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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 NTRS2005

Towards Rocket Engine Components with Increased Strength and Robust Operating Characteristics

High-energy rotating machines, powering liquid propellant rocket engines, are subject to various sources of high and low cycle fatigue generated by unsteady flow phenomena. Given the tremendous need for reliability in a sustainable space exploration program, a fundamental change in the design methodology for engine components is required for both launch and space based systems. A design optimization system based on neural-networks has been applied and demonstrated in the redesign of the Space Shuttle Main Engine (SSME) Low Pressure Oxidizer Turbo Pump (LPOTP) turbine nozzle. One objective of the redesign effort was to increase airfoil thickness and thus increase its strength while at the same time detuning the vane natural frequency modes from the vortex shedding frequency. The second objective was to reduce the vortex shedding amplitude. The third objective was to maintain this low shedding amplitude even in the presence of large manufacturing tolerances. All of these objectives were achieved without generating any detrimental effects on the downstream flow through the turbine, and without introducing any penalty in performance. The airfoil redesign and preliminary assessment was performed in the Exploration Technology Directorate at NASA ARC. Boeing/Rocketdyne and NASA MSFC independently performed final CFD assessments of the design. Four different CFD codes were used in this process. They include WIL DCA T/CORSAIR (NASA), FLUENT (commercial), TIDAL (Boeing Rocketdyne) and, a new family (AardvarWPhantom) of CFD analysis codes developed at NASA MSFC employing LOX fluid properties and a Generalized Equation Set formulation. Extensive aerodynamic performance analysis and stress analysis carried out at Boeing Rocketdyne and NASA MSFC indicate that the redesign objectives have been fully met. The paper presents the results of the assessment analysis and discusses the future potential of robust optimal design for rocket engine components.

Marcu, Bogdan↗
NASA NTRS1997

Room and Low Temperature Performance of High-Speed Neural Network Circuits

We have described a challenging neural-network hardware implementation that would mate a 64x64-pixel infrared sensor directly on to a 3-dimensionally packaged set of neural processing chips with parallel input for high speed image processing. Non linearity effects and range compression are also discussed.

high-speed neural network circuits neural network ↗
NASA NTRS2006

Diurnal Variability of Vertical Structure from a TRMM Passive Microwave "Virtual Radar" Retrieval

Robust description of the diurnal cycle from TRMM observations is complicated by the limitations of Low Earth Orbit (LEO) sampling; from a 'climatological' perspective, sufficient sampling must exist to control for both spatial and seasonal variability, before tackling an additional diurnal component (e.g., with 8 additional 3-hourly or 24 1-hourly bins). For documentation of vertical structure, the narrow sample swath of the TRMM Precipitation Radar limits the resolution of any of these components. A neural-network based 'virtual radar" retrieval has been trained and internally validated, using multifrequency / multipolarization passive microwave(TM1) brightness temperatures and textures parameters and lightning (LIS) observations, as inputs, and PR volumetric reflectivity as targets (outputs). By training the algorithms (essentially highly multivariate, nonlinear regressions) on a very large sample of high-quality co-located data from the center of the TRMM swath, 3D radar reflectivity and derived parameters (VIL, IWC, Echo Tops, etc.) can be retrieved across the entire TMI swath, good to 8-9% over the dynamic range of parameters. As a step in the retrieval (and as an output of the process), each TMI multifrequency pixel (at 85 GHz resolution) is classified into one of the 25 archetypal radar profile vertical structure "types", previously identified using cluster analysis. The dynamic range of retrieved vertical structure appears to have higher fidelity than the current (Version 6) experimental GPROF hydrometeor vertical structure retrievals. This is attributable to correct representation of the prior probabilities of vertical structure variability in the neural network training data, unlike the GPROF cloud-resolving model training dataset used in the V6 algorithms. The LIS lightning inputs are supplementary inputs, and a separate offline neural network has been trained to impute (predict) LIS lightning from passive-microwave-only data. The virtual radar retrieval is thus, in principle, extensible to Aqua/AMSR-E and NPOESS/CMIS passive microwave instruments. The virtual radar approach yields a threefold increase in effective sampling from the mission, albeit of lower-quality "retrieved" data, reducing the variance of local estimates by one third (or the standard deviation by-0.57). In this talk, the variance reduction is leveraged to more finely resolve global diurnal variability in both space and time (local hour).

