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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

SPIKE-Dx : A Low-Power High-Throughput Fault Diagnostics Tool using Spiking Neural Networks for Constrained Systems

Diagnostic systems are important for many aerospace systems, which are severely limited in available power, like cubesats or UAVs. Therefore, traditional diagnostics systems cannot be used due to their substantial footprint and constraints. In this paper, we present our very low power diagnostic tool SPIKE-DX to monitor critical systems with constrained computational and energy resources. This is made possible through spiking neural networks (SNNs), which are executable within optimized simulation environments and further implemented on on cutting-edge neuromorphic hardware. Based upon FMEA (Failure Mode and Effect Analysis) framework, Diagnostic Bayesian Networks (DBNs) can be constructed that provide powerful means for diagnostic reasoning. In this paper, we describe such DBNs and a method to automatically translate the DBN into highly structured networks of spiking neurons for execution in SPIKE-DX.

Spiking Neural Networks↗
NASA NTRS1991

Dynamic edge warping - An experimental system for recovering disparity maps in weakly constrained systems

Dynamic edge warping (DEW), a technique for recovering reasonably accurate disparity maps from uncalibrated stereo image pairs, is presented. No precise knowledge of the epipolar camera geometry is assumed. The technique is embedded in a system including structural stereopsis on the front end and robust estimation in digital photogrammetry on the other for the purpose of self-calibrating stereo image pairs. Once the relative camera orientation is known, the epipolar geometry is computed and the system can use this information to refine its representation of the object space. Such a system will find application in the autonomous extraction of terrain maps from stereo aerial photographs, for which camera position and orientation are unknown a priori, and for online autonomous calibration maintenance for robotic vision applications, in which the cameras are subject to vibration and other physical disturbances after calibration. This work thus forms a component of an intelligent system that begins with a pair of images and, having only vague knowledge of the conditions under which they were acquired, produces an accurate, dense, relative depth map. The resulting disparity map can also be used directly in some high-level applications involving qualitative scene analysis, spatial reasoning, and perceptual organization of the object space. The system as a whole substitutes high-level information and constraints for precise geometric knowledge in driving and constraining the early correspondence process.

Boyer, K. L.↗
NASA NTRSDate not supplied

Habitability Potential of Martian Hydrothermal Systems Constrained from Exploration of a Magma-Sediment Hydrothermal System in the Colorado Plateau

Mars has a crust predominately composed of basalt, which displays wide-spread processes including volcanic hydrothermal systems and reference within. There is an abundance of evidence of previous low temperature hydrous alteration; however, finding evidence of high-temperature hydrothermal activity has been challenging. Therefore, we are investigating a mafic dike and the surrounding metamorphic contact zone to constrain how to find such systems on Mars and their habitability potential on Earth and by extension Mars. DC Dike (DCD) is in the Colorado Plateau, in south-central Utah, and intruded the Entrada formation Sandstone between 3.8 and 4.6 Ma. We chose Mars analog instrumentation to make our results directly applicable to those on Mars: Visible-Near Infrared reflectance spectroscopy (VNIR), X-Ray Diffraction (XRD), and Scanning Electron Microscope (SEM). 21 samples were analyzed with an Ore Express VNIR with wavelengths from 350-2500 nm. Bulk mineralogy is determined by using a Panalytical XRD including for clay fraction and soil separation analysis. Finally, samples are prepped as thin sections and will be analyzed using a SEM. All these techniques will be used to constrain: the mineralogy of the system, how the hydrothermal system cooled, the habitability of the cooling system, and how DCD relates to similar dikes in the area and on Mars. All of these results will then be compared to similar systems Mars, and if they too could have been habitable environments.

R. A. Slank↗
NASA NTRS2007

Corrigendum: New Form of Kane's Equations of Motion for Constrained Systems

A correction to the previously published article "New Form of Kane's Equations of Motion for Constrained Systems" is presented. Misuse of the transformation matrix between time rates of change of the generalized coordinates and generalized speeds (sometimes called motion variables) resulted in a false conclusion concerning the symmetry of the generalized inertia matrix. The generalized inertia matrix (sometimes referred to as the mass matrix) is in fact symmetric and usually positive definite when one forms nonminimal Kane's equations for holonomic or simple nonholonomic systems, systems subject to nonlinear nonholonomic constraints, and holonomic or simple nonholonomic systems subject to impulsive constraints according to Refs. 1, 2, and 3, respectively. The mass matrix is of course symmetric when one forms minimal equations for holonomic or simple nonholonomic systems using Kane s method as set forth in Ref. 4.

