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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Numerical and Experimental Examination of Turbulent Mixing of a Heated Jet in Crossflow

The injection of fully-developed turbulent heated air from a tube into a cooler turbulent duct flow is examined, as an analogy to film cooled turbine blades. A LES numerical model is developed and applied in which tube and duct turbulence inflow effects are emulated using a divergence-free synthetic eddy method (SEM). For direct comparison, a hot-wire experiment is conducted within the ERB test cell SW-6 at NASA Glenn Research Center. Results related to velocity, temperature, and heat flux are obtained numerically and experimentally for a blowing ratio of 1.2, involving a 36 K temperature difference. The relative effect on the solutions of tube and duct inflow turbulence is systematically evaluated. The impact of inherent low-pass filtering of temperature measurements and probe wire offset on the experimental results are addressed.

Large Eddy Simulation↗
NASA NTRS2018

Superconducting Sensors for Microwave and Optical Photon-Starved Communications (Plenary)

NASA mission modeling indicates a desire for approximately a tenfold data improvement per decade from the Deep Space Network through the year 2040. Some of this improvement will come from aperture enhancement and antenna arraying, increased use of Ka-band over X-band, and high performance optical terminals (e.g. 12 m ground telescope). The remainder will depend on "disruptive" technologies. Deep-space communications differs from near earth communications. One way light travel times are measured in minutes rather than seconds and distances involved are large enough such that optical signals are photon limited and microwave signals have extremely low flux density. High frequency SQIF receivers and single photon detectors for optical communications will be described. Specifically, progress towards a "noiseless" receiver based on arrays of incommensurate area SQUIDs, and single photon counting detectors based on superconducting nanowires and kinetic inductance effects, will be discussed. Finally, the design of an optimal array of optical telescopes to emulate performance of a monolithic 12 m telescope will be outlined. In this case, optimal means minimizing the initial capital investment and operational cost while maintaining performance requirements of the deep-space link.

Romanofsky, Bob↗
NASA NTRS2018

The Multiple Access Testbed for Research in Innovative Communications Systems (MATRICS)

NASA is presently in the planning stages for the next generation earth relay architecture which will transition to Ka-band services in a new, potentially commercially-driven architecture, by 2040. To assess the performance of various technologies and architectures, the Multiple Access Testbed for Research in Innovative Communications Systems (The MATRICS), is being developed to offer a flexible emulation platform for system-level architecture and technology assessments of candidate next generation Ka-band relay and user terminal solutions. In this paper, we describe the architecture and setup of the MATRICS, which is presently housed in GRC’s 25-ft anechoic chamber.

next generation relay↗
NASA NTRS2019

NASA IV&V's Cyber Range for Space Systems

A cyber range is a virtual environment that is used for cyberwarfare training and cyber technology development. It provides tools that help strengthen the stability, security and performance of cyber infrastructures and IT systems. NASA IV&V has created an adaptable virtual environment that is representative of a typical NASA mission system of systems environment that serves multiple purposes: Training of network defenders. Ability to perform simulated red vs blue training events. Ability to deploy mission specific technologies in a cyber contested environment. Ability to demonstrate vulnerabilities and their impact to ground and space systemsThe overall system is comprised of more than 70 virtual machines and physical infrastructure to demonstrate real-world cyber attacks against: email users, wireless networks, and physically implanted devices with command and control call-back. The cyber range was built to emulate a NASA precipitation satellite infrastructure.This presentation will give an overview of the virtual cyber range and how it was used to conduct a simulate red vs blue exercise with the goal of compromising the simulated spacecraft that utilizes real NASA flight software.

test and evaluation DT&E↗
NASA NTRS2016

Modeling and Simulation of an Integrated Gate Turnaround Management Concept

An Integrated Gate Turnaround Management (IGTM) prototype was developed at NASA Ames Simulation Laboratories (SimLabs) using Dallas Ft. Worth International Airport (DFW) to demonstrate the IGTM concepts feasibility and benefits. The simulation architecture includes: the IGTM controller, an Airline Operations Control (AOC) application, Big DataAnalytics Input (BAI) application, a terminal traffic simulation or known as NASA-developed Surface Operation Simulator and Scheduler (SOSS), and a Database Server. ActiveMQ, a Java messaging service, was used to emulate the System Wide Information Management (SWIM) data network messaging. This paper describes the modeling and simulation of the IGTM concept, and illustrates selected use cases to demonstrate the feasibility and benefits of the IGTM concept for optimizing gate turnaround operations.