Boccippio, Dennis J.↗
NASA NTRS2009

Processing Electromyographic Signals to Recognize Words

A recently invented speech-recognition method applies to words that are articulated by means of the tongue and throat muscles but are otherwise not voiced or, at most, are spoken sotto voce. This method could satisfy a need for speech recognition under circumstances in which normal audible speech is difficult, poses a hazard, is disturbing to listeners, or compromises privacy. The method could also be used to augment traditional speech recognition by providing an additional source of information about articulator activity. The method can be characterized as intermediate between (1) conventional speech recognition through processing of voice sounds and (2) a method, not yet developed, of processing electroencephalographic signals to extract unspoken words directly from thoughts. This method involves computational processing of digitized electromyographic (EMG) signals from muscle innervation acquired by surface electrodes under a subject's chin near the tongue and on the side of the subject s throat near the larynx. After preprocessing, digitization, and feature extraction, EMG signals are processed by a neural-network pattern classifier, implemented in software, that performs the bulk of the recognition task as described.

Jorgensen, C. C.↗
NASA NTRS2009

Bio-Inspired Neural Model for Learning Dynamic Models

A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.

Duong, Tuan↗
NASA NTRS1995

NASA Tech Briefs, November 1995

The contents include: 1) Mission Accomplished; 2) Resource Report: Marshall Space Flight Center; 3) NASA 1995 Software of the Year Award; 4) Microbolometers Based on Epitaxial YBa2Cu3O(sub 7-x) Thin Films; 5) Garnet Random-Access Memory; 6) Fabrication of SNS Weak Links on SOS Substrates; 7) High-Voltage MOSFET Switching Circuit; 8) Asymmetric Switching for a PWM H-Bridge Power Circuit; 9) Better Ohmic Contacts for InP Semiconductor Devices; 10) Low-Bandgap Thermovoltaic Materials and Devices; 11) Digital Frequency-Differencing Circuit; 12) Imaging Magnetometer; 13) Computer-Assisted Monitoring of a Complex System; 14) Buffered Telemetry Demodulator; 15) Compact Multifunction Inspection Head; 16) Optical Detection of Fractures in Ceramic Diaphragms; 17) Eddy-Current Detection of Cracks in Reinforced Carbon/Carbon; 18) Apparent Thermal Conductivity of Multilayer Insulation; 19) Optimizing Misch-Metal Compositions in Metal Hydride Anodes; 20) Device for Sampling Surface Contamination; 21) Probabilistic Failure Assessment for Fatigue; 22) Probabilistic Fatigue and Flaw-Propagation Analysis; 23) Windows Program for Driving the TDU-850 Printer; 24) Subband/Transform MATLAB Functions for Processing Images; 25) Computing Equilibrium Chemical Compositions; 26) Program Processes Thermocouple Readings; 27) ICAN-Second-Generation Integrated Composite Analyzer; 28) Integrated Composite Analyzer with Damping Capabilities; 29) Computing Efficiency of Transfer of Microwave Power; 30) Program Calculates Power Demands of Electronic Designs; 31) Cost-Estimation Program; 32) Program Estimates Areas Required by Electronic Designs; 33) Program to Balance Mapped Turbopump Assemblies; 34) BiblioTech; 35) Controlling Mirror Tilt With a Bimorph Actuator; 36) Burst-Disk Device Simulates Effect of Pyrotechnic Device; 37) Bearing-Mounting Concept Accommodates Thermal Expansion; 38) Parallel-Plate Acoustic Absorbers for Hot Environments; 39) Adjustable-Length Strut Withstands Large Cyclic Loads; 40) Tool Indicates