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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Intelligent fault-tolerant controllers

A system with fault tolerant controls is one that can detect, isolate, and estimate failures and perform necessary control reconfiguration based on this new information. Artificial intelligence (AI) is concerned with semantic processing, and it has evolved to include the topics of expert systems and machine learning. This research represents an attempt to apply AI to fault tolerant controls, hence, the name intelligent fault tolerant control (IFTC). A generic solution to the problem is sought, providing a system based on logic in addition to analytical tools, and offering machine learning capabilities. The advantages are that redundant system specific algorithms are no longer needed, that reasonableness is used to quickly choose the correct control strategy, and that the system can adapt to new situations by learning about its effects on system dynamics.

Huang, Chien Y.↗
NASA NTRS1991

Using fuzzy logic to integrate neural networks and knowledge-based systems

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

Yen, John↗
NASA NTRS1990

Intelligent control and adaptive systems; Proceedings of the Meeting, Philadelphia, PA, Nov. 7, 8, 1989

Various papers on intelligent control and adaptive systems are presented. Individual topics addressed include: control architecture for a Mars walking vehicle, representation for error detection and recovery in robot task plans, real-time operating system for robots, execution monitoring of a mobile robot system, statistical mechanics models for motion and force planning, global kinematics for manipulator planning and control, exploration of unknown mechanical assemblies through manipulation, low-level representations for robot vision, harmonic functions for robot path construction, simulation of dual behavior of an autonomous system. Also discussed are: control framework for hand-arm coordination, neural network approach to multivehicle navigation, electronic neural networks for global optimization, neural network for L1 norm linear regression, planning for assembly with robot hands, neural networks in dynamical systems, control design with iterative learning, improved fuzzy process control of spacecraft autonomous rendezvous using a genetic algorithm.

Rodriguez, Guillermo↗
NASA NTRS1992

Hydrogen leak detection in the Space Shuttle

This study focuses on a helium gas jet flowing into room air. Measurements of helium concentration and velocity in the jet-air mixture are reported. The objective is to learn about jet characteristics so that dynamically similar hydrogen leaks may be located in the Space Shuttle. The hazardous gas detection system (HGDS) in the mobile launch pad uses mass spectrometers to monitor the shuttle environment for leaks. The mass spectrometers are fed by long sample tubes which draw gas from the payload bay, mid body, aft engine compartment and external tank. The overall purpose of this study is to improve the HGDS especially in its potential for locating hydrogen leaks. A rapid-response leak detection experiment was designed, built, and tested, following on the work done in this program last summer. The apparatus included a Perkin Elmer MGA-1200 mass spectrometer and air velocity transducer, both monitored by a Macintosh IIFX computer using LabVIEW software. A jet of helium flowing into the lab air simulated a gas leak. Steady helium or hydrogen-nitrogen jets were logged for concentration and velocity, and the power spectral density of each was computed. Last year, large eddies and vortices were visually seen with Schlieren imaging, and they were detected in the time plots of the various instruments. The response time of the MGA-1200 was found in the range of 0.05 to 0.1 sec. Pulsed concentration waves were clearly detected at 25 cycles per sec by spectral analysis of MGA data. No peaks were detected in the power spectrum, so in the present study, 10 Hz bandwidth-averaged power levels were examined at regular frequency intervals. The practical consequences of last year's study are as follows: sampling frequency should be increased above the present rate of 1 sample per second so that transients could be observed and analyzed with frequency response methods. Many more experiments and conditions were observed in this second summer, including the effects of orifice diameter, jet velocity, sample tube design, radial effects, vertical flow, and low hydrogen concentrations (1 percent). A frequent observation was that the power spectrum, calculated from the Fourier transform of concentration fluctuations, gives a separate piece of information from concentration. Many of the tests suggest that power is high where mixing occurs at the helium-air interface. This fact is apparently independent of the concentration level, which could be high or low, but depends on the sample location relative to the jet (leak) origin, whereas high concentration may be due to a strong leak far away or a small leak close to the sample tube. If the power is low for any concentration level, this would signify helium is arriving at the sample tube by diffusion, not chaotic mixing caused by the jet interaction with air. The practical result is to propose a modification of the HGDL mass spectrometer data sampling and software so that sampling rates could be capable of observing at least 25 Hz fluctuations.

