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All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Precision Departure Release Capability (PDRC) Technology Description

After takeoff, aircraft must merge into en route (Center) airspace traffic flows which may be subject to constraints that create localized demand-capacity imbalances. When demand exceeds capacity, Traffic Management Coordinators (TMCs) often use tactical departure scheduling to manage the flow of departures into the constrained Center traffic flow. Tactical departure scheduling usually involves use of a Call for Release (CFR) procedure wherein the Tower must call the Center TMC to coordinate a release time prior to allowing the flight to depart. In present-day operations release times are computed by the Center Traffic Management Advisor (TMA) decision support tool based upon manual estimates of aircraft ready time verbally communicated from the Tower to the Center. The TMA-computed release is verbally communicated from the Center back to the Tower where it is relayed to the Local controller as a release window that is typically three minutes wide. The Local controller will manage the departure to meet the coordinated release time window. Manual ready time prediction and verbal release time coordination are labor intensive and prone to inaccuracy. Also, use of release time windows adds uncertainty to the tactical departure process. Analysis of more than one million flights from January 2011 indicates that a significant number of tactically scheduled aircraft missed their en route slot due to ready time prediction uncertainty. Uncertainty in ready time estimates may result in missed opportunities to merge into constrained en route flows and lead to lost throughput. Next Generation Air Transportation System (NextGen) plans call for development of Tower automation systems capable of computing surface trajectory-based ready time estimates. NASA has developed the Precision Departure Release Capability (PDRC) concept that uses this technology to improve tactical departure scheduling by automatically communicating surface trajectory-based ready time predictions to the Center scheduling tool. The PDRC concept also incorporates earlier NASA and FAA research into automation-assisted CFR coordination. The PDRC concept helps reduce uncertainty by automatically communicating coordinated release times with seconds-level precision enabling TMCs to work with target times rather than windows. NASA has developed a PDRC prototype system that integrates the Center's TMA system with a research prototype Tower decision support tool. A two-phase field evaluation was conducted at NASA's North Texas Research Station (NTX) in Dallas-Fort Worth. The field evaluation validated the PDRC concept and demonstrated reduced release time uncertainty while being used for tactical departure scheduling of more than 230 operational flights over 29 weeks of operations. This paper presents the Technology Description. Companion papers include the Final Report and a Concept of Operations.

tactical departure scheduling↗
NASA NTRS2013

Precision Departure Release Capability (PDRC) Concept of Operations

After takeoff, aircraft must merge into en route (Center) airspace traffic flows which may be subject to constraints that create localized demandcapacity imbalances. When demand exceeds capacity Traffic Management Coordinators (TMCs) often use tactical departure scheduling to manage the flow of departures into the constrained Center traffic flow. Tactical departure scheduling usually involves use of a Call for Release (CFR) procedure wherein the Tower must call the Center TMC to coordinate a release time prior to allowing the flight to depart. In present-day operations release times are computed by the Center Traffic Management Advisor (TMA) decision support tool based upon manual estimates of aircraft ready time verbally communicated from the Tower to the Center. The TMA-computed release is verbally communicated from the Center back to the Tower where it is relayed to the Local controller as a release window that is typically three minutes wide. The Local controller will manage the departure to meet the coordinated release time window. Manual ready time prediction and verbal release time coordination are labor intensive and prone to inaccuracy. Also, use of release time windows adds uncertainty to the tactical departure process. Analysis of more than one million flights from January 2011 indicates that a significant number of tactically scheduled aircraft missed their en route slot due to ready time prediction uncertainty. Uncertainty in ready time estimates may result in missed opportunities to merge into constrained en route flows and lead to lost throughput. Next Generation Air Transportation System (NextGen) plans call for development of Tower automation systems capable of computing surface trajectory-based ready time estimates. NASA has developed the Precision Departure Release Capability (PDRC) concept that uses this technology to improve tactical departure scheduling by automatically communicating surface trajectory-based ready time predictions to the Center scheduling tool. The PDRC concept also incorporates earlier NASA and FAA research into automation-assisted CFR coordination. The PDRC concept helps reduce uncertainty by automatically communicating coordinated release times with seconds-level precision enabling TMCs to work with target times rather than windows. NASA has developed a PDRC prototype system that integrates the Center's TMA system with a research prototype Tower decision support tool. A two-phase field evaluation was conducted at NASA's North Texas Research Station (NTX) in DallasFort Worth. The field evaluation validated the PDRC concept and demonstrated reduced release time uncertainty while being used for tactical departure scheduling of more than 230 operational flights over 29 weeks of operations. This paper presents the Concept of Operations. Companion papers include the Final Report and a Technology Description. ? SUBJECT:

