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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Targeted metabolomic analysis identifies increased serum levels of GABA and branched chain amino acids in canine diabetes

Introduction Dogs with naturally occurring diabetes mellitus represent a potential model for human type 1 diabetes, yet signifcant knowledge voids exist in terms of the pathogenic mechanisms underlying the canine disorder. Untargeted metabolomic studies from a limited number of diabetic dogs identifed similarities to humans with the disease. Objective To expand and validate earlier metabolomic studies, identify metabolites that difer consistently between diabetic and healthy dogs, and address whether certain metabolites might serve as disease biomarkers. Methods Untargeted metabolomic analysis via liquid chromatography-mass spectrometry was performed on serum from diabetic (n=15) and control (n=15) dogs. Results were combined with those of our previously published studies using identical methods (12 diabetic and 12 control dogs) to identify metabolites consistently diferent between the groups in all 54 dogs. Thirty-two candidate biomarkers were quantifed using targeted metabolomics. Biomarker concentrations were compared between the groups using multiple linear regression (corrected P<0.0051 considered signifcant). Results Untargeted metabolomics identifed multiple persistent diferences in serum metabolites in diabetic dogs compared with previous studies. Therefore, targeted metabolomics showed increases in gamma amino butyric acid, valine, leucine, isoleucine, citramalate, and 2-hydroxyisobutyric acid in diabetic versus control dogs while indoxyl sulfate, N-acetyl-L-aspartic acid, kynurenine, anthranilic acid, tyrosine, glutamine, and tauroursodeoxycholic acid were decreased. Conclusion Several of these fndings parallel metabolomic studies in both human diabetes and other animal models of this disease. Given recent studies on the role of GABA and branched chain amino acids in human diabetes, the increase in serum concentrations in canine diabetes warrants further study of these metabolites as potential biomarkers, and to identify similarity in mechanisms underlying this disease in humans and dogs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Robot-based Additive Manufacturing of Lego-type Modular Molds for Wind Blades

The objective of this project is to reduce the cost and lead time of horizontal wind turbine blade mold tooling and blade transportation, while maintaining the highest standards of blade quality. The solution involves a smart-design family of modular molds that are easily transportable to fabrication sites near the place of service. Key innovations include the use of additive manufacturing (AM) to integrate conformal thermal management channels, offering enhanced control over the thermal profiles tailored to specific blade materials. This approach enables in-situ quality assurance during mold fabrication, significantly improves mold life, and allows for reuse across multiple production cycles. Ultimately, the solution aims to optimize both tooling and transportation costs, contributing to the scalability of wind turbine blade production. A significant barrier to scaling up the production of large wind turbine blades lies in the high costs associated with tooling and the transportation of blades. Traditional molds are expensive, bulky, and difficult to transport, adding considerable lead time and cost to the overall manufacturing process. Additionally, transporting blades to distant locations for final assembly further exacerbates these challenges. The project aims to address these inefficiencies by demonstrating a modularized, additive-manufactured mold that meets all necessary blade specification requirements, specifically for blade lengths between 120m and 150m.

17 WIND ENERGY↗
NASA NTRS2018

Automated Collision Avoidance (ACA) and Automated Return to Course (ARTC) Requirement and Guidance Review: Final

This report's objectives were to review regulatory and guidance documents to identify requirements and considerations for the design and operation of automated systems that perform the functions of automated collision avoidance (ACA) and/or automated return to course (ARTC). The importance of this work is twofold: 1) to help focus efforts addressing new automated collision avoidance and return to course systems requirements and considerations, and 2) in pulling together available requirements from multiple sources, generate a master resource for these automated system requirements and considerations. Reviewed documents include regulations, industry standards and research papers that specifically address ACA and ARTC systems, other automatedl control and guidance systems, ICAO (International Civil Aviation Organization) and Eurocontrol documents.

