and a library of such models can be used later in this program as contrasts for those computed on the basis of real disk dynamics. The output from these local disk calculations has also been successfully coupled to a program that applies the appropriate relativistic transformations and computes photon trajectories in order to predict the spectrum received by observers located at different polar angles. The principal new result of these calculations is the discovery of potentially observable ionization edges of H-like C and O at frequencies near the peak in flux from these objects. Most of the grant money at UCSB was spent on supporting graduate student Shane Davis. In addition. some money was spent on supporting two other students: Ari Socrates (now a Hubble Fellow at Princeton), and Laura Melling. Davis spent the year constructing stellar atmosphere models of accretion disks appropriate for the high/soft (thermal) state of black hole X-ray binaries. As with AGN models published previously by our collaboration with NASA support. our models include a complete general relativistic treatment of both the disk structure and the propagation of photons from the disk to a distant observer. They also include all important continuum opacity sources, including Compton scattering and bound-free opacity from abundant metal species. The principal new result is that bound-free opacity is very significant in altering the continuum spectral shape, resulting for example in quite different "color correction factors" compared to those predicted previously. In addition, the models predict a relationship between luminosity and inner disk temperature that is, for the first time, in accord with that observed. The primary purpose of the grant was to incorporate more realistic accretion disk physics, learned largely from simulations, into such spectral models. The Davis et al. paper includes consideration of a vertical dissipation profile computed from radiation magneto-hydrodynamic simulations of MRI turbulence by N. J. Turner (2004). So long as the disk is effectively thick, such dissipation profiles do not affect the predicted spectrum significantly. (More work needs to be done on these simulations, however.) A potentially more serious issue is that MRI turbulence produces substantial inhomogeneities, as do photon bubble instabilities. These inhomogeneities can affect the spectra by enhancing the effects of absorption opacity over scattering opacity. We have done some preliminary Monte Carlo calculations to explore these effects.

Krolik, Julian H.↗
NASA NTRS2013

SIM_EXPLORE: Software for Directed Exploration of Complex Systems

Physics-based numerical simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. While such codes may provide the highest- fidelity representation of system behavior, they are often so slow to run that insight into the system is limited. Trying to understand the effects of inputs on outputs by conducting an exhaustive grid-based sweep over the input parameter space is simply too time-consuming. An alternative approach called "directed exploration" has been developed to harvest information from numerical simulators more efficiently. The basic idea is to employ active learning and supervised machine learning to choose cleverly at each step which simulation trials to run next based on the results of previous trials. SIM_EXPLORE is a new computer program that uses directed exploration to explore efficiently complex systems represented by numerical simulations. The software sequentially identifies and runs simulation trials that it believes will be most informative given the results of previous trials. The results of new trials are incorporated into the software's model of the system behavior. The updated model is then used to pick the next round of new trials. This process, implemented as a closed-loop system wrapped around existing simulation code, provides a means to improve the speed and efficiency with which a set of simulations can yield scientifically useful results. The software focuses on the case in which the feedback from the simulation trials is binary-valued, i.e., the learner is only informed of the success or failure of the simulation trial to produce a desired output. The software offers a number of choices for the supervised learning algorithm (the method used to model the system behavior given the results so far) and a number of choices for the active learning strategy (the method used to choose which new simulation trials to run given the current behavior model). The software also makes use of the LEGION distributed computing framework to leverage the power of a set of compute nodes. The approach has been demonstrated on a planetary science application in which numerical simulations are used to study the formation of asteroid families.

Burl, Michael↗
NASA NTRS2015

Physics-Based Modeling of Meteor Entry and Breakup

A new research effort at NASA Ames Research Center has been initiated in Planetary Defense, which integrates the disciplines of planetary science, atmospheric entry physics, and physics-based risk assessment. This paper describes work within the new program and is focused on meteor entry and breakup. Over the last six decades significant effort was expended in the US and in Europe to understand meteor entry including ablation, fragmentation and airburst (if any) for various types of meteors ranging from stony to iron spectral types. These efforts have produced primarily empirical mathematical models based on observations. Weaknesses of these models, apart from their empiricism, are reliance on idealized shapes (spheres, cylinders, etc.) and simplified models for thermal response of meteoritic materials to aerodynamic and radiative heating. Furthermore, the fragmentation and energy release of meteors (airburst) is poorly understood. On the other hand, flight of human-made atmospheric entry capsules is well understood. The capsules and their requisite heatshields are designed and margined to survive entry. However, the highest speed Earth entry for capsules is less than 13 km/s (Stardust). Furthermore, Earth entry capsules have never exceeded diameters of 5 m, nor have their peak aerothermal environments exceeded 0.3 atm and 1 kW/cm2. The aims of the current work are: (i) to define the aerothermal environments for objects with entry velocities from 13 to greater than 20 km/s; (ii) to explore various hypotheses of fragmentation and airburst of stony meteors in the near term; (iii) to explore the possibility of performing relevant ground-based tests to verify candidate hypotheses; and (iv) to quantify the energy released in airbursts. The results of the new simulations will be used to anchor said risk assessment analyses. With these aims in mind, state-of-the-art entry capsule design tools are being extended for meteor entries. We describe: (i) applications of current simulation tools to spherical geometries of diameters ranging from 1 to 100 m for an entry velocity of 20 km/s and stagnation pressures ranging from 1 to 100 atm; (ii) the influence of shape and departure of heating environment predictions from those for a simple spherical geometry; (iii) assessment of thermal response models for silica subject to intense radiation; and (iv) results for porosity-driven gross fragmentation of meteors, idealized as a collection of smaller objects. Lessons learned from these simulations will be used to help understand the Chelyabinsk meteor entry up to its first point of fragmentation.

