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At least 1,063 records · Page 59

Evaluating the Use of Remote Sensing Data in the U.S. Agency for International Development Famine Early Warning Systems Network

The U.S. Agency for International Development (USAID)'s Famine Early Warning System Network (FEWS NET) provides monitoring and early warning support to decision makers responsible for responding to food insecurity emergencies on three continents. FEWS NET uses satellite remote sensing and ground observations of rainfall and vegetation in order to provide information on drought, floods, and other extreme weather events to decision makers. Previous research has presented results from a professional review questionnaire with FEWS NET expert end-users whose focus was to elicit Earth observation requirements. The review provided FEWS NET operational requirements and assessed the usefulness of additional remote sensing data. We analyzed 1342 food security update reports from FEWS NET. The reports consider the biophysical, socioeconomic, and contextual influences on the food security in 17 countries in Africa from 2000 to 2009. The objective was to evaluate the use of remote sensing information in comparison with other important factors in the evaluation of food security crises. The results show that all 17 countries use rainfall information, agricultural production statistics, food prices, and food access parameters in their analysis of food security problems. The reports display large-scale patterns that are strongly related to history of the FEWS NET program in each country. We found that rainfall data were used 84% of the time, remote sensing of vegetation 28% of the time, and gridded crop models 10% of the time, reflecting the length of use of each product in the regions. More investment is needed in training personnel on remote sensing products to improve use of data products throughout the FEWS NET system.

USAID↗

Development of alternative data analysis techniques for improving the accuracy and specificity of natural resource inventories made with digital remote sensing data

An investigation was conducted into ways to improve the involvement of state and local user personnel in the digital image analysis process by isolating those elements of the analysis process which require extensive involvement by field personnel and providing means for performing those activities apart from a computer facility. In this way, the analysis procedure can be converted from a centralized activity focused on a computer facility to a distributed activity in which users can interact with the data at the field office level or in the field itself. A general image processing software was developed on the University of Minnesota computer system (Control Data Cyber models 172 and 74). The use of color hardcopy image data as a primary medium in supervised training procedures was investigated and digital display equipment and a coordinate digitizer were procured.

Lillesand, T. M.↗

Detecting and diagnosing SSME faults using an autoassociative neural network topology

An effort is underway at the University of Tennessee Space Institute to develop diagnostic expert system methodologies based on the analysis of patterns of behavior of physical mechanisms. In this approach, fault diagnosis is conceptualized as the mapping or association of patterns of sensor data to patterns representing fault conditions. Neural networks are being investigated as a means of storing and retrieving fault scenarios. Neural networks offer several powerful features in fault diagnosis, including (1) general pattern matching capabilities, (2) resistance to noisy input data, (3) the ability to be trained by example, and (4) the potential for implementation on parallel computer architectures. This paper presents (1) an autoassociative neural network topology, i.e. the network input and output is identical when properly trained, and hence learning is unsupervised; (2) the training regimen used; and (3) the response of the system to inputs representing both previously observed and unkown fault scenarios. The effects of noise on the integrity of the diagnosis are also evaluated.

Ali, M.↗

Design and Analysis of Precise Pointing Systems

The mathematical models of Glovebox Integrated Microgravity Isolation Technology (g- LIMIT) dynamics/control system, which include six degrees of freedom (DOF) equations of motion, mathematical models of position sensors, accelerometers and actuators, and acceleration and position controller, were developed using MATLAB and TREETOPS simulations. Optimal control parameters of G-LIMIT control system were determined through sensitivity studies and its performance were evaluated with the TREETOPS model of G-LIMIT dynamics and control system. The functional operation and performance of the Tektronix DTM920 digital thermometer were studied and the inputs to the crew procedures and training of the DTM920 were documented.