tactical departure scheduling↗
NASA NTRS1982

Dynamic switch matrix for the TDMA satellite switching system

Future high capacity satellite communication systems require signal processing on board satellites. The on-board signal processing includes switching of RF signals between multiple antennas to provide interconnection between the uplink and downlink beams. This paper describes the development of a dynamic switch matrix for a TDMA satellite switching system to be used in the next generation communications satellites. In this paper, a dynamic switch matrix, which includes the microwave switch matrix, the distribution control unit and the timing source, will be described. Several different microwave switch matrix architectures and switching devices were evaluated and compared. A unique coupler crossbar switch matrix architecture with dual-gate field effect transistor as switching element was developed. Experimental results of both microwave switch matrix (MSM) and distribution control unit (DCU) are presented. These test results verify the MSM with coupler crossbar architecture and dual-gate FET as switching element will meet the future SS-TDMA system requirements. Finally, the reliability of the dynamic switch matrix is addressed. The analysis shows reliability of 0.9981 for 7 year space operation can be achieved for the designed dynamic switch matrix.

Ho, P. T.↗
NASA NTRS2019

Leafy Greens Grown on the International Space Station May Provide a Nutritious Supplement to Astronauts' Diet

Supplemental safe food production has been an essential goal of NASA to meet the nutritional needs of astronauts on the International Space Station (ISS) as well as for future long duration missions to the moon and beyond. Food crops grown in space experience different environmental conditions than plants grown on Earth (i.e. microgravity and spaceflight physical sciences impacts). To test the growth methods and effects of the space environment, red romaine lettuce Lactuca sativa cv. 'Outredgeous', was grown in Veggie plant growth chambers on the ISS. Microbiological food safety of the plants grown on the ISS was determined by heterotrophic plate counts to assess total microbial load for bacteria and fungi as well as screening for specific pathogens and isolate identification. Molecular characterization was completed using Next Generation Sequencing (NGS) to provide valuable information on the taxonomic composition and community structure of the plant microbiome. Chemical analyses of plant tissue were conducted to understand spaceflight-induced changes in key elements in the space diet, phenolics, anthocyanin levels, and Oxygen radical absorbance capacity (ORAC), a measure of antioxidant capacity. Three growth tests of red romaine lettuce were completed on ISS, VEG-01A, VEG-01B, and VEG-03A. Plants were harvested using two harvest methods, either a single terminal harvest (after 33 days) or cut-and-come-again repetitive harvesting (64 days total growth). Ground controls were grown simultaneously with a delay to accommodate condition monitoring and replication. A comparison of the plant tissue returned to Earth showed leaves from the second grow-out had significantly higher bacterial counts than the preceding or subsequent growth test or any of the ground controls. Fungal counts were significantly higher on the final cut-and-come-again harvest of the third grow out. None of the potential foodborne pathogens that were screened for were detected. Bacterial and fungal isolate identification and community characterization indicated similar diversity between VEG-01A and VEG-01B growth tests, however, there appeared to be subtle differences in diversity and distribution among the three growth tests. Chemical analysis of plant tissue revealed significant variation in a few elemental data, but variation in levels of phenolics, anthocyanins, and ORAC was not significantly different. This study indicated that leafy vegetable crops could safely provide an edible supplement to astronauts' diet, and our analysis provided baseline data for continual operation of the Veggie plant growth units on ISS. This research was funded by NASA's space biology program.