Automation↗
DOE OSTI2022

‘Flux+Mutability’: a conditional generative approach to one-class classification and anomaly detection

Abstract Anomaly Detection is becoming increasingly popular within the experimental physics community. At experiments such as the Large Hadron Collider, anomaly detection is growing in interest for finding new physics beyond the Standard Model. This paper details the implementation of a novel Machine Learning architecture, called Flux+Mutability, which combines cutting-edge conditional generative models with clustering algorithms. In the ‘flux’ stage we learn the distribution of a reference class. The ‘mutability’ stage at inference addresses if data significantly deviates from the reference class. We demonstrate the validity of our approach and its connection to multiple problems spanning from one-class classification to anomaly detection. In particular, we apply our method to the isolation of neutral showers in an electromagnetic calorimeter and show its performance in detecting anomalous dijets events from standard QCD background. This approach limits assumptions on the reference sample and remains agnostic to the complementary class of objects of a given problem. We describe the possibility of dynamically generating a reference population and defining selection criteria via quantile cuts. Remarkably this flexible architecture can be deployed for a wide range of problems, and applications like multi-class classification or data quality control are left for further exploration.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1994

Algorithm For Control Of Underactuated Manipulators

Algorithm for improved control of underactuated multiple-link robotic manipulators developed via spatial-algebra-operator approach. Aspects of this approach described in several previous articles in NASA Tech Briefs-most recently "Robot Control Based on Spatial-Operator Algebra" (NPO-17918). Underactuated manipulator has fewer actuators than it has degrees of freedom. Complexity of underactuated systems managed by unified analysis. It has implications for fault-tolerant control, and many practical manipulators underactuated. Examples include manipulators with flexible joints and/or flexible links; space/underwater robots; manipulators that operate with some actuators that turned off because of failure or because of need to conserve energy; manipulators that grasp objects loosely, and manipulators that grasp objects with internal degrees of freedom (e.g., plungers, rollers).

Jain, Abhinandan↗
NASA NTRS2001

Learning Sequences of Actions in Collectives of Autonomous Agents

In this paper we focus on the problem of designing a collective of autonomous agents that individually learn sequences of actions such that the resultant sequence of joint actions achieves a predetermined global objective. We are particularly interested in instances of this problem where centralized control is either impossible or impractical. For single agent systems in similar domains, machine learning methods (e.g., reinforcement learners) have been successfully used. However, applying such solutions directly to multi-agent systems often proves problematic, as agents may work at cross-purposes, or have difficulty in evaluating their contribution to achievement of the global objective, or both. Accordingly, the crucial design step in multiagent systems centers on determining the private objectives of each agent so that as the agents strive for those objectives, the system reaches a good global solution. In this work we consider a version of this problem involving multiple autonomous agents in a grid world. We use concepts from collective intelligence to design goals for the agents that are 'aligned' with the global goal, and are 'learnable' in that agents can readily see how their behavior affects their utility. We show that reinforcement learning agents using those goals outperform both 'natural' extensions of single agent algorithms and global reinforcement, learning solutions based on 'team games'.

Turner, Kagan↗
DOE OSTI2020

Robust Assessment of Real-World Adversarial Examples

We explore rigorous, systematic, and controlled experimental evaluation of adversarial examples in the real world and propose a testing regimen for evaluation of real world adversarial objects. We show that for small scene/ environmental perturbations, large adversarial performance differences exist. Current state of adversarial reporting exists largely as a frequency count over a dynamic collections of scenes. Our work underscores the need for either a more complete report or a score that incorporates scene changes and baseline performance for models and environments tested by adversarial developers. We put forth a score that attempts to address the above issues in a straight-forward exemplar application for multiple generated adversary examples. We contribute the following: 1. a testbed for adversarial assessment, 2. a score for adversarial examples, and 3. a collection of additional evaluations on testbed data.

deep learning, adversarial machine learning, machi↗
NASA NTRS2003

Collectives for Multiple Resource Job Scheduling Across Heterogeneous Servers

Efficient management of large-scale, distributed data storage and processing systems is a major challenge for many computational applications. Many of these systems are characterized by multi-resource tasks processed across a heterogeneous network. Conventional approaches, such as load balancing, work well for centralized, single resource problems, but breakdown in the more general case. In addition, most approaches are often based on heuristics which do not directly attempt to optimize the world utility. In this paper, we propose an agent based control system using the theory of collectives. We configure the servers of our network with agents who make local job scheduling decisions. These decisions are based on local goals which are constructed to be aligned with the objective of optimizing the overall efficiency of the system. We demonstrate that multi-agent systems in which all the agents attempt to optimize the same global utility function (team game) only marginally outperform conventional load balancing. On the other hand, agents configured using collectives outperform both team games and load balancing (by up to four times for the latter), despite their distributed nature and their limited access to information.