Barile, Ronald G↗
NASA NTRS1992

Efficient dynamic optimization of logic programs

A summary is given of the dynamic optimization approach to speed up learning for logic programs. The problem is to restructure a recursive program into an equivalent program whose expected performance is optimal for an unknown but fixed population of problem instances. We define the term 'optimal' relative to the source of input instances and sketch an algorithm that can come within a logarithmic factor of optimal with high probability. Finally, we show that finding high-utility unfolding operations (such as EBG) can be reduced to clause reordering.

Laird, Phil↗
NASA NTRS1992

There is No Free Lunch: Tradeoffs in the Utility of Learned Knowledge

With the recent introduction of learning in integrated systems, there is a need to measure the utility of learned knowledge for these more complex systems. A difficulty arrises when there are multiple, possibly conflicting, utility metrics to be measured. In this paper, we present schemes which trade off conflicting utility metrics in order to achieve some global performance objectives. In particular, we present a case study of a multi-strategy machine learning system, mutual theory refinement, which refines world models for an integrated reactive system, the Entropy Reduction Engine. We provide experimental results on the utility of learned knowledge in two conflicting metrics - improved accuracy and degraded efficiency. We then demonstrate two ways to trade off these metrics. In each, some learned knowledge is either approximated or dynamically 'forgotten' so as to improve efficiency while degrading accuracy only slightly.

Kedar, Smadar T.↗
NASA NTRS1991

Structural Analysis

After an 800-foot-tall offshore oil recovery platform collapsed, the engineers at Engineering Dynamics, Inc., Kenner, LA, needed to learn the cause of the collapse, and analyze the proposed repairs. They used STAGSC-1, a NASA structural analysis program with geometric and nonlinear buckling analysis. The program allowed engineers to determine the deflected and buckling shapes of the structural elements. They could then view the proposed repairs under the pressure that caused the original collapse.

Source record↗
NASA NTRS2004

The PMDP Roadmap

NASA's complex and highly technical missions rely on effective project teams and managers. Since 1993, through its Project Management Development Process (PMDP), the Academy of Program and Project Leadership (APPL) has offered direction to the Agency's project practitioners as they advance in their careers. PMDP helps identify and sequence professional experiences, courses, and other project-based learning experiences that support individual career goals and center activities by outlining competencies at four levels of development. The result is that PMDP provides NASA project practitioners with a road map to the knowledge and competencies appropriate for their job and the jobs to which they aspire. Plus, new this year, APPL has rolled out its electronic Project Management Development Process (ePMDP) tool, a learning management system that includes a dynamic presentation of the PMDP levels, competency areas, competency organizational structures, Individual Development Plans (IDP), and online PMDP enrollment. APPL's website, www.appl.nasa.gov, provides access to ePMDP as well as other online resources for NASA practitioners enrolled in the Project Management Development Process.

Source record↗
NASA NTRS2006

The NASA F-15 Intelligent Flight Control Systems: Generation II

The Second Generation (Gen II) control system for the F-15 Intelligent Flight Control System (IFCS) program implements direct adaptive neural networks to demonstrate robust tolerance to faults and failures. The direct adaptive tracking controller integrates learning neural networks (NNs) with a dynamic inversion control law. The term direct adaptive is used because the error between the reference model and the aircraft response is being compensated or directly adapted to minimize error without regard to knowing the cause of the error. No parameter estimation is needed for this direct adaptive control system. In the Gen II design, the feedback errors are regulated with a proportional-plus-integral (PI) compensator. This basic compensator is augmented with an online NN that changes the system gains via an error-based adaptation law to improve aircraft performance at all times, including normal flight, system failures, mispredicted behavior, or changes in behavior resulting from damage.

