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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Variational Optical Phase Learning on a Continuous-Variable Quantum Compiler

Quantum process learning is a fundamental primitive that draws inspiration from machine learning with the goal of better studying the dynamics of quantum systems. One approach to quantum process learning is quantum compilation, whereby an analog quantum operation is digitized by compiling it into a series of basic gates. While there has been significant focus on quantum compiling for discrete-variable systems, the continuous-variable (CV) framework has received comparatively less attention. We present an experimental implementation of a CV quantum compiler that uses two-mode squeezed light to learn a Gaussian unitary operation. We demonstrate the compiler by learning a parameterized linear phase unitary through the use of target and control phase unitaries to demonstrate a factor of 5.4 increase in the precision of the phase estimation and a 3.6-fold acceleration in the time-to-solution metric when leveraging quantum resources. We further show how our approach can be extended to higher-dimensional compilation tasks. Our results are enabled by the tunable control of our cost landscape via variable squeezing, thus providing a critical framework to simultaneously increase precision and reduce time-to-solution.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Aircraft Classification Using Radar from Small Unmanned Aerial Systems for Scalable Traffic Management Emergency Response Operations

This work investigates two machine learning techniques: Support Vector Machine (SVM) and Autoencoders (AE)with SVM layer for classification of radar trajectories as General Aviation (GA), fixed-wing small Unmanned Aerial System (sUAS), or not-an-aircraft using radar data recorded from sUAS. Onboard identification of intruder aircraft type is useful for planning avoidance maneuvers and is necessary to provide autonomous systems to meet or exceed the avoidance capability of a human pilot. Aircraft classification can identify intruder aircraft that are not part of the team and may be violating a Temporary Flight Restriction. Aircraft classification is needed in monitoring an airspace where multiple aircraft are teaming on a shared task. Scalable Traffic Management for Emergency Response Operations (STEReO) is a NASA project aimed at improving disaster response by enabling large scale aircraft operations through the teaming of manned aircraft with sUAS to maximize emergency response resources. To this end, this work uses trajectories and radar derived features to classify aircraft from a multirotor sUAS. The AE + SVM generated the strongest classification overall accuracy of 93.5% using the first 4 seconds of radar track data for tracks that activated the avoidance system. Subsampling the available track data increased the available training data with the maximum aircraft recall of 0.94 achieved using the SVM with 1 second track data.

Chester V. Dolph↗
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 NTRS2012

Re-Engineering the Mission Operations System (MOS) for the Prime and Extended Mission

One of the most challenging tasks in a space science mission is designing the Mission Operations System (MOS). Whereas the focus of the project is getting the spacecraft built and tested for launch, the mission operations engineers must build a system to carry out the science objectives. The completed MOS design is then formally assessed in the many reviews. Once a mission has completed the reviews, the Mission Operation System (MOS) design has been validated to the Functional Requirements and is ready for operations. The design was built based on heritage processes, new technology, and lessons learned from past experience. Furthermore, our operational concepts must be properly mapped to the mission design and science objectives. However, during the course of implementing the science objective in the operations phase after launch, the MOS experiences an evolutional change to adapt for actual performance characteristics. This drives the re-engineering of the MOS, because the MOS includes the flight and ground segments. Using the Spitzer mission as an example we demonstrate how the MOS design evolved for both the prime and extended mission to enhance the overall efficiency for science return. In our re-engineering process, we ensured that no requirements were violated or mission objectives compromised. In most cases, optimized performance across the MOS, including gains in science return as well as savings in the budget profile was achieved. Finally, we suggest a need to better categorize the Operations Phase (Phase E) in the NASA Life-Cycle Phases of Formulation and Implementation

Extended Mission↗
NASA NTRS1990

An expert system for fault diagnosis in a Space Shuttle main engine

The detection and diagnosis of SSME faults in an early stage is important in order to allow enough time for fault preventive or corrective measurements. Since most of the faults in a complex system like SSME develop rapidly, early detection and diagnosis of faults is critical for the survival of space vehicles. An expert system has been designed for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations. This paper describes an innovative machine learning approach which is employed for the automatic training of this expert system.

Ali, Moonis↗
NASA NTRS2019

NASA Wind Tunnel "Bad Days"

Presentation summarizes NASA's most recent emergency repairs as they relate to large wind tunnel operations. These repairs resulted in lessons being learned that are beneficial for sharing with other organizations that operate wind tunnels.

