SEARCH · Engineering Papers

Results for “scalable representations”

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.

SourceResearch connectionFrom yearThrough year

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

27 records · Page 2

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Upper Class E Traffic Management: NASA's Collaborative Research and Technical Development to Enable Routine, Safe, and Scalable High Altitude Operation

In the past, operations at high altitudes have been limited in number and largely conducted for defense/security purposes. However, with broad technological advances, a growing number of commerical and public good use cases, and a significant rise in the number of capable platforms, the demand for operating in high altitude airspace - known as upper Class E in the United States - is increasing. With that increase in demand comes the need for a means to manage the airspace in a way that does not burden current air traffic services and infrastructure given that provisions for commercial operations are limited. NASA, in collaboration with other government agencies and strong representation from industry, has developed a cooperative approach to airspace management referred to as Upper Class E Traffic Management (ETM). Key aspects of the ETM concept are the ability to exchange information through services that enable the operators to share airspace in cetain areas by adhering to cooperative operating practices (COPs) and by having shared situation awareness. To advance the concept, NASA recently developed and formally tested the first dedicated reference ETM system and supportin architecture. The test involved real-time simulation of high altitude operations with a diverse set of aircraft and encourter situations that included industry partners connected to the system from their remote operating centers. This presentation will provide attendees with an understanding of the ETM concept, the details and importance of the system that has been developed, an overview of the groundbreaking simulation, and a glimpse of things to come in our next steps.

Conrad Dang-Gabriel↗

NASA NTRS2004

Model-based Executive Control through Reactive Planning for Autonomous Rovers

This paper reports on the design and implementation of a real-time executive for a mobile rover that uses a model-based, declarative approach. The control system is based on the Intelligent Distributed Execution Architecture (IDEA), an approach to planning and execution that provides a unified representational and computational framework for an autonomous agent. The basic hypothesis of IDEA is that a large control system can be structured as a collection of interacting agents, each with the same fundamental structure. We show that planning and real-time response are compatible if the executive minimizes the size of the planning problem. We detail the implementation of this approach on an exploration rover (Gromit an RWI ATRV Junior at NASA Ames) presenting different IDEA controllers of the same domain and comparing them with more classical approaches. We demonstrate that the approach is scalable to complex coordination of functional modules needed for autonomous navigation and exploration.

Finzi, Alberto↗

NASA NTRS2006

A Robust Scalable Transportation System Concept

This report documents the 2005 Revolutionary System Concept for Aeronautics (RSCA) study entitled "A Robust, Scalable Transportation System Concept". The objective of the study was to generate, at a high-level of abstraction, characteristics of a new concept for the National Airspace System, or the new NAS, under which transportation goals such as increased throughput, delay reduction, and improved robustness could be realized. Since such an objective can be overwhelmingly complex if pursued at the lowest levels of detail, instead a System-of-Systems (SoS) approach was adopted to model alternative air transportation architectures at a high level. The SoS approach allows the consideration of not only the technical aspects of the NAS", but also incorporates policy, socio-economic, and alternative transportation system considerations into one architecture. While the representations of the individual systems are basic, the higher level approach allows for ways to optimize the SoS at the network level, determining the best topology (i.e. configuration of nodes and links). The final product (concept) is a set of rules of behavior and network structure that not only satisfies national transportation goals, but represents the high impact rules that accomplish those goals by getting the agents to "do the right thing" naturally. The novel combination of Agent Based Modeling and Network Theory provides the core analysis methodology in the System-of-Systems approach. Our method of approach is non-deterministic which means, fundamentally, it asks and answers different questions than deterministic models. The nondeterministic method is necessary primarily due to our marriage of human systems with technological ones in a partially unknown set of future worlds. Our goal is to understand and simulate how the SoS, human and technological components combined, evolve.

