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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Evaluation of Usability and Workload with Paper Strips as Compared to Virtual Flight Strips Used for Ramp Operations

This paper describes an experiment designed to compare the use of paper strips with the use of a new user interface, the Ramp Traffic Console (RTC), designed for use by ramp controllers to be used in place of paper strips. A Human-In-the-Loop (HITL) experiment was performed as the fifth study in a series of six HITL simulation experiments designed to evaluate a concept that provided advisories to the users. The RTC was designed to be used as a Decision Support Tool (DST) that provided advisories to ramp controllers regarding metering or pushback such that most of the delay was taken at the gate to save fuel and emissions. In addition to being a DST, an added benefit of the RTC is that it can provide real-time updates of flight data, airport and airspace status to the controller including Traffic Management Initiatives (TMI). The RTC was designed as new user interface that displays virtual strips on a terminal map drawn on a 27-inch touch screen monitor. The RTC was used in some conditions of the experiment by ramp controllers in place of paper strips and paper maps in the HITL environment. In other conditions the controllers were given paper strips and paper maps similar to what they currently use at Charlotte Douglas International Airport (CLT). The study described here, evaluated the use of the virtual strips displayed on the RTC as compared to the use of paper strips and paper map, using current ramp tower controllers at CLT as participants. The research question being asked was - How does management of ramp traffic affect user workload and usability ratings while using RTC to manage traffic in the ramp verses using paper strips? Workload for our purposes is defined by four components of the NASA-TLX (Task Load Index). Usability was assessed with two sets of usability questions - One set of usability questions addressed traffic management performance and the other set addressed issues of resources and efficiency. Both Post Run and Post Study questionnaire responses were gathered and the results were analyzed to assess controller workload and usability ratings under both conditions, virtual strips shown on RTC and Paper Strips. The results indicate that controllers perceived lower workload while using virtual strips displayed on RTC to manage ramp traffic. Usability ratings for Traffic management performance questions are lower in the virtual strip/RTC condition than in the paper strip condition showing a preference for RTC over Paper. Usability ratings for Resources and efficiency questions show mixed results. Additionally, the Post Study Questions show preference for RTC over paper strips. Results of this data analysis will be presented in this paper. This DST evaluation was an important step in researching and improving the tool, which was planned to be deployed in the field.

Aviation Decision Support Tools↗
NASA NTRS1978

Analysis of a VTOL hover task with predictor displays using an optimal control model of the human operator

The influence of different types of predictor displays in a longitudinal VTOL hover task is analyzed in a theoretical study. It was assumed that pitch angle and position are presented to the pilot in separate displays namely the artificial horizon and position display. The predictive information is calculated by means of a Taylor series. From earlier experimental studies it is well known that predictor displays improve human and system performance and result in reducing human workload. In this study, an optimal control model is used to prove this effect theoretically. Several cases with differing amounts of predictive and rate information are compared.

Johannsen, G.↗
DOE OSTI2023

JIRIAF: JLAB Integrated Research Infrastructure Acros Facilities

The JIRIAF project aims to combine geographically diverse computing facilities into an integrated science infrastructure. This project starts by dynamically evaluating temporarily unallocated or idled compute resources from multiple providers. These resources are integrated to handle additional workloads without affecting local running jobs. This paper describes our approach to launch best-effort batch tasks which exploit these underutilized resources. Our system measures the real-time behavior of jobs running on a machine and learns to distinguish typical performance from outliers. Unsupervised ML techniques are used to analyze hardware-level performance measures, followed by a real-time cross-correlation analysis to determine which applications cause performance degradation. We then ameliorate bad behavior by throttling these processes. We demonstrate that problematic performance interference can be detected and acted on, which makes it possible to continue to share resources between applications and simultaneously maintain high utilization levels in a computing cluster. We relocate the CLAS12 data processing workflow to a remote data center for a case study, preventing file migration and temporal data persistency.

