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NASA NTRS1986

ISCCP reduced resolution satellite radiance data

The International Satellite Cloud Climatology Project (ISCCP) is the first active project of the World Climate Research Program. It is a multinational data collection project focused on collecting a data set that will improve the ability to predict and/or simulate the radiative effects of clouds on climate. For specified cloud parameters, the goals are to archieve values for 3-hour periods over the whole globe for 5 years at 30 km resolution. The task of collecting and processing radiance data from both geosynchronous and polar orbiting satellites began in July 1983. A diagram was shown illustrating the flow of data from the transmitting satellites to the various receiving institutions that handle it. The various stages of processing were then explained in detail, emphasizing Level B3-normalized, reformatted, reduced raw satellite data. The reduction of data by sampling is an essential step in the flow. By the time the ISCCP data reaches the Global Processing Center at Goddard Institute for Space Studies (GISS), the volume has been reduced by a factor of 1000. The Pilot Climate Data System (PLDS) will provide access to the ISCCP data set. It should prove to be one of the cleanest satellite data sets because it will have been through three filters--that of the operational agency, the Global Processing Center, and the PCDS. The ISCCP data set also includes other correlative data sets delivered in compatible format.

Rossow, W.↗

DOE OSTI2023

A Task Based Approach for Co-Scheduling Ensemble Workloads on Heterogeneous Nodes

Scientific workflows consist of multiple, connected applications, with data and results flowing from one to another in a pipeline. Traditionally, such workflows are executed in sequential order, storing intermediate data in storage disks. Co-scheduling application workflows concurrently on the same compute nodes would greatly reduce the cost of moving data to/from storage and allow real-time analysis of intermediate results. Nevertheless, most parallel programming runtimes do not allow seamless integration of various applications in a scientific workflow, in part due to the complexity of managing data and resources. The situation is even more complicated for heterogeneous systems. In this work we extend the Minos Computing Library (MCL) runtime to accelerate pipe-lined and parallel workloads where multiple applications are running in the same system. MCL’s asynchronous task library and runtime dynamically manages resources to allow co-scheduling of multiple processes sharing heterogeneous resources. In addition, we design a custom ex- tension of the Open Compute Language (OpenCL) to enable multiple processes to share device memory. We enable MCL to coordinate these shared buffers to allow for easy, fast data sharing between applications. Using malleable micro-benchmarks and two application workflows that combine scientific simulation and AI-based analysis, we show that our method outperforms traditional approaches.

Index Terms—Parallel systems, Scheduling and Task ↗

DOE OSTI2019

FTIR and Raman Spectroscopy of Reduced Graphene Oxide Carbon Nanoribbons and Graphene Oxide on Copper (Cu)

In the past couple of decades, carbon nanomaterials produced from graphene and graphite have emerged as one of the more important recently discovered substances. These carbon nanomaterials have been found to exhibit high levels of mechanical strength, thermal resistance, and electrical conductivity, thus having a multitude of applications. Therefore, it is important to identify and analyze their traits in order to maximize their effectiveness. Two spectroscopic techniques employed for such analyses of these materials are Fourier-Transform Infrared (FTIR) and Raman Spectroscopy. The advantages of FTIR and Raman include nondestructive analysis, relatively quick measurements under normal atmospheric conditions, and enhanced sensitivity with high resolution capacity. FTIR Spectroscopy was used in this research project to determine the effects of thermal exfoliation and photoreduction on the functionality groups of a carbon nanomaterial. Raman Spectroscopy was used in this research project to determine the effects of thermal exfoliation and photoreduction on the intensity of the disorder peak, which is greater in intensity if the carbon nanomaterial exhibits greater amounts of functionalities or defects in the graphitic material. There were two sample types which were analyzed with both FTIR and Raman Spectroscopy for the research project: graphene nanoribbons and graphene oxide. The m-grade graphene nanoribbons (GNRs) were produced from m-grade carbon nanotubes (CNTs) via Tour's method. The graphene oxide (GO) was produced from graphene via Tour's method, sonicated and centrifuged, dip-coated onto Copper (Cu) foil, and then photoreduced under a UV light source. FTIR measurements and analyses were conducted using a Jasco FTIR Spectrometer. Raman measurements and analyses were conducted using a Jasco Laser Raman Spectrometer. Tasks and techniques for this project included photoreduction, dip-coating, corrosion testing, FTIR and Raman data collection and analysis, and improvement of data collection for the Raman Spectrometer. GNRs exfoliated and reduced (deoxygenation) at 270 deg. C in air showed a significant decrease in O functionalities according to FTIR. However, Raman revealed that a large increase in graphene planar and edge defects are present as indicated by the disorder band at 1340 cm-1. Further optimization could be performed by thermal exfoliation at lower temperatures assuming a need for fewer structural defects. Graphene oxide coatings showed clear reduction with an increase with photoreduction time. Raman revealed that the reduced graphene oxide films had less disruption in the planar phonon lattice vibrations which indicates that the structure is returning to sp2 hybridization of pristine graphene without adding structural defects.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Technical Specification Surveillance Interval Extension Using Self-Diagnostics

