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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Hot planets around cool stars – two short-period mini-Neptunes transiting the late K-dwarf TOI-1260

We present the discovery and characterization of two sub-Neptunes in close orbits, as well as a tentative outer planet of a similar size, orbiting TOI-1260 – a low metallicity K6 V dwarf star. Photometry from Transiting Exoplanet Survey Satellite(TESS) yields radii of R(b) = 2.33 ± 0.10 and R(c) = 2.82 ± 0.15 Rꚛ, and periods of 3.13 and 7.49 d for TOI-1260 b and TOI-1260 c, respectively. We combined the TESS data with a series of ground-based follow-up observations to characterize the planetary system. From HARPS-N high-precision radial velocities we obtain M(b) = 8.6(+1.4,−1.5) and M(c) = 11.8(+3.4,−3.2) Mꚛ. The star is moderately active with a complex activity pattern, which necessitated the use of Gaussian process regression for both the light-curve detrending and the radial velocity modelling, in the latter case guided by suitable activity indicators. We successfully disentangle the stellar-induced signal from the planetary signals, underlining the importance and usefulness of the Gaussian process approach. We test the system’s stability against atmospheric photoevaporation and find that the TOI-1260 planets are classic examples of the structure and composition ambiguity typical for the 2–3 Rꚛ range.

I Y Georgieva↗
NASA NTRS1975

Tectonic analysis of folds in the Colorado plateau of Arizona

Structural mapping and analysis of folds in Phanerozoic rocks in northern Arizona, using LANDSAT-1 imagery, yielded information for a tectonic model useful in identifying regional fracture zones within the Colorado Plateau tectonic province. Since the monoclines within the province developed as a response to differential movements of basement blocks along high-angle faults, the monoclinal fold pattern records the position and trend of many elements of the regional fracture system. The Plateau is divided into a mosaic of complex, polyhedral crustal blocks whose steeply dipping faces correspond to major fracture zones. Zones of convergence and changes in the trend of the monoclinal traces reveal the corners of the blocks. Igneous (and salt) diapirs have been emplaced into many of the designated zones of crustal weakness. As loci of major fracturing, folding, and probably facies changes, the fractures exert control on the entrapment of oil and gas.

Davis, G. H.↗
NASA NTRS1979

Global services systems - Space communication

The requirements projected to the year 2000 for space-based global service systems, including both personal communications and innovative services, are developed based on historic trends and anticipated worldwide demographic and economic growth patterns. The growing demands appear to be best satisfied by developing larger, more sophisticated space systems in order to reduce the size, complexity, and expense of ground terminals. The availability of low-cost ground terminals will, in turn, further stimulate the generation of new services and new customers.

Shepphird, F. H.↗
NASA NTRS1985

Anatomic evidence for peripheral neural processing in mammalian graviceptors

Ultrastructural study of utricular and saccular maculas demonstrates that their innervation patterns are complex. There is a clustering of type I and type II hair cells based upon a sharing of afferents, a system of efferent-type beaded fibers that is of intramacular (mostly calyceal) origin, and a plexus-like arrangement of afferents and efferents at many sites in the neuroepithelium. Results suggest that information concerning linear acceleration is processed peripherally, beginning at the hair cell level, before being sent to the central nervous system. The findings may supply a structural basis for peripheral adaptation to a constant stimulus, and for lateral inhibition to improve signal relative to noise.

Ross, M. D.↗
NASA NTRS1995

AMCC casting development, volume 2

PCC successfully cast and performed nondestructive testing, FPI and x-ray, on seventeen AMCC castings. Destructive testing, lab analysis and chemical milling, was performed on eleven of the castings and the remaining six castings were shipped to NASA or Aerojet. Two of the six castings shipped, lots 015 and 016, were fully processed per blueprint requirements. PCC has fully developed the gating and processing parameters of this part and feels the part could be implemented into production, after four more castings have been completed to ensure the repeatability of the process. The AMCC casting has been a technically challenging part due to its size, configuration, and alloy type. The height and weight of the wax pattern assembly necessitated the development of a hollow gating system to ensure structural integrity of the shell throughout the investment process. The complexity in the jacket area of the casting required the development of an innovative casting technology that PCC has termed 'TGC' or thermal gradient control. This method of setting up thermal gradients in the casting during solidification represents a significant process improvement for PCC and has been successfully implemented on other programs. The alloy, JBK75, is a relatively new alloy in the investment casting arena and required our engineering staff to learn the gating, processing, and dimensional characteristics of the material.

