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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRS2020

Enabling Assurance in the MBSE Environment

A number of specific benefits that fit within the hallmarks of effective development are realized with implementation of model-based approaches to systems and assurance. Model Based Systems Engineering (MBSE) enabled by standardized modeling languages (e.g., SysML®) is at the core. These benefits in the context of spaceflight system challenges can include [1]: • Improved management of complex development • Reduced risk in the development process • Improved cost management • Improved design decisions With appropriate modeling techniques the assurance community also can improve early oversight and insight into project development. NASA has shown the basic constructs of SysML in an MBSE environment offer several key advantages, within a Model Based Mission Assurance (MBMA) initiative [2, 3]. These include the following: • Model viewpoints that promote rapid and systematic assessment of requirements coverage, hazard tagging and risk management • Embedded safety assessments for launch vehicles • Deployment of model assisted development of reliability products - Failure Modes and Effects Analyses (FMEAs) and Fault Trees • Test Planning • Validation and Verification of complex functions • Support of Assurance Case development for complex systems In addition, while there are benefits to be harvested, there is a realization that these do not come without effort and cost. Enabling model-based approaches requires structure, not only in an organizational context, but in a modeling context as well. There can be a steep learning curve and costs associated to train skilled modelers. But, on the other hand, not all of the assurance community need to be modelers. Models themselves must conform to ontologies that enable assurance. This places constraints upon the models and modelers. Optimums have yet to be developed where resources and constraints on modeling must be traded off in the organization and modeling efforts for projects. A number of barriers need to be overcome, as well, which pose challenges to the developers of the software that supports MBSE/MBMA. Information and data must be made to flow seamlessly through the life cycle. Because there is a wide variety of tools used in the community, to avoid the problems of the past of silos, delays, and diverging interests, information should flow among these tools to support the “single source of truth” paradigm of MBSE. This will greatly facilitate MBMA and advancement of assurance functions.

Evans, John W.↗
NASA NTRS2016

Challenges with Electrical, Electronics, and Electromechanical Parts for James Webb Space Telescope

James Webb Space Telescope (JWST) is the space-based observatory that will extend the knowledge gained by the Hubble Space Telescope (HST). Hubble focuses on optical and ultraviolet wavelengths while JWST focuses on the infrared portion of the electromagnetic spectrum, to see the earliest stars and galaxies that formed in the Universe and to look deep into nearby dust clouds to study the formation of stars and planets. JWST, which commenced creation in 1996, is scheduled to launch in 2018. It includes a suite of four instruments, the spacecraft bus, optical telescope element, Integrated Science Instrument Module (ISIM, the platform to hold the instruments), and a sunshield. The mass of JWST is approximately 6200 kg, including observatory, on-orbit consumables and launch vehicle adaptor. Many challenges were overcome while providing the electrical and electronic components for the Goddard Space Flight Center hardware builds. Other difficulties encountered included developing components to work at cryogenic temperatures, failures of electronic components during development and flight builds, Integration and Test electronic parts problems, and managing technical issues with international partners. This paper will present the context of JWST from a EEE (electrical, electronic, and electromechanical) perspective with examples of challenges and lessons learned throughout the design, development, and fabrication of JWST in cooperation with our associated partners including the Canadian Space Agency (CSA), the European Space Agency (ESA), Lockheed Martin and their respective associated partners. Technical challenges and lessons learned will be discussed.

EEE Parts↗
NASA NTRS1989

The least constraint principle for learning in neurodynamics

An adaptive neural network for auto-associative memories operating in continuous time is considered. A new learning algorithm for the weight matrix defined by explicit locations of desirable equilibrium points is introduced. The approach is based upon the minimum 'strength energy' of the weight matrix for each prescribed performance of the neural network.

Zak, Michail↗
NASA NTRS1992

Kennedy Space Center's NASA/Contractor Team-Centered Total Quality Management Seminar: Results, methods, and lessons learned

It is apparent to everyone associated with the Nation's aeronautics and space programs that the challenge of continuous improvement can be reasonably addressed only if NASA and its contractors act together in a fully integrated and cooperative manner that transcends the traditional boundaries of proprietary interest. It is, however, one thing to assent to the need for such integration and cooperation; it is quite another thing to undertake the hard tasks of turning such a need into action. Whatever else total quality management is, it is fundamentally a team-centered and team-driven process of continuous improvement. The introduction of total quality management at KSC, therefore, has given the Center a special opportunity to translate the need for closer integration and cooperation among all its organizations into specific initiatives. One such initiative that NASA and its contractors have undertaken at KSC is a NASA/Contractor team-centered Total Quality Management Seminar. It is this seminar which is the subject of this paper. The specific purposes of this paper are to describe the following: Background, development, and evolution of Kennedy Space Center's Total Quality Management Seminar; Special characteristics of the seminar; Content of the seminar; Meaning and utility of a team-centered design for TQM training; Results of the seminar; Use that one KSC contractor, EG&G Florida, Inc. has made of the seminar in its Total Quality Management initiative; and Lessons learned.

