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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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At least 1,207 records · Page 67

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NASA NTRS2016

Design of the Hybrid Wing Body with Nacelle: N3-X Propulsion-Airframe Configuration

The Hybrid Wing Body (HWB) aircraft is of great interest for future transport concepts due to itspromises of reduced aircraft noise, nitrous-oxide emissions, and fuel consumption. A design parameterizationmethod for HWB configurations with mail slot nacelle has been developed for a fast exploration of designspace in conceptual and preliminary design phases of a HWB configuration. A HWB planform model byLaughlin [11] was implemented, and the Class Shape Transformation (CST) airfoil generation method byKulfan [10] was utilized to construct the needed geometry for computational high fidelity aerodynamicsimulations. Geometric constraints for the parameterization such as internal cabin and cargo hold layoutswere imposed on the geometry generation. A CFD simulation was performed for a HWB configurationgenerated by the current geometric modeler, clearly showing a significant effect of the installed nacelle on theflowfield.

Nacelle Installation↗

NASA NTRS1993

bpshape wk4: a computer program that implements a physiological model for analyzing the shape of blood pressure waveforms

We describe the theory and computer implementation of a newly-derived mathematical model for analyzing the shape of blood pressure waveforms. Input to the program consists of an ECG signal, plus a single continuous channel of peripheral blood pressure, which is often obtained invasively from an indwelling catheter during intensive-care monitoring or non-invasively from a tonometer. Output from the program includes a set of parameter estimates, made for every heart beat. Parameters of the model can be interpreted in terms of the capacitance of large arteries, the capacitance of peripheral arteries, the inertance of blood flow, the peripheral resistance, and arterial pressure due to basal vascular tone. Aortic flow due to contraction of the left ventricle is represented by a forcing function in the form of a descending ramp, the area under which represents the stroke volume. Differential equations describing the model are solved by the method of Laplace transforms, permitting rapid parameter estimation by the Levenberg-Marquardt algorithm. Parameter estimates and their confidence intervals are given in six examples, which are chosen to represent a variety of pressure waveforms that are observed during intensive-care monitoring. The examples demonstrate that some of the parameters may fluctuate markedly from beat to beat. Our program will find application in projects that are intended to correlate the details of the blood pressure waveform with other physiological variables, pathological conditions, and the effects of interventions.

Non-NASA Center↗

NASA NTRS1977

A NASTRAN DMAP alter for the coupling of modal and physical coordinate substructures

A method is described to derive a generalized coordinate model consisting of flexible cantilever modes and rigid body modes from a physical coordinate model using a direct matrix abstraction procedure alter to rigid format 3. This model can readily be coupled to other substructures using modal synthesis techniques. It allows the use of a reduced size model for structural analyses while maintaining the capability of recovering the accelerations, forces, stresses, etc., from the original, large, complex model. This output recovery is accomplished with the use of a loads transformation matrix which relates the output parameters to the modal coordinate accelerations. In addition, a method is described to synthesize structural models consisting of hybrid coordinates for use in dynamic response analyses where one structure is described using physical coordinates and the other using generalized modal coordinates.

Wilson, T. L.↗

NASA NTRS2005

Generating Solid Models from Topographical Data

A method of generating solid models of terrain involves the conversion of topographical data into a form useable by a rapid-prototyping (RP) machine. The method was developed to enable the use of the RP machine to make solid models of Martian terrain from Mars Orbiter laser-altimeter topographical data. The method is equally applicable to the generation of models of the terrains of other astronomical bodies, including other planets, asteroids, and Earth. Topographical data describe a terrain in terms of a set of three-dimensional coordinates [e.g., Cartesian (x,y,z) or polar (latitude, longitude, radius) coordinates] of points or nodes on the terrain surface. The input data for the RP machines are required to provide a three-dimensional description, not of a single surface, but of a volume in this case, a ground volume that underlies the terrain surface. The description is required to be in the form of triangular elements that connect the nodes of all the surfaces and that completely bound the volume, with no open areas, no overlap of triangles, and no extraneous geometric elements. The software used in the present model-generation method was written in IDL - an advanced programming language that affords a number of tools, including subroutines that triangularize surfaces. The software creates a volume from the topographical surface data by adding sides to the edges of the terrain surface and joining the sides with a bottom surface. Each of the sides is triangularized by use of IDL subroutines, and then the software searches for extraneous elements and removes them. Topographical data are usually presented in a grid corresponding to polar coordinates, so that a model generated from such data is equivalent to a topographical map in Mercator projection. However an RP machine is fully capable of including the curvature of a planetary body in a model that it makes. Therefore, the software also offers a capability to transform the topographical data to a projection onto a surface having a curvature corresponding to that of the surface of the modeled planet.