Lawrence, David↗
NASA NTRS1985

Visual scanning behavior

This report summarizes the results and knowledge of scan behavior gained in various simulation and laboratory studies. Results were obtained through various analysis techniques such as real-time viewing of the pilot's scanning behavior and quantitative analysis of scan behavior performance parameters (average dwell time, dwell percentages, instrument transition paths, dwell percentages, instrument transition paths, dwell histograms, and entropy rate measures). Pilot scan behavior is discussed in the following areas; scanning is a subconscious conditioned activity, scanning is situation dependent, pilots' scanning pattern is centered around a home base. Scanning behavior data nave been shown to be useful in determining pilot's workload, evaluating pilot's strategy and role, determining the rate of information transfer of various displays, and aiding in pilot training.

Harris, R. L., Sr.↗
NASA NTRS1998

Dynamic Density: An Air Traffic Management Metric

The definition of a metric of air traffic controller workload based on air traffic characteristics is essential to the development of both air traffic management automation and air traffic procedures. Dynamic density is a proposed concept for a metric that includes both traffic density (a count of aircraft in a volume of airspace) and traffic complexity (a measure of the complexity of the air traffic in a volume of airspace). It was hypothesized that a metric that includes terms that capture air traffic complexity will be a better measure of air traffic controller workload than current measures based only on traffic density. A weighted linear dynamic density function was developed and validated operationally. The proposed dynamic density function includes a traffic density term and eight traffic complexity terms. A unit-weighted dynamic density function was able to account for an average of 22% of the variance in observed controller activity not accounted for by traffic density alone. A comparative analysis of unit weights, subjective weights, and regression weights for the terms in the dynamic density equation was conducted. The best predictor of controller activity was the dynamic density equation with regression-weighted complexity terms.

Laudeman, I. V.↗
NASA NTRS2018

The Influence of Mental Workload in Causes of System Degradation in Air Traffic Control

System safety and resilience is a critical concern in the air traffic domain. An important element of maintaining system safety and resilience is the ability of systems to ‘degrade gracefully’. However, previous research on the causes of system degradation in the air traffic domain are sporadic, and the potential interaction between the causes of degradation, and the resulting possible compound effect on the entire system, has been under-researched. An interview study was conducted with 12 retired controllers as participants. The results of a thematic analysis revealed the key causes of system degradation, and the associated impact on the ability of the controllers to prevent system degradation or recover the system. Findings have direct implications for identifying and mitigating potential risks of increasingly automated air traffic control systems.

Edwards, Tamsyn E.↗
DOE OSTI2019

Performance analysis and optimization for scalable deployment of deep learning models for country-scale settlement mapping on Titan supercomputer

Here, we present a scalable object detection workflow for detecting objects, such as settlements, from remotely sensed (RS) imagery. We have successfully deployed this workflow on Titan supercomputer and utilized it for the task of mapping human settlement at a country scale. The performance of various stages in the workflow was analyzed before making it operational. The workflow implemented various strategies to address issues such as suboptimal resource utilization and long-tail effects due to unbalanced image workload, data loss due to runtime failures, and maximum wall-time constraints imposed by Titan's job scheduling policy. A mean shift clustering–based static load balancing strategy was implemented, which partitions the image load such that each partition contained similar-sized images. Furthermore, a checkpoint-restart strategy was added in the workflow as a fault-tolerance mechanism to prevent the data losses due to unforeseen runtime failures. The performance of the above-mentioned strategies was observed in various scenarios, such as node failure, exceeding wall time, and successful completion. Using this workflow, we have examined an RS data set that has a spatial resolution of 0.31 m and is comprised of 685 675 km 2 of area of the Republic of Zambia in under six hours using 5426 nodes of the Titan supercomputer.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

The Influence of Mental Workload in Causes of System Degradation in Air Traffic Control

System safety and resilience is a critical concern in the air traffic domain. An important element of maintaining system safety and resilience is the ability of systems to ‘degrade gracefully'. However, previous research on the causes of system degradation in the air traffic domain are sporadic, and the potential interaction between the causes of degradation, and the resulting possible compound effect on the entire system, has been under-researched. An interview study was conducted with 12 retired controllers as participants. The results of a thematic analysis revealed the key causes of system degradation, and the associated impact on the ability of the controllers to prevent system degradation or recover the system. Findings have direct implications for identifying and mitigating potential risks of increasingly automated air traffic control systems.

