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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Application of Sparse Identification of Nonlinear Dynamics for Physics-Informed Learning

Advances in machine learning and deep neural networks has enabled complex engineering tasks like image recognition, anomaly detection, regression, and multi-objective optimization, to name but a few. The complexity of the algorithm architecture, e.g., the number of hidden layers in a deep neural network, typically grows with the complexity of the problems they are required to solve, leaving little room for interpreting (or explaining) the path that results in a specific solution. This drawback is particularly relevant for autonomous aerospace and aviation systems, where certifications require a complete understanding of the algorithm behavior in all possible scenarios. Including physics knowledge in such data-driven tools may improve the interpretability of the algorithms, thus enhancing model validation against events with low probability but relevant for system certification. Such events include, for example, spacecraft or aircraft sub-system failures, for which data may not be available in the training phase. This paper investigates a recent physics-informed learning algorithm for identification of system dynamics, and shows how the governing equations of a system can be extracted from data using sparse regression. The learned relationships can be utilized as a surrogate model which, unlike typical data-driven surrogate models, relies on the learned underlying dynamics of the system rather than large number of fitting parameters. The work shows that the algorithm can reconstruct the differential equations underlying the observed dynamics using a single trajectory when no uncertainty is involved. However, the training set size must increase when dealing with stochastic systems, e.g., nonlinear dynamics with random initial conditions.

Corbetta, Matteo↗
NASA NTRSDate not supplied

Retrieve Methane from IR sounder measurements Using Machine Learning-Enhanced Physical Inversion

The sensitivity of IR sounder measurements to atmospheric CH 4 is often limited due to interferences from signals of other trace gases, insufficient thermal contrast, and cloud blockage. In order to resolve the geographical and vertical distribution of atmospheric CH 4 profiles, accurate scene-dependent a priori information is critically needed to support an optimal estimation method-based physical inversion scheme. Following the principles of indexing, representation, and retrieval, a spectral fingerprinting methodology is developed to address the needs for both accuracy and computational efficiency in sounder-based CH 4 retrieval. Within this framework, a clustering method based on machine learning is first employed to stratify and identify the a priori state within the pre-constructed database, using optimized spectral radiances as predictors. The corresponding radiative kernel is then used to establish the physical inversion scheme for finding the solution. High-quality data from CH 4 data assimilation systems like the Carbon-Tracker and the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, as well as the state-of-art sounder products are used to build the training database, including radiative kernels. We will demonstrate the results retrieved from CrIS observations and the associated validation work.

Wan Wu↗
NASA NTRS1985

Modeling solar magnetic structures

Some ideas in the theoretical study of force-free magnetic fields and magnetostatic fields, which are relevant to the effort of using magnetograph data as inputs to model the quasi-static, large-scale magnetic structures in the solar atmosphere are discussed. Basic physical principles will be emphasized. An attempt will be made to assess what we may learn, physically, from the models based on these ideas. There is prospect for learning useful physics and this ought to be an incentive for intensifying the efforts to improve vector magnetograph technology and to solve the basic radiative-transfer problems encountered in the interpretation of magnetograph raw data.

Low, B. C.↗
NASA NTRS2021

Machine Learning the COSMO Model for Predicting Thermodynamics of Electrolyte Mixtures

Bottom-up design of electrolyte mixtures for battery systems requires predicting macro thermodynamic properties from molecular constituents. For instance, molten salt electrolyte batteries require conditions far above room temperature to operate. Therefore, discovering mixtures with increasingly lower eutectic melting points is desirable. A model that can approximate chemical activity is a valuable tool to search through the vast compositional design space. Machine learning can predict properties of materials such as vibrational free energies, electronic energy gaps, and thermal conductivities. Moreover, they can learn physical models such as interatomic potentials. The COSMO-SAC model uses theory and empirical parameterization to predict liquid-vapor and liquid-solid properties using first-principles calculations. However, obtaining activity coefficients required for parameterizing the COSMO-SAC model is costly and limited to a select chemical space. In this work, we explored if machine learning methods could improve the COSMO-SAC model and bridge density functional theory calculations to liquid phase thermodynamic properties. Our data-driven approach uses existing databases for sigma-profiles of organic solvents and reconciles their methodological differences via ensemble averaging. First, an optimal machine learning model is constructed for each dataset. Our machine learning algorithms use the sigma-profile as an input feature to predict binary mixtures' activity coefficients using multi-output regression. Each dataset uses different choices of functionals, methods, and basis sets. Therefore, our ensemble model attempts to predict corrected activity coefficients given the combination of all the model outputs. The activity coefficients used for training are generated using the COSMO-SAC model. This approach enables the extraction of meaningful information from the existing datasets to improve the COSMO-SAC model for obtaining thermodynamic properties of electrolyte mixtures. With the liquid phase activities, we can identify electrolyte mixtures that meet desired phase equilibria conditions.

