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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 271 records · Page 15

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

A Novel Framework for Multi-Path Data Fusion in Earth Observation and New Observing Strategies: Applications to Predicting Forest Canopy Height

Exponential growth of data from Earth Observation (EO) assets has necessitated the development of sophisticated methods for data interpretation and management. NASA’s New Observing Strategy (NOS) approach aims to coordinate operations among complex heterogenous systems of constellations, requiring advanced Artificial Intelligence and Machine Learning (AI/ML) techniques. Despite significant advancements in AI/ML across various domains, the EO and machine learning for satellite (SatML) fields remain fragmented, often relying on adapted techniques rather than domain-specific solutions. We present a novel end-to-end data fusion framework tailored specifically for EO and SatML, addressing this gap by facilitating rapid development of AI/ML applications. This framework, called, Multimodal Earth Observation Workflow for Machine Learning (MEOW-ML), sup- ports the entire AI/ML lifecycle, from dataset manipulation, to model training, evaluation, and logging, and is designed to expedite the development of next-generation NOS deployments and SOTA in EO. We apply our framework to predict canopy height model (CHM) derived from lidar data. We integrate multiple data modalities through a hierarchical, multi-path model architecture, effectively identifying and leveraging the unique strengths of each data source to enhance predictive accuracy. Our experiments demonstrate that the multi-path architecture outperforms traditional single-path models and provides significant advantages in both accuracy and computational efficiency.

Mark Moussa↗

NASA NTRSDate not supplied

Trajectory Planning and Online Performance Model Estimation for Advanced Air Mobility

We propose a framework for adaptive guidance based on optimal control and estimation using closed-loop performance models. In the proposed approach, parameters of a reduced-order performance model are estimated in real time so that trajectories and guidance commands are replanned with more accurate knowledge of the system's response and performance capabilities. To apply this methodology to flight control systems, we introduce a simple yet expressive performance model modified from a reference linear design model. The proposed framework is applied to guidance of a simulated Advanced Air Mobility class concept aircraft experiencing control effector failures. We demonstrate that, by optimizing performance model parameters in real time, guidance commands can be intelligently adjusted to recover system stability and the performance of a full-order vehicle model, even in the event of effector failures. Furthermore, the reduced-order performance model requires a fraction of the computational cost of the full-order model, facilitating real-time use of adaptive, optimal guidance.

Differential dynamic programming↗

NASA NTRSDate not supplied

Trajectory Planning and Online Performance Model Estimation for Advanced Air Mobility

Differential dynamic programming↗

NASA NTRS2000

2nd & 3rd Generation Vehicle Subsystems

This paper contains viewgraph presentation on the "2nd & 3rd Generation Vehicle Subsystems" project. The objective behind this project is to design, develop and test advanced avionics, power systems, power control and distribution components and subsystems for insertion into a highly reliable and low-cost system for a Reusable Launch Vehicles (RLV). The project is divided into two sections: 3rd Generation Vehicle Subsystems and 2nd Generation Vehicle Subsystems. The following topics are discussed under the first section, 3rd Generation Vehicle Subsystems: supporting the NASA RLV program; high-performance guidance & control adaptation for future RLVs; Evolvable Hardware (EHW) for 3rd generation avionics description; Scaleable, Fault-tolerant Intelligent Network or X(trans)ducers (SFINIX); advance electric actuation devices and subsystem technology; hybrid power sources and regeneration technology for electric actuators; and intelligent internal thermal control. Topics discussed in the 2nd Generation Vehicle Subsystems program include: design, development and test of a robust, low-maintenance avionics with no active cooling requirements and autonomous rendezvous and docking systems; design and development of a low maintenance, high reliability, intelligent power systems (fuel cells and battery); and design of a low cost, low maintenance high horsepower actuation systems (actuators).

