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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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NASA NTRS1990

The implementation of STOVL task-tailored control modes in a fighter cockpit

The implementation of Short Takeoff/Vertical Landing (STOVL) specific task tailored control modes in a supersonic fighter/attack aircraft cockpit is investigated. A detailed linear model exhibiting STOVL Level 1 handling qualities is implemented in a real time engineering workstation environment with an F-16 cockpit mock-up. Conventional F-16 control inceptors are utilized to achieve effective STOVL operation and reduced pilot workload throughout the transition to hover flight region. Favorable pilot comments indicate the existing F-16 cockpit configuration with a force sidestick is adaptable to STOVL operation.

Whatley, David W.↗
NASA NTRS1994

Increases in efficiency and enhancements to the Mars Observer non-stored commanding process

The Mars Observer team was, until the untimely loss of the spacecraft on August 21, 1993, performing flight operations with greater efficiency and speed than any previous JPL mission of its size. This level of through-put was made possible by a mission operations system which was composed of skilled personnel using sophisticated sequencing and commanding tools. During cruise flight operations, however, it was realized by the project that this commanding level was not going to be sufficient to support the activities planned for mapping operations. The project had committed to providing the science instrument principle investigators with a much higher level of commanding during mapping. Thus, the project began taking steps to enhance the capabilities of the flight team. One mechanism used by project management was a tool available from total quality management (TQM). This tool is known as a process action team (PAT). The Mars Observer PAT was tasked to increase the capacity of the flight team's nonstored commanding process by fifty percent with no increase in staffing and a minimal increase in risk. The outcome of this effort was, in fact, to increase the capacity by a factor of 2.5 rather than the desired fifty percent and actually reduce risk. The majority of these improvements came from the automation of the existing command process. These results required very few changes to the existing mission operations system. Rather, the PAT was able to take advantage of automation capabilities inherent in the existing system and make changes to the existing flight team procedures.

Brooks, Robert N., Jr.↗
NASA NTRS1997

Wavelet Methods Developed to Detect and Control Compressor Stall

A "wavelet" is, by definition, an amplitude-varying, short waveform with a finite bandwidth (e.g., that shown in the first two graphs). Naturally, wavelets are more effective than the sinusoids of Fourier analysis for matching and reconstructing signal features. In wavelet transformation and inversion, all transient or periodic data features (as in compressor-inlet pressures) can be detected and reconstructed by stretching or contracting a single wavelet to generate the matching building blocks. Consequently, wavelet analysis provides many flexible and effective ways to reduce noise and extract signals which surpass classical techniques - making it very attractive for data analysis, modeling, and active control of stall and surge in high-speed turbojet compressors. Therefore, fast and practical wavelet methods are being developed in-house at the NASA Lewis Research Center to assist in these tasks. This includes establishing user-friendly links between some fundamental wavelet analysis ideas and the classical theories (or practices) of system identification, data analysis, and processing.