Kinlaw, Dennis C.↗
NASA NTRSDate not supplied

Detecting And Characterizing Archetypes of Unintended Consequences in Engineered Systems

When designing engineered systems, the potential for unintended consequences of design policies or design decisions exists despite best intentions. Conditions that might cause the formation of unintended consequences are often known only in hindsight. However, since these conditions are associated with a single event, it is difficult to uncover the general patterns of conditions leading to unintended consequences. In this research, patterns of conditions associated with unintended consequences are learned from historical data and represented in the form of archetypes. While previous work using systems theoretic modeling has identified high-level archetypes, this work leverages a self-organizing map to learn archetypes of unintended consequences from human-tagged risk factors in a large data set of lessons learned from adverse events at NASA. The sixty-six identified archetypes contain patterns of conditions such as complexity and human-machine interaction associated with the formation of unintended consequences. To validate the archetypes, a sample of the archetypes is represented using system dynamics in order to illustrate that the identified archetypes are specialized versions of known high-level archetypes of unintended consequences. While the research is based upon a specific dataset, the archetypes apply to any engineered system and the pattern of leading indicators open a new path to manage unintended consequences and mitigate the magnitude of potentially adverse outcomes.

Hannah S Walsh↗
NASA NTRS1998

NASA/Mir Phase 1: A Lesson Learned in Long Duration Mission Planning and Operations

The NASA/Mir Phase I Program is a precursor to the assembly and operations of the International Space Station (ISS). The Phase I Program covers a four-year period and encompasses over ten shuttle flights to the Mir, culminating with the flight of STS-91 in June 1998. The Mir-Orbiting Station (Mir-OS) provides an excellent environment, both onboard and on the ground, for learning and studying the characteristics associated with crew-tended operations on long duration missions. By the end of the Phase I program, NASA astronauts will have accumulated over two years of continuous operations experience working and living onboard the Mir-OS. Ground controllers, working in Russia and at remote sites in the United States, will have gained valuable experience in the generation, maintenance, and coordination of the information and products required to support the long duration crew members onboard the Mir-OS. The experience gained by the crew and ground support personnel in the area of long duration mission operations has resulted in a number of factors that need to be considered in the definition and implementation of the processes and products for the ISS. While there are many areas in which experience has been gained, this paper focuses on the lessons learned regarding the level of detail and flexibility of crew timelines. Provided in this paper is an overview of the characteristics associated with long duration mission timeline development and execution, a summary of applicable lessons learned identified and documented through participation in the NASA/Mir Phase I Program, a detailed discussion of "Gross Timelines" as implementation which addresses the lessons learned. Also discussed are the challenges that must be overcome in getting the ISS program to recognize, evaluate, and learn from the crew and ground controller experience gained through participation in the NASA/Mir Phase I Program.

Hagopian, Jeff↗
DOE OSTI2024

Machine Learning Aided Modeling of Granular Materials: A Review

Artificial intelligence (AI) has become a buzzy word since Google’s AlphaGo beat a world champion in 2017. In the past five years, machine learning as a subset of the broader category of AI has obtained considerable attention in the research community of granular materials. This work offers a detailed review of the recent advances in machine learning-aided studies of granular materials from the particle-particle interaction at the grain level to the macroscopic simulations of granular flow. This work will start with the application of machine learning in the microscopic particle-particle interaction and associated contact models. Then, different neural networks for learning the constitutive behaviour of granular materials will be reviewed and compared. Finally, the macroscopic simulations of practical engineering or boundary value problems based on the combination of neural networks and numerical methods are discussed. We hope readers will have a clear idea of the development of machine learning-aided modelling of granular materials via this comprehensive review work.