Edwards, Tamsyn↗
NASA NTRS1985

Cross coupling in pilot/vehicle systems

Multiloop pilot/vehicle analysis is applied to the problem of determining crossfeed techniques which may be employed by pilots in minimizing the effects of vehicle cross coupling. As used here, cross coupling refers to unwanted vehicle motion which occurs in one control axis or loop as the result of pilot control actuation in another control axis or loop. The minimization or elimination of such cross coupling can contribute significantly to the 'workload' associated with tasks like nap-of-the-earth helicopter flight. In contrast, situations arise in which pilot's may use vehicle cross coupling to improve performance by coordinating two control actuations in the control of a single response variable. A crossfeed model is developed based upon simple control system design principles and configured in a manner amenable to pilot pursuit or precognitive control activity. A handling qualities theory developed to analyze single loop tasks is applied to the multiloop problem. The crossfeed model is applied to five different vehicles/configurations ranging from helicopters to fighter aircraft. Results indicate relatively simple crossfeed commands can significantly reduce cross coupling and, in some improve handling qualities as predicted by the single loop theory.

Hess, R. A.↗
NASA NTRS2012

Initial Evaluation of a Conflict Detection Tool in the Terminal Area

Despite the recent economic recession and its adverse impact on air travel, the Federal Aviation Administration (FAA) continues to forecast an increase in air traffic demand that may see traffic double or triple by the year 2025. Increases in air traffic will burden the air traffic management system, and higher levels of safety and efficiency will be required. The air traffic controllers primary task is to ensure separation between aircraft in their airspace and keep the skies safe. As air traffic is forecasted to increase in volume and complexity [1], there is an increased likelihood of conflicts between aircraft, which adds risk and inefficiency to air traffic management and increases controller workload. To attenuate these factors, recent ATM research has shown that air and ground-based automation tools could reduce controller workload, especially if the automation is focused on conflict detection and resolution. Conflict Alert is a short time horizon conflict detection tool deployed in the Terminal Radar Approach Control (TRACON), which has limited utility due to the high number of false alerts generated and its use of dead reckoning to predict loss of separation between aircraft. Terminal Tactical Separation Assurance Flight Environment (T-TSAFE) is a short time horizon conflict detection tool that uses both flight intent and dead reckoning to detect conflicts. Results of a fast time simulation experiment indicated that TTSAFE provided a more effective alert lead-time and generated less false alerts than Conflict Alert [2]. TSAFE was previously tested in a Human-In-The-Loop (HITL) simulation study that focused on the en route phase of flight [3]. The current study tested the T-TSAFE tool in an HITL simulation study, focusing on the terminal environment with current day operations. The study identified procedures, roles, responsibilities, information requirements and usability, with the help of TRACON controllers who participated in the experiment. Metrics such as lead alert time, alert response time, workload, situation awareness and other measures were statistically analyzed. These metrics were examined from an overall perspective and comparisons between conditions (altitude resolutions via keyboard entry vs. ADS-B entry) and controller positions (two final approach sectors and two feeder sectors) were also examined. Results of these analyses and controller feedback provided evidence of T-TSAFE s potential promise as a useful air traffic controller tool. Heuristic analysis also provided information on ways in which the T-TSAFE tool can be improved. Details of analyses results will be presented in the full paper.

Verma Savita Arora↗
NASA NTRS1987

Usage analysis of user files in UNIX

Presented is a user-oriented analysis of short term file usage in a 4.2 BSD UNIX environment. The key aspect of this analysis is a characterization of users and files, which is a departure from the traditional approach of analyzing file references. Two characterization measures are employed: accesses-per-byte (combining fraction of a file referenced and number of references) and file size. This new approach is shown to distinguish differences in files as well as users, which cam be used in efficient file system design, and in creating realistic test workloads for simulations. A multi-stage gamma distribution is shown to closely model the file usage measures. Even though overall file sharing is small, some files belonging to a bulletin board system are accessed by many users, simultaneously and otherwise. Over 50% of users referenced files owned by other users, and over 80% of all files were involved in such references. Based on the differences in files and users, suggestions to improve the system performance were also made.

