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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Design, analysis, and control of a large transport aircraft utilizing selective engine thrust as a backup system for the primary flight control

A study has been conducted to determine the capability to control a very large transport airplane with engine thrust. This study consisted of the design of an 800-passenger airplane with a range of 5000 nautical miles design and evaluation of a flight control system, and design and piloted simulation evaluation of a thrust-only backup flight control system. Location of the four wing-mounted engines was varied to optimize the propulsive control capability, and the time constant of the engine response was studied. The goal was to provide level 1 flying qualities. The engine location and engine time constant did not have a large effect on the control capability. The airplane design did meet level 1 flying qualities based on frequencies, damping ratios, and time constants in the longitudinal and lateral-directional modes. Project pilots consistently rated the flying qualities as either level 1 or level 2 based on Cooper-Harper ratings. However, because of the limited control forces and moments, the airplane design fell short of meeting the time required to achieve a 30 deg bank and the time required to respond a control input.

Gerren, Donna S.↗
NASA NTRS2010

Smart Sensor Demonstration Payload

Sensors are a critical element to any monitoring, control, and evaluation processes such as those needed to support ground based testing for rocket engine test. Sensor applications involve tens to thousands of sensors; their reliable performance is critical to achieving overall system goals. Many figures of merit are used to describe and evaluate sensor characteristics; for example, sensitivity and linearity. In addition, sensor selection must satisfy many trade-offs among system engineering (SE) requirements to best integrate sensors into complex systems [1]. These SE trades include the familiar constraints of power, signal conditioning, cabling, reliability, and mass, and now include considerations such as spectrum allocation and interference for wireless sensors. Our group at NASA s John C. Stennis Space Center (SSC) works in the broad area of integrated systems health management (ISHM). Core ISHM technologies include smart and intelligent sensors, anomaly detection, root cause analysis, prognosis, and interfaces to operators and other system elements [2]. Sensor technologies are the base fabric that feed data and health information to higher layers. Cost-effective operation of the complement of test stands benefits from technologies and methodologies that contribute to reductions in labor costs, improvements in efficiency, reductions in turn-around times, improved reliability, and other measures. ISHM is an active area of development at SSC because it offers the potential to achieve many of those operational goals [3-5].

Schmalzel, John↗
NASA NTRS2012

Predicting Loss-of-Control Boundaries Toward a Piloting Aid

This work presents an approach to predicting loss-of-control with the goal of providing the pilot a decision aid focused on maintaining the pilot's control action within predicted loss-of-control boundaries. The predictive architecture combines quantitative loss-of-control boundaries, a data-based predictive control boundary estimation algorithm and an adaptive prediction method to estimate Markov model parameters in real-time. The data-based loss-of-control boundary estimation algorithm estimates the boundary of a safe set of control inputs that will keep the aircraft within the loss-of-control boundaries for a specified time horizon. The adaptive prediction model generates estimates of the system Markov Parameters, which are used by the data-based loss-of-control boundary estimation algorithm. The combined algorithm is applied to a nonlinear generic transport aircraft to illustrate the features of the architecture.

Barlow, Jonathan↗
NASA NTRS2013

Management of the Atmosphere Resource Recovery and Environmental Monitoring Project

The Advanced Exploration Systems Program's Atmosphere Resource Recovery and Environmental Monitoring (ARREM) project is working to further optimize atmosphere revitalization and environmental monitoring system architectures. This paper discusses project management strategies that tap into skill sets across multiple engineering disciplines, projects, field centers, and industry to achieve the project success. It is the project's objective to contribute to system advances that will enable sustained exploration missions beyond Lower Earth Orbit (LEO) and improve affordability by focusing on the primary goals of achieving high reliability, improving efficiency, and reducing dependence on ground-based logistics resupply. Technology demonstrations are achieved by infusing new technologies and concepts with existing developmental hardware and operating in a controlled environment simulating various crewed habitat scenarios. The ARREM project's strengths include access to a vast array of existing developmental hardware that perform all the vital atmosphere revitalization functions, exceptional test facilities to fully evaluate system performance, and a well-coordinated partnering effort among the NASA field centers and industry partners to provide the innovative expertise necessary to succeed.