As part of the Light Water Reactor Sustainability program, an ongoing research effort is being conducted on technical specifications surveillance interval extension of digital equipment in nuclear power plants. The research team is led by Idaho National Laboratory and includes Pacific Northwest National Laboratory, Technology Resources, and Oak Ridge National Laboratory. This research focuses on developing methods for applying the U.S. Nuclear Regulatory Commission (NRC)–approved guidance to implement a licensee-controlled, risk-informed surveillance frequency change program in digital instrumentation and control (I&C) systems that include self-diagnostics and online monitoring (OLM) capabilities. Although approved methods exist for extending technical specifications (TS) surveillance test intervals (STIs) for general equipment, including analog I&C equipment, gaps remain in technology and guidance on crediting newer digital equipment’s internal self-diagnostics and OLM characteristics. Previous research described a general methodology for crediting internal self-diagnostics for extending surveillance test intervals. The methodology used self-diagnostics to detect—and credited recovery from—failure. Self-diagnostics were also applied for performance monitoring during the extended surveillance interval. This report discusses the status of recent activities to evaluate the previously developed methodology using a pilot study. Although both a utility partner for a pilot study and a specific digital asset were identified in FY2020, delays in obtaining proprietary information resulted in a limited ability to fully evaluate the methodology, and further interactions were complicated by the COVID pandemic. Therefore, at that time, the use of public-domain information—along with current processes for surveillance interval extension through a surveillance frequency control program—identified the need to fully assess diagnostic coverage as part of the pilot study. Furthermore, self-diagnostics were also identified as a potential option to replace the drift analyses conducted as part of current STI extension procedures. In FY2022, the project was reconstituted with the industry partner, and information and data were made available by the industry partner to the research team for review. The shared information included failure event descriptions and data for a digital I&C system since its implementation, as well as recent STI extension interval reports developed by the utility partner on that digital I&C system. This report presents an evaluation of this information and data and describes an application of the proposed methodology cited above. The methodology seeks to take advantage of the self-diagnostics and OLM capabilities to reduce risk or reduce the level of qualitative monitoring assessment needed to perform a risk-informed STI extension using existing NRC approved guidance or both. Addressing these issues of STI extension by crediting self-diagnostics is likely to result in benefits for current and future nuclear power plant (NPP) operations, including lowering the barriers to adoption of digital I&C systems and increasing cost savings by deferring or eliminating unneeded preventive maintenance (tasks or checks or activities). Specifically, self-diagnostic and OLM capabilities of newer digital equipment being installed in non-safety and safety applications are designed to detect failures, provide early warning of potential failures, and notify plant operators to take appropriate action to reduce out of service (OOS) time thus protecting safety margins. Moreover, the equipment is expected to provide information that time-related operational degradation is identified early to ensure timely and planned corrective actions instead of a reactive and unplanned approach ahead of an extended-surveillance interval.