Le, Dzu K.↗
NASA NTRS2008

NASA Tech Briefs, June 2008

Topics covered include: Charge-Control Unit for Testing Lithium-Ion Cells; Measuring Positions of Objects Using Two or More Cameras; Lidar System for Airborne Measurement of Clouds and Aerosols; Radiation-Insensitive Inverse Majority Gates; Reduced-Order Kalman Filtering for Processing Relative Measurements; Spaceborne Processor Array; Instrumentation System Diagnoses a Thermocouple; Chromatic Modulator for a High-Resolution CCD or APS; Commercial Product Activation Using RFID; Cup Cylindrical Waveguide Antenna; Aerobraking Maneuver (ABM) Report Generator; ABM Drag_Pass Report Generator; Transformation of OODT CAS to Perform Larger Tasks; Visualization Component of Vehicle Health Decision Support System; Mars Reconnaissance Orbiter Uplink Analysis Tool; Problem Reporting System; G-Guidance Interface Design for Small Body Mission Simulation; DSN Scheduling Engine; Replacement Sequence of Events Generator; Force-Control Algorithm for Surface Sampling; Tool for Merging Proposals Into DSN Schedules; Micromachined Slits for Imaging Spectrometers; Fabricating Nanodots Using Lift-Off of a Nanopore Template; Making Complex Electrically Conductive Patterns on Cloth; Special Polymer/Carbon Composite Films for Detecting SO2; Nickel-Based Superalloy Resists Embrittlement by Hydrogen; Chemical Passivation of Li+-Conducting Solid Electrolytes; Organic/Inorganic Polymeric Composites for Heat-Transfer Reduction; Composite Cathodes for Dual-Rate Li-Ion Batteries; Improved Descent-Rate Limiting Mechanism; Alignment-Insensitive Lower-Cost Telescope Architecture; Micro-Resistojet for Small Satellites; Using Piezoelectric Devices to Transmit Power through Walls; Miniature Latching Valve; Apparatus for Sampling Surface Contamination; Novel Species of Non-Spore-Forming Bacteria; Chamber for Aerosol Deposition of Bioparticles; Hyperspectral Sun Photometer for Atmospheric Characterization and Vicarious Calibrations; Dynamic Stability and Gravitational Balancing of Multiple Extended Bodies; Simulation of Stochastic Processes by Coupled ODE-PDE; Cluster Inter-Spacecraft Communications; Genetic Algorithm Optimizes Q-LAW Control Parameters; Low-Impact Mating System for Docking Spacecraft; Non-Destructive Evaluation of Materials via Ultraviolet Spectroscopy; Gold-on-Polymer-Based Sensing Films for Detection of Organic and Inorganic Analytes in the Air; and Quantum-Inspired Maximizer.

Source record↗
NASA NTRS2005

Predicting Flows of Rarefied Gases

DSMC Analysis Code (DAC) is a flexible, highly automated, easy-to-use computer program for predicting flows of rarefied gases -- especially flows of upper-atmospheric, propulsion, and vented gases impinging on spacecraft surfaces. DAC implements the direct simulation Monte Carlo (DSMC) method, which is widely recognized as standard for simulating flows at densities so low that the continuum-based equations of computational fluid dynamics are invalid. DAC enables users to model complex surface shapes and boundary conditions quickly and easily. The discretization of a flow field into computational grids is automated, thereby relieving the user of a traditionally time-consuming task while ensuring (1) appropriate refinement of grids throughout the computational domain, (2) determination of optimal settings for temporal discretization and other simulation parameters, and (3) satisfaction of the fundamental constraints of the method. In so doing, DAC ensures an accurate and efficient simulation. In addition, DAC can utilize parallel processing to reduce computation time. The domain decomposition needed for parallel processing is completely automated, and the software employs a dynamic load-balancing mechanism to ensure optimal parallel efficiency throughout the simulation.

LeBeau, Gerald J.↗
NASA NTRS2013

Decision Making Training in the Mission Operations Directorate

At JSC, we train our new flight controllers on a set of team skills that we call Space Flight Resource Management (SFRM). SFRM is akin to Crew Resource Management for the airlines and trains flight controllers to work as an effective team to reduce errors and improve safety. We have developed this training over the years with the assistance of Ames Research Center, Wyle Labs and University of Central Florida. One of the skills we teach is decision making/ problem solving (DM/PS). We teach DM/PS first in several classroom sessions, reinforce it in several part task training environments, and finally practice it in full-mission, full-team simulations. What I am proposing to talk about is this training flow: its content and how we teach it.

O'Keefe, William S.↗
NASA NTRS2022

Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, and Psychosocial Adaptions within a Team

The Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, Communication, and Psychosocial Adaptation within a Team (the Team Risk) is primarily performance-focused, with a secondary emphasis on behavioral health outcomes resulting from team performance and interpersonal interactions. Monitoring tools, measures, and countermeasures are aimed at enhancing team processes and team composition configurations to optimize team performance and functioning. Long-duration exploration missions (LDEMs) will include major challenges that could affect team performance, including social isolation, physical confinement, a small and diverse crew, communication delays between crew and ground, limited or no crew rotation or evacuation options, limited or no resupply, and a high-consequence environment. Each of these conditions will affect the crew’s coordination, cooperation, psychological well-being, and performance. Although the International Space Station (ISS) remains important for studies that require spaceflight testing and validation, the current conditions on the ISS do not adequately mimic the exploration environment that is required for National Aeronautics and Space Administration (NASA) teams research, and thus access to terrestrial or ground-based analogs of LDEM conditions is paramount. The emphasis on analogs for research is reflected in this updated evidence review of the Team Risk, and includes data from studies conducted at isolated, confined, extreme (ICE) environments (e.g., Antarctic stations), and from several mission simulation analogs such as the Human Exploration Research Analog (HERA) (HERA Experiment Information Package, 2014), also known as isolated, confined, controlled (ICC) environments. These studies have characterized many team factors regarding LDEMs, and the Team Risk has now matured from risk characterization to focusing more on countermeasure development. Because spaceflight evidence for team-level research is lacking, no reliable data is available to quantify the impact of team-level variables on individual and team-level outcomes during spaceflight missions. Until recently, no systematic attempt had been undertaken to measure the performance effects of team cohesion, team composition, team training, or team-related psychosocial adaptation during spaceflight. The Team Risk is a relatively young research area for NASA, with substantial growth only since the 2000s, and with limited access to spaceflight performance data. As a result, spaceflight evidence is lacking to identify specifically what team composition, level of training, amount of cohesion, or quality of psychosocial adaptation is necessary to reduce the risk of performance errors in space. However, astronaut journals and interviews and reports from spaceflight subject matter experts (SMEs) provide testimonies that team performance during spaceflight is important for mission success and to maintain crew health. Team spaceflight data is now being collected as part of the Spaceflight Standard Measures task (Clement, 2021)—a set of core measurements related to many human spaceflight risks that are collected from astronauts before, during, and after long-duration missions. The team-related standard measures focus on team cohesion, team performance, group living, team climate, and team processes. Collection of standard measures data is ongoing and published data is not yet available. Finally, although spaceflight evidence is lacking, evidence gleaned from ground studies and spaceflight analog studies will help close the gaps outlined in the Team Risk. Ground-based studies provide quantitative evidence for team functioning in ICE environments. Academic research on teams has produced dozens of meta-analyses that can be used to understand the general relationships among team inputs (e.g., team member characteristics and skills, job context), team processes, and emergent states (e.g., coordination, communication, cooperation, cohesion, trust, shared cognition), and team outcomes (e.g., effectiveness, errors, adaptation). Teams are complex, incorporating individual characteristics of team members, but also existing at a level that is greater than the sum of its parts. Therefore, the Team Risk must be integrated with other individual-focused NASA Human Research Program (HRP) risks, including Behavioral Medicine (BMed), Sleep, and Human-Systems Integration Architecture (HSIA), and emerging research indicates more integration may needed between the Team Risk and the physiologically oriented risks. Much of this integration occurs through the Human Systems Risk Board (HSRB). A lack of team functioning may be a stressor in some circumstances, but the team often acts as a countermeasure. For example, support for team leaders and teammates can facilitate individual functioning and encourage psychological and physically healthy behaviors and attitudes. However, more research is needed regarding teams during LDEMs and the remaining gaps in the research are described in the current report.

Lauren Blackwell Landon↗
DOE OSTI2021

Iterative self-organizing SCEne-LEvel sampling (ISOSCELES) for large-scale building extraction