Keller, John W.↗

NASA NTRS2010

Analysis of Sting Balance Calibration Data Using Optimized Regression Models

Calibration data of a wind tunnel sting balance was processed using a candidate math model search algorithm that recommends an optimized regression model for the data analysis. During the calibration the normal force and the moment at the balance moment center were selected as independent calibration variables. The sting balance itself had two moment gages. Therefore, after analyzing the connection between calibration loads and gage outputs, it was decided to choose the difference and the sum of the gage outputs as the two responses that best describe the behavior of the balance. The math model search algorithm was applied to these two responses. An optimized regression model was obtained for each response. Classical strain gage balance load transformations and the equations of the deflection of a cantilever beam under load are used to show that the search algorithm s two optimized regression models are supported by a theoretical analysis of the relationship between the applied calibration loads and the measured gage outputs. The analysis of the sting balance calibration data set is a rare example of a situation when terms of a regression model of a balance can directly be derived from first principles of physics. In addition, it is interesting to note that the search algorithm recommended the correct regression model term combinations using only a set of statistical quality metrics that were applied to the experimental data during the algorithm s term selection process.

Ulbrich, N.↗

NASA NTRSDate not supplied

Materials Informatics at NASA GRC: Machine Learning Surrogate Modeling, Data Management, and Integrated Toolsets for Establishing/Maintaining the Digital Thread

Integrated Computational Materials Engineering (ICME) has recently received widespread attention due to its promises in reducing dependence on physical testing for engineering design by relying on simulation, reducing both time and cost to market for various applications. ICME however requires validated multiscale material models, which heavily depend on available test data with full material and test pedigree, including material processing, test and measurement equipment, raw data collection, and analysis methodology and results that is findable and usable, along with integrated, efficient toolsets for effectively passing information across various length and time scales across such models. At the NASA Glenn Research Center under the Transformational Tools and Technologies Project, significant recent efforts have been directed towards establishing the required cyberinfrastructure to enable optimized ICME processes and the design of “fit-for-purpose” materials to achieve the goals outlined in the NASA Vision 2040 report. Such efforts include development of multiscale physics-based material models, which can be used to train highly efficient surrogate machine learning models, development of best practices and infrastructure for effective, traceable materials information management, and development of toolsets that integrate with physics-based codes, machine learning models, and an information management system to enable high throughput of materials data collection and analysis, establishment of digital twins and the digital thread, and automation of the ICME design process for material optimization.

Machine Learning↗

DOE OSTI2024

Voltage Calculations in Secondary Distribution Networks via Physics-Inspired Neural Network Using Smart Meter Data

The increasing penetration of distributed energy resources (DERs) leads to voltage issues across distribution networks, necessitating voltage calculations by utilities. Electric model-free voltage calculation offers an enticing solution. However, most researches mainly focus on primary distribution networks ignoring secondary distribution networks and commonly overlook extreme voltage case calculations, which require the model’s extrapolation abilities. Here, in addressing the gaps, this paper presents a customized physics-inspired neural network (PINN) model, the structure of which is inspired by the derived coupled power flow model of primary-secondary distribution networks. To ensure precision and rapid convergence, a crafted training framework for the PINN model is proposed. The PINN’s “structure-mimetic” design enables superior extrapolation for unseen scenarios and enhances physical information awareness. We demonstrate this through two applications: hosting capacity analysis and customer-transformer connectivity. The effectiveness and advantages of the proposed PINN model are validated on two public testing systems and one utility distribution feeder model.

Distribution network↗

NASA NTRS1993

Contact stress analysis of spiral bevel gears using nonlinear finite element static analysis

A procedure is presented for performing three-dimensional stress analysis of spiral bevel gears in mesh using the finite element method. The procedure involves generating a finite element model by solving equations that identify tooth surface coordinates. Coordinate transformations are used to orientate the gear and pinion for gear meshing. Contact boundary conditions are simulated with gap elements. A solution technique for correct orientation of the gap elements is given. Example models and results are presented.