Roithmayr, Carlos M.↗
NASA NTRS1982

Solution of nonlinear multivariable constrained systems using a gradient projection digital algorithm that is insensitive to the initial state

Optimal digital control of nonlinear multivariable constrained systems was studied. The optimal controller in the form of an algorithm was improved and refined by reducing running time and storage requirements. A particularly difficult system of nine nonlinear state variable equations was chosen as a test problem for analyzing and improving the controller. Lengthy analysis, modeling, computing and optimization were accomplished. A remote interactive teletype terminal was installed. Analysis requiring computer usage of short duration was accomplished using Tuskegee's VAX 11/750 system.

Hargrove, A.↗
NASA NTRS2022

Chance-Constrained System Identification of Nonlinear Discrete Systems with Safety and Stability Guarantees

This paper presents a discrete-time nonlinear system identification method while satisfying the stability and safety properties of the system with high probability. An Extreme Learning Machine (ELM) is used with a Gaussian assumption on the function reconstruction error. A quadratically constrained quadratic program (QCQP) is developed with probabilistic safety and stability constraints that are only required to be satisfied at sampled points inside the invariant region. The proposed method is validated using two simulation examples: a two degrees-of-freedom (DoF) robot manipulator with constraints on joint angles whose trajectories are guaranteed to remain inside a safe set and on motion trajectories data of a hand-drawn shape.

Iman Salehi↗
NASA NTRS1982

Constrained multibody system dynamics: An automated approach

The governing equations for constrained multibody systems are formulated in a manner suitable for their automated, numerical development and solution. The closed loop problem of multibody chain systems is addressed. The governing equations are developed by modifying dynamical equations obtained from Lagrange's form of d'Alembert's principle. The modifications is based upon a solution of the constraint equations obtained through a zero eigenvalues theorem, is a contraction of the dynamical equations. For a system with n-generalized coordinates and m-constraint equations, the coefficients in the constraint equations may be viewed as constraint vectors in n-dimensional space. In this setting the system itself is free to move in the n-m directions which are orthogonal to the constraint vectors.

Kamman, J. W.↗
NASA NTRS2024

Distribution of -92 Nb in the Early Solar System

Constraining the initial abundances of short-lived radionuclides in planetary materials helps determine the spatial and temporal distribution of interstellar material added during the initial stages of Solar System formation.

short-lived chronometer↗
NASA NTRS1991

Controlling state explosion during automatic verification of delay-insensitive and delay-constrained VLSI systems using the POM verifier

Delay-insensitive VLSI systems have a certain appeal on the ground due to difficulties with clocks; they are even more attractive in space. We answer the question, is it possible to control state explosion arising from various sources during automatic verification (model checking) of delay-insensitive systems? State explosion due to concurrency is handled by introducing a partial-order representation for systems, and defining system correctness as a simple relation between two partial orders on the same set of system events (a graph problem). State explosion due to nondeterminism (chiefly arbitration) is handled when the system to be verified has a clean, finite recurrence structure. Backwards branching is a further optimization. The heart of this approach is the ability, during model checking, to discover a compact finite presentation of the verified system without prior composition of system components. The fully-implemented POM verification system has polynomial space and time performance on traditional asynchronous-circuit benchmarks that are exponential in space and time for other verification systems. We also sketch the generalization of this approach to handle delay-constrained VLSI systems.

Probst, D.↗
NASA NTRS1987

Digital robust active control law synthesis for large order systems using constrained optimization

This paper presents a direct digital control law synthesis procedure for a large order, sampled data, linear feedback system using constrained optimization techniques to meet multiple design requirements. A linear quadratic Gaussian type cost function is minimized while satisfying a set of constraints on the design loads and responses. General expressions for gradients of the cost function and constraints, with respect to the digital control law design variables are derived analytically and computed by solving a set of discrete Liapunov equations. The designer can choose the structure of the control law and the design variables, hence a stable classical control law as well as an estimator-based full or reduced order control law can be used as an initial starting point. Selected design responses can be treated as constraints instead of lumping them into the cost function. This feature can be used to modify a control law, to meet individual root mean square response limitations as well as minimum single value restrictions. Low order, robust digital control laws were synthesized for gust load alleviation of a flexible remotely piloted drone aircraft.