Convolutional neural networks (CNN) provide state-of-the-art performance in many computer vision tasks, including those related to remote-sensing image analysis. Successfully training a CNN to generalize well to unseen data, however, requires training on samples that represent the full distribution of variation of both the target classes and their surrounding contexts. With remote sensing data, acquiring a sufficiently representative training set is a challenge due to both the inherent multi-modal variability of satellite or aerial imagery and the general high cost of labeling data. To address this challenge, we have developed ISOSCELES, an Iterative Self-Organizing SCEne LEvel Sampling method for hierarchical sampling of large image sets. Using affinity propagation, ISOSCELES automates the selection of highly representative training images. Compared to random sampling or using available reference data, the distribution of the training is principally data driven, reducing the chance of oversampling uninformative areas or undersampling informative ones. In comparison to manual sample selection by an analyst, ISOSCELES exploits descriptive features, spectral and/or textural, and eliminates human bias in sample selection. Using a hierarchical sampling approach, ISOSCELES can obtain a training set that reflects both between-scene variability, such as in viewing angle and time of day, and within-scene variability at the level of individual training samples. We verify the method by demonstrating its superiority to stratified random sampling in the challenging task of adapting a pre-trained model to a new image and spatial domain for country-scale building extraction. Using a pair of hand-labeled training sets comprising 1,987 sample image chips, a total of 496,000,000 individually labeled pixels, we show, across three distinct model architectures, an increase in accuracy, as measured by F1-score, of 2.2–4.2%.

42 ENGINEERING↗
NASA NTRS2013

Environmentally Friendly Rotorcraft Concepts: 2011-2014: US/France MoA Helicopter Aeromechanics - Task 5

Motivation: Greenhouse gas concentrations have increased well beyond their pre-industrial levels due to human activities; Aircraft emissions are becoming regulated in industrialized nations, so future rotorcraft will need to be designed for minimal environmental impact. Objectives: Determine suitable metrics to measure environmental impact of rotorcraft, focusing first on air pollution; Integrate performance metrics with rotorcraft design codes; Generate rotorcraft designs with minimal environmental impact. Outcomes: Obtain an understanding of how targeting reduced emissions affects the rotorcraft design process; Advance the capability of existing rotorcraft design tools to include environmental performance metrics. Tools: Evaluation and predesign - NDARC (NASA Design and Analysis of Rotorcraft) and CREATION (Concepts of Rotorcraft Enhanced Assessment Through Integrated Optimization Network); Flight simulation - CAMRAD II (Comprehensive Analytical Model of Rotorcraft Aerodynamics and Dynamics) and HOST (Helicopter Overall Simulation Tool).

Environmentally Friendly↗
NASA NTRS2015

Human-Robot Interaction

Human-robot interaction (HRI) is a discipline investigating the factors affecting the interactions between humans and robots. It is important to evaluate how the design of interfaces affect the human's ability to perform tasks effectively and efficiently when working with a robot. By understanding the effects of interface design on human performance, workload, and situation awareness, interfaces can be developed to appropriately support the human in performing tasks with minimal errors and with appropriate interaction time and effort. Thus, the results of research on human-robot interfaces have direct implications for the design of robotic systems. For efficient and effective remote navigation of a rover, a human operator needs to be aware of the robot's environment. However, during teleoperation, operators may get information about the environment only through a robot's front-mounted camera causing a keyhole effect. The keyhole effect reduces situation awareness which may manifest in navigation issues such as higher number of collisions, missing critical aspects of the environment, or reduced speed. One way to compensate for the keyhole effect and the ambiguities operators experience when they teleoperate a robot is adding multiple cameras and including the robot chassis in the camera view. Augmented reality, such as overlays, can also enhance the way a person sees objects in the environment or in camera views by making them more visible. Scenes can be augmented with integrated telemetry, procedures, or map information. Furthermore, the addition of an exocentric (i.e., third-person) field of view from a camera placed in the robot's environment may provide operators with the additional information needed to gain spatial awareness of the robot. Two research studies investigated possible mitigation approaches to address the keyhole effect: 1) combining the inclusion of the robot chassis in the camera view with augmented reality overlays, and 2) modifying the camera frame of reference. The first study investigated the effects of inclusion and exclusion of the robot chassis along with superimposing a simple arrow overlay onto the video feed of operator task performance during teleoperation of a mobile robot in a driving task. In this study, the front half of the robot chassis was made visible through the use of three cameras, two side-facing and one forward-facing. The purpose of the second study was to compare operator performance when teleoperating a robot from an egocentric-only and combined (egocentric plus exocentric camera) view. Camera view parameters that are found to be beneficial in these laboratory experiments can be implemented on NASA rovers and tested in a real-world driving and navigation scenario on-site at the Johnson Space Center.