42 ENGINEERING↗

DOE OSTI2021

Learning-based framework for sensor fault-tolerant building HVAC control with model-assisted learning

As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort and reducing energy consumption. These HVAC systems in smart buildings rely 'on real-time sensor readings, which in practice often suffer from various faults and could also be vulnerable to malicious attacks. Such faulty sensor inputs may lead to the violation of indoor environment requirements (e.g., temperature, humidity, etc.) and the increase of energy consumption. While many model-based approaches have been proposed in the literature for building HVAC control, it is costly to develop accurate physical models for ensuring their performance and even more challenging to address the impact of sensor faults. In this work, we present a novel learning-based framework for sensor fault-tolerant HVAC control, which includes three deep learning based components for 1) generating temperature proposals with the consideration of possible sensor faults, 2) selecting one of the proposals based on the assessment of their accuracy, and 3) applying reinforcement learning with the selected temperature proposal. Moreover, to address the challenge of training data insufficiency in building-related tasks, we propose a model-assisted learning method leveraging an abstract model of building physical dynamics. Through extensive experiments, we demonstrate that the proposed fault-tolerant HVAC control framework can significantly reduce building temperature violations under a variety of sensor fault patterns while maintaining energy efficiency.

Xu, Shichao↗

DOE OSTI2024

DECOVALEX-2023: Task C Final Report

The Full-scale Emplacement (FE) heater experiment at the Mont Terri Underground Rock Laboratory (URL) was designed and conducted by Nagra to replicate an emplacement tunnel of Nagra’s reference repository design at 1:1 scale. Alongside testing the technical feasibility of constructing disposal tunnels, emplacing waste containers in the tunnels and then backfilling them, the main goals of the FE experiment are (1) to obtain a better understanding of the coupled effects of induced thermo-hydro-mechanical (THM) processes that may occur and (2) to validate existing coupled THM models (Müller et al., 2017). A key aspect of ensuring safety for repositories located in low-permeability rock involves minimizing any damage to the rock itself, thereby preserving its integrity and promoting a stable environment Amongst a number of processes that could damage the rock is the increase in pore pressure due to thermal loading caused by heat emitted from the waste. To reduce the potential damage of the rock, it is important to analyse the evolution of heat over time due to the heat load of the containers and assess possible consequences by coupled THM models. The aim of Task C of DECOVALEX-2023 was to build 3D numerical models of the FE experiment, focussing in particular on the heating induced pore pressure change in the Opalinus Clay. Data from a large number of sensors were available from the FE experiment for model comparison. These sensors measured temperature and relative humidity in the bentonite around the heaters, and temperature, pressure and displacement/strain in the surrounding Opalinus clay. Data were available from the start of excavation (April 2012) up to August 2020 for most sensors (more than 5 years from the start of heating in December 2014). To fulfil the overall aim of the task, the work was broken down into a number of steps, starting with simpler models to build confidence in each team’s approach and then moving to more complex models that better represent the FE experiment. Step 0 consisted of 2D benchmark models, gradually increasing the number of processes that are represented from thermal (T) only models in Step 0a, to coupled thermal hydraulic (TH) models in Step 0b with a representation of changing porosity, to coupled thermo-hydro-mechanical (THM) models in Step 0c, where porosity changes are calculated by the mechanical model. A detailed specification of processes, parameters, initial and boundary conditions was provided for this step, with the ambition that all teams would work towards close agreement in their model results, thus building confidence in the model implementations. vi It was not straightforward to achieve agreement between the teams, so additional steps (Step 0b2, 0b3, 0c2, 0c3) were added along with derivation of some analytical solutions against which the models could be compared. The reasons for the differences between teams were investigated and found to be caused primarily by different conceptual model assumptions (including temperature dependence of the thermal expansion of water), different model formulations (including porosity evolution) and differences in modelled domain sizes, boundary conditions and grid discretisation. This demonstrates that comparisons between multiple modelling teams and/or comparison with analytical results and experimental data are highly beneficial in providing an indication of uncertainty in model predictions. At the conclusion of Step 0, almost all teams had achieved a close agreement in model results and those that had not achieved an agreement knew the reason for this. Step 1 moved from 2D models to 3D models of the FE experiment without adding technical features like shotcrete or EDZ, and only considering the heating phase. Initially the 3D model was tightly specified to continue to build confidence in the model implementations (Step 1a). The results of Step 1a were compared to the data from the FE-experiment without the teams seeing the data. The teams were then provided with a sub-set of the data from the FE-experiment and invited to consider how best to use the large dataset for model comparison (Step 1b). Teams were then asked to use the data provided to calibrate their models, only changing material property values rather than adding features or processes to their models (Step 1c). In Step 1, teams were asked to only model the heating phase of the experiment, so pressure in the Opalinus Clay was reported as change in pressure since the initial conditions were specified rather than modelled. The change from 2D to 3D models was accompanied by an increase in the dispersion of results between the teams. Some of this was resolved during the task, but some remained and is potentially due to model discretisation. Calibration of parameters was useful in improving the fit of the models to the data but the remaining differences indicated that the models were missing features or processes. In Step 2, the teams were asked to update their models with additional features and processes as well as calibrating parameters to try and improve the fit of the models to the data. Teams were encouraged to represent ventilation of the open FE tunnel prior to backfilling with heaters and bentonite and in Step 2, the absolute pressure in the Opalinus Clay was compared between the teams. Teams took different approaches, but there was consideration of adding shotcrete and an EDZ into the model, representing stress change during excavation and different approaches to modelling ventilation of the FE tunnel. Overall, the documented results showed a very good agreement for temperature. The results for porewater pressure evolution showed a significant improvement for most teams compared to Step 1c with a good agreement to the measurements for several teams whereas some teams overpredicted the pressure increase and others overpredicted the drainage effect especially for the sensors close to the heater. Step 3 was an opportunity for teams to use the models developed in Step 1 and Step 2 to make predictions about the temperature and pressure changes that will be expected at the FE experiment over the next few years in light of the planned changes in thermal output of the heaters.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2014