gate turnaround management↗
NASA NTRS2019

High-Performance Spaceflight Computing (HPSC) Middleware Overview

High Performance Spacecraft Computing (HPSC) is a joint project between the National Aeronautics and Space Administration (NASA) and Air Force Research Lab (AFRL) to develop a high-performance multi-core radiation hardened flight processor. HPSC offers a new flight computing architecture to meet the needs of NASA missions through 2030 and beyond. Providing on the order of 100X the computational capacity of current flight processors for the same amount of power, the multicore architecture of the HPSC processor, or "Chiplet" provides unprecedented flexibility in a flight computing system by enabling the operating point to be set dynamically, trading among needs for computational performance, energy management and fault tolerance. The HPSC Chiplet is being developed by Boeing under contract to NASA, and is expected to provide prototypes in 2021. The HPSC Chiplet prototypes will be delivered with an evaluation board, system emulators, comprehensive system software, and a software development kit. In addition to the vendor deliverables, the AFRL is funding the development of a flexible Middleware to be developed by NASA Jet Propulsion Laboratory and NASA Goddard Space Flight Center. The HPSC Middleware provides a suite of thirteen high level services to manage the compute, memory and I/O resources of this complex device.This presentation will provide an overview of the HPSC project, including a hardware overview, system software overview, Middleware overview, and mission use cases. The hardware overview will provide a look at the 8 core High Performance Processing Subsystem (HPPS), the Real Time Processing Subsystem (RTPS), the Chiplet Configuration Management Subsystem, on chip peripherals, and high speed I/O. The system software overview will introduce the boot loaders, operating systems, device drivers, and software development environment. The Middleware overview will provide insight into the high-level services that will be provided to help mission developers manage the many resources and configurations made possible with the Chiplet. Finally, the presentation will provide a brief look at the mission use cases that can be enabled with this next generation architecture.

middleware↗
NASA NTRS2019

Characterization of a Photon Counting Test Bed for Space to Ground Optical Pulse Position Modulation Communications Links

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) has developed a laboratory transmitter and receiver prototype of a space to ground optical communications link. The system is meant to emulate future deep space optical communication links, such as the first crewed flight of Orion, in which the transmitted laser is modulated using pulse position modulation and the receiver is capable of detecting single photons. The transmitter prototype consists of a software defined radio, a high extinction ratio electro-optic modulator system, and 1550 nm laser. The receiver is a scalable concept and utilizes a single-pixel array of fiber coupled superconducting nanowire single photon detectors. The transmit and receive waveforms follow the Consultative Committee for Space Data Systems (CCSDS) Optical Communications High Photon Efficiency Standard. This paper describes the transmitter and receiver prototypes as well as the system test configuration. System level tests results are presented and compared to predictions from software simulations.

Nappier, Jennifer M.↗
NASA NTRS2019

Atmospheric Chemistry Modeling Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS2019

Application of Machine Learning Techniques to Delay Tolerant Network Routing

This dissertation discusses several machine learning techniques to improve routing in delay tolerant networks (DTNs). These are networks in which there may be long one-way trip times, asymmetric links, high error rates, and deterministic as well as non-deterministic loss of contact between network nodes, such as interplanetary satellite networks, mobile ad hoc networks and wireless sensor networks. This work uses historical network statistics to train a multi-label classifier to predict reliable paths through the network. In addition, a clustering technique is used to predict future mobile node locations. Both of these techniques are used to reduce the consumption of resources such as network bandwidth, memory and data storage that is required by replication routing methods often used in opportunistic DTN environments. Thesis contributions include: an emulation tool chain developed to create a DTN test bed for machine learning, the network and software architecture for a machine learning based routing method, the development and implementation of classification and clustering techniques and performance evaluation in terms of machine learning and routing metrics.

Dudukovich, Rachel M.↗
NASA NTRS2019

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O (sup 1) D (Deuterium) to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS2019

Atmospheric Chemistry Modeling and Air Quality Forecasting Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model.We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS2019

Characterization of a Photon Counting Test Bed for Space to Ground Optical Pulse Position Modulation Communications Links

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) has developed a laboratory transmitter and receiver prototype of a space-to-ground optical communications link. The system is meant to emulate future deep space optical communication links, such as the first crewed flight of Orion, in which the transmitted laser is modulated using pulse position modulation and the receiver is capable of detecting single photons. The transmitter prototype consists of a software defined radio, a high extinction ratio electro-optic modulator system, and a 1550 nm laser. The receiver is a scalable concept and utilizes a single-pixel array of fiber coupled superconducting nanowire single photon detectors. The transmit and receive waveforms follow the Consultative Committee for Space Data Systems (CCSDS) Optical Communications Coding and Synchronization Standard. A software model of the optical transmitter and receiver has also been implemented to predict performance of the optical test bed. This paper describes the transmitter and receiver prototypes as well as the system test configuration. System level tests results are presented and shown to align with predictions from software simulations. The validated software model can be used to in the future to reduce the design cycle of optical communications systems.