Buschbacher, Mark↗
NASA NTRSDate not supplied

CLAIRE: Enabling Heterogeneous Communication Network Optimization for Robust and Resilient Operations

In this paper, we present the capabilities of the CLAIRE System which provides resilient communications for NASA in presence of interference and congestion for a heterogeneous multi-vendor network. CLAIRE increases mission science data return to improve resource efficiencies and ensures resilience in the unpredictable space environment for NASA missions and communication networks. CLAIRE provides technology / waveform agnostic cognitive control plane that is instantiated at the Application Layer (APP) so that it can ride on NASA’s HDTN bundle protocol or any other protocol stack that is used by the network. The cognitive control plane is instantiated using Heartbeats (HTBTs). CLAIRE is assisted by Wideband UHF-Ka Band RF Sensing that leverages advances in the Direct Digital Transceiver (DDTRX) technology. The Wideband RF Sensing is driven by statistical signal processing and machine learning algorithms. Interference is mitigated using Dynamic Spectrum Access (DSA). Finally, CLAIRE addresses congestion using spectrum aware packet forwarding algorithm. CLAIRE provides an extensible protocol that allows passing of RF spectrum situational awareness, cross-layer sensing, delay tolerant networking and dynamic spectrum access information that can help with network optimization. Cross-Layer Sensing (CLS) and CLAIRE Decision Engine (CDE) enable spectrum and delay aware packet forwarding and Dynamic Spectrum Access during cases of severe interference.

cognitive communications↗
DOE OSTI2026

Data from: "Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics"

This data package was generated to support the manuscript “Towards CONUS-Wide Machine Learning-Augmented Conceptually Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics.” It provides input files, model outputs, plotting data, scripts, notebooks, and documentation used to develop, evaluate, and reproduce Mass-Conserving Perceptron (MCP)-based hydrologic modeling experiments across 513 selected Catchment Attributes and Meteorology for Large-sample Studies in the United States (CAMELS-US) basins. The files are organized by modeling component and analysis purpose, including rainfall–runoff experiments, snow module experiments, coupled hydrologic-snow experiments, Long Short-Term Memory (LSTM) benchmark results, model skill metrics, initialization and epoch records, cell-state normalization files, Akaike Information Criterion (AIC)-based model comparison files, and data used to generate manuscript figures. Tabular files can be opened using standard spreadsheet software or Python/R data-analysis tools. Python scripts, Jupyter notebooks, and selected MATLAB scripts are included for model execution, postprocessing, plotting, and statistical analysis. Quality assurance and quality control were conducted through the source-data selection and modeling workflow. Meteorological forcing, streamflow, and static catchment attributes were derived from the CAMELS-US dataset, and snow water equivalent data were derived from the University of Arizona (UA) Snow Water Equivalent dataset. Selected basins and time periods were screened during the associated research workflow to avoid missing observations or poor-quality cases. Static geospatial features were processed primarily using Quantum Geographic Information System (QGIS) and Geospatial Data Abstraction Library (GDAL) workflows. Additional details are provided in the associated manuscript and documentation.

ESS-DIVE CSV File Formatting Guidelines Reporting ↗
NASA NTRS2003

Biogeochemistry of hypersaline microbial mats illustrates the dynamics of modern microbial ecosystems and the early evolution of the biosphere

Photosynthetic microbial mats are remarkably complete self-sustaining ecosystems at the millimeter scale, yet they have substantially affected environmental processes on a planetary scale. These mats may be direct descendents of the most ancient biological communities in which even oxygenic photosynthesis might have developed. Photosynthetic mats are excellent natural laboratories to help us to learn how microbial populations associate to control dynamic biogeochemical gradients.