Improving Early Adaptation Following Long Duration Spaceflight by Enhancing Vestibular Information

Crewmember adapted to the microgravity state may need to egress the vehicle within a few minutes for safety and operational reasons after g-transitions. The transition from one sensorimotor state to another consists of two main mechanisms: strategic and plastic-adaptive and have been demonstrated in astronauts returning after long duration space flight. Strategic modifications represent "early adaptation" -immediate and transitory changes in control that are employed to deal with short-term changes in the environment. If these modifications are prolonged then plastic-adaptive changes are evoked that modify central nervous system function, automating new behavioral responses. More importantly, this longer term adaptive recovery mechanism was significantly associated with their strategic ability to recover on the first day after return to Earth G. We are developing a method based on stochastic resonance (SR) to enhance information transfer by improving the brain's ability to detect vestibular signals especially when combined with balance training exercises for rapid improvement in functional skill, for standing and mobility. The countermeasure to improve post-flight balance and locomotor disturbances is a stimulus delivery system that is wearable/portable providing low imperceptible levels of white noise based binaural bipolar electrical stimulation of the vestibular system (stochastic vestibular stimulation, SVS). The techniques for improving signal detection using SVS may thus provide additional information to improve such strategic abilities and thus help in significantly reducing the number of days required to recover functional performance to preflight levels after long duration space flight. We have conducted a series of studies to document the efficacy of SVS stimulation on balance/locomotion tasks on unstable surfaces and motion tracking tasks during intra-vestibular system conflicts. In an initial study, we showed that SVS improved overall balance performance while standing on an unstable surface indicating that SVS may be sufficient to provide a comprehensive countermeasure approach for improving postural stability. In a second study, we showed that SVS improved locomotor performance on a treadmill mounted on an oscillating platform indicating that SVS may also be used to maximize locomotor performance during walking in unstable environments. In a third study, SVS was evaluated during an otolith-canal conflict scenario in a variable radius centrifuge at low frequency of oscillation (0.1 Hz) on both eye movements and perceptual responses (using a joystick) to track imposed oscillations. The variable radius centrifuge provides a selective tilting sensation that is detectable only by the otolith organs providing conflicting information from the canal organs of the vestibular system (intra-vestibular conflict). Results show that SVS significantly reduced the timing difference between both the eye movement responses as well as the perceptual tracking responses with respect to the imposed tilt sensations. These results indicate that SVS can improve performance in sensory conflict scenarios like that experienced during space flight. Such a SR countermeasure will act synergistically along with the pre-and in-flight adaptability training protocols providing an integrated, multi-disciplinary countermeasure capable of fulfilling multiple requirements making it a comprehensive and cost effective countermeasure approach to enhance sensorimotor capabilities following long-duration space flight.