42 ENGINEERING↗
NASA NTRS2012

Image Mapping and Visual Attention on the Sensory Ego-Sphere

The Sensory Ego-Sphere (SES) is a short-term memory for a robot in the form of an egocentric, tessellated, spherical, sensory-motor map of the robot s locale. Visual attention enables fast alignment of overlapping images without warping or position optimization, since an attentional point (AP) on the composite typically corresponds to one on each of the collocated regions in the images. Such alignment speeds analysis of the multiple images of the area. Compositing and attention were performed two ways and compared: (1) APs were computed directly on the composite and not on the full-resolution images until the time of retrieval; and (2) the attentional operator was applied to all incoming imagery. It was found that although the second method was slower, it produced consistent and, thereby, more useful APs. The SES is an integral part of a control system that will enable a robot to learn new behaviors based on its previous experiences, and that will enable it to recombine its known behaviors in such a way as to solve related, but novel, task problems with apparent creativity. The approach is to combine sensory-motor data association and dimensionality reduction to learn navigation and manipulation tasks as sequences of basic behaviors that can be implemented with a small set of closed-loop controllers. Over time, the aggregate of behaviors and their transition probabilities form a stochastic network. Then given a task, the robot finds a path in the network that leads from its current state to the goal. The SES provides a short-term memory for the cognitive functions of the robot, association of sensory and motor data via spatio-temporal coincidence, direction of the attention of the robot, navigation through spatial localization with respect to known or discovered landmarks, and structured data sharing between the robot and human team members, the individuals in multi-robot teams, or with a C3 center.

Fleming, Katherine Achim↗
DOE OSTI2024

MLOps for Beam Controls

Machine learning operations (MLOps) is the standardization and streamlining of the ML development lifecycle to address the challenges associated with large-scale machine learning applications. The full MLOps pipeline consists of open-source tools: DataHub, MinIO and MLflow. It is being used for dataset management and model development to handle changing data dependencies, varying business needs, reproducibility, and diverse teams working with differing tools and skills. To demonstrate the completion of an MLOps pipeline for particle accelerator operations, we are deploying a simple script that computes settings for the Booster’s gradient magnet power supply. Once the demonstration is complete, we will develop and deploy ML-based optimization algorithms to improve Booster’s overall efficiency. This MLOps pipeline opens the gate to systematically develop and deploy ML applications for accelerator controls and diagnostics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

MLOps for Beam Controls

Machine learning operations (MLOps) is the standardization and streamlining of the ML development lifecycle to address the challenges associated with large-scale machine learning applications. The full MLOps pipeline consists of open-source tools: DataHub, MinIO and MLflow. It is being used for dataset management and model development to handle changing data dependencies, varying business needs, reproducibility, and diverse teams working with differing tools and skills. To demonstrate the completion of an MLOps pipeline for particle accelerator operations, we are deploying a simple script that computes settings for the Booster’s gradient magnet power supply. Once the demonstration is complete, we will develop and deploy ML-based optimization algorithms to improve Booster’s overall efficiency. This MLOps pipeline opens the gate to systematically develop and deploy ML applications for accelerator controls and diagnostics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

MLOps for Beam Controls

Machine learning operations (MLOps) is the standardization and streamlining of the ML development lifecycle to address the challenges associated with large-scale machine learning applications. The full MLOps pipeline consists of open-source tools: DataHub, MinIO and MLflow. It is being used for dataset management and model development to handle changing data dependencies, varying business needs, reproducibility, and diverse teams working with differing tools and skills. To demonstrate the completion of an MLOps pipeline for particle accelerator operations, we are deploying a simple script that computes settings for the Booster’s gradient magnet power supply. Once the demonstration is complete, we will develop and deploy ML-based optimization algorithms to improve Booster’s overall efficiency. This MLOps pipeline opens the gate to systematically develop and deploy ML applications for accelerator controls and diagnostics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Constitutive and inducible oleoresin defenses share genetic architectures and mechanisms in Pinus taeda

The oleoresin defense system of loblolly pine (Pinus taeda) protects trees from insects and pathogens and is an important source of renewable biofuels and chemicals, but the genetic basis of oleoresin production is poorly understood. We characterized the genetic architecture of oleoresin flow, resin canal number, stem wood terpene content, and monoterpene composition in two clonal populations of P. taeda. We used quantitative genetic analyses, genome-wide association studies (GWASs), multiplex network learning, and gene expression profiling to elucidate shared gene networks underlying defense traits and to identify high-quality candidates for breeding and engineering loblolly pine. Genetic analyses revealed polygenic inheritance and trait-to-trait correlations provide strong evidence for shared genes regulating constitutive and induced oleoresin flow. We identified 236 single nucleotide polymorphisms associated with oleoresin flow, resin canal number, and terpene composition and highlight candidate genes likely involved in terpene biosynthesis, cambial meristem reprogramming, and pathogen perception and immune signaling. Fourteen GWAS candidates were methyl jasmonate-responsive in tissues where resin canals initiate and terpene production occurs. Integrating quantitative genetics, GWAS, gene expression, and multiplex network analyses enabled the prioritization of high-quality candidate genes. This work advances the development of more resilient loblolly pine optimized for ecological performance, renewable chemical, and biofuel production.