Food Production↗
NASA NTRS2013

Precision Departure Release Capability (PDRC) Final Report

After takeoff, aircraft must merge into en route (Center) airspace traffic flows that may be subject to constraints that create localized demand/capacity imbalances. When demand exceeds capacity, Traffic Management Coordinators (TMCs) and Frontline Managers (FLMs) often use tactical departure scheduling to manage the flow of departures into the constrained Center traffic flow. Tactical departure scheduling usually involves a Call for Release (CFR) procedure wherein the Tower must call the Center to coordinate a release time prior to allowing the flight to depart. In present-day operations release times are computed by the Center Traffic Management Advisor (TMA) decision support tool, based upon manual estimates of aircraft ready time verbally communicated from the Tower to the Center. The TMA-computed release time is verbally communicated from the Center back to the Tower where it is relayed to the Local controller as a release window that is typically three minutes wide. The Local controller will manage the departure to meet the coordinated release time window. Manual ready time prediction and verbal release time coordination are labor intensive and prone to inaccuracy. Also, use of release time windows adds uncertainty to the tactical departure process. Analysis of more than one million flights from January 2011 indicates that a significant number of tactically scheduled aircraft missed their en route slot due to ready time prediction uncertainty. Uncertainty in ready time estimates may result in missed opportunities to merge into constrained en route flows and lead to lost throughput. Next Generation Air Transportation System plans call for development of Tower automation systems capable of computing surface trajectory-based ready time estimates. NASA has developed the Precision Departure Release Capability (PDRC) concept that improves tactical departure scheduling by automatically communicating surface trajectory-based ready time predictions and departure runway assignments to the Center scheduling tool. The PDRC concept also incorporates earlier NASA and FAA research into automation-assisted CFR coordination. The PDRC concept reduces uncertainty by automatically communicating coordinated release times with seconds-level precision enabling TMCs and FLMs to work with target times rather than windows. NASA has developed a PDRC prototype system that integrates the Center's TMA system with a research prototype Tower decision support tool. A two-phase field evaluation was conducted at NASA's North Texas Research Station in Dallas/Fort Worth. The field evaluation validated the PDRC concept and demonstrated reduced release time uncertainty while being used for tactical departure scheduling of more than 230 operational flights over 29 weeks of operations. This paper presents research results from the PDRC research activity. Companion papers present the Concept of Operations and a Technology Description.

tactical departure scheduling↗
NASA NTRS2017

ATD-2 Surface Scheduling and Metering Concept

This presentation describes the concept of ATD-2 tactical surface scheduling and metering. The concept is composed of several elements, including data exchange and integration; surface modeling; surface scheduling; and surface metering. The presentation explains each of the elements. Surface metering is implemented to balance demand and capacity• When surface metering is on, target times from surface scheduler areconverted to advisories for throttling demand• Through the scheduling process, flights with CTOTs will not get addedmetering delay (avoids potential for ‘double delay’)• Carriers can designate certain flights as exempt from metering holds• Demand throttle in Phase 1 at CLT is through advisories sent to rampcontrollers for pushback instructions to the flight deck– Push now– Hold for an advised period of time (in minutes)• Principles of surface metering can be more generally applied to otherairports in the NAS to throttle demand via spot-release times (TMATs Strong focus on optimal use of airport resources• Flexibility enables stakeholders to vary the amount of delay theywould like transferred to gate• Addresses practical aspects of executing surface metering in aturbulent real world environment• Algorithms designed for both short term demand/capacityimbalances (banks) or sustained metering situations• Leverage automation to enable surface metering capability withoutrequiring additional positions• Represents first step in Tactical/Strategic fusion• Provides longer look-ahead calculations to enable analysis ofstrategic surface metering potential usage

Departure↗
NASA NTRS2005

Development of the Surface Management System Integrated with CTAS Arrival Tools

The Surface Management System (SMS) developed by NASA Ames Research Center in coordination with the Federal Aviation Administration (FAA) is a decision support tool to help tower traffic coordinators and Ground/Local controllers in managing and controlling airport surface traffic in order to increase capacity, efficiency, and flexibility. SMS provides common situation awareness to personnel at various air traffic control facilities such as airport traffic control towers (ATCT s), airline ramp towers, Terminal Radar Approach Control (TRACON), and Air Route Traffic Control Center (ARTCC). SMS also provides a traffic management tool to assist ATCT traffic management coordinators (TMCs) in making decisions such as airport configuration and runway load balancing. The Build 1 of the SMS tool was installed and successfully tested at Memphis International Airport (MEM) and received high acceptance scores from ATCT controllers and coordinators, as well as airline ramp controllers. NASA Ames Research Center continues to develop SMS under NASA s Strategic Airspace Usage (SAU) project in order to improve its prediction accuracy and robustness under various modeling uncertainties. This paper reports the recent development effort performed by the NASA Ames Research Center: 1) integration of Center TRACON Automation System (CTAS) capability with SMS and 2) an alternative approach to obtain airline gate information through a publicly available website. The preliminary analysis results performed on the air/surface traffic data at the DFW airport have shown significant improvement in predicting airport arrival demand and IN time at the gate. This paper concludes with recommendations for future research and development.