Mulavara, Ajitkumar↗

DOE OSTI2017

Less is More: Bigger Data from Compressive Measurements

Compressive sensing approaches are beginning to take hold in (scanning) transmission electron microscopy (S/TEM) [1,2,3]. Compressive sensing is a mathematical theory about acquiring signals in a compressed form (measurements) and the probability of recovering the original signal by solving an inverse problem [4]. The inverse problem is underdetermined (more unknowns than measurements), so it is not obvious that recovery is possible. Compression is achieved by taking inner products of the signal with measurement weight vectors. Both Gaussian random weights and Bernoulli (0,1) random weights form a large class of measurement vectors for which recovery is possible. The measurements can also be designed through an optimization process. The key insight for electron microscopists is that compressive sensing can be used to increase acquisition speed and reduce dose. Building on work initially developed for optical cameras, this new paradigm will allow electron microscopists to solve more problems in the engineering and life sciences. We will be collecting orders of magnitude more data than previously possible. The reason that we will have more data is because we will have increased temporal/spatial/spectral sampling rates, and we will be able ability to interrogate larger classes of samples that were previously too beam sensitive to survive the experiment. For example consider an in-situ experiment that takes 1 minute. With traditional sensing, we might collect 5 images per second for a total of 300 images. With compressive sensing, each of those 300 images can be expanded into 10 more images, making the collection rate 50 images per second, and the decompressed data a total of 3000 images [3]. But, what are the implications, in terms of data, for this new methodology? Acquisition of compressed data will require downstream reconstruction to be useful. The reconstructed data will be much larger than traditional data, we will need space to store the reconstructions during analysis, and the computational demands for analysis will be higher. Moreover, there will be time costs associated with reconstruction. Deep learning [5] is an approach to address these problems. Deep learning is a hierarchical approach to find useful (for a particular task) representations of data. Each layer of the hierarchy is intended to represent higher levels of abstraction. For example, a deep model of faces might have sinusoids, edges and gradients in the first layer; eyes, noses, and mouths in the second layer, and faces in the third layer. There has been significant effort recently in deep learning algorithms for tasks beyond image classification such as compressive reconstruction [6] and image segmentation [7]. A drawback of deep learning, however, is that training the model requires large datasets and dedicated computational resources (to reduce training time to a few days). A second issue is that deep learning is not user-friendly and the meaning behind the results is usually not interpretable. We have shown it is possible to reduce the data set size while maintaining model quality [8] and developed interpretable models for image classification [9], but the demands are still significant. The key to addressing these problems is to NOT reconstruct the data. Instead, we should design computational sensors that give answers to specific problems. A simple version of this idea is compressive classification [10], where the goal is to classify signal type from a small number of compressed measurements. Classification is a much simpler problem than reconstruction, so 1) much fewer measurements will be necessary, and 2) these measurements will probably not be useful for reconstruction. Other simple examples of computational sensing include determining object volume or the number of objects present in the field of view [11].

Stevens, Andrew↗

DOE OSTI2025

Improved Gas Plume Identification Using Nearest Neighbor Methods for Background Estimation

Longwave infrared (LWIR) hyperspectral imaging (HSI) can be used for many tasks in remote sensing, including detecting and identifying effluent gases by LWIR sensors on airborne platforms. Identification is used after detection to increase confidence in weakly detected plumes, reduce false positives from detection, and distinguish between similar and confounding material signatures. Background estimation is an important step used to reveal the unique spectral characteristics of the detected gas, allowing the identification model to determine what the gas is specifically. The importance of proper background estimation increases when dealing with weak signals, large libraries of gases of interest, and uncommon or heterogeneous backgrounds. In this article, we propose two methods for background estimation: a novel k-nearest segments (KNS) algorithm and the standard k-nearest neighbors (KNN) algorithm. We test our methods and three existing background estimation methods for comparison against global background estimation to determine which performs best at estimating the true background radiance under a plume and for increasing identification confidence using a neural network classification model. We compare the different methods using 640 simulated weak plumes in an urban environment. For identification, our KNS algorithm improves median neural network identification confidence by 53.2%. For background radiance estimation, the KNN algorithm provides a median of 49 times less RMSE than global background estimation. Furthermore, KNN is the easiest method to tune for different plumes, making it an excellent “out of the box” background estimator.