Sandor, Aniko↗
DOE OSTI2019

Parameter inference with deep jointly informed neural networks

Abstract A common challenge in modeling inertial confinement fusion (ICF) experiments with computer simulations is that many of the simulation inputs are unknown and cannot be directly measured. Often, parameters that are measured in the experiment are used to infer the unknown inputs by solving the inverse problem: finding the set of simulation inputs that result in outputs consistent with the experimental observations. In ICF, this process is often referred to as a “post‐shot analysis.” Post‐shot analyses are challenging as the inverse problem is often highly degenerate, the input parameter space is vast, and simulations are computationally expensive. In this work, deep neural network models equipped with model uncertainty estimates are used to train inverse models, which map directly from output to input space, to find the distribution of post‐shot simulations that are consistent with experimental observations. The inverse model approach is compared to Markov chain Monte Carlo (MCMC) sampling of the forward model, which maps from input to output space, for parameter inference tasks of varying complexity. The inverse models perform best when searching vast parameter spaces for post‐shot simulations that are consistent with a large number of observables, where MCMC sampling can be prohibitively expensive. We demonstrate how augmenting inverse models with autoencoders enable the inclusion of several dozen observables in the inverse mapping, reducing the degeneracy of the model and improving the accuracy of the post‐shot analysis.

Humbird, Kelli D.↗
DOE OSTI2016

Massively parallel and linear-scaling algorithm for second-order Moller–Plesset perturbation theory applied to the study of supramolecular wires

Here, we present a scalable cross-platform hybrid MPI/OpenMP/OpenACC implementation of the Divide–Expand–Consolidate (DEC) formalism with portable performance on heterogeneous HPC architectures. The Divide–Expand–Consolidate formalism is designed to reduce the steep computational scaling of conventional many-body methods employed in electronic structure theory to linear scaling, while providing a simple mechanism for controlling the error introduced by this approximation. Our massively parallel implementation of this general scheme has three levels of parallelism, being a hybrid of the loosely coupled task-based parallelization approach and the conventional MPI +X programming model, where X is either OpenMP or OpenACC. We demonstrate strong and weak scalability of this implementation on heterogeneous HPC systems, namely on the GPU-based Cray XK7 Titan supercomputer at the Oak Ridge National Laboratory. Using the “resolution of the identity second-order Moller–Plesset perturbation theory” (RI-MP2) as the physical model for simulating correlated electron motion, the linear-scaling DEC implementation is applied to 1-aza-adamantane-trione (AAT) supramolecular wires containing up to 40 monomers (2440 atoms, 6800 correlated electrons, 24 440 basis functions and 91 280 auxiliary functions). This represents the largest molecular system treated at the MP2 level of theory, demonstrating an efficient removal of the scaling wall pertinent to conventional quantum many-body methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Uncertainty quantification for molecular property predictions with graph neural architecture search

Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify uncertainties in the predictions. This capability is crucial for ensuring the trustworthy use and deployment of models in downstream tasks. To that end, we introduce AutoGNNUQ, an automated uncertainty quantification (UQ) approach for molecular property prediction. AutoGNNUQ leverages architecture search to generate an ensemble of high-performing GNNs, enabling the estimation of predictive uncertainties. Our approach employs variance decomposition to separate data (aleatoric) and model (epistemic) uncertainties, providing valuable insights for reducing them. In our computational experiments, we demonstrate that AutoGNNUQ outperforms existing UQ methods in terms of both prediction accuracy and UQ performance on multiple benchmark datasets, and generalizes well to out-of-distribution datasets. Additionally, we utilize t-SNE visualization to explore correlations between molecular features and uncertainty, offering insight for dataset improvement. AutoGNNUQ has broad applicability in domains such as drug discovery and materials science, where accurate uncertainty quantification is crucial for decision-making.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Lorentz group equivariant autoencoders