Source record↗
NASA NTRS1995

AMCC casting development. Volume 1: Executive Summary

The Advanced Combustion Chamber Casting (AMCC) has been a technically challenging part due to its size, configuration, and alloy type. The height and weight of the wax pattern assembly necessitated the development of a hollow gating system to ensure structural integrity of the shell throughout the investment process. The complexity in the jacket area of the casting required the development of an innovative casting technology that PCC has termed 'TGC' or Thermal Gradient Control. This method, of setting up thermal gradients in the casting during solidification, represents a significant process improvement for PCC and has been successfully implemented on other programs. Metallurgical integrity of the final four castings was very good. Only the areas of the parts that utilized 'TGC Shape & Location System #2' showed any significant areas of microshrinkage when evaluated by non-destructive tests. Alumina oxides detected by FPI on the 'float' surfaces (top sid surfaces of the casting during solidification) of the part were almost entirely less than the acceptance criteria of .032 inches in diameter. Destructive chem mill of the castings was required to determine the effect of the process variables used during the processing of these last four parts (with the exception of the 'Shape & Location of TGC' variable).

Source record↗
NASA NTRS1991

Design of thrust vectoring exhaust nozzles for real-time applications using neural networks

Thrust vectoring continues to be an important issue in military aircraft system designs. A recently developed concept of vectoring aircraft thrust makes use of flexible exhaust nozzles. Subtle modifications in the nozzle wall contours produce a non-uniform flow field containing a complex pattern of shock and expansion waves. The end result, due to the asymmetric velocity and pressure distributions, is vectored thrust. Specification of the nozzle contours required for a desired thrust vector angle (an inverse design problem) has been achieved with genetic algorithms. This approach is computationally intensive and prevents the nozzles from being designed in real-time, which is necessary for an operational aircraft system. An investigation was conducted into using genetic algorithms to train a neural network in an attempt to obtain, in real-time, two-dimensional nozzle contours. Results show that genetic algorithm trained neural networks provide a viable, real-time alternative for designing thrust vectoring nozzles contours. Thrust vector angles up to 20 deg were obtained within an average error of 0.0914 deg. The error surfaces encountered were highly degenerate and thus the robustness of genetic algorithms was well suited for minimizing global errors.

Prasanth, Ravi K.↗
NASA NTRS1994

Empirical modeling for intelligent, real-time manufacture control

Artificial neural systems (ANS), also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such, they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, nonlinear, mathematical relationship or transform. These constructs have two significant properties that have proven useful to the authors in signal processing and process modeling: noise tolerance and complex pattern recognition. Specifically, the authors have developed a new network learning algorithm that has resulted in the successful application of ANS's to high speed signal processing and to developing models of highly complex processes. Two of the applications, the Weld Bead Geometry Control System and the Welding Penetration Monitoring System, are discussed in the body of this paper.

Xu, Xiaoshu↗
NASA NTRS1975

Optimization of system reliability by the sequential unconstrained minimization technique

The reliability of a complex system was optimized by a new approach for implementing the sequential unconstrained minimization technique (SUMT) with the aid of Hooke and Jeeves pattern search and heuristic programming. Two optimization problems were considered. In the first, the system reliability was maximized subject to a nonlinear weight constraint. In the second, the weight of the system was minimized without violating the requirements of the minimal system reliability and the minimum reliability for each component. The sensitivities of the system reliability and that of the system weight to the reliability of each component were determined under optimal conditions.

Hwang, C. L.↗
NASA NTRS2001

Interface Pattern Selection in Directional Solidification

The central focus of this research is to establish key scientific concepts that govern the selection of cellular and dendritic patterns during the directional solidification of alloys. Ground-based studies have established that the conditions under which cellular and dendritic microstructures form are precisely where convection effects are dominant in bulk samples. Thus, experimental data can not be obtained terrestrially under pure diffusive regime. Furthermore, reliable theoretical models are not yet possible which can quantitatively incorporate fluid flow in the pattern selection criterion. Consequently, microgravity experiments on cellular and dendritic growth are designed to obtain benchmark data under diffusive growth conditions that can be quantitatively analyzed and compared with the rigorous theoretical model to establish the fundamental principles that govern the selection of specific microstructure and its length scales. In the cellular structure, different cells in an array are strongly coupled so that the cellular pattern evolution is controlled by complex interactions between thermal diffusion, solute diffusion and interface effects. These interactions give infinity of solutions, and the system selects only a narrow band of solutions. The aim of this investigation is to obtain benchmark data and develop a rigorous theoretical model that will allow us to quantitatively establish the physics of this selection process.