Source record↗

NASA NTRSDate not supplied

Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility

Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process.

machine learning↗

NASA NTRSDate not supplied

Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility

machine learningautonomous systems; flight simulat↗

DOE OSTI2011

Overview of ATLAS PanDA Workload Management

The Production and Distributed Analysis System (PanDA) plays a key role in the ATLAS distributed computing infrastructure. All ATLAS Monte-Carlo simulation and data reprocessing jobs pass through the PanDA system. We will describe how PanDA manages job execution on the grid using dynamic resource estimation and data replication together with intelligent brokerage in order to meet the scaling and automation requirements of ATLAS distributed computing. PanDA is also the primary ATLAS system for processing user and group analysis jobs, bringing further requirements for quick, flexible adaptation to the rapidly evolving analysis use cases of the early datataking phase, in addition to the high reliability, robustness and usability needed to provide efficient and transparent utilization of the grid for analysis users. We will describe how PanDA meets ATLAS requirements, the evolution of the system in light of operational experience, how the system has performed during the first LHC data-taking phase and plans for the future.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1991

The 3rd Annual Controlled Structures Technology Symposium

Viewgraphs of presentations at the Controlled Structures Technology (CST) MIT Space Engineering Research Center 3rd Annual Symposium are included. Topics covered include optical interferometer testbed; active impedence matching of complex structural systems; application of CST to adaptive optics; middeck 0-G dynamics Experiment (MODE); inhibiting multiple mode vibration in controlled flexible systems; the middeck active control experiment (MACE); robust control for uncertain structures; cost averaging techniques for robust structural control; and intelligent structures technology.

Source record↗

DOE OSTI2012

Modular Power Converters for PV Applications

This report describes technical opportunities to serve as parts of a technological roadmap for Shoals Technologies Group in power electronics for PV applications. There are many different power converter circuits that can be used for solar inverter applications. The present applications do not take advantage of the potential for using common modules. We envision that the development of a power electronics module could enable higher reliability by being durable and flexible. Modules would have fault current limiting features and detection circuits such that they can limit the current through the module from external faults and can identify and isolate internal faults such that the remaining modules can continue to operate with only minimal disturbance to the utility or customer. Development of a reliable, efficient, low-cost, power electronics module will be a key enabling technology for harnessing more power from solar panels and enable plug and play operation. Power electronics for computer power supplies, communication equipment, and transportation have all targeted reliability and modularity as key requirements and have begun concerted efforts to replace monolithic components with collections of common smart modules. This is happening on several levels including (1) device level with intelligent control, (2) functional module level, and (3) system module. This same effort is needed in power electronics for solar applications. Development of modular units will result in standard power electronic converters that will have a lower installed and operating cost for the overall system. These units will lead to increased adaptability and flexibility of solar inverters. Incorporating autonomous fault current limiting and reconfiguration capabilities into the modules and having redundant modules will lead to a durable converter that can withstand the rigors of solar power generation for more than 30 years. Our vision for the technology roadmap is that there is no need for detailed design of new power converters for each new application or installation. One set of modules and controllers can be pre-developed and the only design question would be how many modules need to be in series or parallel for the specific power requirement. Then, a designer can put the modules together and add the intelligent reconfigurable controller. The controller determines how many modules are connected, but it might also ask for user input for the specific application during setup. The modules include protection against faults and can reset it, if necessary. In case of a power device failure, the controller reconfigures itself to continue limited operation until repair which might be as simple as taking the faulty module out and inserting a new module. The result is cost savings in design, maintenance, repair, and a grid that is more reliable and available. This concept would be a perfect fit for the recently announced funding opportunity announcement (DE-FOA-0000653) on Plug and Play Photovoltaics.

14 SOLAR ENERGY↗

DOE OSTI2023

The INTERSECT Open Federated Architecture for the Laboratory of the Future

A federated instrument-to-edge-to-center architecture is needed to autonomously collect, transfer, store, process, curate, and archive scientific data and reduce human-in-the-loop needs with (a) common interfaces to leverage community and custom software, (b) pluggability to permit adaptable solutions, reuse, and digital twins, and (c) an open standard to enable adoption by science facilities world-wide. The Selfdriven Experiments for Science/Interconnected Science Ecosystem (INTERSECT) Open Architecture enables science breakthroughs using intelligent networked systems, instruments and facilities with autonomous experiments, “self-driving” laboratories, smart manufacturing and artificial intelligence (AI) driven design, discovery and evaluation. It creates an open federated architecture for the laboratory of the future using a novel approach, consisting of (1) science use case design patterns, (2) a system of systems architecture, and (3) a microservice architecture.