Kim, Young K.↗

The Successful Implementation of NASA Orbital Debris Requirements for the Retirement of TDRS-1

TDRS-1 was decommissioned on October 28th 2009 following more than 26 years of operation. The Grand Old Dame of the TDRSS constellation wa s launched aboard the maiden voyage of the Space Shuttle Challenger ( STS-6) in April 1983. TDRS-1 survived a malfunction of the Inertial Upper Stage eventually utilizing its own propulsion system to success fully reach its assigned station in geosynchronous orbit. The anomalo us beginning of the TDRS-1 mission was not without lasting consequenc es as the primary reaction control system (A-side) was completely di sabled with an apparent propellant leak and the secondary system (B-s ide) suffered damage to its negative roll thruster rendering the thru ster inoperable. Following decommissioning the challenge to completin g a successful TDRS-1 end-of-mission (EOM) was the implementation of the stringent orbital debris requirements of NPR 8715.6 with a parti ally functioning spacecraft not originally designed to meet those req uirements. The TDRS-1 EOM had three key goals: 1) removal of the spac ecraft from geosynchronous orbit; 2) depletion of the remaining prope llant; and 3) passivation of all other sources of energy storage or generation. The TDRS-1 EOM approach was one of minimizing risks while accomplishing the goals above. The orbit raising portion of EOM was accomplished using deltavelocity operations already proven during pre vious stationchanging maneuvers. The propellant depletion approach wa s necessarily more aggressive as over 20 hours of burn time was requ ired to deplete the remaining fuel. A novel approach utilizing a spin ning, thrusting, passively controlled spacecraft configuration was ut ilized to achieve reasonable burn durations that met schedule constra ints. This nonstandard configuration required careful analysis of ele ctrical, thermal, and communication subsystems. The configuration wa s thoroughly simulated prior to the start of operations and carefully characterized during the initial spin period and first burn. Passiva tion was by definition a unique operation not previously performed wi thin the TDRS 1-7 constellation. Use was made of a TDRS spacecraft si mulator to verify the operational procedure to mitigate risks and pro vide crew training. TDRS-1 orbit raising maneuvers commenced on June 5th 2010 and completed on June 14th with an apogee 370 km and a peri gee 352 km above geosynchronous altitude. 127 kg of propellant were e stimated to be remaining in the tanks at the completion of orbit rais ing. TDRS-1 was placed in its spinning orientation on June 16th and the first fuel depletion burn was performed the following day. A seri es of 10 depletion burns were performed ending on June 26th when both propellant tank pressures experienced dramatic drops. Final passivat ion was performed on June 27 th deactivating electronics, removing t he batteries from the bus and solar arrays, and disabling the space-t o-ground communications equipment. The Second TDRS Ground Terminal (S TGT) continued to open loop track TDRS-1 for several days attempting command reacquisition several times a day. All attempts were unsucce ssful confirming passivation was achieved. The TDRS-1 orbit at passiv ation was an orbital debris compliant 36,319 x 36,128 km in height. W hile differing spacecraft designs may preclude mimicking the exact TD RS-1 EOM approach, the TDRS-1 campaign serves to demonstrate that pr e-NPR 8715.6 designs can be made to meet the requirements resulting i n a reduced orbit debris environment for future missions

Mirczak, Walter↗

A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature, and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS. The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations. Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically different between the merged and unmerged models are expected to improve overall performance. These new parameters are then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5 providing the best results.

George Priftis↗

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

NASA NDE Fracture Critical Detectable Flaw Sizes History and Methodology

NASA requires that NDE methods and inspectors demonstrate 90% Probability of Detection (POD) with 95% confidence for critical flaw sizes when inspecting fracture critical metallic components. NASA addresses the known variability of NDE inspector capability in two ways. The first, Special NDE, requires that every inspector demonstrate the required 90/95 POD, which is resource intensive. The second approach is Standard NDE for which conservative flaw sizes for different NDE methods are provided such that it is expected that most properly trained inspectors will exceed the 90/95 POD requirement. As such, individual POD demonstration testing is not required. The origin of NASA Standard NDE dates to the start of the Space Shuttle Program in the early 1970’s. In the first study to quantitatively assess NDE methods and inspectors, the performance of multiple inspectors was evaluated for different NDE methods using a large set of fatigue cracked specimens. A rudimentary POD analysis was performed to estimate the 90/95 POD flaw size for each inspector for each method. Additionally, the average and standard deviation of the 90/95 POD flaw size across the multiple inspectors was calculated to estimate the flaw size for which 95 percent of inspectors would provide the 90/95 POD capability. These estimated 90/95/95 POD flaw sizes evolved into the NASA Standard NDE flaw sizes still in use for structural analysis five decades later. The methodology for performing Standard NDE POD studies was never documented in NASA requirements. Furthermore, POD analysis methods have significantly evolved since this seminal study. Likewise, NDE methods have improved and there has been a push to reassess Standard NDE flaw sizes for existing methods, and to develop Standard NDE flaw sizes for new methods such as digital radiography. In this study, a Standard NDE POD methodology was developed and baselined using the historical data. This reanalysis of the historical data identified several deficiencies in the original test plan as well as an overall lack of conservatism in the estimated 90/95/95 POD flaw sizes. The results of this historical review and the new methodology are being incorporated into an update of NASA NDE POD requirements.