Tumer, K.↗
DOE OSTI2019

High Energy Storage Capacity Low Cost Iron Flow Battery

A new flow battery was proposed that utilizes low cost materials: iron as the only active element, cheap aqueous electrolytes, and inexpensive separators. During charging, ferrous iron (Fe 2+ ) is oxidized to ferric iron (Fe 3+ ) at the positive electrode while it (Fe 2+ ) is reduced to form iron metal (Fe 0 ) at the negative electrode. Because iron is plated at the negative electrode during charging, conventional electrode structures couple the energy storage capacity and the power rating of the battery. In order to decouple the energy and power ratings and regain the economic advantages of a flow battery, a slurry electrode design was proposed for use as the negative electrode. The slurry electrode is made by flowing electrically conductive particles in an electrolyte containing the dissolved iron species. On charging, iron is plated onto the particles. The particles can then carry the iron metal out of the cell to be stored in external reservoirs. This project looked to develop a slurry for the negative electrode of the all-iron flow battery that can operate at 200 mA/cm 2 with <100 mV overpotential, be pumped with parasitic energy costs < 10%, and promote plating on the particles. A scalable and stackable cell design of 1100 cm 2 was developed. The project was initially under contract in mid-January 2013. The project was converted from a one-year proof of concept seedling to an additional two-year program, then extended with an 18-month plus-up, and finally with an additional 6-month approved cost-overrun. During the seeding project, the concept of plating on carbon particles in a slurry was demonstrated. However, this was accomplished with high-cost multi-walled carbon nanotubes as the slurry material. The following two-year program discovered several low-cost carbon blacks that could also be used in this application. These findings were supported by an analysis that demonstrated the effectiveness of the carbon that takes into account not just the effective electronic conductivity of the slurry, but also the surface area per volume of the slurry. It was demonstrated that stable, flowable slurries could be created with these carbons, even when the carbons where fully loaded with iron during charging. During the two-year program, laboratory-scale 50 cm 2 single-cells demonstrated charge-discharge cycling at current densities of 150 mA/cm 2 and energy efficiencies of >50%. Meanwhile, cost modeling continued to show promise for this energy storage approach. Following the conversion period, the overall objective of the nominally 18-month plus up period was to close the technical gap of a 25X scale-up and reduce risk to enable commercial investment required to develop a slurry iron flow battery product. A specific objective was to demonstrate 150 cycles of performance exceeding 70% energy efficiency with a 10-cell stack of 1156 cm 2 cells operating at 100 mA/cm 2 . The 10-cell short stack was designed to have a power delivery rating of approximately 1 kW, which represents a size commensurate with the requirements of our commercialization partner at that time. A complete battery support system for the full-size cell was designed, fabricated, and tested. This includes pumps (multiple types were considered, and constant volume hose pumps were found to be acceptable for performance and cost), electrolyte rebalance reactor, and sensors and control systems. The support system proved to be well-behaved and worked well. A new low-cost membrane was developed for this system based on a microporous polymer support with a layer of hydrogel polymer. The membrane had low resistivity, and significantly reduced pressure driven fluid flow across the membrane (as compared to the un-coated microporous support). This membrane approach was scaled-up in conjunction with a commercial toll coating company, and a large amount of membrane was manufactured to support large cell and stack testing. A new concept for an electrolyte rebalance reactor to react hydrogen gas from the negative electrolyte (from plating inefficiency) with excess ferric ion in the positive electrolyte was developed. This design was very simple, low cost, and effective. The full scale 1156 cm 2 single-cell was designed, modeled, fabricated and tested. Modeling showed that uniform flow-distribution should be obtained with the designed flow distributor. The design took into consideration the need to stack the cells, and components were fabricated for a ten-cell stack. Testing of the cell for charge and discharge demonstrated current densities of 50 mA/cm 2 with efficiencies of 50%. However, optimization and long cycling could not be accomplished because the slurry electrode would plug within the cell and block the flow. This would shut down the cell after several charge-discharge cycles. Various approaches were pursued to address this problem with some progress. However, there was not adequate time to find solutions to the problem although several promising approaches were identified for future work. The final deliverable for this proposed 18-month effort was to be a 1 kW, 6 kWh slurry flow battery, with complete balance of plant, ready for testing at an ARPA-E ‘Charges’ site with an estimated capital cost less than $\$ $45/kWh for a six-hour system (excluding power electronics). The deliverable milestone was not achieved for reasons mentioned above related to the slurry plugging. At the completion of this project, it was determined that the cost targets are achievable. The cost estimate for a 1 MW system with a one-hour capacity was estimated at $\$ $140/kWh (including power electronics) and for a six-hour system was estimated at $\$ $40/kWh (including power electronics). These costs include the battery, the balance of plant, the power conditioning and a concrete pad. This program resulted in three patents including the slurry-based iron flow battery, the rebalance reactor, and the membrane separator. At the time of this report, slurry-based iron flow battery patents have been issued in the US, China, Japan, Korea, and Europe. The other patent applications were under various stages of examination. A licensee was identified and the company initiated a scale-up and development program to take this technology to the marketplace, and CWRU staff have transferred their know-how and knowledge base to the company. This technology will enhance the economic and energy security of the United States by enabling the use of intermittent renewable energy technology, such as wind and solar power, as dispatchable resources. This report summarizes the results of this research and development program.

