Search NASA⌕ Search

SEARCH · Search NASA

Results for “Task complexity”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

A programming environment for distributed complex computing. An overview of the Framework for Interdisciplinary Design Optimization (FIDO) project. NASA Langley TOPS exhibit H120b

The Framework for Interdisciplinary Design Optimization (FIDO) is a general programming environment for automating the distribution of complex computing tasks over a networked system of heterogeneous computers. For example, instead of manually passing a complex design problem between its diverse specialty disciplines, the FIDO system provides for automatic interactions between the discipline tasks and facilitates their communications. The FIDO system networks all the computers involved into a distributed heterogeneous computing system, so they have access to centralized data and can work on their parts of the total computation simultaneously in parallel whenever possible. Thus, each computational task can be done by the most appropriate computer. Results can be viewed as they are produced and variables changed manually for steering the process. The software is modular in order to ease migration to new problems: different codes can be substituted for each of the current code modules with little or no effect on the others. The potential for commercial use of FIDO rests in the capability it provides for automatically coordinating diverse computations on a networked system of workstations and computers. For example, FIDO could provide the coordination required for the design of vehicles or electronics or for modeling complex systems.

Townsend, James C.↗
NASA NTRS1985

Characterization of concurrent processing

Computer architectures designed for concurrent processing are characterized by the number of processing elements, ensemble speed, random access memory, input/output routes, and modes of operation. The important attributes of processing tasks are then identified, and some processing stratagems are examined. It is shown that the greater the complexity of a given task, the wider the range of possible stratagems which can accomplish the task. For relatively simple tasks, the optimum stratagem can be found by analytical reasoning. For more complex tasks, however, optimum scheduling techniques may have to be employed for the assignment of segments of the task to the available processing elements.

Utku, S.↗
NASA NTRS2006

Using Apex To Construct CPM-GOMS Models

process for automatically generating computational models of human/computer interactions as well as graphical and textual representations of the models has been built on the conceptual foundation of a method known in the art as CPM-GOMS. This method is so named because it combines (1) the task decomposition of analysis according to an underlying method known in the art as the goals, operators, methods, and selection (GOMS) method with (2) a model of human resource usage at the level of cognitive, perceptual, and motor (CPM) operations. CPM-GOMS models have made accurate predictions about behaviors of skilled computer users in routine tasks, but heretofore, such models have been generated in a tedious, error-prone manual process. In the present process, CPM-GOMS models are generated automatically from a hierarchical task decomposition expressed by use of a computer program, known as Apex, designed previously to be used to model human behavior in complex, dynamic tasks. An inherent capability of Apex for scheduling of resources automates the difficult task of interleaving the cognitive, perceptual, and motor resources that underlie common task operators (e.g., move and click mouse). The user interface of Apex automatically generates Program Evaluation Review Technique (PERT) charts, which enable modelers to visualize the complex parallel behavior represented by a model. Because interleaving and the generation of displays to aid visualization are automated, it is now feasible to construct arbitrarily long sequences of behaviors. The process was tested by using Apex to create a CPM-GOMS model of a relatively simple human/computer-interaction task and comparing the time predictions of the model and measurements of the times taken by human users in performing the various steps of the task. The task was to withdraw $80 in cash from an automated teller machine (ATM). For the test, a Visual Basic mockup of an ATM was created, with a provision for input from (and measurement of the performance of) the user via a mouse. The times predicted by the automatically generated model turned out to approximate the measured times fairly well (see figure). While these results are promising, there is need for further development of the process. Moreover, it will also be necessary to test other, more complex models: The actions required of the user in the ATM task are too sequential to involve substantial parallelism and interleaving and, hence, do not serve as an adequate test of the unique strength of CPM-GOMS models to accommodate parallelism and interleaving.