47 OTHER INSTRUMENTATION↗

NASA NTRS1974

Turbulence flight director analysis and preliminary simulation

A control column and trottle flight director display system is synthesized for use during flight through severe turbulence. The column system is designed to minimize airspeed excursions without overdriving attitude. The throttle system is designed to augment the airspeed regulation and provide an indication of the trim thrust required for any desired flight path angle. Together they form an energy management system to provide harmonious display indications of current aircraft motions and required corrective action, minimize gust upset tendencies, minimize unsafe aircraft excursions, and maintain satisfactory ride qualities. A preliminary fixed-base piloted simulation verified the analysis and provided a shakedown for a more sophisticated moving-base simulation to be accomplished next. This preliminary simulation utilized a flight scenario concept combining piloting tasks, random turbulence, and discrete gusts to create a high but realistic pilot workload conducive to pilot error and potential upset. The turbulence director (energy management) system significantly reduced pilot workload and minimized unsafe aircraft excursions.

Johnson, D. E.↗

NASA NTRS1984

Manual-control Analysis Applied to the Money-supply Control Task

The recent procedure implemented by the Federal Reserve Board to control the money supply is formulated in the form of a tracking model as used in the study of manual-control tasks. Using this model, an analysis is made to determine the effect of monetary control on the fluctuations in economic output. The results indicate that monetary control can reduce the amplitude of fluctuations at frequencies near the region of historic business cycles. However, with significant time lags in the control loop, monetary control tends to increase the amplitude of the fluctuations at the higher frequencies. How the investigator or student can use the tools developed in the field of manual-control analysis to study the nature of economic fluctuations and to examine different strategies for stabilization is examined.

Wingrove, R. C.↗

NASA NTRS1990

Using Expert Systems For Computational Tasks

Transformation technique enables inefficient expert systems to run in real time. Paper suggests use of knowledge compiler to transform knowledge base and inference mechanism of expert-system computer program into conventional computer program. Main benefit, faster execution and reduced processing demands. In avionic systems, transformation reduces need for special-purpose computers.

Duke, Eugene L.↗

NASA NTRS1990

Shuttle extravehicular mobility unit (EMU) operational enhancements

An EMU program activity is considered aimed at achieving a 25 percent reduction in ground turnaround man-hours and processing time between missions and extending EVA on-orbit capabilities. Tasks being implemented with expected benefits to NASA-manned spaceflight programs are identified and described. It is concluded that the EMU enhancements made to increase durability and reduce ground processing time will also provide the capability for on-orbit suit resizing and make possible the extension of the present on-orbit certification limit from 3 to 5 EVAs.

Balinskas, Robert↗

NASA NTRS1992

Geometric modeling for computer aided design

The goal was the design and implementation of software to be used in the conceptual design of aerospace vehicles. Several packages and design studies were completed, including two software tools currently used in the conceptual level design of aerospace vehicles. These tools are the Solid Modeling Aerospace Research Tool (SMART) and the Environment for Software Integration and Execution (EASIE). SMART provides conceptual designers with a rapid prototyping capability and additionally provides initial mass property analysis. EASIE provides a set of interactive utilities that simplify the task of building and executing computer aided design systems consisting of diverse, stand alone analysis codes that result in the streamlining of the exchange of data between programs, reducing errors and improving efficiency.