Thermodynamics↗
NASA NTRS1993

A multimedia adult literacy program: Combining NASA technology, instructional design theory, and authentic literacy concepts

For a number of years, the Software Technology Branch of the Information Systems Directorate has been involved in the application of cutting edge hardware and software technologies to instructional tasks related to NASA projects. The branch has developed intelligent computer aided training shells, instructional applications of virtual reality and multimedia, and computer-based instructional packages that use fuzzy logic for both instructional and diagnostic decision making. One outcome of the work on space-related technology-supported instruction has been the creation of a significant pool of human talent in the branch with current expertise on the cutting edges of instructional technologies. When the human talent is combined with advanced technologies for graphics, sound, video, CD-ROM, and high speed computing, the result is a powerful research and development group that both contributes to the applied foundations of instructional technology and creates effective instructional packages that take advantage of a range of advanced technologies. Several branch projects are currently underway that combine NASA-developed expertise to significant instructional problems in public education. The branch, for example, has developed intelligent computer aided software to help high school students learn physics and staff are currently working on a project to produce educational software for young children with language deficits. This report deals with another project, the adult literacy tutor. Unfortunately, while there are a number of computer-based instructional packages available for adult literacy instruction, most of them are based on the same instructional models that failed these students when they were in school. The teacher-centered, discrete skill and drill-oriented, instructional strategies, even when they are supported by color computer graphics and animation, that form the foundation for most of the computer-based literacy packages currently on the market may not be the most effective or most desirable way to use computer technology in literacy programs. This project is developing a series of instructional packages that are based on a different instructional model - authentic instruction. The instructional development model used to create these packages is also different. Instead of using the traditional five stage linear, sequential model based on behavioral learning theory, the project uses the recursive, reflective design and development model (R2D2) that is based on cognitive learning theory, particularly the social constructivism of Vygotsky, and an epistemology based on critical theory. Using alternative instructional and instructional development theories, the result of the summer faculty fellowship is LiteraCity, a multimedia adult literacy instructional package that is a simulation of finding and applying for a job. The program, which is about 120 megabytes, is distributed on CD-ROM.

Willis, Jerry W.↗
NASA NTRS1985

Computer architectures for computational physics work done by Computational Research and Technology Branch and Advanced Computational Concepts Group

Slides are reproduced that describe the importance of having high performance number crunching and graphics capability. They also indicate the types of research and development underway at Ames Research Center to ensure that, in the near term, Ames is a smart buyer and user, and in the long-term that Ames knows the best possible solutions for number crunching and graphics needs. The drivers for this research are real computational physics applications of interest to Ames and NASA. They are concerned with how to map the applications, and how to maximize the physics learned from the results of the calculations. The computer graphics activities are aimed at getting maximum information from the three-dimensional calculations by using the real time manipulation of three-dimensional data on the Silicon Graphics workstation. Work is underway on new algorithms that will permit the display of experimental results that are sparse and random, the same way that the dense and regular computed results are displayed.