Bibel, G. D.↗

NASA NTRS1993

Contact stress analysis of spiral bevel gears using nonlinear finite element static analysis

Bibel, G. D.↗

NASA NTRS1995

Contact Stress Analysis of Spiral Bevel Gears Using Finite Element Analysis

Bibel, G. D.↗

DOE OSTI2026

Dynamic Evolution of Mass and Physical Properties of Atmospheric Organic Aerosol under Solar Irradiance

Organic aerosol (OA) particles constitute a substantial fraction of sub-micron particulate mass in the atmosphere and play a critical role in climate system. OA undergoes dynamic aging processes in the atmosphere, with photolytic aging induced by ultraviolet solar irradiance being an important yet poorly characterized mechanism. Knowledge gaps persist regarding the role of volatility transformations during photolytic aging on the OA mass decay kinetics and the evolution of climate-relevant properties, such as hygroscopicity, hindering the model evaluation of OA spatiotemporal distributions and atmospheric budgets. In this study, we conduct isothermal photolytic aging experiments on both laboratory-generated secondary organic aerosols and ambient-collected particles from urban Atlanta, utilizing a high-sensitivity Quartz Crystal Microbalance. Our results reveal that photolytic aging reduces 40–66% of the low-volatility OA mass with lifetimes ranging from 8 to 200 hours under solar irradiance, and 44–92% of the photolytic mass loss is through slow evaporation of semi- or intermediate-volatile products, kinetically limited by their volatility. We observe up to ±50% changes in OA hygroscopicity with the transformation of fresh OA to photo-recalcitrant low-volatility products, associated with changes in oxygen-to-carbon ratio and molecular weight. A kinetic model incorporating photolytic volatility transformation provides a cohesive explanation for the observed photolysis-induced changes in mass, volatility, and hygroscopicity. Our results can help constrain model representation of the dynamic evolutions of mass and climate-relevant properties during photolytic aging processes of the ambient OA, improving our understanding of OA atmospheric behavior and climate impact.

Bai, Bin↗

DOE OSTI2021

Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling

The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.

58 GEOSCIENCES↗

DOE OSTI2023

The high pressure dependence of x-ray induced decomposition of cadmium oxalate

The high proclivity of x rays to destabilize and distort molecular structures has been previously utilized in the synthesis of novel compounds. Here, we show that x-ray induced decomposition of cadmium oxalate induces chemical and structural transformations only at 0.5 and 1 GPa. Using x-ray diffraction and Raman spectroscopy, the synthesized product is identified as cadmium carbonate with cadmium oxalate remnants, which is stable under ambient conditions. At ambient and >1 GPa pressures, only degradation of the electronic density distribution is observed. The transformation kinetics are examined in terms of Avrami’s model, which demonstrates that despite the necessity of high pressure for efficient x-ray induced synthesis of cadmium carbonate, the rate and geometry of structural synthesis in the 0.5–1 GPa pressure range do not depend on the applied pressure. In addition, the possible role of intermolecular distance and molecular mobility in transformation yield is also discussed. Our experimental results indicate that x-ray induced photochemical synthetic pathways can be modulated and optimized by specific parameter selection such as high pressure.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Variance Reduction within Implicit Monte Carlo Thermal Radiation Transport using the Local Importance Function Transform [Slides]

System of equations derived by Fleck and Cummings to model time-dependent thermal radiative transfer (TRT) problems: Solution of radiation specific intensity (I), non-linearly coupled to material internal energy (U); Absorption and re-emission approximated by effective scattering events. Useful for high-energy density physics simulations: i.e., astrophysics, inertial confinement fusion (ICF). Whereas deterministic methods are fully discretized and free of statistical noise, Monte Carlo methods allow for dynamic sampling of the phase space at the price of statistical noise; Monte Carlo methods are also characterized by a slow $\frac{1}{√Ν}$ convergence rate.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1970

Empirical analytic transformations between geographic and corrected geomagnetic coordinates

Based upon a mathematical model of contours of constant corrected geomagnetic latitude in a polar projection of geographic coordinates, analytic equations are developed for converting geographic coordinates to corrected geomagnetic coordinates and vice versa. The equations were programmed for use on a small computer. This treatment is restricted to the Northern Hemisphere.