Trivedi, Rohit↗
NASA NTRS1982

Advances in information extraction techniques

Sundry recent developments are presented which show some potential for affecting the automatic extraction of information from remotely sensed data. Pattern representations more abstract than Euclidean vector spaces offer some hope of unifying structural and decision theoretical approaches. The estimation of expected classification error rates is becoming more sophisticated and rigorous, but useful finite-sample results for nonparametric distributions appear unobtainable. Focus on computational complexity allows comparison of algorithms, while software engineering techniques reduce the effort necessary to develop and maintain complex image processing systems. Advances in computer systems architecture, commercial database technology, and man-machine communications should be closely monitored by the remote sensing community. A NASA-sponsored recommendation for research directions in mathematical pattern recognition are offered.

Nagy, G.↗
NASA NTRS1998

Multi-Scale Fractal Analysis of Image Texture and Pattern

Fractals embody important ideas of self-similarity, in which the spatial behavior or appearance of a system is largely independent of scale. Self-similarity is defined as a property of curves or surfaces where each part is indistinguishable from the whole, or where the form of the curve or surface is invariant with respect to scale. An ideal fractal (or monofractal) curve or surface has a constant dimension over all scales, although it may not be an integer value. This is in contrast to Euclidean or topological dimensions, where discrete one, two, and three dimensions describe curves, planes, and volumes. Theoretically, if the digital numbers of a remotely sensed image resemble an ideal fractal surface, then due to the self-similarity property, the fractal dimension of the image will not vary with scale and resolution. However, most geographical phenomena are not strictly self-similar at all scales, but they can often be modeled by a stochastic fractal in which the scaling and self-similarity properties of the fractal have inexact patterns that can be described by statistics. Stochastic fractal sets relax the monofractal self-similarity assumption and measure many scales and resolutions in order to represent the varying form of a phenomenon as a function of local variables across space. In image interpretation, pattern is defined as the overall spatial form of related features, and the repetition of certain forms is a characteristic pattern found in many cultural objects and some natural features. Texture is the visual impression of coarseness or smoothness caused by the variability or uniformity of image tone or color. A potential use of fractals concerns the analysis of image texture. In these situations it is commonly observed that the degree of roughness or inexactness in an image or surface is a function of scale and not of experimental technique. The fractal dimension of remote sensing data could yield quantitative insight on the spatial complexity and information content contained within these data. A software package known as the Image Characterization and Modeling System (ICAMS) was used to explore how fractal dimension is related to surface texture and pattern. The ICAMS software was verified using simulated images of ideal fractal surfaces with specified dimensions. The fractal dimension for areas of homogeneous land cover in the vicinity of Huntsville, Alabama was measured to investigate the relationship between texture and resolution for different land covers.

Emerson, Charles W.↗
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 NTRS2008

Generating Customized Verifiers for Automatically Generated Code

Program verification using Hoare-style techniques requires many logical annotations. We have previously developed a generic annotation inference algorithm that weaves in all annotations required to certify safety properties for automatically generated code. It uses patterns to capture generator- and property-specific code idioms and property-specific meta-program fragments to construct the annotations. The algorithm is customized by specifying the code patterns and integrating them with the meta-program fragments for annotation construction. However, this is difficult since it involves tedious and error-prone low-level term manipulations. Here, we describe an annotation schema compiler that largely automates this customization task using generative techniques. It takes a collection of high-level declarative annotation schemas tailored towards a specific code generator and safety property, and generates all customized analysis functions and glue code required for interfacing with the generic algorithm core, thus effectively creating a customized annotation inference algorithm. The compiler raises the level of abstraction and simplifies schema development and maintenance. It also takes care of some more routine aspects of formulating patterns and schemas, in particular handling of irrelevant program fragments and irrelevant variance in the program structure, which reduces the size, complexity, and number of different patterns and annotation schemas that are required. The improvements described here make it easier and faster to customize the system to a new safety property or a new generator, and we demonstrate this by customizing it to certify frame safety of space flight navigation code that was automatically generated from Simulink models by MathWorks' Real-Time Workshop.