Aerothermodynamics↗
NASA NTRS2015

Physics-Based Modeling of Meteor Entry and Breakup

A new research effort at NASA Ames Research Center has been initiated in Planetary Defense, which integrates the disciplines of planetary science, atmospheric entry physics, and physics-based risk assessment. This paper describes work within the new program and is focused on meteor entry and breakup. Over the last six decades significant effort was expended in the US and in Europe to understand meteor entry including ablation, fragmentation and airburst (if any) for various types of meteors ranging from stony to iron spectral types. These efforts have produced primarily empirical mathematical models based on observations. Weaknesses of these models, apart from their empiricism, are reliance on idealized shapes (spheres, cylinders, etc.) and simplified models for thermal response of meteoritic materials to aerodynamic and radiative heating. Furthermore, the fragmentation and energy release of meteors (airburst) is poorly understood. On the other hand, flight of human-made atmospheric entry capsules is well understood. The capsules and their requisite heat shields are designed and margined to survive entry. However, the highest speed Earth entry for capsules is 13 kms (Stardust). Furthermore, Earth entry capsules have never exceeded diameters of 5 m, nor have their peak aerothermal environments exceeded 0.3 atm and 1 kWcm2. The aims of the current work are: (i) to define the aerothermal environments for objects with entry velocities from 13 to 20 kms; (ii) to explore various hypotheses of fragmentation and airburst of stony meteors in the near term; (iii) to explore the possibility of performing relevant ground-based tests to verify candidate hypotheses; and (iv) to quantify the energy released in airbursts. The results of the new simulations will be used to anchor said risk assessment analyses.With these aims in mind, state-of-the-art entry capsule design tools are being extended for meteor entries. We describe: (i) applications of current simulation tools to spherical geometries of diameters ranging from 1 to 100 m for an entry velocity of 20 kms and stagnation pressures ranging from 1 to 100 atm; (ii) the influence of shape and departure of heating environment predictions from those for a simple spherical geometry; (iii) assessment of thermal response models for silica subject to intense radiation; and (iv) results for porosity-driven gross fragmentation of meteors, idealized as a collection of smaller objects. Lessons learned from these simulations will be used to help understand the Chelyabinsk meteor entry up to its first point of fragmentation.

Atmospheric Entry↗
NASA NTRS2015

Physics-Based Modeling of Meteor Entry and Breakup

A new research effort at NASA Ames Research Center has been initiated in Planetary Defense, which integrates the disciplines of planetary science, atmospheric entry physics, and physics-based risk assessment. This paper describes work within the new program and is focused on meteor entry and breakup.Over the last six decades significant effort was expended in the US and in Europe to understand meteor entry including ablation, fragmentation and airburst (if any) for various types of meteors ranging from stony to iron spectral types. These efforts have produced primarily empirical mathematical models based on observations. Weaknesses of these models, apart from their empiricism, are reliance on idealized shapes (spheres, cylinders, etc.) and simplified models for thermal response of meteoritic materials to aerodynamic and radiative heating. Furthermore, the fragmentation and energy release of meteors (airburst) is poorly understood.On the other hand, flight of human-made atmospheric entry capsules is well understood. The capsules and their requisite heatshields are designed and margined to survive entry. However, the highest speed Earth entry for capsules is 13 kms (Stardust). Furthermore, Earth entry capsules have never exceeded diameters of 5 m, nor have their peak aerothermal environments exceeded 0.3 atm and 1 kW/sq cm. The aims of the current work are: (i) to define the aerothermal environments for objects with entry velocities from 13 to 20 kms; (ii) to explore various hypotheses of fragmentation and airburst of stony meteors in the near term; (iii) to explore the possibility of performing relevant ground-based tests to verify candidate hypotheses; and (iv) to quantify the energy released in airbursts. The results of the new simulations will be used to anchor said risk assessment analyses. With these aims in mind, state-of-the-art entry capsule design tools are being extended for meteor entries. We describe: (i) applications of current simulation tools to spherical geometries of diameters ranging from 1 to 100 m for an entry velocity of 20 kms and stagnation pressures ranging from 1 to 100 atm; (ii) the influence of shape and departure of heating environment predictions from those for a simple spherical geometry; (iii) assessment of thermal response models for silica subject to intense radiation; and (iv) results for porosity-driven gross fragmentation of meteors, idealized as a collection of smaller objects. Lessons learned from these simulations will be used to help understand the Chelyabinsk meteor entry up to its first point of fragmentation.

Near-Earth Asteroids↗
NASA NTRS2016

An Overview of Air-Breathing Propulsion Efforts for 2015 SBIR Phase I

NASA's Small Business Innovation Research (SBIR) program focuses on technological innovation by investing in development of innovative concepts and technologies to help NASA mission directorates address critical research needs for Agency programs. This report highlights 24 of the innovative SBIR 2015 Phase I projects that emphasize one of NASA Glenn Research Center's six core competencies-Air-Breathing Propulsion. The technologies cover a wide spectrum of applications such as hybrid nanocomposites for efficient aerospace structures; plasma flow control for drag reduction; physics-based aeroanalysis methods for open rotor conceptual designs; vertical lift by series hybrid power; fast pressure-sensitive paint systems for production wind tunnel testing; rugged, compact, and inexpensive airborne fiber sensor interrogators based on monolithic tunable lasers; and high sensitivity semiconductor sensor skins for multi-axis surface pressure characterization. Each featured technology describes an innovation and technical objective and highlights NASA commercial and industrial applications. This report provides an opportunity for NASA engineers, researchers, and program managers to learn how NASA SBIR technologies could help their programs and projects, and lead to collaborations and partnerships between the small SBIR companies and NASA that would benefit both.

aerospace structures↗