Mukhopadhyay, Vivek↗
NASA NTRS1981

Accelerated convergence of structured banded systems using constrained corrections

An efficient iterative method for solving a structured banded system of equations is described. The method was developed for a full potential flow program and uses a basic interation step, a dynamic relation step, and a multigrid concept of constraining iterative corrections. The solution of a large linear system of equations is examined. Efficient iterative methods have become attractive for large problems. In the nonlinear cases, these iterations may be effectively merged to improve convergence rates.

Kneile, K.↗
NASA NTRS1974

Analytical Dynamics and Nonrigid Spacecraft Simulation

Application to the simulation of idealized spacecraft are considered both for multiple-rigid-body models and for models consisting of combination of rigid bodies and elastic bodies, with the elastic bodies being defined either as continua, as finite-element systems, or as a collection of given modal data. Several specific examples are developed in detail by alternative methods of analytical mechanics, and results are compared to a Newton-Euler formulation. The following methods are developed from d'Alembert's principle in vector form: (1) Lagrange's form of d'Alembert's principle for independent generalized coordinates; (2) Lagrange's form of d'Alembert's principle for simply constrained systems; (3) Kane's quasi-coordinate formulation of D'Alembert's principle; (4) Lagrange's equations for independent generalized coordinates; (5) Lagrange's equations for simply constrained systems; (6) Lagrangian quasi-coordinate equations (or the Boltzmann-Hamel equations); (7) Hamilton's equations for simply constrained systems; and (8) Hamilton's equations for independent generalized coordinates.

Likins, P. W.↗
NASA NTRS2004

Adaptive Multi-Agent Systems for Constrained Optimization

Product Distribution (PD) theory is a new framework for analyzing and controlling distributed systems. Here we demonstrate its use for distributed stochastic optimization. First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (probability distribution of) the joint state of the agents. When the game in question is a team game with constraints, that equilibrium optimizes the expected value of the team game utility, subject to those constraints. The updating of the Lagrange parameters in the Lagrangian can be viewed as a form of automated annealing, that focuses the MAS more and more on the optimal pure strategy. This provides a simple way to map the solution of any constrained optimization problem onto the equilibrium of a Multi-Agent System (MAS). We present computer experiments involving both the Queen s problem and K-SAT validating the predictions of PD theory and its use for off-the-shelf distributed adaptive optimization.

Macready, William↗
NASA NTRS1988

The expanded Lagrangian system for constrained optimization problems

Smooth penalty functions can be combined with numerical continuation/bifurcation techniques to produce a class of robust and fast algorithms for constrained optimization problems. The key to the development of these algorithms is the Expanded Lagrangian System which is derived and analyzed in this work. This parameterized system of nonlinear equations contains the penalty path as a solution, provides a smooth homotopy into the first-order necessary conditions, and yields a global optimization technique. Furthermore, the inevitable ill-conditioning present in a sequential optimization algorithm is removed for three penalty methods: the quadratic penalty function for equality constraints, and the logarithmic barrier function (an interior method) and the quadratic loss function (an interior method) for inequality constraints. Although these techniques apply to optimization in general and to linear and nonlinear programming, calculus of variations, optimal control and parameter identification in particular, the development is primarily within the context of nonlinear programming.

Poore, A. B.↗
NASA NTRS1986

The expanded LaGrangian system for constrained optimization problems

Smooth penalty functions can be combined with numerical continuation/bifurcation techniques to produce a class of robust and fast algorithms for constrainted optimization problems. The key to the development of these algorithms is the Expanded Lagrangian System which is derived and analyzed in this work. This parameterized system of nonlinear equations contains the penalty path as a solution, provides a smooth homotopy into the first-order necessary conditions, and yields a global optimization technique. Furthermore, the inevitable ill-conditioning present in a sequential optimization algorithm is removed for three penalty methods: the quadratic penalty function for equality constraints, and the logarithmic barrier function (an interior method) and the quadratic loss function (an interior method) for inequality constraints. Although these techniques apply to optimization in general and to linear and nonlinear programming, calculus of variations, optimal control and parameter identification in particular, the development is primarily within the context of nonlinear programming.