Engelmann, Christian↗

NASA NTRS1971

Delta modulation

The conclusions of the design research of the song adaptive delta modulator are presented for source encoding voice signals. The variation of output SNR vs input signal power/when 8, 9, and 10 bit internal arithmetic is employed. Voice intelligibility tapes to test the 10-bit system are used. An analysis of a delta modulator is also presented designed to minimize the in-band rms error. This is accomplished by frequency shaping the error signal in the modulator prior to hard limiting. The result is a significant increase in the output SNR measured after low pass filtering.

Schilling, D. L.↗

NASA NTRS1988

Programming model for distributed intelligent systems

A programming model and architecture which was developed for the design and implementation of complex, heterogeneous measurement and control systems is described. The Multigraph Architecture integrates artificial intelligence techniques with conventional software technologies, offers a unified framework for distributed and shared memory based parallel computational models and supports multiple programming paradigms. The system can be implemented on different hardware architectures and can be adapted to strongly different applications.

Sztipanovits, J.↗

NASA NTRS1994

Intelligent robotics can boost America's economic growth

A case is made for strategic investment in intelligent robotics as a part of the solution to the problem of improved global competitiveness for U.S. manufacturing, a critical industrial sector. Similar cases are made for strategic investments in intelligent robotics for field applications, construction, and service industries such as health care. The scope of the country's problems and needs is beyond the capability of the private sector alone, government alone, or academia alone to solve independently of the others. National cooperative programs in intelligent robotics are needed with the private sector supplying leadership direction and aerospace and non-aerospace industries conducting the development. Some necessary elements of such programs are outlined. The National Aeronautics and Space Administration (NASA) and the Lyndon B. Johnson Space Center (JSC) can be key players in such national cooperative programs in intelligent robotics for several reasons: (1) human space exploration missions require supervised intelligent robotics as enabling tools and, hence must develop supervised intelligent robotic systems; (2) intelligent robotic technology is being developed for space applications at JSC (but has a strong crosscutting or generic flavor) that is advancing the state of the art and is producing both skilled personnel and adaptable developmental infrastructure such as integrated testbeds; and (3) a NASA JSC Technology Investment Program in Robotics has been proposed based on commercial partnerships and collaborations for precompetitive, dual-use developments.

Erickson, Jon D.↗

DOE OSTI2024

Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES)

The DOE CYDRES project is driven by the urgent need to address critical research gaps in the domain of cyber-physical security of smart buildings, including Grid-interactive Efficient Buildings (GEBs). CYDRES, a real-time advanced building resilient platform, aims to enhance the cyber-attack-immune capabilities of buildings through multi-layered prevention, detection, and adaptation mechanisms. CYDRES consists of five key modules: a multi-layer network analyzer, an Automatic Fault Detection, Diagnosis, and Prognosis (AFDDP) framework, an intelligent mode selector, a cyber-resilient control framework, and a situation awareness platform. The Network Analyzer employs a data-driven framework that includes a protocol state learning tool and a CRF (Conditional Random Field) command validator. In Hardware-In-the-Loop (HIL) testbeds, it achieved 100% detection accuracy with a false alarm rate of 3%, validating its efficacy in identifying selected cyber-attacks. The AFDDP framework leverages pattern matching, PCA (Principal Component Analysis)-based strategies, and a DBN (Dynamic Bayesian Network)-based fault diagnosis approach to pinpoint the causes of physical system abnormalities using Building Automation System (BAS) data. In HIL experiments, the AFDDP module attained a detection accuracy of over 95% with a false alarm rate below 7%. Additionally, the fault detector utilized machine learning (Random Forest) and deep learning (Multi-Layer Perceptron) methods with acoustic sensor data to achieve a 100% fault detection accuracy in Heating, Ventilation, and Air-Conditioning (HVAC) equipment. The Mode Selector offered real-time impact analysis, allowing immediate actions to protect BASs in the face of emerging threats. The cyber-resilient control framework included an adaptive Model Predictive Control (MPC) and a measurement compensator, reducing temperature violations by up to 94% and improving the total demand flexibility by up to 70% in HIL experiments. Such HIL experiments covered a cyber-attack case and a physical fault case, showcasing CYDRES’ efficiency in maintaining operational continuity during threats. The situation awareness platform in Grafana enhanced real-time threat detection and response visualization, augmenting the operational awareness for building operators. CYDRES demonstrated high technical effectiveness in various test scenarios, particularly in HIL environments. The project's phased development approach ensured efficient use of resources, highlighting its practical feasibility and readiness for commercialization. By enhancing the security and resilience of building operations, CYDRES represents a significant advance in mitigating risks associated with cyber-physical systems, thereby enhancing public confidence in the safety of modern building infrastructure. Future directions for the project include expanding testing protocols, refining AFDDP methodologies, exploring more comprehensive resilient control strategies, and testing in real commercial buildings.