25 ENERGY STORAGE↗
NASA NTRS2010

NASA Airborne Science Program: NASA Stratospheric Platforms

The National Aeronautics and Space Administration conducts a wide variety of remote sensing projects using several unique aircraft platforms. These vehicles have been selected and modified to provide capabilities that are particularly important for geophysical research, in particular, routine access to very high altitudes, long range, long endurance, precise trajectory control, and the payload capacity to operate multiple, diverse instruments concurrently. While the NASA program has been in operation for over 30 years, new aircraft and technological advances that will expand the capabilities for airborne observation are continually being assessed and implemented. This presentation will review the current state of NASA's science platforms, recent improvements and new missions concepts as well as provide a survey of emerging technologies unmanned aerial vehicles for long duration observations (Global Hawk and Predator). Applications of information technology that allow more efficient use of flight time and the ability to rapidly reconfigure systems for different mission objectives are addressed.

Curry, Robert E.↗
DOE OSTI2026

Integrated Simulation of Weld Residual Stress Evolution and Crack Propagation Using XFEM

Nuclear power plant components operate in environments that promote multiple degradation mecha- nisms, several of which involve crack initiation and growth. An ongoing effort in the U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program is developing a general capability within the Multiphysics Object Oriented Simulation Environment (MOOSE) framework for simulating three-dimensional crack growth under a range of driving conditions, including fatigue, stress corrosion cracking (SCC), brittle fracture, and stress-relaxation cracking. This report demonstrates an end-to-end workflow that uses this capability to model weld-residual-stress-driven SCC in the J-groove weld of a pressurized-water reactor control rod drive mechanism penetration in the vessel head. The workflow consists of a thermomechanical welding simulation with temperature-dependent plasticity, followed by cooldown to ambient conditions, and a restart of the simulation using the MOOSE extended finite element method (XFEM) module to propagate a three-dimensional crack through the residual stress field. New welding capabilities were developed to properly initialize newly activated elements in the weld region, and robustness improvements were made to the mesh-based algorithm for defining cutting planes in the 3D XFEM algorithm, allowing it to handle complex crack fronts and stress fields. Together these advances allowed the simulated SCC crack to grow from an initial elliptical flaw in the weld, across the weld, through the tube wall, and almost to the triple point (where the weld, tube, and reactor pressure vessel head intersect) over roughly 36 years of simulated service. These results demonstrate a workflow that can be extended to fully three-dimensional welding simulations and more complex crack interaction problems.

42 - ENGINEERING↗
DOE OSTI2023

Control oriented model order reduction for the hydraulic system of an autonomous wheel loader

Control design for an autonomous wheel loader requires a high-fidelity and low-order dynamics model. The objective of order reduction is to decrease the number of states in the model while maintaining performance comparable to the original model. Of all the power components in a wheel loader, the model order reduction for steering and working hydraulic systems is most demanding due to its nonlinear and stiff dynamical nature. This paper describes a physics-inspired model order reduction method that can reduce the model order by close to thirty percent. It is achieved by discarding orders with fast dynamics and consolidating multiple states into less new states. Furthermore, this method can also be extended to other types of off-road vehicles such as excavators, bulldozers, etc. Simulation results demonstrate that the reduced-order model exhibits very similar performance to the full-order model under typical operating conditions of wheel loaders, with an output error of less than six percent.