Roman, Monsi↗
NASA NTRSDate not supplied

Characterizing the Risk: Review of Sensorimotor Evidence and Research Roadmap

BACKGROUND: NASA’s Artemis program will take astronauts back to the lunar surface for the first time in almost 50 years. Despite the successes of the previous Apollo program, the Artemis missions will differ in duration, vehicle characteristics, and landing tasks that may exacerbate the risks to crew health/safety and mission objectives. NASA’s Human Research Program identifies the risk of altered sensorimotor/vestibular function impacting critical mission tasks as one of the top priority risks to lunar exploration missions. This session will review the existing evidence and remaining gaps in knowledge for the sensorimotor risk. DESCRIPTION: Alterations in sensorimotor processing during spaceflight can lead to motion sickness, spatial disorientation, and decrements in postural control, locomotion, and fine motor control during and following gravity-transitions. The risk of impairment is greatest during and soon after gravity-transitions, when performance decrements may have high operational impacts (e.g., manual landings, immediate egress following landing, and early extravehicular activities (EVAs)). Recent studies have specifically improved the risk characterization of changes in perception, motion sickness, postural and locomotor control, manual control, and fine-motor coordination. However, given the difficulty in obtaining measurements during and soon after gravity-transitions, evidence for initial decrements immediately following gravity-transitions remains limited. The most significant gaps in the risk include manual control ability around gravity-transitions, the incidence and severity of motion sickness during landings, and the ability to perform egress/EVAs soon after gravity-transition. To address these gaps, current research roadmaps leverage both spaceflight studies and ground-based analogs for risk characterization and countermeasure development/validation. DISCUSSION: This panel will further describe the current sensorimotor research strategies with an emphasis on the operational scenarios of manual control, crew egress, and EVA. The goal of this research is to accommodate the needs of the crew and facilitate human capabilities to ensure lunar mission success. This work will prepare NASA for successful Artemis missions and enable the next giant leap, the exploration of Mars.

Timothy Ryan Macaulay↗
NASA NTRS2017

Multi-Objective Reinforcement Learning-Based Deep Neural Networks for Cognitive Space Communications

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

space archtiecture↗
NASA NTRS2017

Multi-Objective Reinforcement Learning-based Deep Neural Networks for Cognitive Space Communications

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

space archtiecture↗
NASA NTRS1996

Methodology for Physics and Engineering of Reliable Products

Physics of failure approaches have gained wide spread acceptance within the electronic reliability community. These methodologies involve identifying root cause failure mechanisms, developing associated models, and utilizing these models to inprove time to market, lower development and build costs and higher reliability. The methodology outlined herein sets forth a process, based on integration of both physics and engineering principles, for achieving the same goals.