Jung, Yoon C.↗
NASA NTRS2013

Precision Departure Release Capability (PDRC): NASA to FAA Research Transition

After takeoff, aircraft must merge into en route (Center) airspace traffic flows which may be subject to constraints that create localized demand-capacity imbalances. When demand exceeds capacity, Traffic Management Coordinators (TMCs) and Frontline Managers (FLMs) often use tactical departure scheduling to manage the flow of departures into the constrained Center traffic flow. Tactical departure scheduling usually involves use of a Call for Release (CFR) procedure wherein the Tower must call the Center to coordinate a release time prior to allowing the flight to depart. In present-day operations release times are computed by the Center Traffic Management Advisor (TMA) decision support tool based upon manual estimates of aircraft ready time verbally communicated from the Tower to the Center. The TMA-computed release time is verbally communicated from the Center back to the Tower where it is relayed to the Local controller as a release window that is typically three minutes wide. The Local controller will manage the departure to meet the coordinated release time window. Manual ready time prediction and verbal release time coordination are labor intensive and prone to inaccuracy. Also, use of release time windows adds uncertainty to the tactical departure process. Analysis of more than one million flights from January 2011 indicates that a significant number of tactically scheduled aircraft missed their en route slot due to ready time prediction uncertainty. Uncertainty in ready time estimates may result in missed opportunities to merge into constrained en route flows and lead to lost throughput. Next Generation Air Transportation System plans call for development of Tower automation systems capable of computing surface trajectory-based ready time estimates. NASA has developed the Precision Departure Release Capability (PDRC) concept that improves tactical departure scheduling by automatically communicating surface trajectory-based ready time predictions and departure runway assignments to the Center scheduling tool. The PDRC concept also incorporates earlier NASA and FAA research into automation-assisted CFR coordination. The PDRC concept reduces uncertainty by automatically communicating coordinated release times with seconds-level precision enabling TMCs and FLMs to work with target times rather than windows. NASA has developed a PDRC prototype system that integrates the Center's TMA system with a research prototype Tower decision support tool. A two-phase field evaluation was conducted at NASA's North Texas Research Station in Dallas-Fort Worth. The field evaluation validated the PDRC concept and demonstrated reduced release time uncertainty while being used for tactical departure scheduling of more than 230 operational flights over 29 weeks of operations.

ATM↗
NASA NTRS2016

Basin-Scale Assessment of the Land Surface Water Budget in the National Centers for Environmental Prediction Operational and Research NLDAS-2 Systems

The purpose of this study is to evaluate the components of the land surface water budget in the four land surface models (Noah, SAC-Sacramento Soil Moisture Accounting Model, (VIC) Variable Infiltration Capacity Model, and Mosaic) applied in the newly implemented National Centers for Environmental Prediction (NCEP) operational and research versions of the North American Land Data Assimilation System version 2 (NLDAS-2). This work focuses on monthly and annual components of the water budget over 12 National Weather Service (NWS) River Forecast Centers (RFCs). Monthly gridded FLUX Network (FLUXNET) evapotranspiration (ET) from the Max-Planck Institute (MPI) of Germany, U.S. Geological Survey (USGS) total runoff (Q), changes in total water storage (dS/dt, derived as a residual by utilizing MPI ET and USGS Q in the water balance equation), and Gravity Recovery and Climate Experiment (GRACE) observed total water storage anomaly (TWSA) and change (TWSC) are used as reference data sets. Compared to these ET and Q benchmarks, Mosaic and SAC (Noah and VIC) in the operational NLDAS-2 overestimate (underestimate) mean annual reference ET and underestimate (overestimate) mean annual reference Q. The multimodel ensemble mean (MME) is closer to the mean annual reference ET and Q. An anomaly correlation (AC) analysis shows good AC values for simulated monthly mean Q and dS/dt but significantly smaller AC values for simulated ET. Upgraded versions of the models utilized in the research side of NLDAS-2 yield largely improved performance in the simulation of these mean annual and monthly water component diagnostics. These results demonstrate that the three intertwined efforts of improving (1) the scientific understanding of parameterization of land surface processes, (2) the spatial and temporal extent of systematic validation of land surface processes, and (3) the engineering-oriented aspects such as parameter calibration and optimization are key to substantially improving product quality in various land data assimilation systems.