Denney, Ewen↗
NASA NTRS1999

Microgravity Two-Phase Flow Transition

Two-phase flows under microgravity condition find a large number of important applications in fluid handling and storage, and spacecraft thermal management. Specifically, under microgravity condition heat transfer between heat exchanger surfaces and fluids depend critically on the distribution and interaction between different fluid phases which are often qualitatively different from the gravity-based systems. Heat transfer and flow analysis in two-phase flows under these conditions require a clear understanding of the flow pattern transition and development of appropriate dimensionless scales for its modeling and prediction. The physics of this flow is however very complex and remains poorly understood. This has led to various inadequacies in flow and heat transfer modeling and has made prediction of flow transition difficult in engineering design of efficient thermal and flow systems. In the present study the available published data for flow transition under microgravity condition are considered for mapping. The transition from slug to annular flow and from bubbly to slug flow are mapped using dimensionless variable combination developed in a previous study by the authors. The result indicate that the new maps describe the flow transitions reasonably well over the range of the data available. The transition maps are examined and the results are discussed in relation to the presumed balance of forces and flow dynamics. It is suggested that further evaluation of the proposed flow and transition mapping will require a wider range of microgravity data expected to be made available in future studies.

Parang, M.↗
NASA NTRS1974

On the recognition of complex structures: Computer software using artificial intelligence applied to pattern recognition

An approach to simultaneous interpretation of objects in complex structures so as to maximize a combined utility function is presented. Results of the application of a computer software system to assign meaning to regions in a segmented image based on the principles described in this paper and on a special interactive sequential classification learning system, which is referenced, are demonstrated.

Yakimovsky, Y.↗
NASA NTRS2012

A First Order Wavefront Estimation Algorithm for P1640 Calibrator

P1640 calibrator is a wavefront sensor working with the P1640 coronagraph and the Palomar 3000 actuator adaptive optics system (P3K) at the Palomar 200 inch Hale telescope. It measures the wavefront by interfering post-coronagraph light with a reference beam formed by low-pass filtering the blocked light from the coronagraph focal plane mask. The P1640 instrument has a similar architecture to the Gemini Planet Imager (GPI) and its performance is currently limited by the quasi-static speckles due to non-common path wavefront errors, which comes from the non-common path for the light to arrive at the AO wavefront sensor and the coronagraph mask. By measuring the wavefront after the coronagraph mask, the non-common path wavefront error can be estimated and corrected by feeding back the error signal to the deformable mirror (DM) of the P3K AO system. Here, we present a first order wavefront estimation algorithm and an instrument calibration scheme used in experiments done recently at Palomar observatory. We calibrate the P1640 calibrator by measuring its responses to poking DM actuators with a sparse checkerboard pattern at different amplitudes. The calibration yields a complex normalization factor for wavefront estimation and establishes the registration of the DM actuators at the pupil camera of the P1640 calibrator, necessary for wavefront correction. Improvement of imaging quality after feeding back the wavefront correction to the AO system demonstrated the efficacy of the algorithm.

Wavefront sensing↗
NASA NTRS1994

Design of a walking robot

Carnegie Mellon University's Autonomous Planetary Exploration Program (APEX) is currently building the Daedalus robot; a system capable of performing extended autonomous planetary exploration missions. Extended autonomy is an important capability because the continued exploration of the Moon, Mars and other solid bodies within the solar system will probably be carried out by autonomous robotic systems. There are a number of reasons for this - the most important of which are the high cost of placing a man in space, the high risk associated with human exploration and communication delays that make teleoperation infeasible. The Daedalus robot represents an evolutionary approach to robot mechanism design and software system architecture. Daedalus incorporates key features from a number of predecessor systems. Using previously proven technologies, the Apex project endeavors to encompass all of the capabilities necessary for robust planetary exploration. The Ambler, a six-legged walking machine was developed by CMU for demonstration of technologies required for planetary exploration. In its five years of life, the Ambler project brought major breakthroughs in various areas of robotic technology. Significant progress was made in: mechanism and control, by introducing a novel gait pattern (circulating gait) and use of orthogonal legs; perception, by developing sophisticated algorithms for map building; and planning, by developing and implementing the Task Control Architecture to coordinate tasks and control complex system functions. The APEX project is the successor of the Ambler project.

Whittaker, William↗