Nondestructive Evaluation↗

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

PALMO: An OVERFLOW Machine Learning Airfoil Performance Database

The OVERFLOW Machine Learning Airfoil Performance (PALMO) database has been created to enable robust modeling of airfoil performance in a variety of applications. The PALMO database uses OVERFLOW simulation data second-order accurate in time and fourth-order accurate in space with Spalart-Allmaras turbulence closure. The foundation of the in-development PALMO database is the airfoil base cube. Each base cube includes simulation data parametrized over a range of Mach numbers, Reynolds numbers, and angles-of-attack. This database includes the NACA 4-series airfoils, with parametrization in airfoil thickness and camber from an NACA 0006 to an NACA 4424. In total, 52,480 NACA 4-series OVERFLOW calculations were run on the NASA High-End Compute Capability (HECC) supercomputer. This provides high-order-accurate simulation data covering a wide range of aerospace design applications, which enables users to develop accurate airfoil performance look-up tables without additional high-performance computing. In addition to engineering design and analysis of aerospace vehicles, PALMO is well suited to be a benchmark dataset for the development and testing of machine learning methods in aerospace engineering. This work presents an example PALMO surrogate model that enables accurate airfoil performance predictions for any arbitrary combination of camber, thickness, Mach number, Reynolds number, and angle of attack within the bounds of the database. Airfoil performance tables predicted for an airfoil not used in training the model are used in three-dimensional OVERFLOW simulations to quantify the downstream accuracy on aggregate rotor performance metrics. For the NACA 3415 airfoil, which had no common thickness or camber with the training data, the surrogate predicted and CFD generated tables were within 2.1% of each other in the forward flight lift to drag metric. This suggests that performance tables generated for airfoils within the bounds of the PALMO database will yield aggregate rotor performance predictions on par with tables generated from directly running OVERFLOW airfoil calculations. The PALMO airfoil performance coefficients are available publicly.

Database↗

Mapping of the wildland fuel characteristics of the Santa Monica mountains of Southern California

LANDSAT digital data was successfully used to map and evaluate the wildland fuels of the Santa Monica Mountains in Southern California. A mixed classification scheme was used where training areas of known vegetation types were entered and the maximum likelihood classifier run, followed by an evaluation of the results and an unsupervised retraining of the classifier using an image of the probability of misclassification. Estimation of maturity class and crown closure percents of the major cover types were assigned to each computer class by associating the photointerpretation of 159 large scale photo samples with the resultant computer classes using analysis of variance and analysis of categorized data. The result of the computer classification and statistical analysis were then transformed from the LANDSAT Coordinate California State Plane Coordinate system for use in a digital format in the FIRESCOPE data retrieval and fire modeling system.

Nichols, J. D.↗

A teleoperation training simulator with visual and kinesthetic force virtual reality

A force-reflecting teleoperation training simulator with a high-fidelity real-time graphics display has been developed for operator training. A novel feature of this simulator is that it enables the operator to feel contact forces and torques through a force-reflecting controller during the execution of the simulated peg-in-hole task, providing the operator with the feel of visual and kinesthetic force virtual reality. A peg-in-hole task is used in our simulated teleoperation trainer as a generic teleoperation task. A quasi-static analysis of a two-dimensional peg-in-hole task model has been extended to a three-dimensional model analysis to compute contact forces and torques for a virtual realization of kinesthetic force feedback. The simulator allows the user to specify force reflection gains and stiffness (compliance) values of the manipulator hand for both the three translational and the three rotational axes in Cartesian space. Three viewing modes are provided for graphics display: single view, two split views, and stereoscopic view.