42 ENGINEERING↗

DOE OSTI2023

Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models

Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at different parameter settings. Using intelligent and adaptive selection of parameters to build the emulator can drastically improve the efficiency of the calibration process. The article proposes a sequential framework with a novel criterion for parameter selection that targets learning the posterior density of the parameters. The emergent behavior from this criterion is that exploration happens by selecting parameters in uncertain posterior regions while simultaneously exploitation happens by selecting parameters in regions of high posterior density. Furthermore, the advantages of the proposed method are illustrated using several simulation experiments and a nuclear physics reaction model.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Enhancing Biopreparedness through a Model System to Understand the Molecular Mechanisms that Lead to Pathogenesis and Disease Transmission: NW-BRaVE

The science of biopreparedness to counter biological threats hinges on understanding the fundamental principles and molecular mechanisms that lead to pathogenesis and disease transmission. Our vision to address this challenge is to create a powerful and user-friendly platform to elucidate the fundamental principles of how molecular interactions drive pathogen-host relationships and host shifts. We will enable groundbreaking discoveries by integrating a wide range of structural, genomics, proteomics, and other advanced omics measurements, along with evolutionary and artificial intelligence predictions. To make sure the system is applicable to real-world problems, we will develop it in the context of a tractable model system, the small, abundant, and accessible photosynthetic cyanobacteria and their constantly co-adapting viral pathogens, cyanophages. This model will maintain the system’s applicability to real-world problems and techniques, but the overall focus will be on elucidating general principles of detecting, assessing, and surveilling molecular interaction, adaptation, and coevolution that are system agnostic and therefore extensible to other viral-host interactions. Our overall objectives are to (1) identify the molecular complexes that comprise the cyanobacteria redox macromolecular subsystem and how they dynamically change with bacteriophage infection in situ, using cryo-electron tomography; (2) profile regulatory changes during infection using proteomics, multiomics, and experimental validation, and integrate the data with in situ structures; (3) use genomics and metagenomics to determine environmental and population factors across time scales that impact the interactions between marine cyanobacteria and their cyanophage parasites, predicting the evolutionary origins of in situ structural and functional interactions, convergence and coevolution; and (4) develop a data integration and transformation platform that facilitates the integration of in situ, proteomic, and evolutionary measurements of molecular interactions to surveil diverse hosts and parasites in various environmental contexts. These objectives address Focus Area 2 Reveal Molecular Interactions Across Biological Scales for Design of Targeted Interventions. Our powerful and user-friendly platform will enhance connections between the often-siloed fields of structure, molecular phenotype, and evolutionary genomics that are key to biopreparedness, but in need of integration (Figure 1). We will build an integrated navigation tool to facilitate the effective use of globally distributed experimental data for integrated analysis and predictive modeling. The project will develop, implement, and test a platform to assess host-pathogen molecular interactions, adaptation to hosts and host shifts, and coevolution between hosts and pathogens, successfully impacting the research community by revolutionizing abilities to study any host-pathogen interaction, encourage diverse community contributions, and gain fundamental insights into how proteins adapt to new contexts. This ability will be critical for designing early interventions to address future threats. We will build surveillance training capability, aiming for a fair and equitable response to future pandemics and biothreats.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1987

Real time AI expert system for robotic applications

A computer controlled multi-robot process cell to demonstrate advanced technologies for the demilitarization of obsolete chemical munitions was developed. The methods through which the vision system and other sensory inputs were used by the artificial intelligence to provide the information required to direct the robots to complete the desired task are discussed. The mechanisms that the expert system uses to solve problems (goals), the different rule data base, and the methods for adapting this control system to any device that can be controlled or programmed through a high level computer interface are discussed.

Follin, John F.↗

DOE OSTI2024

From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments

The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. Our ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.

artificial intelligence↗