NLDAS-2↗
NASA NTRS2015

Simple Tool for Aircraft Noise-Reduction Route Design

The design of arrival and departure routes from an airport has to balance the conflicting requirements of fuel efficiency, airport capacity utilization and community emission and noise considerations. The commonly used tools for aircraft noise assessment are the FAA’s Integrated Noise Model (INM) and Aviation Environmental Design Tool (AEDT). These tools are suitable to generate precise noise contours. However, they are harder to use with other tools for route design optimization involving evaluation of a large number of aircraft trajectories. A simplified aircraft noise computation tool, named AIRNOISE, is developed for preliminary aircraft noise-reduction route design in this paper. AIRNOISE computes aircraft noise based on the same SAE-AIR-1845 procedures used by INM and AEDT. AIRNOISE does not consider components related to terrain and atmosphere adjustments. As a result, it is not only computationally efficient but also flexible to use for customized aircraft profiles. The aircraft noise results are compared with the FAA’s AEDT2b and show that the level of accuracy achieved by AIRNOISE can be used to reduce the number of route design options to a small number from a large pool for subsequent accurate analysis by INM.

Li, Jinhua↗
NASA NTRSDate not supplied

Lessons learned from replicating services for flood prediction and monitoring in Asia to the assessment of hurricane impacts in Central America

In October and November 2020, two dangerous back-to-back hurricanes, Eta and Iota, made landfall in Central America. The SERVIR program - a joint effort of NASA and the U.S. Agency for International Development, and whose motto is “connecting space to village” was able to leverage two tools originally developed for use in other regions for predicting and assessing the flood impacts of the hurricanes. The GEOGLoWS Streamflow Prediction tool - originally implemented in the Hindu Kush Himalayan region - was used for predicting potential flooding ahead of landfall by Eta and Iota. In conjunction, the Hydrologic Remote Sensing Analysis for Floods (HYDRAFloods) framework - originally developed along with SERVIR-Mekong - was used for post-event flood mapping, leveraging its ability to map floods in cloud-covered areas using synthetic aperture radar (SAR) imagery from the Copernicus program. Both tools were used in support of disaster coordination efforts being led by the Central American Regional Disaster Prevention Center (CEPREDENAC), in conjunction with its sister agency, the Regional Water Resources Committee (CRRH). The support provided to regional entities - and to their stakeholder national governments - served as an example of rapid generation of Earth observation products for disaster response. Feedback on those products was also provided, especially in terms of the implications of (i) calibration of predicted river volumes, and (ii) the latency of the input Earth observation imagery and attempts to map the floods’ maximum extents. An upcoming NASA DEVELOP project will also seek to strengthen the capability of CEPREDENAC and CRRH to apply HYDRAFloods for future extreme events. The application of the tools also provides a useful case study on capacity building, in terms of how Earth observation data and models can be replicated across regions.

Capacity building↗
NASA NTRS1998

The Aviation System Analysis Capability Noise Impact Model

To meet its objective of assisting the U.S. aviation industry with the technological challenges of the future, NASA must identify research areas that have the greatest potential for improving the operation of the air transportation system. To accomplish this, NASA is building an Aviation System Analysis Capability (ASAC). The Noise Impact Model (NIM) has been developed as part of the ASAC. Its primary purpose is to enable users to examine the impact that quieter aircraft technologies and/or operations might have on community noise impact and air carrier operating efficiency at any of 16 large- and medium-sized U.S. airports. The analyst chooses an airport and case year for study, selects a runway use configuration and set of flight tracks for the scenario, and has the option of reducing the noise of the aircraft that operate at the airport by 3, 6, or 10 decibels. NIM computes the resultant noise impact and estimates any airline operations improvements. Community noise impact is characterized in three ways: the size of the noise contour footprint, the number of people living within the.contours, and the number of homes located in the same contours. Distance and time savings are calculated by comparing the noise abatement flight path length to a less circuitous alternate routing. For a more efficient runway use configuration, the increase in capacity and reduction in delay are shown.