John, Bonnie↗
DOE OSTI2026

Reduced‐Order Modeling of Energetic Materials Using Physics‐Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC)

Physics-aware deep learning (PADL) has gained popularity for use in spatiotemporal dynamics simulations, such as those in computational modeling of energetic materials (EM). We show that the challenge PADL methods face while learning complex field evolution problems can be simplified and accelerated by decoupling it into two tasks: learning complex geometric features in evolving fields and modeling dynamics over these features in a lower-dimensional feature space. We build upon our previous work on physics-aware recurrent convolutional neural networks (PARC). PARC embeds knowledge of underlying physics into its neural network architecture for more robust and accurate prediction of evolving physical fields. PARC was shown to effectively learn complex nonlinear features such as the formation of hotspots and coupled shock fronts in various initiation scenarios of EMs, as a function of microstructures, serving effectively as a microstructure-aware burn model. Here, we further accelerate PARC and reduce its computational cost by projecting the original dynamics onto a lower-dimensional invariant manifold, or “latent space.” The projected latent representation encodes the complex geometry of evolving fields (e.g., temperature and pressure) in a set of data-driven features. The reduced dimension of this latent space allows us to learn the dynamics during the initiation of EM with a lighter and more efficient model. We observe a significant decrease in training and inference time while maintaining results comparable to PARC at inference. This work takes steps towards enabling rapid prediction of EM thermomechanics at larger scales and characterization of EM structure–property–performance linkages at a full application scale.

Mathematics and Computing↗
NASA NTRS1996

Air Traffic Complexity Measurement Environment (ACME): Software User's Guide

A user's guide for the Air Traffic Complexity Measurement Environment (ACME) software is presented. The ACME consists of two major components, a complexity analysis tool and user interface. The Complexity Analysis Tool (CAT) analyzes complexity off-line, producing data files which may be examined interactively via the Complexity Data Analysis Tool (CDAT). The Complexity Analysis Tool is composed of three independently executing processes that communicate via PVM (Parallel Virtual Machine) and Unix sockets. The Runtime Data Management and Control process (RUNDMC) extracts flight plan and track information from a SAR input file, and sends the information to GARP (Generate Aircraft Routes Process) and CAT (Complexity Analysis Task). GARP in turn generates aircraft trajectories, which are utilized by CAT to calculate sector complexity. CAT writes flight plan, track and complexity data to an output file, which can be examined interactively. The Complexity Data Analysis Tool (CDAT) provides an interactive graphic environment for examining the complexity data produced by the Complexity Analysis Tool (CAT). CDAT can also play back track data extracted from System Analysis Recording (SAR) tapes. The CDAT user interface consists of a primary window, a controls window, and miscellaneous pop-ups. Aircraft track and position data is displayed in the main viewing area of the primary window. The controls window contains miscellaneous control and display items. Complexity data is displayed in pop-up windows. CDAT plays back sector complexity and aircraft track and position data as a function of time. Controls are provided to start and stop playback, adjust the playback rate, and reposition the display to a specified time.

Source record↗
NASA NTRS2020

Application of Sparse Identification of Nonlinear Dynamics for Physics-Informed Learning

Advances in machine learning and deep neural networks has enabled complex engineering tasks like image recognition, anomaly detection, regression, and multi-objective optimization, to name but a few. The complexity of the algorithm architecture, e.g., the number of hidden layers in a deep neural network, typically grows with the complexity of the problems they are required to solve, leaving little room for interpreting (or explaining) the path that results in a specific solution. This drawback is particularly relevant for autonomous aerospace and aviation systems, where certifications require a complete understanding of the algorithm behavior in all possible scenarios. Including physics knowledge in such data-driven tools may improve the interpretability of the algorithms, thus enhancing model validation against events with low probability but relevant for system certification. Such events include, for example, spacecraft or aircraft sub-system failures, for which data may not be available in the training phase. This paper investigates a recent physics-informed learning algorithm for identification of system dynamics, and shows how the governing equations of a system can be extracted from data using sparse regression. The learned relationships can be utilized as a surrogate model which, unlike typical data-driven surrogate models, relies on the learned underlying dynamics of the system rather than large number of fitting parameters. The work shows that the algorithm can reconstruct the differential equations underlying the observed dynamics using a single trajectory when no uncertainty is involved. However, the training set size must increase when dealing with stochastic systems, e.g., nonlinear dynamics with random initial conditions.

