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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Ares I-X Ground Diagnostic Prototype

Automating prelaunch diagnostics for launch vehicles offers three potential benefits. First, it potentially improves safety by detecting faults that might otherwise have been missed so that they can be corrected before launch. Second, it potentially reduces launch delays by more quickly diagnosing the cause of anomalies that occur during prelaunch processing. Reducing launch delays will be critical to the success of NASA's planned future missions that require in-orbit rendezvous. Third, it potentially reduces costs by reducing both launch delays and the number of people needed to monitor the prelaunch process. NASA is currently developing the Ares I launch vehicle to bring the Orion capsule and its crew of four astronauts to low-earth orbit on their way to the moon. Ares I-X will be the first unmanned test flight of Ares I. It is scheduled to launch on October 27, 2009. The Ares I-X Ground Diagnostic Prototype is a prototype ground diagnostic system that will provide anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage thrust vector control (TVC) and for the associated ground hydraulics while it is in the Vehicle Assembly Building (VAB) at John F. Kennedy Space Center (KSC) and on the launch pad. It will serve as a prototype for a future operational ground diagnostic system for Ares I. The prototype combines three existing diagnostic tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool that is commercially produced by Qualtech Systems, Inc. It uses a qualitative model of failure propagation to perform fault isolation and diagnostics. We adapted an existing TEAMS model of the TVC to use for diagnostics and developed a TEAMS model of the ground hydraulics. The second tool, Spacecraft Health Inference Engine (SHINE), is a rule-based expert system developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification. The prototype uses the outputs of SHINE as inputs to TEAMS. The third tool, the Inductive Monitoring System (IMS), is an anomaly detection tool developed at NASA Ames Research Center and is currently used to monitor the International Space Station Control Moment Gyroscopes. IMS automatically "learns" a model of historical nominal data in the form of a set of clusters and signals an alarm when new data fails to match this model. IMS offers the potential to detect faults that have not been modeled. The three tools have been integrated and deployed to Hangar AE at KSC where they interface with live data from the Ares I-X vehicle and from the ground hydraulics. The outputs of the tools are displayed on a console in Hangar AE, one of the locations from which the Ares I-X launch will be monitored. The full paper will describe how the prototype performed before the launch. It will include an analysis of the prototype's accuracy, including false-positive rates, false-negative rates, and receiver operating characteristics (ROC) curves. It will also include a description of the prototype's computational requirements, including CPU usage, main memory usage, and disk usage. If the prototype detects any faults during the prelaunch period then the paper will include a description of those faults. Similarly, if the prototype has any false alarms then the paper will describe them and will attempt to explain their causes.

Schwabacher, Mark↗
NASA NTRS2015

Urine Pretreatment History and Perspective in NASA Human Spaceflight

Urine pretreatment is a technology that may seem to have small mass impacts in future spaceflight missions, but can have significant impacts on reliability, life, and performance of the rest of the wastewater management and recovery systems. NASA has experience with several different urine pretreatment systems, including those flow on the space shuttle, evaluated for NASA waste collection systems or used in Russian commodes on ISS, or developed by NASA or industry as alternatives. Each has had unique requirements for shelf life, operational life, and the life or conditions of the stored, treated urine. Each was evaluated under different test conditions depending on mission, and depending on testing experience developed over NASA's history. Those that were flown led to further lessons learned about hardware compatibility and control. As NASA looks forward to human spaceflight missions beyond low Earth orbit, these techniques need to be evaluated in new light. Based on published design reference missions, candidate requirements can be derived for future systems. Initial comparisons between these requirements and previous performance or test results can be performed. In many cases these comparisons reveal data gaps. Successful previous performance is not enough to address current needs.