Kim, Won S.↗

Enhancing Fault Isolation for Health Monitoring of Electric Aircraft Propulsion by Embedding Failure Mode and Effect Analysis into Bayesian Networks

This paper describes a fault isolation approach for electric powertrains of unmanned aerial vehicles. The approach leverages the combination of failure mode and effect analysis (FMEA) and Bayesian networks, thus introducing depend-ability structures into a diagnostic framework. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded within FMEAs. This framework helps the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. The framework is applied to an electric power-train system of a small, rotary-wing unmanned aerial vehicle, demonstrating how a Bayesian network enhanced by FMEA helps disambiguate between root causes of incipient failures, which would otherwise be considered as equally probable.

Fault Isolation↗

Lessons Learned From the Construction of a Portable Cleanroom for NASA OSIRIS-REx Mission Deintegration

NASA Johnson Space Center (JSC) Infrastructure and Astromaterials Acquisition & Curation Office completed construction and commissioning of the OSIRIS-REx (OREx) Deintegration portable cleanroom at the Utah Test and Training Range (UTTR). The new portable cleanroom was designed to receive the OREx sample return capsule from the landing point on the range to an ISO7 environment. Scientists used the portable clean-room to deintegrate the sample canister from the sample return capsule. Once separated, the sample canister was put in a container under nitrogen purge for transportation to B31 at the Johnson Space Center for astromaterial sample extraction, preliminary analysis, and long-term curation. The portable cleanroom was built by a subcontractor at their facility and then deconstructed to be transported to the remote location at UTTR. Since construction was completed in a remote location all tools and materials had to be transported from contractor site in Dallas, TX. The cleanroom was constructed within an existing facility, which provided conditioned air, electric power, and protection from the elements. Careful coordination was required between the host facility, cleanroom contractor, mission scientists, and JSC facilities and curation personnel. An existing anteroom at JSC was transported to UTTR and added to the portable cleanroom after there was concern about contamination without one for personnel entry/exit. The scientific study of organics is critical for the mission, so a stringent contamination control plan was implemented for low organics. Given these mission requirements the cleanroom construction materials were carefully selected to not hinder the scientific search for amino acids and the study of organics in the samples. The same cleanroom contractor that built the long-term astromaterial curation cleanroom back at JSC Houston, TX was selected to build the portable cleanroom and instructed to use the same materials. The cleanroom had double doors to open and allow the sample return capsule to fit into the cleanroom on its stand and be transferred to a clean stand already in the cleanroom. The portable cleanroom successfully completed its mission and the sample canister was safely deintegrated and transported to JSC under nitrogen purge.

astromaterials curation↗

Portable Cleanroom for NASA OSIRIS-REx Mission Deintegration

NASA Johnson Space Center (JSC) Infrastructure and Astromaterials Acquisition & Curation Office completed construction and commissioning of the OSIRIS-REx (OREx) Deintegration portable cleanroom at the Utah Test and Training Range (UTTR). The new portable cleanroom was designed to receive the OREx sample return capsule from the landing point on the range to an ISO7 environment. Scientists used the portable clean-room to deintegrate the sample canister from the sample return capsule. Once separated, the sample canister was put in a container under nitrogen purge for transportation to B31 at the Johnson Space Center for astromaterial sample extraction, preliminary analysis, and long-term curation. The portable cleanroom was built by a subcontractor at their facility and then deconstructed to be transported to the remote location at UTTR. Since construction was completed in a remote location all tools and materials had to be transported from contractor site in Dallas, TX. The cleanroom was constructed within an existing facility, which provided conditioned air, electric power, and protection from the elements. Careful coordination was required between the host facility, cleanroom contractor, mission scientists, and JSC facilities and curation personnel. An existing anteroom at JSC was transported to UTTR and added to the portable cleanroom after there was concern about contamination without one for personnel entry/exit. The scientific study of organics is critical for the mission, so a stringent contamination control plan was implemented for low organics. Given these mission requirements the cleanroom construction materials were carefully selected to not hinder the scientific search for amino acids and the study of organics in the samples. The same cleanroom contractor that built the long-term astromaterial curation cleanroom back at JSC Houston, TX was selected to build the portable cleanroom and instructed to use the same materials. The cleanroom had double doors to open and allow the sample return capsule to fit into the cleanroom on its stand and be transferred to a clean stand already in the cleanroom. The portable cleanroom successfully completed its mission and the sample canister was safely deintegrated and transported to JSC under nitrogen purge.