Contact Angles in Bearing Raceways; 41) Gravity Slides With Magnetic Braking; 42) High-Torque, Lightweight, Pneumatically Driven Wrench for Small Spaces; 43) Device for Testing Compatibility of an O-Ring; 44) Magnetic Heat Pump Containing Flow Diverters; 45) Variable-Tilt Helicopter Rotor Mast; 46) "Beach-Ball" Robotic Rovers; 47) Apparatus Would Measure Temperatures of Ball Bearings; 48) Flexible Borescope for Inspecting Ducts; 49) Texturing Copper To Reduce Secondary Emission of Electrons; 50) Automated Laser Cutting in Three Dimensions; 51) Algorithm Helps Monitor Engine Operation; 52) Flexible Revision of Data-Processing Communications; 53) Software for Managing the Use of Land; 54) Thermal Strap Increases Cryocooling Efficiency; 55) Reversible Nut With Engagement Indication; 56) Control Algorithms for Kinematically Redundant Manipulators; 57) Computed Hydrogen-Flow Splits in a Rocket Engine; 58) Pressure and Thermal Modeling of Rocket Launches; 59) Field of View of a Spacecraft Antenna: Analysis and Software; 60) Digital Controller for Laser-Beam-Steering Subsystem; 61) More About Beam-Steering Subsystem for Laser Communication; 62) Digital Controller for Laser-Beam-Steering Subsystem: Part 2; 63) Interface Circuit Board for Space-Shuttle Communications; 64) Automated Planning of Spacecraft Telecommunications; 65) Artifacts of Spectral Analysis of Instrument Readings; 66) Neural-Network Controller for Vibration Suppression; 67) Adaptive Finite-Element Computation in Fracture Mechanics; 68) Attitude Control for the Cassini Spacecraft; 69) Analytical Model for Fluid Dynamics in a Microgravity Environment; 70) Study of Rocket-Engine Joints Bonded by NVCU/NARloy-Z; 71) Improved Silicon Nitride for Advanced Heat Engines; 72) Parameters for Welding Aluminum/Lithium Alloys; 73) Lightweight Composite Intertank Structure; 74) Foil Patches Seal Small Vacuum Leaks; 75) Data Base on Cables and Connectors; 76) Effect of Clock Mode on Radiation Hardnessf an ADC; and 77) Fault-Tolerant Control for a Robotic Inspection System.

Source record↗
NASA NTRS2011

Ice Shape Characterization Using Self-Organizing Maps

A method for characterizing ice shapes using a self-organizing map (SOM) technique is presented. Self-organizing maps are neural-network techniques for representing noisy, multi-dimensional data aligned along a lower-dimensional and possibly nonlinear manifold. For a large set of noisy data, each element of a finite set of codebook vectors is iteratively moved in the direction of the data closest to the winner codebook vector. Through successive iterations, the codebook vectors begin to align with the trends of the higher-dimensional data. In information processing, the intent of SOM methods is to transmit the codebook vectors, which contains far fewer elements and requires much less memory or bandwidth, than the original noisy data set. When applied to airfoil ice accretion shapes, the properties of the codebook vectors and the statistical nature of the SOM methods allows for a quantitative comparison of experimentally measured mean or average ice shapes to ice shapes predicted using computer codes such as LEWICE. The nature of the codebook vectors also enables grid generation and surface roughness descriptions for use with the discrete-element roughness approach. In the present study, SOM characterizations are applied to a rime ice shape, a glaze ice shape at an angle of attack, a bi-modal glaze ice shape, and a multi-horn glaze ice shape. Improvements and future explorations will be discussed.