Ecosystem↗
DOE OSTI2024

Weak-form inference for hybrid dynamical systems in ecology

Species subject to predation and environmental threats commonly exhibit variable periods of population boom and bust over long timescales. Understanding and predicting such behaviour, especially given the inherent heterogeneity and stochasticity of exogenous driving factors over short timescales, is an ongoing challenge. A modelling paradigm gaining popularity in the ecological sciences for such multi-scale effects is to couple short-term continuous dynamics to long-term discrete updates. We develop a data-driven method utilizing weak-form equation learning to extract such hybrid governing equations for population dynamics and to estimate the requisite parameters using sparse intermittent measurements of the discrete and continuous variables. The method produces a set of short-term continuous dynamical system equations parametrized by long-term variables, and long-term discrete equations parametrized by short-term variables, allowing direct assessment of interdependencies between the two timescales. We demonstrate the utility of the method on a variety of ecological scenarios and provide extensive tests using models previously derived for epizootics experienced by the North American spongy moth ( Lymantria dispar dispar ).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Earth Science and Weather Research: A Satellite’s View of Our Dynamic Planet

The earth and its phenomena affect every aspect of our daily lives. NASA satellite earth observation platforms make it possible for earth scientists to develop detection algorithms, improve prediction, and understand the climatologies of earth phenomena even in places where traditional observations are inconsistent or unavailable, and use these observations to monitor our changing Earth system. NASA is committed to making its data open and accessible to the public. Learn how to access NASA’s earth observation data for your community and around the world, and ways to engage your community to learn more about understanding and conserving our dynamic home planet.

Sarah D Bang↗
DOE OSTI2026

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Data-driven framework↗
DOE OSTI2024

Neuromorphic overparameterisation and few-shot learning in multilayer physical neural networks

Abstract Physical neuromorphic computing, exploiting the complex dynamics of physical systems, has seen rapid advancements in sophistication and performance. Physical reservoir computing, a subset of neuromorphic computing, faces limitations due to its reliance on single systems. This constrains output dimensionality and dynamic range, limiting performance to a narrow range of tasks. Here, we engineer a suite of nanomagnetic array physical reservoirs and interconnect them in parallel and series to create a multilayer neural network architecture. The output of one reservoir is recorded, scaled and virtually fed as input to the next reservoir. This networked approach increases output dimensionality, internal dynamics and computational performance. We demonstrate that a physical neuromorphic system can achieve an overparameterised state, facilitating meta-learning on small training sets and yielding strong performance across a wide range of tasks. Our approach’s efficacy is further demonstrated through few-shot learning, where the system rapidly adapts to new tasks.

Science & Technology - Other Topics↗
NASA NTRS1992

Discrete time learning control in nonlinear systems

In this paper digital learning control methods are developed primarily for use in single-input, single-output nonlinear dynamic systems. Conditions for convergence of the basic form of learning control based on integral control concepts are given, and shown to be satisfied by a large class of nonlinear problems. It is shown that it is not the gross nonlinearities of the differential equations that matter in the convergence, but rather the much smaller nonlinearities that can manifest themselves during the short time interval of one sample time. New algorithms are developed that eliminate restrictions on the size of the learning gain, and on knowledge of the appropriate sign of the learning gain, for convergence to zero error in tracking a feasible desired output trajectory. It is shown that one of the new algorithms can give guaranteed convergence in the presence of actuator saturation constraints, and indicate when the requested trajectory is beyond the actuator capabilities.

Longman, Richard W.↗
DOE OSTI2026

Enabling accurate chemical modeling of shocked energetic materials using a machine learning interatomic potential

Understanding the complex chemistry of organic materials under dynamic compression is important for many applications, but it is challenging due to the large number of reactions occurring at various time scales. Here, in this study, we develop a machine learning potential based on Chebyshev polynomials to study the insensitive energetic material 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) under detonation. We discuss a strategy for constructing diverse training data needed to capture the complex chemistry of TATB. Our potential demonstrates strong transferability across a wide range of thermodynamic conditions and other explosives, enabling accurate and reliable chemical modeling of organic materials under extreme conditions. The efficiency of our approach allows for simulations over several nanoseconds and for large system sizes, providing detailed insights into the chemistry of shocked TATB. The model accurately reproduces experimental Hugoniot equation of state data, and our simulations reveal the rapid formation of nitrogen-rich carbon clusters following shock. The methods and datasets developed here offer a robust framework for accurate chemical modeling of other shocked organic energetic materials.

Chemistry↗