Abstract There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection. Often these models are adapted from those designed for datasets in computer vision or natural language processing, which lack inductive biases suited to HEP data, such as equivariance to its inherent symmetries. Such biases have been shown to make models more performant and interpretable, and reduce the amount of training data needed. To that end, we develop the Lorentz group autoencoder (LGAE), an autoencoder model equivariant with respect to the proper, orthochronous Lorentz group $$\textrm{SO}^+(3,1)$$ SO + ( 3 , 1 ) , with a latent space living in the representations of the group. We present our architecture and several experimental results on jets at the LHC and find it outperforms graph and convolutional neural network baseline models on several compression, reconstruction, and anomaly detection metrics. We also demonstrate the advantage of such an equivariant model in analyzing the latent space of the autoencoder, which can improve the explainability of potential anomalies discovered by such ML models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Benchmarking treewidth as a practical component of tensor network simulations

Tensor networks are powerful factorization techniques which reduce resource requirements for numerically simulating principal quantum many-body systems and algorithms. The computational complexity of a tensor network simulation depends on the tensor ranks and the order in which they are contracted. Unfortunately, computing optimal contraction sequences (orderings) in general is known to be a computationally difficult (NP-complete) task. In 2005, Markov and Shi showed that optimal contraction sequences correspond to optimal (minimum width) tree decompositions of a tensor network’s line graph, relating the contraction sequence problem to a rich literature in structural graph theory. While treewidth-based methods have largely been ignored in favor of dataset-specific algorithms in the prior tensor networks literature, we demonstrate their practical relevance for problems arising from two distinct methods used in quantum simulation: multi-scale entanglement renormalization ansatz (MERA) datasets and quantum circuits generated by the quantum approximate optimization algorithm (QAOA). We exhibit multiple regimes where treewidth-based algorithms outperform domain-specific algorithms, while demonstrating that the optimal choice of algorithm has a complex dependence on the network density, expected contraction complexity, and user run time requirements. We further provide an open source software framework designed with an emphasis on accessibility and extendability, enabling replicable experimental evaluations and future exploration of competing methods by practitioners.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Development of Technologies and Capabilities for Coal Energy Resources

This is the final report for the Aerojet Rocketdyne (AR) Advanced Gasification Systems Development (AGSD) project that was initiated on October 1, 2004. The project’s primary objective is to demonstrate technologies that improve the availability and efficiency of gasification-based power plants and to reduce plant capital and operational costs. The objective will be accomplished by (1) development and testing of cooled refractory liners, (2) feed system development and demonstration, (3) pilot plant definition and planning, (4) transport gasifier feed analysis, (5) coal-biomass mixture feed analysis, and (6) coal-biomass mixture pilot scale tests. Task 1, development and testing of cooled refractory liners, was conducted at CANMET Energy Technology Centre in Ottawa, Canada. The test objectives were to screen high conductivity CMC materials with alternate manufacturing methods and a variety of surface treatments for cooled metal tubes in a slagging gasifier environment. During the CMC testing, it was found that a constantly cooled CMC will survive in good conditions and that high porosity CMC resulted in high material erosion and corrosion. During cooled tube testing, it was found that hard metal spray coat and bare Haynes 188 performed the best. Task 2, feed system development and demonstration, was conducted at at the Energy and Environmental Research Center (EERC) at the University of North Dakota (UND). For tasks 2.1-2.6, the test objective was to demonstrate 400-tons/day coal feed system operation (to 1,000 psia gasifier pressures) in ultra-dense phase flow without plugging and to achieve multi-element injector flow-split non-uniformities below 2 %RSD. Testing was successfully completed with no feed system plugging of the feed system. The majority of the other Task 2 activities were associated with the Dry Solids Pump design (Task 2.8 and 2.11), development (Task 2.10), fabrication (Task 2.12), amd testing (Task 2.13-2.15). A variety of tests were performed that verified the capability of the Dry Solids Pump to consolidate various pulverized feedstocks to a state capable of sealing and sustaining downstream outlet pressures during mechanical operation of the pump as well as during periods when the pump was turned off. However, the final operating goals were not achieved. Task 3, pilot plant definition and planning, was to define requirements for an advanced commercial gasifier with a cooled liner and to establish a preferred commercial gasifier system configuration to serve as the reference point for an advanced gasifier pilot plant concept definition. A pilot plant concept was successfully defined that is scalable and traceable to the commercial gasifier configuration. The schedule and cost for design, installation, and long-duration test operation of a pilot plant were estimated at 33 months and $\$$25.6M. Taks 4, transport gasifier feed analysis, was to determine the effects of larger sized transport gasifier low rank coals within the AR dry solids pump. It was found for the two low-rank +100 mesh size transport gasifier coals that the cohesion of these larger materials was significantly less than the coals ground to minus 200 mesh. Also, the gas permeability of the MSL transport coal was found to be significantly higher than the permeability of the PRB transport coal. Task 5, coal-biomass mixture feed analysis, was to determine the effects of coal biomass blends within the AR ultra-dense phase feed system and dry solids pump, and to determine the cost of electricity increase of an integrated gasification combined cycle (IGCC) operating on coal/biomass blends with the AR dry solids pump and ultra-dense phase feed system. In summary, it was found that 75/25 wt% coal/corn stover blends produce reasonable economic results for IGCC operation with minimal dry solids pump modifications – especially if the coal is a low rank coal and the corn stover biomass is pelletized. The increase in electricity cost was found to be 1.1 cents/kWh. Task 6, coal-biomass mixture pilot scale tests, was to determine which coal/biomass blends could be tested in the current dry solids pump without major modifications. As reported in detail in the reports by Sprouse (2010) and Sprouse and Darby (2013), the coal/biomass blend having the least impact on pump configuration and operation is the 90/10 wt% Illinois #6 to corn stover ratio blend. The AGSD program was initiated on October 1, 2004. The interim final report was submitted on October 21, 2010 and covered Task 1, Task 2.1-2.9, and Task 3, performed during budget period 1. This final report covers Task 2.10-2.16, 4, 5, and 6, performed during budget period 2. The interim final report is attached as part of the appendix to this report.