McClain, Stephen T.↗
NASA NTRS2005

Training Software in Artificial-Intelligence Computing Techniques

The Artificial Intelligence (AI) Toolkit is a computer program for training scientists, engineers, and university students in three soft-computing techniques (fuzzy logic, neural networks, and genetic algorithms) used in artificial-intelligence applications. The program promotes an easily understandable tutorial interface, including an interactive graphical component through which the user can gain hands-on experience in soft-computing techniques applied to realistic example problems. The tutorial provides step-by-step instructions on the workings of soft-computing technology, whereas the hands-on examples allow interaction and reinforcement of the techniques explained throughout the tutorial. In the fuzzy-logic example, a user can interact with a robot and an obstacle course to verify how fuzzy logic is used to command a rover traverse from an arbitrary start to the goal location. For the genetic algorithm example, the problem is to determine the minimum-length path for visiting a user-chosen set of planets in the solar system. For the neural-network example, the problem is to decide, on the basis of input data on physical characteristics, whether a person is a man, woman, or child. The AI Toolkit is compatible with the Windows 95,98, ME, NT 4.0, 2000, and XP operating systems. A computer having a processor speed of at least 300 MHz, and random-access memory of at least 56MB is recommended for optimal performance. The program can be run on a slower computer having less memory, but some functions may not be executed properly.

Howard, Ayanna↗
NASA NTRS2003

Cascade Back-Propagation Learning in Neural Networks

The cascade back-propagation (CBP) algorithm is the basis of a conceptual design for accelerating learning in artificial neural networks. The neural networks would be implemented as analog very-large-scale integrated (VLSI) circuits, and circuits to implement the CBP algorithm would be fabricated on the same VLSI circuit chips with the neural networks. Heretofore, artificial neural networks have learned slowly because it has been necessary to train them via software, for lack of a good on-chip learning technique. The CBP algorithm is an on-chip technique that provides for continuous learning in real time. Artificial neural networks are trained by example: A network is presented with training inputs for which the correct outputs are known, and the algorithm strives to adjust the weights of synaptic connections in the network to make the actual outputs approach the correct outputs. The input data are generally divided into three parts. Two of the parts, called the "training" and "cross-validation" sets, respectively, must be such that the corresponding input/output pairs are known. During training, the cross-validation set enables verification of the status of the input-to-output transformation learned by the network to avoid over-learning. The third part of the data, termed the "test" set, consists of the inputs that are required to be transformed into outputs; this set may or may not include the training set and/or the cross-validation set. Proposed neural-network circuitry for on-chip learning would be divided into two distinct networks; one for training and one for validation. Both networks would share the same synaptic weights.

Duong, Tuan A.↗
NASA NTRS2003

Vector-Ordering Filter Procedure for Data Reduction

The vector-ordering filter (VOF) technique involves a procedure for sampling a large population of data vectors to select a subset of data vectors that fully characterize the state space of the large population. The VOF technique enables a large reduction of the volume of data that must be handled in the automated monitoring system and method discussed in the two immediately preceding articles. In so doing, the VOF technique enables the development of data-driven mathematical models of a monitored asset from sets of data that would otherwise exceed the memory capacities of conventional engineering computers. Data-driven mathematical models have been shown to offer high fidelity for purposes of control and monitoring of assets. In practice, a collection of asset-operating observations is acquired with the intention that the collection contain observations characteristic of the full dynamic range of operation of the asset. Often, such a collection contains an extremely large number of observations, many of which are redundant. The VOF technique fills the need for a means to extract, from the original collection of observational data, a reduced data matrix that excludes redundant data while maintaining the full statistical character and dynamic range of the original data. The reduced data matrix can then be used as the input data for development of a mathematical model of the monitored asset, or as training data for a neural-network substitute for an explicit mathematical model of the asset. Alternatively, the reduced data matrix can, itself, be used directly as a mathematical model of the monitored asset, as is commonly done in multivariate state-estimation techniques. The original data are collected from the asset over a range of operating states and are put in matrix form. Each column vector in the original data matrix represents the signal values acquired at a particular operational state of the asset. Thus, the number of columns of the original data matrix equals the number of observed states and the number of rows in this matrix equals the number of signals acquired at each observation. In the VOF technique, one extracts the reduced data matrix from the original data matrix through the selection of a representative subset of the column (state) vectors.