Corbetta, Matteo↗
NASA NTRS1989

Planning time-optimal robotic manipulator motions and work places for point-to-point tasks

A method is presented which combines simple time-optimal motions in an optimal manner to yield the minimum-time motions for an important class of complex manipulator tasks composed of point-to-point moves such as assembly, electronic component insertion, and spot welding. This method can also be used to design manipulator actions and work places so that tasks can be completed in minimum time. The method has been implemented in a computer-aided design software system. Several examples are presented. Experimental results show the method's validity and utility.

Dubowsky, S.↗
NASA NTRS1972

The elusive tradeoff - Speed vs accuracy in visual discrimination tasks.

Explicit payoffs were used to control pressures for speed and accuracy in experiments seeking tradeoffs for difficult visual discrimination tasks. The experiments involved stimuli on a cathode ray tube display controlled by a PDP-1 computer, with the subjects performing increasingly complex discrimination tasks and their earnings increasing with their performance. Models for discrimination under time pressure are proposed on the basis of the results.

Swensson, R. G.↗
NASA NTRS2005

Human Centered Computing for Mars Exploration

The science objectives are to determine the aqueous, climatic, and geologic history of a site on Mars where conditions may have been favorable to the preservation of evidence of prebiotic or biotic processes. Human Centered Computing is a development process that starts with users and their needs, rather than with technology. The goal is a system design that serves the user, where the technology fits the task and the complexity is that of the task not of the tool.

Trimble, Jay↗
NASA NTRSDate not supplied

Dual Glovebox Thermal Vacuum Chamber: Testing Capabilities for Spacesuit Arms and Gloves

The development of Extravehicular Activity (EVA) suits and hand mobility EVA tasks are complex, high risk, and difficult to test in a simulated space environment. During the early assembly of the International Space Station, the Crew and Thermal Systems Division at NASA JSC was tasked to design a chamber that could use two Extravehicular Mobility Unit (EMU) arms in a simulated space environment versus testing with a full suit. The Dual Glove Box (DGB) Chamber was built and served to help develop EVA tools and operations to assist with Return to Flight for the Space Shuttle after the Columbia accident. With the recent development of the Exploration Extravehicular Mobility Unit (xEMU) and new commercial suits through the Extravehicular Activities Services (xEVAS) contract, the DGB can support the need to do suit component testing at thermal extremes and EVA operations without the cost of full suit testing. The DGB can simulate realistic delta pressures, vacuum down to 10^-5 Torr with roughing and cryogenic pumps, and a wide range of shroud temperatures achieved via a combination of Liquid Nitrogen (LN2), conditioned Gaseous Nitrogen (GN2), Infrared (IR) lamps, and heater plates. Recent developmental work has verified operational status of the chamber and expanded the capabilities of the DGB to include thermal contact testing of suit gloves through the development of 2 temperature-controlled grab bars. This paper will discuss the history and capabilities of the DGB, and the chamber’s future role in the development of new spacesuit systems.

Extravehicular Activity (EVA) suits↗
NASA NTRS1984

Parallel, iterative solution of sparse linear systems: Models and architectures

A model of a general class of asynchronous, iterative solution methods for linear systems is developed. In the model, the system is solved by creating several cooperating tasks that each compute a portion of the solution vector. A data transfer model predicting both the probability that data must be transferred between two tasks and the amount of data to be transferred is presented. This model is used to derive an execution time model for predicting parallel execution time and an optimal number of tasks given the dimension and sparsity of the coefficient matrix and the costs of computation, synchronization, and communication. The suitability of different parallel architectures for solving randomly sparse linear systems is discussed. Based on the complexity of task scheduling, one parallel architecture, based on a broadcast bus, is presented and analyzed.

Reed, D. A.↗
NASA NTRS1985

Parallel, iterative solution of sparse linear systems - Models and architectures

Solving large, sparse, linear systems of equations is a fundamental problem in large scale scientific and engineering computation. A model of a general class of asynchronous, iterative solution methods for linear systems is developed. In the model, the system is solved by creating several cooperating tasks that each compute a portion of the solution vector. A data transfer model predicting both the probability that data must be transferred between two tasks and the amount of data to be transferred is presented. This model is used to derive an execution time model for predicting parallel execution time and an optimal number of tasks given the dimension and sparsity of the coefficient matrix and the costs of computation, synchronization, and communication. The suitability of different parallel architectures for solving randomly sparse linear systems is discussed. Based on the complexity of task scheduling, one parallel architecture, based on a broadcast bus, is presented and analyzed.