42 ENGINEERING↗
DOE OSTI2022

High-Silicon Steel Strip by Single-Step Shear Deformation Processing

It is well known that Fe-Si alloys with Si content higher than in conventional electrical sheet steels (>3.2% Si) can make a significant impact in improving the efficiency of electrical motors if they are available in sheet/foil (strip) forms at suitable cost. While the magnetic and electrical attributes (e.g., resistivity, core loss) of these high-Si Fe alloys, of relevance to electrical motor core laminations, are known to be exceptional, the alloys have limited workability, making them difficult to produce consistently in sheet/foil (strip) forms. Current processing techniques such as rolling, while adequate for producing conventional electrical steel sheet, do have important disadvantages - large energy consumption and emissions, limitations in processing of low-workability alloys (e.g., high-Si content steels), large-scale plant infrastructure, and less than adequate capability to engineer sheet metals with specific microstructures (e.g., fine-grained) and crystallographic textures (e.g., shear textures). It is therefore of interest to have an alternative commercial process that can produce sheet/foil (strip) from high-Si Fe alloys and which can also overcome some of the deficiencies of current multistage strip processes. The goal of the present project was design and demonstration of a new energy-efficient pilot process for producing high-Si electrical steel strip of commercial widths and thickness, and with superior electrical and magnetic properties than current electrical steels (Fe-3.2% Si as benchmark). The applications domain for these steels is electrical motor core laminations. We have addressed this goal by accomplishment of the following specific objectives and tasks: a) Development of an Fe-4Si-4Cr alloy with electrical resistivity >80 μΩ-cm, induction flux density >1.48 T at 5000 A/m and core loss 35% lower than the benchmark 3.2% Si alloy. The alloy which meets DOE target specifications for motor core attributes was designed with the Si content controlled for the electrical properties and the Cr content tailored to meet material/process workability requirements. b) A unique machining-based deformation processing system was designed and scaled-up to produce strip of commercial width (25 mm to 150 mm) and thickness (up to 0.5 mm) from the Fe-4Si-4Cr alloy and other alloys of varied workability including copper, Al6061-T6 and naval brass. The key attributes of the machining-based strip production are deformation processing by concentrated simple-shear; single-step production of strip from ingot using compact machine infrastructure; strip surface finish of Ra 0.35 to 1 micrometer that is comparable/superior to that of rolled strip; discrete production of strip that can potentially be done at point of use; and controllability of strip mechanical/formability properties by deformation control. c) The electrical, magnetic, surface quality, mechanical, formability, and metallurgical properties/attributes of the machining-based strip were established by direct ASTM standard or equivalent measurement techniques. d) Punching characteristics of the strip in terms of load, edge quality and macro defects were similar to those of conventional 3.2% Si electrical steels. These punching characteristics are critical from a manufacturability perspective for motor/transformer core applications. e) A modeling framework for energy analysis of multistage rolling and the machining-based deformation processing has been established. Application of this modeling to the two strip-processes showed that the machining-based process requires significantly lower specific energy for processing, ~ 25% of that for rolling. The modeling framework can be adapted for a range of sheet-metal forming, bulk metal forming, and machining processes. It can be used to identify key parameters controlling process specific energy. f) A comparative analysis of advantages and disadvantages of machining-based processing against rolling for strip production. The single stage machining-based processing, with compact infrastructure, represents a new manufacturing paradigm for sheet and foil manufacturing that can potentially also be applied to advanced titanium, aluminum, copper and magnesium alloys. The goals and objectives were accomplished by a cross-disciplinary project team comprising of personnel from Purdue University; M4 Sciences LLC, a small-business focused on advanced manufacturing technology development; the Pacific Northwest National Labs; and tool manufacturers. The team is currently in advanced discussions with multiple entities for future process development for commercialization.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The relationship between urinary selenium levels and risk of gestational diabetes mellitus: A nested case–control study

Background Selenium (Se) is an essential trace element for the human body. Serum Se and urinary Se are also biomarkers to assess Se exposure status. However, studies focusing on the association between urinary Se and the risk of gestational diabetes mellitus (GDM) are rare. Objective To investigate the association between urinary Se and the risk of GDM. Methods A nested case–control study based on a prospective birth cohort in Wuhan, China, which focuses on the effects of prenatal environmental factors exposure on pregnant women and children’s health was conducted. Two hundred and twenty-six cases and 452 controls were included. Maternal urine samples were collected before GDM diagnosis, and the urinary Se levels were determined. We assessed the association of urinary Se with GDM by conditional logistic regression with maternal urinary Se level as a categorical variable, and estimated the association between Se and glucose levels by multiple linear regression. The potential modifier roles of maternal age and fetal sex have also been assessed. Results Lower urinary level of Se was significantly associated with a higher risk of GDM (OR = 2.35 for the tertile 1, 95% CI:1.36-4.06; adjusted OR = 1.79 for the tertile 2, 95%CI:1.09-2.95; p for trend = 0.01). Fetal sex had an interaction with Se in the association with GDM. The association was more pronounced among pregnant women with female fetuses than with male fetuses. Discussion Our study suggested a significant negative association between urinary Se and the risk of GDM, and this association may vary depending on the fetal sex.