failure analysis electronics reliability PACT (pre↗
DOE OSTI2019

Integrated Solutions for Optimized Performance (ISOP) Final Technical Report

The goal of this project was to demonstrate technical feasibility of an integrated approach for deep energy retrofits by packaging innovative technologies with smart communicating controls that represent the next generation of energy efficiency. The integrated solutions for optimized performance (ISOP) package integrates (i) advanced lighting and plug load controls; (ii) intelligent daylight harvesting through automated self-powered shades and window films; (iii) advanced HVAC controls sequences (ASHRAE Guideline (GDL) 36); and (iv) fault detection and diagnosis (FDD) and continuous commissioning (CCx) using advanced analytics. These technologies address energy use across multiple building end uses that represent more than 60% of the energy consumption in all commercial buildings. The project was structured to be implemented over three budget periods: Budget Period 1 scope included site selection, project design, site-specific customization of technology packages, procurement of technologies, initiation of installation of technologies, the development of a measurement and verification plan and collection of pre-retrofit data collection. The first year go/no-go decision was based on securing host sites representing 250,000 sq. ft. of occupied operational spaces, savings analysis, technology procurement and initiation of installation. Budget Period 2 planned to complete technology installations and initiate data collection of post-retrofit performance data. The second year go/no-go was to be the completion of technology installations and commissioning, metering and data collecting to support the requirements laid out in site specific M&V plans. Budget Period 3 planned to collect and analyze post-retrofit performance data and provide data and reporting to support energy efficiency rebate programs. After the team completed activities planned for Budget Period 1, the team reached a Go/No-Go Decision Point. At that point, TRC recommended that DOE discontinue the project without prejudice due to challenges with securing demonstration sites to meet the full 250,000 sf of occupied operational spaces and DOE concurred. In Budget Period 1, the TRC team developed criteria to recruit and evaluate potential demonstration host sites. TRC worked with Princeton University, the project demonstration partner, to identify buildings on their campus as potential candidates for the demonstration. Following the selection of demonstration sites, the TRC team customized energy technology packages according to the needs and characteristics of each site. TRC defined technology specifications, identified vendors, and conducted analysis to estimate energy and cost savings potential. The project team then developed schedules for technology procurement processes and scheduled for installation at all selected demonstration sites. The project team worked with Princeton University and DOE to develop an approach to verify energy savings achieved through measurement and verification (M&V) 2.0 methods. Princeton University initiated open bids to procure the necessary materials and installation contractors. Bid results came back higher than estimates due to a couple of factors – lack of familiarity of the bidders with the proposed technologies and lack of local trained installers to execute the installation of these new measures. Due to the higher bids, projects no longer met the internal cost-effectiveness criteria used by Princeton to proceed with procurement of the full suite of technologies as proposed in ISOP. Princeton had already procured parts of the ISOP package as part of a campus-wide initiative for FDD and lighting retrofits, but with the higher costs for other ISOP components, Princeton could no longer commit to the full ISOP package on the selected sites. Based on this, the team reached the Go/No-Go decision point, where they decided not to proceed with the project. Princeton is separately installing portions of the ISOP package where they individually meet Princeton’s payback criteria but on a different schedule than would have been needed for the DOE ISOP project. The team has learned several lessons from project progress in the first budget period. The team will utilize these lessons in future projects, as should any team which proceeds with a project around the ISOP technology. The first lesson is that more information about the proposed sites is required earlier in the process (perhaps even at the proposal stage) in order to have a successful demonstration. Technical details not evident earlier in the process (such as the TLED limitations for ALCS) can derail project execution. The second lesson is that pricing of emerging technologies is highly variable due to lack of familiarity of local installers and distributor with the proposed technologies as well as alternate bids using products they were more familiar with, but that cost more than the ones proposed by TRC. Finally, the last major lesson is that the host site’s internal project criteria is of critical importance. Based on these higher installed costs and lack of an informed installer base for the products proposed for this project, the project does not meet Princeton’s simple payback goals of five years.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2011

ISSLive!

The ISSLive! project is a JSC innovation award- winning, combined MOD/Education project to publish export control and PAO-approved ISS telemetry, and simplified and scrubbed crew timelines. The publication of this data will be real-time or near real time and will include links to the crew's social media feeds and existing streaming public video/audio feeds, via public-friendly website, mobile devices and tablet applications. Additionally, the project will offer interactive virtual 3D views of an ISS model based on real-time telemetry and a 3D virtual mission control center based on existing Front Room console positions in made for public displays. The ISSLive! project is MOD-managed and includes collaborations with subject-matter expertise from the ISS flight controllers regarding daily operations and planning, education program specialists from the JSC Office of Education, instructional designers, human computer interface experts, and software/hardware experts from MOD facility organization, and senior web designers. In support of the Agency s Strategic Goal #6 with respect to using the ISS National Laboratory for education activities, ISSLive! uses the Station itself as STEM education subject matter and provides data for STEM-based lessons plans using national standards. Specifically, ISSLive! supports and enables the National Laboratory Education (NLE) project to address the Agency s Strategic Goal #6. This goal mandates, sharing NASA with the public, educators, and students to provide opportunities to participate in our Mission, foster innovation .. ISSLive! satisfies the Agency s outcomes of Strategic Goal; that is, engages the public in NASA's missions by providing new pathways for participation (Outcome 6.3) and it informs, engages, and inspires the public by sharing NASA s missions, challenges, and results (Outcome 6.4). Additionally, ISSLive! enables MOD s support of JSC Outreach and NASA's Open Data and Open Government Initiatives. The audience for the ISSLive! website and its application(s) are: teachers, students, citizen scientists, and the general public who will be given new and interactive insights on how the ISS Operates.