Wingrove, Earl R., III↗
NASA NTRS1999

The Aviation System Analysis Capability Noise Impact Model

To meet its objective of assisting the US aviation industry with the technological challenges of the future, NASA must identify research areas that have the greatest potential for improving the operation of the air transportation system. To accomplish this, NASA is building an Aviation System Analysis Capability (ASAC). The Noise Impact Model (NIM) has been developed as part of the ASAC. Its primary purpose is to enable users to examine the impact that quieter aircraft technologies and/or operation might have on community noise impact and air carrier operating efficiency at any of 16 large and medium size US airports. The analyst chooses an airport and case year for study, selects a runway use configuration and set of flight tracks for the scenario, and has the option of reducing the noise of the aircraft that operate at the airport by 3, 6, and 10 decibels, NIM computes the resultant noise impact and estimates any airline operational improvements. Community noise impact is characterized in three ways: the size of the noise contour footprint, the number of people living within the contours, and the number of homes located in the same contours. Distance and time savings are calculated by comparing the noise abatement flight path length to a less circuitous alternated routing. For a more efficient runway use configuration, the increase in capacity and reduction in delay are shown.

Ege, Russell A.↗
NASA NTRS2016

Analyzing Double Delays at Newark Liberty International Airport (EWR)

When weather or congestion impacts the National Airspace System, multiple different Traffic Management Initiatives can be implemented, sometimes with unintended consequences. One particular perceived inequity that is commonly identified is in the interaction between Ground Delay Programs (GDPs) and time based scheduling of internal departures by the Traffic Management Advisor (TMA) (now operationally superseded by the FAA's the Time-Based Flow Management system). Internal departures under TMA scheduling can take large GDP delays, followed by large TMA scheduling delays, because they cannot easily fit into the arrival flow at the runway. In this paper we examine the causes of these double delays through an analysis of arrival operations at Newark Liberty International Airport (EWR) from June to August 2010. TMA scheduling delays are found to be generally higher than TMA airborne metering delays, regardless of prior GDP delays. Depending on how the double delay is defined, between 42 and 62 of all internal departures in GDP and TMA scheduling experienced double delays in this period. A deep dive into the data reveals that contributors to double delays include upstream flights departing before their Expect Departure Clearance Times (EDCTs); differences in the rates used for setting EDCTs and TMA Scheduled Times of Arrival; differences in the arrival demand expected based on EDCTs and the arrival demand entering TMA; and shorter en route times between takeoff and entry into TMA than assumed in the calculation of flight EDCTs, all of which undermine the sequencing and spacing underlying flight EDCTs. Double delays are also found to coincide with periods in which the virtual runway arrival queue being served by a TMA is large, there are periods of high demand relative to capacity, and there are high airborne metering delays. Data mining techniques are used to confirm that each of these factors contribute to the occurrence of double delay andor high internal departure scheduling delay across three months of data from June to August 2010. Predictors of the occurrence of double delay and high TMA scheduling delay are built using logistic regression, providing prediction accuracies of 69 and 73, respectively.

traffic flow management↗
NASA NTRS1994

Models of Human Information Requirements: "When Reasonable Aiding Systems Disagree"

Aircraft flight management and Air Traffic Control (ATC) automation are under development to maximize the economy of flight and to increase the capacity of the terminal area airspace while maintaining levels of flight safety equal to or better than current system performance. These goals are being realized by the introduction of flight management automation aiding and operations support systems on the flight deck and by new developments of ATC aiding systems that seek to optimize scheduling of aircraft while potentially reducing required separation and accounting for weather and wake vortex turbulence. Aiding systems on both the flight deck and the ground operate through algorithmic functions on models of the aircraft and of the airspace. These models may differ from each other as a result of variations in their models of the immediate environment. The resultant flight operations or ATC commands may differ in their response requirements (e.g. different preferred descent speeds or descent initiation points). The human operators in the system must then interact with the automation to reconcile differences and resolve conflicts. We have developed a model of human performance including cognitive functions (decision-making, rule-based reasoning, procedural interruption recovery and forgetting) that supports analysis of the information requirements for resolution of flight aiding and ATC conflicts. The model represents multiple individuals in the flight crew and in ATC. The model is supported in simulation on a Silicon Graphics' workstation using Allegro Lisp. Design guidelines for aviation automation aiding systems have been developed using the model's specification of information and team procedural requirements. Empirical data on flight deck operations from full-mission flight simulation are provided to support the model's predictions. The paper describes the model, its development and implementation, the simulation test of the model predictions, and the empirical validation process. The model and its supporting data provide a generalizable tool that is being expanded to include air/ground compatibility and ATC crew interactions in air traffic management.