Liu, Yuanxia↗
DOE OSTI2020

How Low Can You Go? Using Synthetic 3D Imagery to Drastically Reduce Real-World Training Data for Object Detection

Deep convolutional neural networks (DCNNs) currently provide state-of-the-art performance on image classification and object detection tasks, and there are many global security mission areas where such models could be extremely useful. Crucially, the success of these models is driven in large part by the widespread availability of high-quality open source data sets such as Image Net, Common Objects in Context (COCO), and KITTI, which contain millions of images with thousands of unique labels. However, global security relevant objects-of-interest can be difficult to obtain: relevant events are low frequency and high consequence; the content of relevant images is sensitive; and adversaries and proliferators seek to obscure their activities. For these cases where exemplar data is hard to come-by, even fine-tuning an existing model with available data can be effectively impossible. Recent work demonstrated that models can be trained using a combination of real-world and synthetic images generated from 3D representations; that such models can exceed the performance of models trained using real-world data alone; and that the generated images need not be perfectly realistic (Tremblay, et al., 2018). However, this approach still required hundreds to thousands of real-world images for training and fine tuning, which for sparse, global security-relevant datasets can be an unrealistic hurdle. In this research, we validate the performance and behavior of DCNN models as we drive the number of real-world images used for training object detection tasks down to a minimal set. We perform multiple experiments to identify the best approach to train DCNNs from an extremely small set of real-world images. In doing so, we: Develop state-of-the-art, parameterized 3D models based on real-world images and sample from their parameters to increase the variance in synthetic image training data; Use machine learning explainability techniques to highlight and correct through targeted training the biases that result from training using completely synthetic images; and Validate our results by comparing the performance of the models trained on synthetic data to one another, and to a control model created by fine-tuning an existing ImageNet-trained model with a limited number (hundreds) of real-world images.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Practical input optimization for aircraft parameter estimation experiments

The object of this research was to develop an algorithm for the design of practical, optimal flight test inputs for aircraft parameter estimation experiments. A general, single pass technique was developed which allows global optimization of the flight test input design for parameter estimation using the principles of dynamic programming with the input forms limited to square waves only. Provision was made for practical constraints on the input, including amplitude constraints, control system dynamics, and selected input frequency range exclusions. In addition, the input design was accomplished while imposing output amplitude constraints required by model validity and considerations of safety during the flight test. The algorithm has multiple input design capability, with optional inclusion of a constraint that only one control move at a time, so that a human pilot can implement the inputs. It is shown that the technique can be used to design experiments for estimation of open loop model parameters from closed loop flight test data. The report includes a new formulation of the optimal input design problem, a description of a new approach to the solution, and a summary of the characteristics of the algorithm, followed by three example applications of the new technique which demonstrate the quality and expanded capabilities of the input designs produced by the new technique. In all cases, the new input design approach showed significant improvement over previous input design methods in terms of achievable parameter accuracies.

Morelli, Eugene A.↗
NASA NTRS2014

RS-34 Phoenix In-Space Propulsion System Applied to Active Debris Removal Mission