Devarakonda, Murthy V.↗
DOE OSTI2025

Considerations for Distributed Edge Data Centers and Use of Building Loads to Support Large Interconnections

The rapid expansion of artificial intelligence (AI) and machine learning is driving unprecedented electricity demand from data centers. It is predicted that by 2030, 90% of AI workloads will be inference-based, requiring interconnection of multiple low-latency edge data centers (<20 MW) sited closer to end users - often on already constrained distribution feeders. Although individually small, these loads can aggregate to large loads per feeder, straining infrastructure, creating multi-year interconnection delays, and driving up customer costs. This paper proposes a data center-focused grid-integration framework that combines feeder hosting capacity analysis with building energy efficiency, building load flexibility, and waste heat reuse to expand effective feeder and substation headroom. Such approaches can reduce interconnection delays, lower costs for ratepayers, and accelerate AI-ready infrastructure deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Toward Large-Scale Image Segmentation on Summit

Semantic segmentation of images is an important computer vision task that emerges in a variety of application domains such as medical imaging, robotic vision and autonomous vehicles to name a few. While these domain-specific image analysis tasks involve relatively small image sizes (~ 10 2 × 10 2 ), there are many applications that need to train machine learning models on image data with extents that are orders of magnitude larger (~10 4 × 10 4 ). Training deep neural network (DNN) models on large extent images is extremely memory-intensive and often exceeds the memory limitations of a single graphical processing unit, a hardware accelerator of choice for computer vision workloads. Here, an efficient, sample parallel approach to train U-Net models on large extent image data sets is presented. Its advantages and limitations are analyzed and near-linear strong-scaling speedup demonstrated on 256 nodes (1536 GPUs) of the Summit supercomputer. Using a single node of the Summit supercomputer, an early evaluation of a recently released model parallel framework called GPipe is demonstrated to deliver ~ 2X speedup in executing a U-Net model with an order of magnitude larger number of trainable parameters than reported before. Performance bottlenecks for pipelined training of U-Net models are identified and mitigation strategies to improve the speedups are discussed. Together, these results open up the possibility of combining both approaches into a unified scalable pipelined and data parallel algorithm to efficiently train U-Net models with very large receptive fields on data sets of ultra-large extent images.

Seal, Sudip↗
NASA NTRS2012

Extending Validated Human Performance Models to Explore NextGen Concepts

To meet the expected increases in air traffic demands, NASA and FAA are researching and developing Next Generation Air Transportation System (NextGen) concepts. NextGen will require substantial increases in the data available to pilots on the flight deck (e.g., weather,wake, traffic trajectory predictions, etc.) to support more precise and closely coordinated operations (e.g., self-separation, RNAV/RNP, and closely spaced parallel operations, CSPOs). These NextGen procedures and operations, along with the pilot's roles and responsibilities, must be designed with consideration of the pilot's capabilities and limitations. Failure to do so will leave the pilots, and thus the entire aviation system, vulnerable to error. A validated Man-machine Integration and design Analysis System (MIDAS) v5 model was extended to evaluate anticipated changes to flight deck and controller roles and responsibilities in NextGen approach and Land operations. Compared to conditions when the controllers are responsible for separation on decent to land phase of flight, the output from these model predictions suggest that the flight deck response time to detect the lead aircraft blunder will decrease, pilot scans to the navigation display will increase, and workload will increase.