Corker, Kevin↗
NASA NTRSDate not supplied

Assessing the Relationships Between Sensorimotor Biomarkers and Post-Landing Functional Task Performance

Spaceflight drives adaptive changes in healthy individuals appropriate for sensorimotor function in a microgravity environment. These changes are maladaptive for return to earth's gravity. The inter-individual variability of sensorimotor decrements is striking, although poorly understood. The goal of this study was to identify a set of behavioral, neuroimaging and genetic measures that can be used to predict early post-flight performance on a set of sensorimotor tasks. Astronauts were recruited who previously participated in sensorimotor field tests and/or posturography soon after long-duration spaceflight. Behavioral tests included assessments of sensory dependency and adaptability. Visual dependency involved treadmill walking while viewing a moving virtual visual scene. Vestibular perceptual thresholds were measured while seated during lateral translations. Proprioception dependency was measured during one-legged stance on a horizontal air-bearing surface. Ground assessment of adaptability was performed (1) during treadmill walking with a virtual linear hallway and a moving walking surface, and (2) during multiple trials of navigating an obstacle course while wearing reversing prisms (adaptive Functional Mobility Test, aFMT). The neuroimaging tests characterized individual differences in regional brain volumes (using Structural MRI) and white matter microstructure (using Diffusion Tensor Imaging) to serve as potential predictors of adaptive capacity. The genetic tests utilized saliva samples to examine variations in four genes chosen because of their ability to differentiate sensorimotor adaptation ability in a normative population, including Catechol-O-methyltransferase (COMT), Dopamine Receptor D2 (DRD2), Brain-derived neurotrophic factor (BDNF) and the α2-adrenergic receptor. Thirty ISS crewmembers were recruited, including 12 first-time fliers, 6F, and mission durations lasting 185.5 ± 45.5 days, mean ± std. We utilized a combination of three post-flight functional task outcomes: tandem walk, recovery from fall and dynamic posturography. There was considerable variability among all post-flight performance outcomes. Based on an ordinal scale survey, 72% indicated their ability to perform functional tasks were more impacted postflight relative to inflight with 50% indicating they needed to restrict movements for a longer period postflight relative to inflight. While there is a strong association within tests obtained at different R+0 timepoints, by R+24 hr performance on one post-flight test does not necessarily correlate with performance on other post-flight tests. Preliminary statistical analysis indicates combining biomarkers will increase predictive power and this will be explored with future analyses. Our preliminary findings underscore the importance of a comprehensive post-flight test battery including different types of tasks with varying sensory feedback. Understanding the relationships between these sensorimotor biomarkers and post-flight functional task performance improve both our understanding of the individual variability and our strategy to optimize sensorimotor countermeasures.

biomarkers↗
NASA NTRS1993

Relationships between pigment composition variation and reflectance for plant species from a coastal savannah in California