Anderson, Molly↗
NASA NTRS2018

Fractographic Analysis of 60-Nitinol Bearing Races

Fracture surfaces on a failed bearing race made from 60-Nitinol (60 wt% Ni - 40 wt% Ti) were analyzed. The bearing had experienced excessive localized heat due to an issue during machining that may have resulted in dimensional distortion. However, the classical fracture features that were identified by scanning electron microscopy and the fracture origins coincided with high concentrations of TiC-based contamination. Lessons learned for processing and quality control are discussed. An attempt to use x-ray diffraction to directly measure residual stresses imparted from the required heat treatment is also discussed.

intermetallics↗
NASA NTRSDate not supplied

Modified Autogenic Feedback Training Produces Effect on Motion Sickness

Motion sickness is a common physiological reaction to provocative motion and is characterized by a constellation of symptoms, including stomach awareness, nausea, emesis, pallor, sweating, hypersalivation, and fatigue. The emergence of these symptoms can pose a significant threat to safety, particularly in the context of aviation. Given its prevalence among aviators and its detrimental impact on performance, researchers have endeavored to identify effective countermeasures for motion sickness. Currently, many of the existing interventions are pharmacological in nature and while effective, they present a problem due to their associated adverse side effects. A modified two-hour version of Autogenic Feedback Training Exercise (AFTE) could be an effective countermeasure to motion sickness without adverse side effects. AFTE combines principles of autogenic therapy, biofeedback, and learning to teach individuals to control their own internal physiological reactions through a series of relaxation and arousal exercises. AFTE was administered over six days. On the first day, participants were exposed to provocative motion via a rotating chair test to collect baseline physiological data. AFTE sessions were conducted on four consecutive days and lasted approximately 30 minutes each session. Participants were exposed to the rotating chair again on the 6th day. Results: Participants (n = 16) were evaluated on the number of rotations experienced and cumulative minutes spun in the chair. Participants tended to ride longer (M = 21.0 min, SD = 16.56) and tolerated more rotations (M = 244.8 rotations, SD = 310.62) on their second rotating chair test compared to baseline (M = 14.0 min, SD = 9.35; M = 125.8 rotations, SD = 124.08; t(15) = 2.21, p = .02). A moderate effect size was recorded (Hedges’s g = 0.44). A modified two-hour version of the AFTE is effective at increasing tolerance of symptoms associated with motion sickness.

motion sickness↗
DOE OSTI2021

Transforming Energy through Computational Science: Computing for Clean Energy

This fact sheet discusses the opportunity space for NREL's computational science capabilities to address national clean energy objectives. Achieving a carbon-free power sector by 2035 as a step towards a decarbonized U.S. energy economy in 2050 will require major advances in power generation, autonomous energy systems, transportation, and buildings/communities. Development of integrated modeling approaches for complex energy systems will be essential for deployment. Success requires developments in optimization and control theory, complemented by machine learning (ML) and artificial intelligence (AI), all of which in turn need targeted investments in breadth and scale of computing. This document defines an opportunity space where: embracing computing can link established research and development (R&D) to a decarbonization agenda; pursuing emerging approaches can accelerate the pace of technology advancement across the portfolio; and leading by example could reduce the carbon footprint of computing worldwide.

advanced computing↗
DOE OSTI2021

BOOSTR: A Dataset for Accelerator Control Systems

The Booster Operation Optimization Sequential Time-series for Regression (BOOSTR) dataset was created to provide a cycle-by-cycle time series of readings and settings from instruments and controllable devices of the Booster, Fermilab's Rapid-Cycling Synchrotron (RCS) operating at 15~Hz. BOOSTR provides a time series from 55 device readings and settings that pertain most directly to the high-precision regulation of the Booster's gradient magnet power supply (GMPS). To our knowledge, this is one of the first well-documented datasets of accelerator device parameters made publicly available. We are releasing it in the hopes that it can be used to demonstrate aspects of artificial intelligence for advanced control systems, such as reinforcement learning and autonomous anomaly detection.

43 PARTICLE ACCELERATORS↗
NASA NTRS2010

The Right Stuff: A Look Back at Three Decades of Flight Controller Training for Space Shuttle Mission Operations