Colantonio, Ron↗
NASA NTRS2010

Space Medicine in the Human System Integration Process

This slide presentation reviews the importance of integration of space medicine in the human system of lunar exploration. There is a review of historical precedence in reference to lunar surface operations. The integration process is reviewed in a chart which shows the steps from research to requirements development, requirements integration, design, verification, operations and using the lessons learned, giving more information and items for research. These steps are reviewed in view of specific space medical issues. Some of the testing of the operations are undertaken in an environment that is an analog to the exploration environment. Some of these analog environments are reviewed, and there is some discussion of the benefits of use of an analog environment in testing the processes that are derived.

Scheuring, Richard A.↗
NASA NTRS2006

Achieving Operability via the Mission System Paradigm

In the past, flight and ground systems have been developed largely-independently, with the flight system taking the lead, and dominating the development process. Operability issues have been addressed poorly in planning, requirements, design, I&T, and system-contracting activities. In many cases, as documented in lessons-learned, this has resulted in significant avoidable increases in cost and risk. With complex missions and systems, operability is being recognized as an important end-to-end design issue. Never-the-less, lessons-learned and operability concepts remain, in many cases, poorly understood and sporadically applied. A key to effective application of operability concepts is adopting a 'mission system' paradigm. In this paradigm, flight and ground systems are treated, from an engineering and management perspective, as inter-related elements of a larger mission system. The mission system consists of flight hardware, flight software, telecom services, ground data system, testbeds, flight teams, science teams, flight operations processes, procedures, and facilities. The system is designed in functional layers, which span flight and ground. It is designed in response to project-level requirements, mission design and an operations concept, and is developed incrementally, with early and frequent integration of flight and ground components.

ground systems↗
NASA NTRS2021

NASA SpaceCube Edge TPU SmallSat Card for Autonomous Operations and Onboard Science-Data Analysis

Using state-of-the-art artificial intelligence (AI)frameworks onboard spacecraft is challenging because common spacecraft processors cannot provide comparable performance to datacenters with server-grade CPUs and GPUs available for terrestrial applications and advanced deep-learning networks. This limitation makes small, lo w-p o we r AI microchip architectures, such as the Google Coral Edge Tensor Processing Unit (TPU), attractive for space missions where the application-specific design enables both high-performance and power-efficient computing for AI applications. To address these challenging considerations for space deployment, this research introduces the design and capabilities of a CubeSat-sized Edge TPU-based co-processor card, known as the SpaceCube Low-power Ed g e Artificial Intelligence Resilient Node (SC-LEARN). This design conforms to NASA’s CubeSat Card Specification (CS2) for integration into next-generation SmallSat and CubeSat systems. This paper describes the overarching architecture and design of the SC-LEARN, as well as, the supporting test card designed for rapid prototyping and evaluation. The SC-LEARN was developed with three operational modes: (1) a high-performance parallel-processing mode,(2)a fault-tolerant mode for onboard resilience, and (3) a power-saving mode with cold spares. Importantly, this research also elaborates on both training and quantization of Tensor Flow models for the SC-LEARN for use onboard with representative, open-source datasets. Lastly, we describe future research plans, including radiation-beam testing and flight demonstration.

Advanced avionics↗
NASA NTRS1981

1981 LTA technology assessment - Past and present

A four-part presentation is made of (1) lessons learned from the design and operation of the 'classic' airships of the 1920s and 30s, with respect to such issues as construction, propulsion, control, instrumentation, ground handling, habitability, aerodynamics, and structure and construction; (2) lessons learned from the development of such specialized lighter-than-air (LTA) concepts as metal-clad airships, semi-buoyant lifting bodies, experimental, sport and commercial free balloons, high-altitude platforms, and tethered aerostats; (3) the current status of LTA technology in various countries, with emphasis on significant developments in configuration, dynamics, control, structures, materials, and propulsion; and (4) a projection of future trends. It is concluded that socio-economic factors will strongly influence and encourage LTA development in the 1990s.

Ashford, R. L.↗
DOE OSTI2026

NN-OpInf

SAND2026-18878O The NN-OpInf tool is a PyTorch-based approach to operator inference that uses composable, structure-preserving neural networks to represent nonlinear operators. Operator inference is a machine learning method for inferring low-dimensional systems from data and polynomial models for system dynamics. However, many systems do not conform to polynomial structures, which NN-OpInf addresses by parameterizing operators with neural networks. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
NASA NTRS2001