Price, Jennifer B.↗
DOE OSTI2021

Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency

Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic (PV) technology and electric battery storage, are increasingly being considered as solutions to support carbon reduction goals and increase grid reliability and resiliency. However, dynamic control of these resources in concert with traditional building loads, to effect efficiency and demand flexibility, is not yet commonplace in commercial control products. Traditional rule-based control algorithms do not offer integrated closed-loop control to optimize across systems, and most often, PV and battery systems are operated for energy arbitrage and demand charge management, and not for the provision of grid services. More advanced control approaches, such as MPC control have not been widely adopted in industry because they require significant expertise to develop and deploy. Recent advances in deep reinforcement learning (DRL) offer a promising option to optimize the operation of DER systems and building loads with reduced setup effort. However, there are limited studies that evaluate the efficacy of these methods to control multiple building subsystems simultaneously. Additionally, most of the research has been conducted in simulated environments as opposed to real buildings. This paper proposes a DRL approach that uses a deep deterministic policy gradient algorithm for integrated control of HVAC and electric battery storage systems in the presence of on-site PV generation. The DRL algorithm, trained on synthetic data, was deployed in a physical test building and evaluated against a baseline that uses the current best-in-class rule-based control strategies. Performance in delivering energy efficiency, load shift, and load shed was tested using price-based signals. The results showed that the DRL-based controller can produce cost savings of up to 39.6% as compared to the baseline controller, while maintaining similar thermal comfort in the building. The project team has also integrated the simulation components developed during this work as an OpenAIGym environment and made it publicly available so that prospective DRL researchers can leverage this environment to evaluate alternate DRL algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Nanosecond Repetitively Pulsed (NRP) Plasmas: Relationship Between Induced Flow and Plasma Characteristics at Atmospheric Pressure (Final Technical Report)

The local flow fields induced by plasma discharges at atmospheric pressure can play a critical role in aerodynamic flow and combustion control and have significant implications for plasma-based technologies in medicine and environmental engineering. However, the coupling between the plasma characteristics, the geometry and the induced flow field is not well understood. The primary goal of this work is to determine the relationship between the plasma characteristics and the induced flow field for nanosecond pulsed discharges at atmospheric pressure. A comprehensive experimental investigation of plasmas produced by nanosecond high-voltage pulses in a pin-to-pin electrode configuration was conducted, employing several measurement techniques including microwave and laser Rayleigh scattering, optical emission spectroscopy, and high-speed imaging. The flow field induced by the nanosecond plasma was measured and characterized using measurements of the velocity (particle image velocimetry) and density (background oriented schlieren) with unprecedented spatiotemporal resolution. The experimental measurements were complemented by development of a one-dimensional model of the plasma discharge and a high-fidelity computational fluid dynamics (CFD) simulation of the subsequent plasma-induced flow field.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1987

MTK: An AI tool for model-based reasoning

A 1988 goal for the Systems Autonomy Demonstration Project Office of the NASA Ames Research Center is to apply model-based representation and reasoning techniques in a knowledge-based system that will provide monitoring, fault diagnosis, control and trend analysis of the space station Thermal Management System (TMS). A number of issues raised during the development of the first prototype system inspired the design and construction of a model-based reasoning tool called MTK, which was used in the building of the second prototype. These issues are outlined, along with examples from the thermal system to highlight the motivating factors behind them. An overview of the capabilities of MTK is given.