Hahn, Andrew↗

NASA NTRS2006

Trade Studies of Space Launch Architectures using Modular Probabilistic Risk Analysis

A top-down risk assessment in the early phases of space exploration architecture development can provide understanding and intuition of the potential risks associated with new designs and technologies. In this approach, risk analysts draw from their past experience and the heritage of similar existing systems as a source for reliability data. This top-down approach captures the complex interactions of the risk driving parts of the integrated system without requiring detailed knowledge of the parts themselves, which is often unavailable in the early design stages. Traditional probabilistic risk analysis (PRA) technologies, however, suffer several drawbacks that limit their timely application to complex technology development programs. The most restrictive of these is a dependence on static planning scenarios, expressed through fault and event trees. Fault trees incorporating comprehensive mission scenarios are routinely constructed for complex space systems, and several commercial software products are available for evaluating fault statistics. These static representations cannot capture the dynamic behavior of system failures without substantial modification of the initial tree. Consequently, the development of dynamic models using fault tree analysis has been an active area of research in recent years. This paper discusses the implementation and demonstration of dynamic, modular scenario modeling for integration of subsystem fault evaluation modules using the Space Architecture Failure Evaluation (SAFE) tool. SAFE is a C++ code that was originally developed to support NASA s Space Launch Initiative. It provides a flexible framework for system architecture definition and trade studies. SAFE supports extensible modeling of dynamic, time-dependent risk drivers of the system and functions at the level of fidelity for which design and failure data exists. The approach is scalable, allowing inclusion of additional information as detailed data becomes available. The tool performs a Monte Carlo analysis to provide statistical estimates. Example results of an architecture system reliability study are summarized for an exploration system concept using heritage data from liquid-fueled expendable Saturn V/Apollo launch vehicles.

Mathias, Donovan L.↗

NASA NTRS2013

The NASA-Goddard Multi-Scale Modeling Framework - Land Information System: Global Land/atmosphere Interaction with Resolved Convection

The present generation of general circulation models (GCM) use parameterized cumulus schemes and run at hydrostatic grid resolutions. To improve the representation of cloud-scale moist processes and landeatmosphere interactions, a global, Multi-scale Modeling Framework (MMF) coupled to the Land Information System (LIS) has been developed at NASA-Goddard Space Flight Center. The MMFeLIS has three components, a finite-volume (fv) GCM (Goddard Earth Observing System Ver. 4, GEOS-4), a 2D cloud-resolving model (Goddard Cumulus Ensemble, GCE), and the LIS, representing the large-scale atmospheric circulation, cloud processes, and land surface processes, respectively. The non-hydrostatic GCE model replaces the single-column cumulus parameterization of fvGCM. The model grid is composed of an array of fvGCM gridcells each with a series of embedded GCE models. A horizontal coupling strategy, GCE4fvGCM4Coupler4LIS, offered significant computational efficiency, with the scalability and I/O capabilities of LIS permitting landeatmosphere interactions at cloud-scale. Global simulations of 2007e2008 and comparisons to observations and reanalysis products were conducted. Using two different versions of the same land surface model but the same initial conditions, divergence in regional, synoptic-scale surface pressure patterns emerged within two weeks. The sensitivity of largescale circulations to land surface model physics revealed significant functional value to using a scalable, multi-model land surface modeling system in global weather and climate prediction.

cumulus schemes↗

NASA NTRS1998

Preliminary Work for Examining the Scalability of Reinforcement Learning

Researchers began studying automated agents that learn to perform multiple-step tasks early in the history of artificial intelligence (Samuel, 1963; Samuel, 1967; Waterman, 1970; Fikes, Hart & Nilsonn, 1972). Multiple-step tasks are tasks that can only be solved via a sequence of decisions, such as control problems, robotics problems, classic problem-solving, and game-playing. The objective of agents attempting to learn such tasks is to use the resources they have available in order to become more proficient at the tasks. In particular, each agent attempts to develop a good policy, a mapping from states to actions, that allows it to select actions that optimize a measure of its performance on the task; for example, reducing the number of steps necessary to complete the task successfully. Our study focuses on reinforcement learning, a set of learning techniques where the learner performs trial-and-error experiments in the task and adapts its policy based on the outcome of those experiments. Much of the work in reinforcement learning has focused on a particular, simple representation, where every problem state is represented explicitly in a table, and associated with each state are the actions that can be chosen in that state. A major advantage of this table lookup representation is that one can prove that certain reinforcement learning techniques will develop an optimal policy for the current task. The drawback is that the representation limits the application of reinforcement learning to multiple-step tasks with relatively small state-spaces. There has been a little theoretical work that proves that convergence to optimal solutions can be obtained when using generalization structures, but the structures are quite simple. The theory says little about complex structures, such as multi-layer, feedforward artificial neural networks (Rumelhart & McClelland, 1986), but empirical results indicate that the use of reinforcement learning with such structures is promising. These empirical results make no theoretical claims, nor compare the policies produced to optimal policies. A goal of our work is to be able to make the comparison between an optimal policy and one stored in an artificial neural network. A difficulty of performing such a study is finding a multiple-step task that is small enough that one can find an optimal policy using table lookup, yet large enough that, for practical purposes, an artificial neural network is really required. We have identified a limited form of the game OTHELLO as satisfying these requirements. The work we report here is in the very preliminary stages of research, but this paper provides background for the problem being studied and a description of our initial approach to examining the problem. In the remainder of this paper, we first describe reinforcement learning in more detail. Next, we present the game OTHELLO. Finally we argue that a restricted form of the game meets the requirements of our study, and describe our preliminary approach to finding an optimal solution to the problem.