genome-wide association study↗
NASA NTRS2002

Stimulus-response compatibility and psychological refractory period effects: implications for response selection

The purpose of this paper was to provide insight into the nature of response selection by reviewing the literature on stimulus-response compatibility (SRC) effects and the psychological refractory period (PRP) effect individually and jointly. The empirical findings and theoretical explanations of SRC effects that have been studied within a single-task context suggest that there are two response-selection routes-automatic activation and intentional translation. In contrast, all major PRP models reviewed in this paper have treated response selection as a single processing stage. In particular, the response-selection bottleneck (RSB) model assumes that the processing of Task 1 and Task 2 comprises two separate streams and that the PRP effect is due to a bottleneck located at response selection. Yet, considerable evidence from studies of SRC in the PRP paradigm shows that the processing of the two tasks is more interactive than is suggested by the RSB model and by most other models of the PRP effect. The major implication drawn from the studies of SRC effects in the PRP context is that response activation is a distinct process from final response selection. Response activation is based on both long-term and short-term task-defined S-R associations and occurs automatically and in parallel for the two tasks. The final response selection is an intentional act required even for highly compatible and practiced tasks and is restricted to processing one task at a time. Investigations of SRC effects and response-selection variables in dual-task contexts should be conducted more systematically because they provide significant insight into the nature of response-selection mechanisms.

Review Literature↗
DOE OSTI2024

Task-specific sensor optical designs

A method and system architecture for designing a compressive sensing matrix for machine learning includes receiving an image associated with a classification task and; generating a sensing matrix. The sensing matrix includes an array of nonzero elements of the image. A prism array of prism elements is in communication with the sensing matrix. A row of values corresponding with an input angle of the prism array is mapped to a respective column corresponding with a detector. Then the detector detects light refracted at an output angle dictated by the physical shape of the prism element. A physical model of the detector is fabricated and generates a compressed representation of the image. A machine learning classification algorithm is applied to the compressed representation of the image and generates an optimized non-invertible final determination of the image.

Birch, Gabriel Carlisle↗
DOE OSTI2026

Neural entropy-stable conservative flux form neural networks for learning hyperbolic conservation laws

We propose a neural entropy-stable conservative flux form neural network (NESCFN) for learning hyperbolic conservation laws and their associated entropy functions directly from solution trajectories, without requiring any predefined numerical discretization. While recent neural network architectures have successfully integrated classical numerical principles into learned models, most rely on prior knowledge of the governing equations or assume a fixed discretization. Our approach removes this dependency by embedding entropy-stable design principles into the learning process itself, enabling the discovery of physically consistent dynamics in a fully data-driven setting. By jointly learning both the flux function and a corresponding entropy, NESCFN promotes conservation and entropy dissipation, which is critical for long-term stability and fidelity in the system of hyperbolic conservation laws. Furthermore, numerical results demonstrate that the method achieves stability and conservation over extended time horizons and accurately captures shock propagation speeds, even without oracle access to future-time solution profiles in the training data.

Conservative flux form↗
DOE OSTI2025

A physics-constrained neural ordinary differential equations approach for robust learning of stiff chemical kinetics

The high computational cost associated with solving for detailed chemistry poses a significant challenge for predictive computational fluid dynamics (CFD) simulations of turbulent reacting flows. While deep learning techniques have been explored to develop faster surrogate models, they often fail to integrate reliably with CFD solvers. This instability arises because traditional deep learning approaches optimize for training error without ensuring compatibility with ordinary differential equation (ODE) solvers, resulting in accumulation of errors over time. Recently, neuralODE (NODE) based approaches have been shown to be a promising technique to emulate and accelerate detailed chemistry computations. Here, in the present work, we extend this NODE framework for stiff chemical kinetics by incorporating mass conservation constraints directly into the loss function during training. This ensures that the total mass as well as the individual elemental species masses are conserved in an a-posteriori manner. Proof-of-concept studies are performed with the novel physics-constrained NODE (PC-NODE) approach for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions. It is demonstrated that the PC-NODE framework not only improves the physical consistency of the resulting data-driven model with respect to mass conservation criteria, but also improves training efficiency. PC-NODE is shown to achieve 2–100× speedup relative to the hydrogen-air detailed chemical mechanism depending on the type of the ODE solver (implicit or explicit) used during autoregressive inference tests. Lastly, a-posteriori studies are performed wherein the trained PC-NODE model is coupled with a CFD solver. It is shown that higher accuracy is achieved with PC-NODE relative to the purely data-driven NODE approach. Moreover, PC-NODE also exhibits robustness and generalizability to unseen initial conditions from within (interpolative capability) as well as outside (extrapolative capability) the training regime.

computational combustion↗