Erickson, William K.↗
NASA NTRS1988

MTK: An AI tool for model-based reasoning

A 1988 goal for the Systems Autonomy Demonstration Project Office of the NASA Ames Research Office is to apply model-based representation and reasoning techniques in a knowledge-based system that will provide monitoring, fault diagnosis, control, and trend analysis of the Space Station Thermal Control System (TCS). A number of issues raised during the development of the first prototype system inspired the design and construction of a model-based reasoning tool called MTK, which was used in the building of the second prototype. These issues are outlined here with examples from the thermal system to highlight the motivating factors behind them, followed by an overview of the capabilities of MTK, which was developed to address these issues in a generic fashion.

Erickson, William K.↗
NASA NTRS1988

Model based trajectory planning using preview

The control of manipulators is seen here as a two-level process. A method is described to convert information available at the programming level into trajectories suitable to be tracked by a servo control system. The goal of the servo is to absorb the unmodeled dynamics. Tracking accuracy will depend mainly on the acceleration demand of the nominal trajectory setpoint, in particular, the actuator output demand must remain bounded. Our scheme adaptively takes into consideration at the trajectory computation level the dynamics of the underlying system, dynamically available information acquired through sensors, various types of constraints, such as path accuracy, and manipulator optimization. This scheme is meant to be implemented on-line, to drive mechanical systems such as manipulators. It is developed in the context of a multi-manipulator programming and control environment for space applications developed as part of a collaborative effort between McGill University and the Jet Propulsion Laboratory.

Hayward, Vincent↗
NASA NTRS1994

Development and demonstration of a telerobotic excavation system

Oak Ridge National Laboratory is developing remote excavation technologies for the Department of Energy's Office (DOE) of Technology Development, Robotics Technology Development Program, and also for the Department of Defense (DOD) Project Manager for Ammunition Logistics. This work is being done to meet the need for remote excavation and removal of radioactive and contaminated buried waste at several DOE sites and unexploded ordnance at DOD sites. System requirements are based on the need to uncover and remove waste from burial sites in a way that does not cause unnecessary personnel exposure or additional environmental contamination. Goals for the current project are to demonstrate dexterous control of a backhoe with force feedback and to implement robotic operations that will improve productivity. The Telerobotic Small Emplacement Excavator is a prototype system that incorporates the needed robotic and telerobotic capabilities on a commercially available platform. The ability to add remote dexterous teleoperation and robotic operating modes is intended to be adaptable to other commercially available excavator systems.

Burks, Barry L.↗
NASA NTRS2009

Prototype Interoperability Document between NASA-JSC and DLR-GSOC Describing the CCSDS SM and C Mission Operations Prototype

The purpose of the PROTOTYPE INTEROPERABILITY DOCUMENT is to document the design and interfaces for the service providers and consumers of a Mission Operations prototype between JSC-OTF and DLR-GSOC. The primary goal is to test the interoperability sections of the CCSDS Spacecraft Monitor & Control (SM&C) Mission Operations (MO) specifications between both control centers. An additional goal is to provide feedback to the Spacecraft Monitor and Control (SM&C) working group through the Review Item Disposition (RID) process. This Prototype is considered a proof of concept and should increase the knowledge base of the CCSDS SM&C Mission Operations standards. No operational capabilities will be provided. The CCSDS Mission Operations (MO) initiative was previously called Spacecraft Monitor and Control (SM&C). The specifications have been renamed to better reflect the scope and overall objectives. The working group retains the name Spacecraft Monitor and Control working group and is under the Mission Operations and Information Services Area (MOIMS) of CCSDS. This document will refer to the specifications as SM&C Mission Operations, Mission Operations or just MO.

Lucord, Steve A.↗