Darby, Alan K.↗
NASA NTRS1987

System engineering techniques for establishing balanced design and performance guidelines for the advanced telerobotic testbed

Novel system engineering techniques have been developed and applied to establishing structured design and performance objectives for the Telerobotics Testbed that reduce technical risk while still allowing the testbed to demonstrate an advancement in state-of-the-art robotic technologies. To estblish the appropriate tradeoff structure and balance of technology performance against technical risk, an analytical data base was developed which drew on: (1) automation/robot-technology availability projections, (2) typical or potential application mission task sets, (3) performance simulations, (4) project schedule constraints, and (5) project funding constraints. Design tradeoffs and configuration/performance iterations were conducted by comparing feasible technology/task set configurations against schedule/budget constraints as well as original program target technology objectives. The final system configuration, task set, and technology set reflected a balanced advancement in state-of-the-art robotic technologies, while meeting programmatic objectives and schedule/cost constraints.

Zimmerman, W. F.↗
NASA NTRS2003

Agent Based Software for the Autonomous Control of Formation Flying Spacecraft

Distributed satellite systems is an enabling technology for many future NASA/DoD earth and space science missions, such as MMS, MAXIM, Leonardo, and LISA [1, 2, 3]. While formation flying offers significant science benefits, to reduce the operating costs for these missions it will be essential that these multiple vehicles effectively act as a single spacecraft by performing coordinated observations. Autonomous guidance, navigation, and control as part of a coordinated fleet-autonomy is a key technology that will help accomplish this complex goal. This is no small task, as most current space missions require significant input from the ground for even relatively simple decisions such as thruster burns. Work for the NMP DS1 mission focused on the development of the New Millennium Remote Agent (NMRA) architecture for autonomous spacecraft control systems. NMRA integrates traditional real-time monitoring and control with components for constraint-based planning, robust multi-threaded execution, and model-based diagnosis and reconfiguration. The complexity of using an autonomous approach for space flight software was evident when most of its capabilities were stripped off prior to launch (although more capability was uplinked subsequently, and the resulting demonstration was very successful).

How, Jonathan P.↗