Bickford, Randall L.↗
NASA NTRS2013

Assessment of Ice Shape Roughness Using a Self-Orgainizing Map Approach

Self-organizing maps are neural-network techniques for representing noisy, multidimensional data aligned along a lower-dimensional and nonlinear manifold. For a large set of noisy data, each element of a finite set of codebook vectors is iteratively moved in the direction of the data closest to the winner codebook vector. Through successive iterations, the codebook vectors begin to align with the trends of the higher-dimensional data. Prior investigations of ice shapes have focused on using self-organizing maps to characterize mean ice forms. The Icing Research Branch has recently acquired a high resolution three dimensional scanner system capable of resolving ice shape surface roughness. A method is presented for the evaluation of surface roughness variations using high-resolution surface scans based on a self-organizing map representation of the mean ice shape. The new method is demonstrated for 1) an 18-in. NACA 23012 airfoil 2 AOA just after the initial ice coverage of the leading 5 of the suction surface of the airfoil, 2) a 21-in. NACA 0012 at 0AOA following coverage of the leading 10 of the airfoil surface, and 3) a cold-soaked 21-in.NACA 0012 airfoil without ice. The SOM method resulted in descriptions of the statistical coverage limits and a quantitative representation of early stages of ice roughness formation on the airfoils. Limitations of the SOM method are explored, and the uncertainty limits of the method are investigated using the non-iced NACA 0012 airfoil measurements.

Icing↗
NASA NTRSDate not supplied

Runtime Monitoring with R2U2 for Aircraft Systems with Neural Networks

R2U2 (Realizable, Responsive, Unobtrusive Unit) is a hardware-supported tool and framework for real-time system monitoring and software health management of cyber-physical systems. During system operation, R2U2 continuously monitors properties about safety, performance, and security of the vehicle and its vital components and can perform diagnostic reasoning. Efficient observers for past-time and future-time Metric Temporal Logic, fast reasoners for Bayesian Networks, and model-based prognostics algorithms are key components of R2U2 and designed for minimal computational footprint. R2U2 has been implemented in software supporting ROS, NASA's cFS/cFE, and Simulink and as an FPGA configuration. The synergistic combination of monitors and observers in R2U2 makes it possible to design powerful models for system runtime monitoring, diagnostics, software health management, prognostics, and security monitoring. In this presentation, I will give a detailed overview of the R2U2 architecture and its features and will discuss the application of R2U2 for safety-monitoring of a neural-network based autonomous centerline tracking system (ACT) for autonomous aircraft.

Runtime Monitoring↗
NASA NTRSDate not supplied

A Machine Learning Approach for the Remote Sensing Retrieval of Oil Spills from Polarimetric Measurements

The Fresnel laws of specular reflection directly connect remote sensing observations of light polarization within the sunglint region to the ocean surface refractive index. This parameter is instrumental to identify areas affected by oil, or any other floating substance capable of modifying the refractive index of pure seawater. In preparation for the NASA Plankton, Aerosol, ocean Ecosystem (PACE) mission, we are developing an advanced retrieval scheme that exploits such measurements to deliver the refractive index as an operational product. The original method was developed based on observations of the airborne Research Scanning Polarimeter. Here we present several aspects related to the extension of this technique to the PACE HARP-2 sensor (especially regarding the projected accuracy), and discuss preliminary results obtained by applying neural-network trainings to look-up tables produced with the forward radiative transfer code, with the goal of improving the computational performance. Success in the final implementation will guarantee a version of the retrieval algorithm suitable to fast-response needs and disaster management.

machine learning↗
NASA NTRS2026

Benchmarking Bayesian Optimization Frameworks and Acquisition Strategies for Materials Discovery and Autonomous Laboratories

Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.