Schwing, James L.↗

NASA NTRS1993

Report of the SSME assessment team

In response to a request from the House of Representatives Committee on Science, Space, and Technology in its Report No. 102-500 of April 22, 1992, the Aerospace Safety Advisory Panel (ASAP) created an ad hoc task force to conduct a thorough assessment of the Space Shuttle Main Engine (SSME). The membership was drawn mostly from organizations other than ASAP, and this report represents the views of that task force. Its task was to assess the risk that the SSME poses to the safe operation of the Space Shuttle, to identify and evaluate improvements to the engine that would reduce the risk, and to recommend a set of priorities for the implementation of these improvements. The SSME Assessment Team, as it opted to call itself, convened in mid-1992 and, subsequently, met with and gathered information from all the principal organizations involved in the SSME program. These included the Rocketdyne Division of Rockwell International, the Marshall Space Flight Center of NASA, and the Pratt & Whitney Division of United Technologies Corporation. The information in this report reflects the program status as of October 1992. From the information received, the Team formed its conclusions and recommendations. Changes in the program status have, of course, occurred since that time; however, they did not affect the Team's conclusions and recommendations.

Source record↗

NASA NTRS1992

An advanced teleoperator control system - Design and evaluation

The design goal of an advanced teleoperator control system is twofold: 1) to allow the operator's manual control to be robust to system nonlinearities such as time delays and operator's control errors, and 2) to support the high performance of teleoperation while reducing the operator's control burden by providing the master and slave arms with desirable dynamic properties and by allowing the slave arm to automatically perform such control tasks as compliance and force control in the form of task sharing. The authors present a novel teleoperator control system achieving the above design goal by taking the following into consideration: the human dynamics involved in generating control command based on visual and forced feedback is modeled and incorporated into the controller design and evaluation; the dynamic characteristics of slave and master arms are actively modified in such a way as to implement the desirable dynamic characteristics; and the force feedback is redefined in terms of the combination of opposition and force discrepancies in order to establish the required man/machine dynamic coordination under shared control. The proposed control system with human dynamics in the control loop is simulated and compared with a number of conventional methods in the presence of human control errors and time delays.

Lee, Sukhan↗

NASA NTRS1997

Measurement of Carbon Dioxide Accumulation and Physiological Function in the Launch and Entry and Advanced Crew Escape Suits

The Launch and Entry Suit (LES) and Advanced Crew Escape Suit (ACES) are worn by astronauts for launch and entry. Previous work by Waligora, et al., 1992, Waligora and Gilbert, 1992, and Dalrymple 1996, have found that carbon dioxide (CO2) accumulation in the LES/ACES helmet may be problematic. CO2 accumulation is important because high inspired levels of CO2 reduce physical function and pose a safety hazard (e.g. levels of CO2 accumulation of 3.6% in the Extravehicular Mobility Unit are sufficient to terminate Extra Vehicular Activities). My task was to design a suitable test protocol for determining the important physiological aspects of LES/ACES use. Three basic issues arose. First was the determination of the astronaut's CO2 inspiration during visor-down use at rest and during walking at 3.5 mph. A sub-issue was the impact of a pneumotach on CO2 since it has been previously observed that when the Aerosport pneumotach was used, performance seemed improved, which might be attributable to a lowered respiration rate when using the pneumotach. The second issue was the energy costs of waLking in the LES/ACES with various G-suit inflation levels, since G-suit inflation increases metabolic costs and metabolic costs influence the C02 production in the LES/ACES helmet. Since G-suit inflation improves orthostatic tolerance after space flight, but likely increases the energy costs of walking, the balance between G-suit inflation and C02 accumulation is an important safety consideration. The third issue which arose from pilot work was the substantial reduction in physical function after a 10 min visor-down period prior to walk.

Bishop, Phillip↗

NASA NTRS2000

Reducing Risk for the Next Generations

It is the goal of this activity to develop 50 kW class Hall thruster technology in support of cost and time critical mission applications such as orbit insertion. NASA Marshall Space Flight Center is tasked to develop technologies that enable cost and travel time reduction of interorbital transportation. Therefore, a key challenge is development of moderate specific impulse (2000-3000 s), high thrust-to-power electric propulsion. NASA Glenn Research Center is responsible for development of a Hall propulsion system to meet these needs. First-phase, sub-scale Hall engine development has been completed, the 10 kW engine designed, fabricated, and tested. Performance demonstrated > 2400 s, > 500 mN thrust over 1000 hrs of operation documented.

Jankovsky, Robert↗