Clouse, Jeff↗

NASA NTRSDate not supplied

Identifying Information Needs and Tools to Support Interactions between Upper Class E Traffic Management (ETM) Operations and the Air Traffic System (ATS)

With the introduction of high-altitude long endurance (HALE) vehicles and balloons designed to operate above 60,000 feet, the frequency and duration of operations in Upper Class E airspace are expected to increase. In response to the need for scalable traffic management for these diverse operations at higher altitudes, the FAA introduced the Upper Class E Traffic Management (ETM) concept. Like the successful demonstration of Uncrewed Aircraft System (UAS) Traffic Management (UTM), the ETM concept is also designed as a community-based, industry-driven cooperative approach to traffic management. As these vehicles and balloons ascend to/descend from ETM Cooperative Areas in Upper Class E, they will transit through Class A controlled airspace where they will interact with various entities of the conventional Air Traffic System (ATS) (e.g., Air Traffic Control (ATC)). This work explores tools that will help support ETM-ATS interactions for users throughout the ATS, as well as ETM Operators. An information needs analysis using ETM-ATS interaction use cases, revealed that the needed functionalities generally grouped themselves into two main themes, the visualization of flights and airspace designations, and digital communication capabilities across various human users. In this paper, we describe two envisioned tools, 1) an Integrated Visualization Tool to display flight information and airspace designations, and 2) an Integrated Digital Communication Tool to facilitate two-way information exchange between users about vehicle position information, the coordination of airspace approvals, and notifications. The tools we describe create an integrated visual representation of vehicles and airspace designations with a set of communication capabilities to consolidate information into a single display interface. These tools may be used to guide the development of prototype tools for demonstrations at the National Aeronautics and Space Administration (NASA) Ames Research Center to further explore ETM-ATS interactions within the ETM concept.

Upper Class E Traffic Management (ETM)↗

NASA NTRSDate not supplied

Identifying Information Needs and Tools to Support Interactions between Upper Class E Traffic Management (ETM) Operations and the Air Traffic System (ATS)

Upper Class E Traffic Management (ETM)↗

NASA NTRS2013

Scheduling Operations for Massive Heterogeneous Clusters

High-performance computing (HPC) programming has become increasingly difficult with the advent of hybrid supercomputers consisting of multicore CPUs and accelerator boards such as the GPU. Manual tuning of software to achieve high performance on this type of machine has been performed by programmers. This is needlessly difficult and prone to being invalidated by new hardware, new software, or changes in the underlying code. A system was developed for task-based representation of programs, which when coupled with a scheduler and runtime system, allows for many benefits, including higher performance and utilization of computational resources, easier programming and porting, and adaptations of code during runtime. The system consists of a method of representing computer algorithms as a series of data-dependent tasks. The series forms a graph, which can be scheduled for execution on many nodes of a supercomputer efficiently by a computer algorithm. The schedule is executed by a dispatch component, which is tailored to understand all of the hardware types that may be available within the system. The scheduler is informed by a cluster mapping tool, which generates a topology of available resources and their strengths and communication costs. Software is decoupled from its hardware, which aids in porting to future architectures. A computer algorithm schedules all operations, which for systems of high complexity (i.e., most NASA codes), cannot be performed optimally by a human. The system aids in reducing repetitive code, such as communication code, and aids in the reduction of redundant code across projects. It adds new features to code automatically, such as recovering from a lost node or the ability to modify the code while running. In this project, the innovators at the time of this reporting intend to develop two distinct technologies that build upon each other and both of which serve as building blocks for more efficient HPC usage. First is the scheduling and dynamic execution framework, and the second is scalable linear algebra libraries that are built directly on the former.

Humphrey, John↗