In-space propulsion is a high percentage of the cost when considering Active Debris Removal mission. For this reason it is desired to research if existing designs with slight modification would meet mission requirements to aid in reducing cost of the overall mission. Such a system capable of rendezvous, close proximity operations, and de-orbit of Envisat class resident space objects has been identified in the existing RS-34 Phoenix. RS-34 propulsion system is a remaining asset from the de-commissioned United States Air Force Peacekeeper program; specifically the pressure-fed storable bi-propellant Stage IV Post Boost Propulsion System. The National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) gained experience with the RS-34 propulsion system on the successful Ares I-X flight test program flown in the Ares I-X Roll control system (RoCS). The heritage hardware proved extremely robust and reliable and sparked interest for further utilization on other potential in-space applications. Subsequently, MSFC has obtained permission from the USAF to obtain all the remaining RS-34 stages for re-use opportunities. The MSFC Advanced Concepts Office (ACO) was commissioned to lead a study for evaluation of the Rocketdyne produced RS-34 propulsion system as it applies to an active debris removal design reference mission for resident space object targets including Envisat. Originally designed, the RS-34 Phoenix provided in-space six-degrees-of freedom operational maneuvering to deploy payloads at multiple orbital locations. The RS-34 Concept Study lead by sought to further understand application for a similar orbital debris design reference mission to provide propulsive capability for rendezvous, close proximity operations to support the capture phase of the mission, and deorbit of single or multiple large class resident space objects. Multiple configurations varying the degree of modification were identified to trade for dry mass optimization and propellant load. The results of the RS-34 Phoenix Concept Study show that the system is technically sufficient to successfully support all of the missions to rendezvous, capture, and de-orbit targets including Envisat and Hubble Space Telescope. The results and benefits of the RS-34 Orbital Debris Application Concept Study are presented in this paper.

Esther, Elizabeth A.↗
NASA NTRS2000

Autonomy Architectures for a Constellation of Spacecraft

Until the past few years, missions typically involved fairly large expensive spacecraft. Such missions have primarily favored using older proven technologies over more recently developed ones, and humans controlled spacecraft by manually generating detailed command sequences with low-level tools and then transmitting the sequences for subsequent execution on a spacecraft controller. This approach toward controlling a spacecraft has worked spectacularly on previous missions, but it has limitations deriving from communications restrictions - scheduling time to communicate with a particular spacecraft involves competing with other projects due to the limited number of deep space network antennae. This implies that a spacecraft can spend a long time just waiting whenever a command sequence fails. This is one reason why the New Millennium program has an objective to migrate parts of mission control tasks onboard a spacecraft to reduce wait time by making spacecraft more robust. The migrated software is called a "remote agent" and has 4 components: a mission manager to generate the high level goals, a planner/scheduler to turn goals into activities while reasoning about future expected situations, an executive/diagnostics engine to initiate and maintain activities while interpreting sensed events by reasoning about past and present situations, and a conventional real-time subsystem to interface with the spacecraft to implement an activity's primitive actions. In addition to needing remote planning and execution for isolated spacecraft, a trend toward multiple-spacecraft missions points to the need for remote distributed planning and execution. The past few years have seen missions with growing numbers of probes. Pathfinder has its rover (Sojourner), Cassini has its lander (Huygens), and the New Millenium Deep Space 3 (DS3) proposal involves a constellation of 3 spacecraft for interferometric mapping. This trend is expected to continue to progressively larger fleets. For example, one mission proposed to succeed DS3 would have 18 spacecraft flying in formation in order to detect earth-sized planets orbiting other stars. A proposed magnetospheric constellation would involve 5 to 500 spacecraft in Earth orbit to measure global phenomena within the magnetosphere. This work describes and compares three autonomy architectures for a system that continuously plans to control a fleet of spacecraft using collective mission goals instead of goals or command sequences for each spacecraft. A fleet of self-commanding spacecraft would autonomously coordinate itself to satisfy high level science and engineering goals in a changing partially-understood environment making feasible the operation of tens or even a hundred spacecraft (such as for interferometry or plasma physics missions). The easiest way to adapt autonomous spacecraft research to controlling constellations involves treating the constellation as a single spacecraft. Here one spacecraft directly controls the others as if they were connected. The controlling "master" spacecraft performs all autonomy reasoning, and the slaves only have real-time subsystems to execute the master's commands and transmit local telemetry/observations. The executive/diagnostics module starts actions and the master's real-time subsystem controls the action either locally or remotely through a slave. While the master/slave approach benefits from conceptual simplicity, it relies on an assumption that the master spacecraft's executive can continuously monitor the slaves' real-time subsystems, and this relies on high-bandwidth highly-reliable communications. Since unintended results occur fairly rarely, one way to relax the bandwidth requirements involves only monitoring unexpected events in spacecraft. Unfortunately, this disables the ability to monitor for unexpected events between spacecraft and leads to a host of coordination problems among the slaves. Also, failures in the communications system can result in losing slaves. The other two architectures improve robustness while reducing communications by progressively distributing more of the other three remote agent components across the constellation. In a teamwork architecture, all spacecraft have executives and real-time subsystems - only the leader has the planner/scheduler and mission manager. Finally, distributing all remote agent components leads to a peer-to-peer approach toward constellation control.

Barrett, Anthony↗