Gore, Brian Francis↗
NASA NTRS1993

Workload management and geographic disorientation in aviation incidents: A review of the ASRS data base

NASA's Aviation Safety Reporting System (ASRS) incident reports are reviewed in two related areas: pilots' failures to appropriately manage tasks, and breakdowns in geographic orientation. Examination of 51 relevant reports on task management breakdowns revealed that altitude busts and inappropriate runway usee were the most frequently reported consequences. Task management breakdowns appeared to occur at all levels of expertise, and prominent causal factors were related to breakdowns in crew communications, over-involvement with the flight management system and, for small (general aviation) aircraft, preoccupation with weather. Analysis of the 83 cases of geographic disorientation suggested that these too occurred at all levels of pilot experience. With regard to causal factors, a majority was related to poor cockpit resource management, in which inattention led to a loss of geographic awareness. Other leading causes were related to poor weather and poor decision making. The potential of the ASRS database for contributing to research and design issues is addressed.

Williams, Henry P.↗
DOE OSTI2024

Integrating the PanDA Workload Management System with the Vera C. Rubin Observatory

The Vera C. Rubin Observatory will produce an unprecedented astronomical data set for studies of the deep and dynamic universe. Its Legacy Survey of Space and Time (LSST) will image the entire southern sky every three to four days and produce tens of petabytes of raw image data and associated calibration data over the course of the experiment’s run. More than 20 terabytes of data must be stored every night, and annual campaigns to reprocess the entire dataset since the beginning of the survey will be conducted over ten years. The Production and Distributed Analysis (PanDA) system was evaluated by the Rubin Observatory Data Management team and selected to serve the Observatory’s needs due to its demonstrated scalability and flexibility over the years, for its Directed Acyclic Graph (DAG) support, its support for multi-site processing, and its highly scalable complex workflows via the intelligent Data Delivery Service (iDDS). PanDA is also being evaluated for prompt processing where data must be processed within 60 seconds after image capture. This paper will briefly describe the Rubin Data Management system and its Data Facilities (DFs). Finally, it will describe in depth the work performed in order to integrate the PanDA system with the Rubin Observatory to be able to run the Rubin Science Pipelines using PanDA.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Modeling Large-Scale Slim Fly Networks Using Parallel Discrete-Event Simulation

As supercomputers approach exascale performance, the increased number of processors translates to an increased demand on the underlying network interconnect. We present that the slim fly network topology, a new low-diameter, low-latency, and low-cost interconnection network, is gaining interest as one possible solution for next-generation supercomputing interconnect systems. In this article, we present a high-fidelity slim fly packet-level model leveraging the Rensselaer Optimistic Simulation System (ROSS) and Co-Design of Exascale Storage (CODES) frameworks. We validate the model with published work before scaling the network size up to an unprecedented 1 million compute nodes and confirming that the slim fly observes peak network throughput at extreme scale. In addition to synthetic workloads, we evaluate large-scale slim fly models with real communication workloads from applications in the Design Forward program with over 110,000 MPI processes. We show strong scaling of the slim fly model on an Intel cluster achieving a peak network packet transfer rate of 2.3 million packets per second and processing over 7 billion discrete events using 128 MPI tasks. Enabled by the strong performance capabilities of the model, we perform a detailed application trace and routing protocol performance study. Lastly, through analysis of metrics such as packet latency, hop count, and congestion, we find that the slim fly network is able to leverage simple minimal routing and achieve the same performance as more complex adaptive routing for tested DOE benchmark applications.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1988

Run-time parallelization and scheduling of loops

The class of problems that can be effectively compiled by parallelizing compilers is discussed. This is accomplished with the doconsider construct which would allow these compilers to parallelize many problems in which substantial loop-level parallelism is available but cannot be detected by standard compile-time analysis. We describe and experimentally analyze mechanisms used to parallelize the work required for these types of loops. In each of these methods, a new loop structure is produced by modifying the loop to be parallelized. We also present the rules by which these loop transformations may be automated in order that they be included in language compilers. The main application area of the research involves problems in scientific computations and engineering. The workload used in our experiment includes a mixture of real problems as well as synthetically generated inputs. From our extensive tests on the Encore Multimax/320, we have reached the conclusion that for the types of workloads we have investigated, self-execution almost always performs better than pre-scheduling. Further, the improvement in performance that accrues as a result of global topological sorting of indices as opposed to the less expensive local sorting, is not very significant in the case of self-execution.

Saltz, Joel H.↗