Source record↗
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 NTRS2018

Adventures in Low Disc Loading VTOL Design

This memoir covers the first eight years of my 37 year career in VTOL aircraft design. It starts with family and how I came to be an engineer with a passion for aviation and a desire to make a difference. At MIT I acquired a solid understanding of basic physics, learned the basics of the various engineering disciplines and gained design experience. After over a decade on the East Coast I was homesick for Northern California. I decided to take a chance on working for the government instead of industry in order to return home. I was hired by Dr. Richard M. Carlson in March 1975 and joined a wonderful Army/NASA technical environment. The Interservice Helicopter Commonality Study was an important introduction to Joint Service aircraft design. The Advanced Attack Helicopter Source Selection Evaluation Board was an opportunity to learn acquisition system fundamentals and to lead a small team in a major technical evaluation. The Advanced Scout Helicopter Concept Formulation was an opportunity to learn how an aircraft development program is created and it formed a partnership between Dr. Carlson's Labs and Charlie Crawford's Development and Qualification directorate. The Army was Executive Service for the first year (1982) of the Joint Services Advanced Vertical Lift Aircraft (JVX) program. The JVX Joint Technology Assessment concluded that there was at least one design configuration, the tilt rotor, which could satisfy all JVX mission requirements with a high degree of inter-service commonality. The Navy became Executive Service at the end of the year and promptly released a JVX RFP to industry. This RFP resulted in the V-22 Osprey tilt rotor as the third type of VTOL aircraft to enter production and service. I was very lucky to have a useful role early in this program.

VTOL Design↗
NASA NTRSDate not supplied

Developing and Testing a Physics Guided Machine Learning NeuralNetwork to Predict Tonal Noise Emitted by a Propeller

Artificial neural networks offer a highly nonlinear and adaptive model for predicting complex interactions between input-output parameters. However, these networks require large datasets which often exceed practical considerations in modeling experimental results. To alleviate the dataset size requirement, a method known as physics guided machine learning has been applied to construct several neural networks for predicting propeller tonal noise in the time domain over a broad range of flight conditions. Three space-filling designs, namely, Latin-Hypercube, Sphere-Packing, and Grid-Space, were used to distribute points throughout the input parameter space encompassing nondimensional flight conditions and observer geometry. Each neural network’s performance was validated by conditions outside of the training set and compared to the Propeller Analysis System tool from the NASA Aircraft Noise Prediction Program. Compared to the Grid-Space input design, the Latin-Hypercube and the Sphere-Packing designs provided a better representation of the domain for training. Regarding the network archetype, a fully connected perceptron was found to outperform the partially connected perceptron in their ability to predict tonal noise for small datasets. The black-box nature of these neural networks was also explored to understand how the networks constructed the waveform and understand why some network designs produce better models.

Propeller noise↗
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 NTRS1997

Learning about jet physics from gamma-ray blazars

The spectral properties of the MeV radiation-dominated blazars are used to place constraints on the physical parameters of relativistic jets in quasars. The luminosities and positions of high energy and low energy spectral components are used to derive constraints on the jet speeds, magnetic fields and the distances at which most of the nonthermal radiation is produced. By comparing the theoretically predicted bulk-Compton radiation with the observed soft X-ray luminosities, upper limits on the optical thickness and lower limits on the distance where the relativistic jet is formed and collimated, are identified. The results show that these jets should be Thomson optically thin and, in the case of gamma ray production dominated by the external radiation Compton process, favor proton-electron jets. Weaker constraints on the pair production are provided if the gamma ray production is dominated by the synchrotron self Compton (SSC) process. The values of the jet Lorentz factors predicted by the SSC models are smaller than those observed in quasars.

Sikora, M.↗
NASA NTRS2006

The NASA Radiation Interuniversity Science and Engineering(RaISE) Project: A Model for Inter-collaboration and Distance Learning in Radiation Physics and Nuclear Engineering

This viewgraph document reviews the Radiation Interuniversity Science and Engineering (RaISE) Project, which is a project that has as its goals strengthening and furthering the curriculum in radiation sciences at two Historically Black Colleges and Universities (HBCU), Prairie View A&M University and Texas Southern University. Those were chosen in part because of the proximity to NASA Johnson Space Center, a lead center for the Space Radiation Health Program. The presentation reviews the courses that have been developed, both in-class, and on-line.

Denkins, Pamela S.↗