This paper will summarize the thirty-year history of Space Shuttle operations from the perspective of training in NASA Johnson Space Center's Mission Control Center. It will focus on training and development of flight controllers and instructors, and how training practices have evolved over the years as flight experience was gained, new technologies developed, and programmatic needs changed. Operations of human spaceflight systems is extremely complex, therefore the training and certification of operations personnel is a critical piece of ensuring mission success. Mission Control Center (MCC-H), at the Lyndon B. Johnson Space Center, in Houston, Texas manages mission operations for the Space Shuttle Program, including the training and certification of the astronauts and flight control teams. This paper will give an overview of a flight control team s makeup and responsibilities during a flight, and details on how those teams are trained and certified. The training methodology for developing flight controllers has evolved significantly over the last thirty years, while the core goals and competencies have remained the same. In addition, the facilities and tools used in the control center have evolved. These changes have been driven by many factors including lessons learned, technology, shuttle accidents, shifts in risk posture, and generational differences. Flight controllers will share their experiences in training and operating the Space Shuttle throughout the Program s history. A primary method used for training Space Shuttle flight control teams is by running mission simulations of the orbit, ascent, and entry phases, to truly "train like you fly." The audience will learn what it is like to perform a simulation as a shuttle flight controller. Finally, we will reflect on the lessons learned in training for the shuttle program, and how those could be applied to future human spaceflight endeavors.

Dittemore, Gary D.↗
NASA NTRS1990

Knowledge/geometry-based Mobile Autonomous Robot Simulator (KMARS)

Ongoing applied research is focused on developing guidance system for robot vehicles. Problems facing the basic research needed to support this development (e.g., scene understanding, real-time vision processing, etc.) are major impediments to progress. Due to the complexity and the unpredictable nature of a vehicle's area of operation, more advanced vehicle control systems must be able to learn about obstacles within the range of its sensor(s). A better understanding of the basic exploration process is needed to provide critical support to developers of both sensor systems and intelligent control systems which can be used in a wide spectrum of autonomous vehicles. Elcee Computek, Inc. has been working under contract to the Flight Dynamics Laboratory, Wright Research and Development Center, Wright-Patterson AFB, Ohio to develop a Knowledge/Geometry-based Mobile Autonomous Robot Simulator (KMARS). KMARS has two parts: a geometry base and a knowledge base. The knowledge base part of the system employs the expert-system shell CLIPS ('C' Language Integrated Production System) and necessary rules that control both the vehicle's use of an obstacle detecting sensor and the overall exploration process. The initial phase project has focused on the simulation of a point robot vehicle operating in a 2D environment.

Cheng, Linfu↗
NASA NTRS2011

The Right Stuff: A Look Back at Three Decades of Flight Controller Training for Space Shuttle Mission Operations

Operations of human spaceflight systems is extremely complex, therefore the training and certification of operations personnel is a critical piece of ensuring mission success. Mission Control Center (MCC-H), at the Lyndon B. Johnson Space Center, in Houston, Texas manages mission operations for the Space Shuttle Program, including the training and certification of the astronauts and flight control teams. This paper will give an overview of a flight control team s makeup and responsibilities during a flight, and details on how those teams are trained and certified. The training methodology for developing flight controllers has evolved significantly over the last thirty years, while the core goals and competencies have remained the same. In addition, the facilities and tools used in the control center have evolved. These changes have been driven by many factors including lessons learned, technology, shuttle accidents, shifts in risk posture, and generational differences. Flight controllers will share their experiences in training and operating the Space Shuttle throughout the Program s history. A primary method used for training Space Shuttle flight control teams is by running mission simulations of the orbit, ascent, and entry phases, to truly "train like you fly." The reader will learn what it is like to perform a simulation as a shuttle flight controller. Finally, the paper will reflect on the lessons learned in training for the shuttle program, and how those could be applied to future human spaceflight endeavors. These endeavors could range from going to the moon or to Mars. The lessons learned from operating the space shuttle for over thirty years will help the space industry build the next human transport space vehicle and inspire the next generation of space explorers.

Dittemore, Gary D.↗
DOE OSTI2026

Physics vs structure: A systematic benchmark of learning strategies for multi-zone building thermal dynamics

Recent advances in physics-informed and data-driven machine learning promise improved thermal models for advanced building control, yet there is limited quantitative evidence on when added physics structure and architectural complexity are beneficial. Here, this work presents a systematic benchmark of five representative system identification methods for modeling multi-zone building thermal dynamics: linear state-space models, multi-layer perceptrons, neural state-space models, neural ordinary differential equations, and physically-consistent neural networks. The methods are evaluated across multiple data regimes and zone coupling strategies. Using a high-fidelity multi-zone commercial building emulator, we examine short-term and long-term prediction accuracy, computational efficiency, and ease of development. Our results reveal critical trade-offs between prediction performance, model complexity, and physical consistency. We demonstrate that decoupled, nonlinear black-box models consistently outperform coupled physics-constrained architectures in both predictive accuracy and out-of-distribution robustness in majority of the test cases for the building type considered in the study. Our findings quantify the cost of complexity in building thermal modeling and provide concrete, actionable, scenario-based guidelines for selecting model classes for control-oriented applications.