Poore, A. B.↗
NASA NTRS2008

Using Observations of Deep Convective Systems to Constrain Atmospheric Column Absorption of Solar Radiation in the Optically Thick Limit

Atmospheric column absorption of solar radiation A(sub col) is a fundamental part of the Earth's energy cycle but is an extremely difficult quantity to measure directly. To investigate A(sub col), we have collocated satellite-surface observations for the optically thick Deep Convective Systems (DCS) at the Department of Energy Atmosphere Radiation Measurement (ARM) Tropical Western Pacific (TWP) and Southern Great Plains (SGP) sites during the period of March 2000 December 2004. The surface data were averaged over a 2-h interval centered at the time of the satellite overpass, and the satellite data were averaged within a 1 deg X 1 deg area centered on the ARM sites. In the DCS, cloud particle size is important for top-of-atmosphere (TOA) albedo and A(sub col) although the surface absorption is independent of cloud particle size. In this study, we find that the A(sub col) in the tropics is approximately 0.011 more than that in the middle latitudes. This difference, however, disappears, i.e., the A(sub col) values at both regions converge to the same value (approximately 0.27 of the total incoming solar radiation) in the optically thick limit (tau greater than 80). Comparing the observations with the NASA Langley modified Fu_Liou 2-stream radiative transfer model for optically thick cases, the difference between observed and model-calculated surface absorption, on average, is less than 0.01, but the model-calculated TOA albedo and A(sub col) differ by 0.01 to 0.04, depending primarily on the cloud particle size observation used. The model versus observation discrepancies found are smaller than many previous studies and are just within the estimated error bounds. We did not find evidence for a large cloud absorption anomaly for the optically thick limit of extensive ice cloud layers. A more modest cloud absorption difference of 0.01 to 0.04 cannot yet be ruled out. The remaining uncertainty could be reduced with additional cases, and by reducing the current uncertainty in cloud particle size.

Dong, Xiquan↗
NASA NTRS2018

Constraining Solar System Bombardment Using In Situ Radiometric Dating

The leading, but contentious, model for lunar impact history includes a pronounced increase in impact events at around 3.9 Ga. This late heavy bombardment would have scarred Mars and the terrestrial planets, influenced the course of biologic evolution on the early Earth, and rearranged the very architecture of our Solar System. But what if it's not true? In the last decade, new observations and sample analyses have reinterpreted basin ages and "pulled the pin" on the cataclysm - we may only have the age of one large basin (Imbrium). The Curie mission would constrain the onset of the cataclysm by determining the age of a major pre-Imbrium lunar basin (Nectaris or Crisium), characterize new lunar lithologies far from the Apollo and Luna landing sites, including the basalts in the basin-filling maria and olivine-rich lithologies in the basin margins, and provide a unique vantage point to assess volatiles in the lunar regolith from dawn to dusk.

Cohen, Barbara A.↗
DOE OSTI2025

TRIM: AI Guided Random Number Generation for Resource-Constrained IoT Systems

Random numbers often serve as the backbone for many security solutions in diverse domains such as cryptography, side channel leakage prevention, and moving target defense. However, generating true random numbers requires a physical source of entropy (e.g. hardware, quantum, environmental phenomenon) making it difficult to realize at a large scale and at a low cost. On the flip side, pseudorandom number generators (easy to implement) following a specific distribution (e.g. Gaussian) can be easily compromised given a sufficient amount of traces. In this work, we have developed a machine learning-guided generative approach that can be used to create portable, resource-efficient, and cost-effective random number generators with high throughput and true randomness characteristics. We implement the proposed approach as a highly parameterized framework and perform extensive evaluation for different settings. The framework was able to learn from true random sources such as irrational numbers and environmental audio noise and imitate those sources towards generating new good quality random numbers on demand. We have generated more than 1 billion bits and observed robust performance in terms of true randomness metrics obtained from NIST SP 800-22 and FIPS 140-1 randomness test suites achieving a throughput of up to 142.85 Mbps. Compared to the state-of-the-art (SOTA) technique, the iso-cost setup of our framework can achieve more than 500 Mbps in a distributed setting. We have evaluated the efficacy of running the true randomness imitation AI models on target edge devices such as Raspberry Pi 4 (Model B), Nvidia Jetson Nano, Nvidia Jetson Orin Nano and Nvidia Jetson Xavier. We have also looked at the security of the TRIM framework itself against different adversarial threat models.

Cybersecurity↗