Advances in imaging spectroscopy have indicated that remotely sensed reflectance measurements of the plant canopy may be used to identify and qualify some classes of canopy biochemicals; however, the manner in which differences in biochemical compositions translate into differences is not well understood. Most frequently, multiple linear regression routines have been used to correlate narrow band reflectance values with measured biochemical concentrations. Although some success has been achieved with such methods for given data sets, the bands selected by multiple regression are not consistent between data sets, nor is it always clear what physical or biological basis underlies the correlation. To examine the relationship between biochemical concentration and leaf reflectance signal we chose to focus on the visible spectrum where the primary biochemical absorbances are due to photosynthetic pigments. Pigments provide a range of absorbance features, occur over a range of concentrations in natural samples, and are ecophysiologically important. Concentrations of chlorophyll, for example, have been strongly correlated to foliar nitrogen levels within a species and to photosynthetic capacity across many species. In addition pigments effectively absorb most of the photosynthetically active radiation between 400-700 nm, a spectral region for which silicon detectors have good signal/noise characteristics. Our strategy has been to sample a variety of naturally occurring species to measure leaf reflectance and pigment compositions. We hope to extend our understanding of pigment reflectance effects to interpret small overlapping absorbances of other biochemicals in the infrared region. For this reason, selected samples were also tested to determine total nitrogen, crude protein, cellulose, and lignin levels. Leaf reflectance spectra measured with AVIRIS bandwidths and wavelengths were compared between species and within species and for differences between seasons, for changes in the the shape of the spectra. We attempt to statistically correlate these shape changes with differences in pigment compositions. In parallel with our comparisons of pigment composition and leaf reflectance, we have modified the PROSPECT leaf reflectance model to test the contributions of pigments or pigment group concentrations. PROSPECT considers a leaf as a multi-layer dielectric plane with an uneven surface. Jacquemoud adapted the basic analysis of Allen for surface effects, a leaf thickness factor, and the absorption of water and chlorophyll (actually all pigments) and the plant matrix. Our modifications to PROSPECT in the forward direction include breaking out the pigment concentration parameter into separate components for chlorophyll a and b and a number of xanthophylls and carotenes, and introducing a shift and convolution function to model the spread and shift from their in vitro measurements to their in vivo state. Further, we have considered how the matrix elements (i.e., all biochemicals and structural effects not modeled explicity) vary with species.

Ustin, Susan L.↗
NASA NTRS2008

Effect of Surface Traffic Count on Taxi Time at Dallas-Fort Worth (DFW) International Airport

As the amount of air traffic increases over the years, most airports simply do not have the means of expanding to handle the intensified traffic on the surface that will ensue. Precise surveillance equipment and automation concepts, as well as advanced surface traffic algorithms are being developed to improve airport efficiency. These surface algorithms require inputs unique to each airport to ensure maximum efficiency, and minimal taxi delay. This study analyzes surface traffic at Dallas-Fort Worth International Airport (DFW) to determine the effect of the number of aircraft on the surface and the amount of stop and go situations they experience to the amount of additional taxi time encountered. If the surface capacity of an airport is known, minimal delay can be accomplished by limiting the number of taxiing aircraft to that capacity. This concept is related to highways, where traffic flow drastically decreases as more cars occupy the road. An attempt to minimize this effect on highways is seen with the use of metering lights at freeway on-ramps. Since the surface traffic at airports is highly regulated, and aircraft are less mobile on the ground, limiting the surface count to a certain number can greatly reduce the amount of additional taxi time encountered, as well as reduce hazardous emissions. This study will also find the regions of an airport that encounter the most additional taxi time when the number of aircraft in that area is increased. This could help surface traffic algorithms avoid congesting that area, or re-route aircraft to different runways when that area reaches its capacity. The relationship between the amount of stop and go situations an aircraft encounters and their effect on the taxi time of that aircraft will also be investigated. This will help to determine the effect of holding an aircraft on the taxiway as opposed to re-routing it. The lesser of the two should be used when developing surface traffic algorithms to further minimize the delay encountered. The fields investigated in this study include taxi time, the number of aircraft on the surface, the number of stop and go situations, and the time stopped for each aircraft. Taxi time is defined as spot to runway for departures, and runway to spot for arrivals. It does not include ramp area taxi time because the ramp area is controlled differently, and surface traffic schedulers do not currently incorporate them. Taxi time is found by finding the difference between take-off time (OFF) and spot crossing time for departures, and spot crossing time and landing time (ON) for arrivals. All surface data was either found directly using the Surface Operations Data Analysis and Adaptation (SODAA), a tool to analyze the Surface Management System (SMS) generated log files, or indirectly from SODAA using Matlab to derive values from SODAA data. The number of aircraft on the surface is found by looping through the ON times, OFF times, and spot times for each aircraft during a particular day. For each departure aircraft, surface counts are taken at its spot crossing and OFF time. The average of these two is used as the surface count for that aircraft. For arrivals, surface counts are taken at its ON time and its spot crossing time. The average of these two is used.

Kistler, Matthew Stephen↗