astromaterials curation↗

Locomotor Adaptation Improves Balance Control, Multitasking Ability and Reduces the Metabolic Cost of Postural Instability

During exploration-class missions, sensorimotor disturbances may lead to disruption in the ability to ambulate and perform functional tasks during the initial introduction to a novel gravitational environment following a landing on a planetary surface. The overall goal of our current project is to develop a sensorimotor adaptability training program to facilitate rapid adaptation to these environments. We have developed a unique training system comprised of a treadmill placed on a motion-base facing a virtual visual scene. It provides an unstable walking surface combined with incongruent visual flow designed to enhance sensorimotor adaptability. Greater metabolic cost incurred during balance instability means more physical work is required during adaptation to new environments possibly affecting crewmembers? ability to perform mission critical tasks during early surface operations on planetary expeditions. The goal of this study was to characterize adaptation to a discordant sensory challenge across a number of performance modalities including locomotor stability, multi-tasking ability and metabolic cost. METHODS: Subjects (n=15) walked (4.0 km/h) on a treadmill for an 8 -minute baseline walking period followed by 20-minutes of walking (4.0 km/h) with support surface motion (0.3 Hz, sinusoidal lateral motion, peak amplitude 25.4 cm) provided by the treadmill/motion-base system. Stride frequency and auditory reaction time were collected as measures of locomotor stability and multi-tasking ability, respectively. Metabolic data (VO2) were collected via a portable metabolic gas analysis system. RESULTS: At the onset of lateral support surface motion, subj ects walking on our treadmill showed an increase in stride frequency and auditory reaction time indicating initial balance and multi-tasking disturbances. During the 20-minute adaptation period, balance control and multi-tasking performance improved. Similarly, throughout the 20-minute adaptation period, VO2 gradually decreased following an initial increase after the onset of support surface motion. DISCUSSION: Resu lts confirmed that walking in discordant conditions not only compromises locomotor stability and the ability to multi-task, but comes at a quantifiable metabolic cost. Importantly, like locomotor stability and multi-tasking ability, metabolic expenditure while walking in discordant sensory conditions improved during adaptation. This confirms that sensorimotor adaptability training can benefit multiple performance parameters central to the successful completion of critical mission tasks.

Bloomberg, J. J.↗

Cross-Cutting Computational Modeling Project: Exploration Medical Station Analysis

Astronauts will be away from Earth-based medical care for long periods during future exploration missions. Thus, it will be necessary for the astronauts to perform various medical tasks to monitor and maintain their health in the microgravity environment of space. Performance of these tasks will be constrained due to the limited volume available to perform the task, the absence of gravity and the limited resources and capabilities available in the medical work area. It is therefore necessary to evaluate exploration medical workstation designs for how well the designs will support crew performance of medical tasks. This evaluation featured two trained medical caregivers (99th percentile male, 26th percentile female) performing emergent care procedures (alone and in tandem) on a medical manikin. The procedures came from the The procedures came from the International Space Station Medical Checklist, and they are designed for spaceflight. The objectives of the evaluation included determining the operational volume required to perform the tasks, examining the effect of constraining the operational volume with partitions, determining candidate locations for foot restraints and equipment placements and determining the effect of single vs. dual caregiver on the operational volume.A marker-based motion capture system collected the motion data, which enabled computation of operational volumes and foot placement maps using custom Python code. Additional data collected included heart rate, time to perform the procedures, and feedback from the caregivers in the form of the NASA Task Load Index (TLX), the US Government System Usability Survey, and an open-ended questionnaire.

Christopher A Gallo↗