Building thermal modeling↗
NASA NTRS2011

Space Vehicle Powerdown Philosophies Derived from the Space Shuttle Program

In spaceflight, electrical power is a vital but limited resource. Almost every spacecraft system, from avionics to life support systems, relies on electrical power. Since power can be limited by the generation system s performance, available consumables, solar array shading, or heat rejection capability, vehicle power management is a critical consideration in spacecraft design, mission planning, and real-time operations. The purpose of this paper is to capture the powerdown philosophies used during the Space Shuttle Program. This paper will discuss how electrical equipment is managed real-time to adjust the overall vehicle power level to ensure that systems and consumables will support changing mission objectives, as well as how electrical equipment is managed following system anomalies. We will focus on the power related impacts of anomalies in the generation systems, air and liquid cooling systems, and significant environmental events such as a fire, decrease in cabin pressure, or micrometeoroid debris strike. Additionally, considerations for executing powerdowns by crew action or by ground commands from Mission Control will be presented. General lessons learned from nearly 30 years of Space Shuttle powerdowns will be discussed, including an in depth case-study of STS-117. During this International Space Station (ISS) assembly mission, a failure of computers controlling the ISS guidance, navigation, and control system required that the Space Shuttle s maneuvering system be used to maintain attitude control. A powerdown was performed to save power generation consumables, thus extending the docked mission duration and allowing more time to resolve the issue.

Willsey, Mark↗
NASA NTRS2012

Controllable Solid Propulsion Combustion and Acoustic Knowledge Base Improvements

Controllable solid propulsion systems have distinctive combustion and acoustic environments that require enhanced testing and analysis techniques to progress this new technology from development to production. In a hot gas valve actuating system, the movement of the pintle through the hot gas exhibits complex acoustic disturbances and flow characteristics that can amplify induced pressure loads that can damage or detonate the rocket motor. The geometry of a controllable solid propulsion gas chamber can set up unique unsteady flow which can feed acoustic oscillations patterns that require characterization. Research in this area aids in the understanding of how best to design, test, and analyze future controllable solid rocket motors using the lessons learned from past government programs as well as university research and testing. This survey paper will give the reader a better understanding of the potentially amplifying affects propagated by a controllable solid rocket motor system and the knowledge of the tools current available to address these acoustic disturbances in a preliminary design. Finally the paper will supply lessons learned from past experiences which will allow the reader to come away with understanding of what steps need to be taken when developing a controllable solid rocket propulsion system. The focus of this survey will be on testing and analysis work published by solid rocket programs and from combustion and acoustic books, conference papers, journal articles, and additionally from subject matter experts dealing currently with controllable solid rocket acoustic analysis.

McCauley, Rachel↗
NASA NTRS1990

Neural networks for aircraft control

Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

Linse, Dennis↗
NASA NTRS2000

Intelligence Applied to Air Vehicles

The exponential growth in information technology has provided the potential for air vehicle capabilities that were previously unavailable to mission and vehicle designers. The increasing capabilities of computer hardware and software, including new developments such as neural networks, provide a new balance of work between humans and machines. This paper will describe several NASA projects, and review results and conclusions from ground and flight investigations where vehicle intelligence was developed and applied to aeronautical and space systems. In the first example, flight results from a neural network flight control demonstration will be reviewed. Using, a highly-modified F-15 aircraft, a NASA/Dryden experimental flight test program has demonstrated how the neural network software can correctly identify and respond to changes in aircraft stability and control characteristics. Using its on-line learning capability, the neural net software would identify that something in the vehicle has changed, then reconfigure the flight control computer system to adapt to those changes. The results of the Remote Agent software project will be presented. This capability will reduce the cost of future spacecraft operations as computers become "thinking" partners along with humans. In addition, the paper will describe the objectives and plans for the autonomous airplane program and the autonomous rotorcraft project. Technologies will also be developed.