Automating Trend Analysis for Spacecraft Constellations

Spacecraft trend analysis is a vital mission operations function performed by satellite controllers and engineers, who perform detailed analyses of engineering telemetry data to diagnose subsystem faults and to detect trends that may potentially lead to degraded subsystem performance or failure in the future. It is this latter function that is of greatest importance, for careful trending can often predict or detect events that may lead to a spacecraft's entry into safe-hold. Early prediction and detection of such events could result in the avoidance of, or rapid return to service from, spacecraft safing, which not only results in reduced recovery costs but also in a higher overall level of service for the satellite system. Contemporary spacecraft trending activities are manually intensive and are primarily performed diagnostically after a fault occurs, rather than proactively to predict its occurrence. They also tend to rely on information systems and software that are oudated when compared to current technologies. When coupled with the fact that flight operations teams often have limited resources, proactive trending opportunities are limited, and detailed trend analysis is often reserved for critical responses to safe holds or other on-orbit events such as maneuvers. While the contemporary trend analysis approach has sufficed for current single-spacecraft operations, it will be unfeasible for NASA's planned and proposed space science constellations. Missions such as the Dynamics, Reconnection and Configuration Observatory (DRACO), for example, are planning to launch as many as 100 'nanospacecraft' to form a homogenous constellation. A simple extrapolation of resources and manpower based on single-spacecraft operations suggests that trending for such a large spacecraft fleet will be unmanageable, unwieldy, and cost-prohibitive. It is therefore imperative that an approach to automating the spacecraft trend analysis function be studied, developed, and applied to missions such as DRACO with the intent that mission operations costs be significantly reduced. The goal of the Constellation Spacecraft Trend Analysis Toolkit (CSTAT) project is to serve as the pathfinder for a fully automated trending system to support spacecraft constellations. The development approach to be taken is evolutionary. In the first year of the project, the intent is to significantly advance the state of the art in current trending systems through improved functionality and increased automation. In the second year, the intent is to add an expert system shell, likely through the adaptation of an existing commercial-off-the-shelf (COTS) or government-off-the-shelf (GOTS) tool to implement some level of the trending intelligence that humans currently provide in manual operations. In the third year, the intent is to infuse the resulting technology into a near-term constellation or formation-flying mission to test it and gain experience in automated trending. The lessons learned from the real missions operations experience will then be used to improve the system, and to ultimately incorporate it into a fully autonomous, closed-loop mission operations system that is truly capable of supporting large constellations. In this paper, the process of automating trend analysis for spacecraft constellations will be addressed. First, the results of a survey on automation in spacecraft mission operations in general, and in trending systems in particular will be presented to provide an overview of the current state of the art. Next, a rule-based model for implementing intelligent spacecraft subsystem trending will be then presented, followed by a survey of existing COTS/GOTS tools that could be adapted for implementing such a model. The baseline design and architecture of the CSTAT system will be presented. Finally, some results obtained from initial software tests and demonstrations will be presented.

Davis, George↗
NASA NTRS2000

The Advanced Communications Technology Satellite - Performance, Reliability and Lessons Learned

The Advanced Communications Satellite (ACTS) was conceived and developed in the mid- 1980s as an experimental satellite to demonstrate unproven Ka-band technology, and potential new commercial applications and services. Since launch into geostationary orbit in September 1993. ACTS has accumulated almost seven years of essentially trouble-free operation and met all program objectives. The unique technology, service experiments. and system level demonstrations accomplished by ACTS have been reported in many forums over the past several years. As ACTS completes its final experiments activity, this paper will relate the top-level program goals that have been achieved in the design, operation, and performance of the particular satellite subsystems. Pre-launch decisions to ensure satellite reliability and the subsequent operational experiences contribute to lessons learned that may be applicable to other comsat programs.

Krawczyk, Richard J.↗
NASA NTRS2010

The Applied Meteorology Unit: Nineteen Years Successfully Transitioning Research into Operations for America's Space Program

The Applied Meteorology Unit (AMU) provides technology transition and technique development to improve operational weather support to the Space Shuttle and the entire American space program. The AMU is funded and managed by NASA and operated by a contractor that provides five meteorologists with a diverse mix of advanced degrees, operational experience, and associated skills including data processing, statistics, and the development of graphical user interfaces. The AMU's primary customers are the U.S. Air Force 45th Weather Squadron at Patrick Air Force Base, the National Weather Service Spaceflight Meteorology Group at NASA Johnson Space Center, and the National Weather Service Melbourne FL Forecast Office. The AMU has transitioned research into operations for nineteen years and worked on a wide range of topics, including new forecasting techniques for lightning probability, synoptic peak winds,.convective winds, and summer severe weather; satellite tools to predict anvil cloud trajectories and evaluate camera line of sight for Space Shuttle launch; optimized radar scan strategies; evaluated and implemented local numerical models; evaluated weather sensors; and many more. The AMU has completed 113 projects with 5 more scheduled to be completed by the end of 2010. During this rich history, the AMU and its customers have learned many lessons on how to effectively transition research into operations. Some of these lessons learned include collocating with the operational customer and periodically visiting geographically separated customers, operator submitted projects, consensus tasking process, use of operator primary advocates for each project, customer AMU liaisons with experience in both operations and research, flexibility in adapting the project plan based on lessons learned during the project, and incorporating training and other transition assistance into the project plans. Operator involvement has been critical to the AMU's remarkable success and many awards from NASA, the National Weather Association, and two citations from the Navy's Center of Excellence for Best Manufacturing Practices. This paper will present the AMU's proven methods and explain how they may be applied by other organizations to effectively transition research into operations.