Reed, D. A.↗
NASA NTRSDate not supplied

Dual Glovebox Thermal Vacuum Chamber: Testing Capabilities for Spacesuit Arms and Gloves

The development of Extravehicular Activity (EVA) suits and hand mobility EVA tasks are complex, high risk, and difficult to test in a simulated space environment. During the early assembly of the International Space Station, the Crew and Thermal Systems Division’s Systems Test Branch at NASA JSC was tasked to design a chamber that could use two Extravehicular Mobility Unit (EMU) arms in a simulated space environment versus testing with a full suit. The Dual Glove Box (DGB) Chamber was built and served to help develop EVA tools and operations to assist with Return to Flight for the Space Shuttle after the Columbia accident. With the recent development of the Exploration Extravehicular Mobility Unit (xEMU) and new commercial suits through the Extravehicular Activities Services (xEVAS) contract, the DGB can support the need to do suit component testing at thermal extremes and EVA operations without the cost of full suit testing. The DGB can simulate realistic delta pressures, vacuum down to 10^-5 Torr with roughing and cryogenic pumps, and a wide range of shroud temperatures achieved via a combination of Liquid Nitrogen (LN2), conditioned Gaseous Nitrogen (GN2), and Infrared (IR) lamps. Recent developmental work has verified operational status of the chamber and expanded the capabilities of the DGB to include thermal contact testing of suit gloves through the development of 2 temperature-controlled grab bars. This paper will discuss the history and capabilities of the DGB, and the chamber’s future role in the development of new spacesuit systems.

Kaixin Cui↗
NASA NTRS2024

Dual Glovebox Thermal Vacuum Chamber: Testing Capabilities for Spacesuit Arms and Gloves

The development of Extravehicular Activity (EVA) suits and hand mobility EVA tasks are complex, high risk, and difficult to test in a simulated space environment. During the early assembly of the International Space Station (ISS), the Crew and Thermal Systems Division (CTSD) at NASA Johnson Space Center (JSC) was tasked to design a chamber that could use two Extravehicular Mobility Unit (EMU) arms and gloves in a simulated space environment versus testing with a full suit. The Dual Glovebox (DGB) Chamber was built and served to help develop EVA tools and operations to assist with Return to Flight for the Space Shuttle after the Columbia accident. With the recent development of the Exploration Extravehicular Mobility Unit (xEMU) and new commercial suits through the Extravehicular Activities Services (xEVAS) contract, the DGB can support the need to do suit component testing at thermal extremes and EVA operations without the cost of full suit testing. The DGB can simulate realistic delta pressures, vacuum down to 5x10-4 Torr (0.066 Pa) with roughing and cryogenic pumps, and a wide range of temperatures achieved via a combination of Liquid Nitrogen (LN2), conditioned Gaseous Nitrogen (GN2), Infrared (IR) lamps, and heater cables. Recent developmental work has verified operational status of the chamber and expanded the capabilities of the DGB to include thermal contact testing of suit gloves through two temperature-controlled grab bars. This paper will discuss the history and capabilities of the DGB, and the chamber’s future role in the development of new spacesuit systems.