Rosen, Robert↗
DOE OSTI2026

Numerical Modeling & Optimization of the iProTech Pitching Inertial Pump (PIP) Wave Energy Converter (WEC) (CRADA Final Report)

This project represents a continuation of the collaboration between iProTech and NLR to simulate, optimize and design the iProTech Pitching Inertial Pump (PIP) device. The objectives of this TEAMER project are twofold: 1. Refining the physical characteristics of the existing iProTech PIP WEC-Sim model to enhance the model’s fidelity and include controllable components. Key model enhancements target the inclusion of Coulomb friction, the introduction of a controllable bypass valve, and the replacement of traditional check valves with advanced motorized ones. 2. Exploring traditional and advanced control algorithms. From traditional methods like latching control to cutting-edge reinforcement learning (RL) algorithms, the goal is to ensure the PIP device's adaptability and optimal performance across a range of ocean conditions. NLR is tasked with augmenting the WEC-Sim model and implementing the control algorithms, culminating in performance comparison analyses. iProTech will update their existing 3D models, advise on model improvements, and determine crucial system metrics. WEC-Sim, developed in MATLAB/SIMULINK with Simscape Multibody, is the main piece of software that will be used in this project. Coupled with the MATLAB RL Toolbox, it offers a robust platform for in-depth simulation and optimization of the iProTech PIP device. Building on previous work to explore the PIP design space and optimize its geometry, mass distribution, center of gravity and other key parameters, this project aims to refine iProTech’s existing numerical models and develop effective control algorithms that can seamlessly integrate into their future hardware testing campaigns.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Differentiable multiphase flow model for physics-informed machine learning in reservoir pressure management

Accurate subsurface reservoir pressure control is extremely challenging due to geological heterogeneity and multiphase fluid-flow dynamics. Predicting behavior in this setting relies on high-fidelity physics-based simulations that are computationally expensive. Yet, the uncertain, heterogeneous properties that control these flows make it necessary to perform many of these expensive simulations, which is often prohibitive. To address these challenges, we introduce a physics-informed machine learning workflow that couples a fully differentiable multiphase flow simulator, which is implemented in the DPFEHM framework with a convolutional neural network (CNN). The CNN learns to predict fluid extraction rates from heterogeneous permeability fields to enforce pressure limits at critical reservoir locations. By incorporating transient multiphase flow physics into the training process, our method enables more practical and accurate predictions for realistic injection-extraction scenarios compared to previous works. To speed up training, we pretrain the model on single-phase, steady-state simulations and then finetune it on full multiphase scenarios, which dramatically reduces the computational cost. We demonstrate that high-accuracy training can be achieved with fewer than three thousand full-physics multiphase flow simulations – compared to previous estimates requiring up to ten million. This drastic reduction in the number of simulations is achieved by leveraging transfer learning from much less expensive single phase simulations.

25 ENERGY STORAGE↗
NASA NTRS2011

Quadrocopter Control Design and Flight Operation

A limiting factor in control system design and analysis for spacecraft is the inability to physically test new algorithms quickly and cheaply. Test flights of space vehicles are costly and take much preparation. As such, EV41 recently acquired a small research quadrocopter that has the ability to be a test bed for new control systems. This project focused on learning how to operate, fly, and maintain the quadrocopter, as well as developing and testing protocols for its use. In parallel to this effort, developing a model in Simulink facilitated the design and analysis of simple control systems for the quadrocopter. Software provided by the manufacturer enabled testing of the Simulink control system on the vehicle.

Karwoski, Katherine↗
NASA NTRS2011

Ensuring Success of Adaptive Control Research Through Project Lifecycle Risk Mitigation

Lessons Learne: 1. Design-out unnecessary risk to prevent excessive mitigation management during flight. 2. Consider iterative checkouts to confirm or improve human factor characteristics. 3. Consider the total flight test profile to uncover unanticipated human-algorithm interactions. 4. Consider test card cadence as a metric to assess test readiness. 5. Full-scale flight test is critical to development, maturation, and acceptance of adaptive control laws for operational use.

Pavlock, Kate M.↗