Bayesian optimization↗
DOE OSTI2026

Component-Level Inverse Design of Transmon Qubits Using Neural Networks

Designing a superconducting qubit to realize specific Hamiltonian parameters typically requires iterating through a time and compute-intensive forward loop in which the designer chooses a layout geometry, simulates it, extracts circuit parameters such as capacitances, and refines the geometry. We study the inverse version of this task using a neural-network workflow that maps target Hamiltonian parameters directly to component-level layout parameters, which we subsequently demonstrate on a planar transmon layout. During training, we pair the inverse model with a frozen forward surrogate model and evaluate the loss in Hamiltonian space rather than in layout-parameter space. In validation against a conventional EM solver, 97% of generated designs produce usable geometries, and the inverse-plus-surrogate pipeline reaches mean percent errors of 0.73% for qubit frequency and 1.58% for anharmonicity, comparable to or below the fabrication and simulation-to-measurement uncertainty expected for academic-process transmon devices of this type. A single pipeline query takes ~60 ms on CPU, versus ~2 min for a conventional EM capacitance extraction on the same hardware, a speedup of approximately 2,000x. Batching minimizes the AI model inference overhead, reducing the runtime to 3.1 microseconds per sample on CPU and 2.6 microseconds per sample on GPU at a batch size of 2048, resulting in speedups of 3.9 x 10^7 and 4.6 x 10^7, respectively, relative to a single conventional CPU EM extraction. Our results indicate that component-level inverse design usefully extends and complements conventional EM simulation, including for small datasets on the order of 1,000 samples.

Seidel, Olivia [Fermilab; Texas U., Arlington]↗
DOE OSTI2026

On-Detector Machine Learning for Beam-Induced Background Rejection at a 10 TeV Muon Collider

A 10 TeV Muon Collider is a compelling candidate for a future energy-frontier facility, offering unprecedented opportunities to explore the fundamental laws of particle physics. Muon decays in the collider ring produce intense beam-induced background (BIB) that can overwhelm detector occupancy and exceed readout bandwidth constraints. We investigate the potential of on-detector Machine Learning for BIB rejection in the vertex detector, exploiting pixel cluster shapes to distinguish background from collision products. We study three classes of lightweight neural-network architectures, and evaluate their implementation feasibility using high-level synthesis. Selected architectures achieve 88 to 90% data reduction at 99% signal efficiency, while requiring hardware resources compatible with potential ASIC implementation. These results demonstrate the potential of performing substantial BIB rejection directly in the pixel readout, providing a strategy for meeting the tracker readout requirements at a future Muon Collider.

Abadjiev, Daniel [Chicago U.]↗
DOE OSTI2023

Probing the self-ionization of liquid water with ab initio deep potential molecular dynamics

The chemical equilibrium between self-ionized and molecular water dictates the acid–base chemistry in aqueous solutions, yet understanding the microscopic mechanisms of water self-ionization remains experimentally and computationally challenging. Herein, Density Functional Theory (DFT)–based deep neural network (DNN) potentials are combined with enhanced sampling techniques and a global acid–base collective variable to perform extensive atomistic simulations of water self-ionization for model systems of increasing size. The explicit inclusion of long-range electrostatic interactions in the DNN potential is found to be crucial to accurately reproduce the DFT free energy profile of solvated water ion pairs in small (64 and 128 H 2 O) cells. The reversible work to separate the hydroxide and hydronium to a distance S is found to converge for simulation cells containing more than 500 H 2 O, and a distance of ∼ 8 Å is the threshold beyond which the work to further separate the two ions becomes approximately zero. The slow convergence of the potential of mean force with system size is related to a restructuring of water and an increase of the local order around the water ions. Calculation of the dissociation equilibrium constant illustrates the key role of long-range electrostatics and entropic effects in the water autoionization process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