Madura, John T.↗
DOE OSTI2024

Transient anisotropic kernel for probabilistic learning on manifolds

PLoM (Probabilistic Learning on Manifolds) is a method introduced in 2016 for handling small training datasets by projecting an Itô equation from a stochastic dissipative Hamiltonian dynamical system, acting as the MCMC generator, for which the KDE-estimated probability measure with the training dataset is the invariant measure. PLoM performs a projection on a reduced-order vector basis related to the training dataset, using the diffusion maps (DMAPS) basis constructed with a time-independent isotropic kernel. In this paper, we propose a new ISDE projection vector basis built from a transient anisotropic kernel, providing an alternative to the DMAPS basis to improve statistical surrogates for stochastic manifolds with heterogeneous data. The construction ensures that for times near the initial time, the DMAPS basis coincides with the transient basis. For larger times, the differences between the two bases are characterized by the angle of their spanned vector subspaces. The optimal instant yielding the optimal transient basis is determined using an estimation of mutual information from Information Theory, which is normalized by the entropy estimation to account for the effects of the number of realizations used in the estimations. Consequently, this new vector basis better represents statistical dependencies in the learned probability measure for any dimension. Three applications with varying levels of statistical complexity and data heterogeneity validate the proposed theory, showing that the transient anisotropic kernel improves the learned probability measure.

Diffusion maps↗
NASA NTRS2011

Human Spaceflight Conjunction Assessment: Lessons Learned

This viewgraph presentation reviews the process of a human space flight conjunction assessment and lessons learned from the more than twelve years of International Space Station (ISS) operations. Also, the application of these lessons learned to a recent ISS conjunction assessment with object 84180 on July 16, 2009 is also presented.

Smith, Jason T.↗
NASA NTRS2018

Human-Centered Operations

Efforts to improve operational safety often focus on preventing human error. But humans don't just make mistakes. They do, in fact, make a tremendous contribution to operational safety, and there is much to learn from what goes right. To support people in their role, the operation should be human-centered. To make the operation human-centered, the framework of the 4Ps can be used to create a clear, coherent, consistent and comprehensive guidance.

flight safety↗
NASA NTRS2011

Acquisition of Skill Proficiency Over Multiple Sessions of a Novel Rover Simulation

Following long-duration exploration transits, adaptive changes in sensorimotor function may impair the crew's ability to safely perform manual control tasks such as operating pressurized rovers. Postflight performance will also be influenced by the level of preflight skill proficiency they have attained. The purpose of this study was to characterize the acquisition of skills in a motion-based rover simulation over multiple sessions, and to investigate the effects of varying the simulation scenarios. METHODS: Twenty healthy subjects were tested in 5 sessions, with 1-3 days between sessions. Each session consisted of a serial presentation of 8 discrete tasks to be completed as quickly and accurately as possible. Each task consisted of 1) perspective-taking, using a map that defined a docking target, 2) navigation toward the target around a Martian outpost, and 3) docking a side hatch of the rover to a visually guided target. The simulator utilized a Stewart-type motion base (CKAS, Australia), single-seat cabin with triple scene projection covering 150 deg horizontal by 50 deg vertical, and joystick controller. Subjects were randomly assigned to a control group (tasks identical in the first 4 sessions) or a varied-practice group. The dependent variables for each task included accuracy toward the target and time to completion. RESULTS: The greatest improvements in time to completion occurred during the docking phase. The varied-practice group showed more improvement in perspective-taking accuracy. Perspective-taking accuracy was also affected by the relative orientation of the rover to the docking target. Skill acquisition was correlated with self-ratings of previous gaming experience. DISCUSSION: Varying task selection and difficulty will optimize the preflight acquisition of skills when performing novel operational tasks. Simulation of operational manual control will provide functionally relevant evidence regarding the impact of sensorimotor adaptation on early surface operations and what countermeasures are needed. Learning Objective: The use of a motion-based simulation to investigate decrements in the proficiency to operate pressurized rovers during early surface operations of space exploration missions, along with the acquisition of skill proficiency needed during the preflight phase of the mission.

Dean, S. L.↗