Extravehicular Activity (EVA)↗
NASA NTRSDate not supplied

Dual Glovebox Thermal Vacuum Chamber: Testing Capabilities for Spacesuit Arms and Gloves

The development of Extravehicular Activity (EVA) suits and hand mobility EVA tasks are complex, high risk, and difficult to test in a simulated space environment. During the early assembly of the International Space Station (ISS), the Crew and Thermal Systems Division (CTSD) at NASA Johnson Space Center (JSC) was tasked to design a chamber that could use two Extravehicular Mobility Unit (EMU) arms and gloves in a simulated space environment versus testing with a full suit. The Dual Glovebox (DGB) Chamber was built and served to help develop EVA tools and operations to assist with Return to Flight for the Space Shuttle after the Columbia accident. With the recent development of the Exploration Extravehicular Mobility Unit (xEMU) and new commercial suits through the Extravehicular Activities Services (xEVAS) contract, the DGB can support the need to do suit component testing at thermal extremes and EVA operations without the cost of full suit testing. The DGB can simulate realistic delta pressures, vacuum down to 5x10-4 Torr (0.066 Pa) with roughing and cryogenic pumps, and a wide range of temperatures achieved via a combination of Liquid Nitrogen (LN2), conditioned Gaseous Nitrogen (GN2), Infrared (IR) lamps, and heater cables. Recent developmental work has verified operational status of the chamber and expanded the capabilities of the DGB to include thermal contact testing of suit gloves through two temperature-controlled grab bars. This paper will discuss the history and capabilities of the DGB, and the chamber’s future role in the development of new spacesuit systems.

Extravehicular Activity (EVA)↗
NASA NTRSDate not supplied

Dual Glovebox Thermal Vacuum Chamber: Testing Capabilities for Spacesuit Arms and Gloves

The development of Extravehicular Activity (EVA) suits and hand mobility EVA tasks are complex, high risk, and difficult to test in a simulated space environment. During the early assembly of the International Space Station (ISS), the Crew and Thermal Systems Division (CTSD) at NASA Johnson Space Center (JSC) was tasked to design a chamber that could use two Extravehicular Mobility Unit (EMU) arms and gloves in a simulated space environment versus testing with a full suit. The Dual Glovebox (DGB) Chamber was built and served to help develop EVA tools and operations to assist with Return to Flight for the Space Shuttle after the Columbia accident. With the recent development of the Exploration Extravehicular Mobility Unit (xEMU) and new commercial suits through the Extravehicular Activities Services (xEVAS) contract, the DGB can support the need to do suit component testing at thermal extremes and EVA operations without the cost of full suit testing. The DGB can simulate realistic delta pressures, vacuum down to 5x10-4 Torr (0.066 Pa) with roughing and cryogenic pumps, and a wide range of temperatures achieved via a combination of Liquid Nitrogen (LN2), conditioned Gaseous Nitrogen (GN2), Infrared (IR) lamps, and heater cables. Recent developmental work has verified operational status of the chamber and expanded the capabilities of the DGB to include thermal contact testing of suit gloves through two temperature-controlled grab bars. This paper will discuss the history and capabilities of the DGB, and the chamber’s future role in the development of new spacesuit systems.

spacesuit arms↗
DOE OSTI2022

DECOVALEX-2019: An international collaboration for advancing the understanding and modeling of coupled thermo-hydro-mechanical-chemical (THMC) processes in geological systems

The DECOVALEX Project is an international research collaboration for advancing the understanding and modeling of coupled thermo-hydro-mechanical-chemical (THMC) processes in geological systems. DECOVALEX stands for “DEvelopment of COupled Models and VALidation against EXperiments”. The creation of this international initiative, now running for almost 30 years, was motivated by the recognition that prediction of these coupled effects is an essential part of the performance and safety assessment of geologic disposal systems for radioactive waste and spent nuclear fuel, and also for a range of other sub-surface engineering activities. DECOVALEX emphasizes joint analysis and comparative modeling of state-of-the-art field and laboratory experiments, across a range of host rock options and repository designs. Participating research teams are from radioactive waste management organizations, national research institutes, regulatory agencies, universities, and consulting groups, providing a wide range of perspectives and solutions to these complex problems. The most recent phase of the DECOVALEX Project, here referred to as DECOVALEX-2019, started in 2016 and ended in 2019. Modeling teams from 13 international partner organizations participated in the comparative evaluation of seven modeling tasks involving complex field and/or laboratory experiments. Furthermore, this Virtual Special Issue on DECOVALEX-2019 provides an in-depth overview of these collaborative research efforts and how these have advanced the state-of-the-art of understanding and modeling coupled THMC processes.

58 GEOSCIENCES↗