Comfort, R. H.↗

DOE OSTI2023

Atomic protein structure refinement using all-atom graph representations and SE(3)-equivariant graph transformer

Abstract Motivation The state-of-art protein structure prediction methods such as AlphaFold are being widely used to predict structures of uncharacterized proteins in biomedical research. There is a significant need to further improve the quality and nativeness of the predicted structures to enhance their usability. In this work, we develop ATOMRefine, a deep learning-based, end-to-end, all-atom protein structural model refinement method. It uses a SE(3)-equivariant graph transformer network to directly refine protein atomic coordinates in a predicted tertiary structure represented as a molecular graph. Results The method is first trained and tested on the structural models in AlphaFoldDB whose experimental structures are known, and then blindly tested on 69 CASP14 regular targets and 7 CASP14 refinement targets. ATOMRefine improves the quality of both backbone atoms and all-atom conformation of the initial structural models generated by AlphaFold. It also performs better than two state-of-the-art refinement methods in multiple evaluation metrics including an all-atom model quality score—the MolProbity score based on the analysis of all-atom contacts, bond length, atom clashes, torsion angles, and side-chain rotamers. As ATOMRefine can refine a protein structure quickly, it provides a viable, fast solution for improving protein geometry and fixing structural errors of predicted structures through direct coordinate refinement. Availability and implementation The source code of ATOMRefine is available in the GitHub repository (https://github.com/BioinfoMachineLearning/ATOMRefine). All the required data for training and testing are available at https://doi.org/10.5281/zenodo.6944368.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1999

Cell and molecular biology of simian virus 40: implications for human infections and disease

Simian virus 40 (SV40), a polyomavirus of rhesus macaque origin, was discovered in 1960 as a contaminant of polio vaccines that were distributed to millions of people from 1955 through early 1963. SV40 is a potent DNA tumor virus that induces tumors in rodents and transforms many types of cells in culture, including those of human origin. This virus has been a favored laboratory model for mechanistic studies of molecular processes in eukaryotic cells and of cellular transformation. The viral replication protein, named large T antigen (T-ag), is also the viral oncoprotein. There is a single serotype of SV40, but multiple strains of virus exist that are distinguishable by nucleotide differences in the regulatory region of the viral genome and in the part of the T-ag gene that encodes the protein's carboxyl terminus. Natural infections in monkeys by SV40 are usually benign but may become pathogenic in immunocompromised animals, and multiple tissues can be infected. SV40 can replicate in certain types of simian and human cells. SV40-neutralizing antibodies have been detected in individuals not exposed to contaminated polio vaccines. SV40 DNA has been identified in some normal human tissues, and there are accumulating reports of detection of SV40 DNA and/or T-ag in a variety of human tumors. This review presents aspects of replication and cell transformation by SV40 and considers their implications for human infections and disease pathogenesis by the virus. Critical assessment of virologic and epidemiologic data suggests a probable causative role for SV40 in certain human cancers, but additional studies are necessary to prove etiology.

Review↗

NASA NTRSDate not supplied

Probabilistic Modeling of a Three-Stage Human Landing System Architecture

Unmitigated uncertainties are known to have previously led to failed development programs; in order to combat these uncertainties, risks and their impacts must be understood and handled to ensure program success. In this paper, a probabilistic methodology to handle uncertainties is demonstrated on a three-element Human Landing System (HLS) concept, which allows tracking of current best estimates of the vehicle’s performance and assessment of its robustness against uncertainties. This methodology has two key parts: ﬁrst, the creation of a dynamic architecture model of a three-element HLS concept; and second, its use with surrogate modeling and range estimating techniques to capture and propagate uncertainties. The DYnamic Rocket EQuation Tool (DYREQT), a space systems synthesis and sizing framework used by NASA, was used as to model the HLS architecture. For the probabilistic analysis, uncertainties of interest within the HLS concept were enumerated and represented as parameters within the DYREQT model as inputs for vehicle stages or mission proﬁle events. Range estimating — a probabilistic method that combines Monte Carlo sampling, focus on critical parameters, and heuristics to assess risk and opportunities — is then adapted with operational parameters as well as vehicle parameters in the DYREQT model to capture mission uncertainty alongside vehicle uncertainty. To perform the range estimation portion of this methodology, the DYREQT model was sampled using a Design of Experiments (DoE) to eﬃciently explore the architecture design space with respect to the set of uncertainty parameters. Then, the results were used to create surrogate models, multivariate regressions that can visualize hypercube trends in the design space, of the architecture with respect to the uncertainty parameters. Using a correlation matrix constructed for the uncertainty parameters, previously independent samples were transformed to perform a Correlated Monte Carlo on the surrogate models. This probabilistic methodology was proved to provide insight into the underlying uncertainties of the three-element HLS architecture.

Stephanie Y Zhu↗