software defined radio↗
NASA NTRS2019

Few-Mode Fiber Coupled Superconducting Nanowire Single-Photon Detectors for Photon Efficient Optical Communications

The NASA Glenn Research Center's development of a high-photon efficiency real-time optical communications ground receiver has added superconducting nanowire single-photon detectors (SNSPDs) coupled with few-mode fibers (FMF). High data rate space-to-ground optical communication links require enhanced ground receiver sensitivity to reduce spacecraft transmitter constraints, and therefore require highly efficient coupling from fiber to detector. In the presence of atmospheric turbulence the received optical wave front can be severely distorted introducing higher-order spatial mode components to the received signal. To reduce mode filtering and mismatch loss and the resulting degradations to detector coupling efficiency, we explore the use of few-mode fiber coupling to commercial single-pixel SNSPDs. Graded index 20-µm few-mode fibers allow the commercial single pixel SNSPD's active area to couple with equal efficiency as single mode fibers. Here we determine detector characteristics such as count rate, detection efficiency, dark counts, and jitter, as well as detection efficiencies for higher-order fiber spatial modes. Additionally, we assess the laboratory performance of the detectors in an optical system which emulates future deep space optical communications links.

Vyhnalek, Brian E.↗
NASA NTRS2015

Proposal for a Sample Pilot Response Model

Regulations to establish operational and performance requirements for unmanned aircraft systems (UAS) are being developed by a consortium of government, industry and academic institutions. Those requirements will apply to the new detect and avoid (DAA) systems and other equipment necessary to integrate UAS with the National Airspace System (NAS) and are determined according to their contribution to the overall safety case for such an integration. In order to perform end-to-end verification and validation of DAA requirements, a committee accepted pilot response model is needed to emulate UAS pilots in closed-loop Monte Carlo fast-time simulations. This briefing focuses on background of the pilot model used in previous Airspace Concept Evaluation System studies, identification of strengths and weaknesses in that model, and proposals on model improvements. The briefing also looks to build consensus around a common architecture and assumptions for modeling the pilot for UAS DAA systems.

Santiago, Confesor↗
NASA NTRS2015

Enhancing the Traffic Management Advisor's Schedule by Time Advance

A time advance algorithm associated with the scheduling functionalities of the Traffic Management Advisor (TMA) for arrival flights is presented and evaluated. The algorithm enhances TMA's meter fix schedule by advancing the flights' Scheduled Time of Arrival (STA) by an amount that minimizes their systemic operating cost. The systemic operating cost leverages the inherent trade-off of time and fuel efficiency resident in the cost index of modern flight management systems. The resulting STAs are achievable by speeding up the leading flights from their desired nominal speed profiles. A key advantage of this approach is that it reduces systemic delay to tight groupings of arriving aircraft as well as increases sustained throughput of the operation. A fast-time, Monte Carlo simulation that emulates TMA's scheduling functionalities is performed for arrival flights to the Phoenix Airport to quantify the benefit of the time advance algorithm. Results show consistent time saving benefits, ranging from 3 to 50 minutes for 112 flights with varying levels of traffic congestion.

arrival traffic management↗
NASA NTRS2019

Planetary Rover Simulation for Lunar Exploration Missions

When planning planetary rover missions it is useful to develop intuition and skills driving in, quite literally, alien environments before incurring the cost of reaching said locales. Simulators make it possible to operate in environments that have the physical characteristics of target locations without the expense and overhead of extensive physical tests. To that end, NASA Ames and Open Robotics collaborated on a Lunar rover driving simulator based on the open source Gazebo simulation platform and leveraging ROS (Robotic Operating System) components. The simulator was integrated with research and mission software for rover driving, system monitoring, and science instrument simulation to constitute an end-to-end Lunar mission simulation capability. Although we expect our simulator to be applicable to arbitrary Lunar regions, we designed to a reference mission of prospecting in polar regions. The harsh lighting and low illumination angles at the Lunar poles combine with the unique reflectance properties of Lunar regolith to present a challenging visual environment for both human and computer perception. Our simulator placed an emphasis on high fidelity visual simulation in order to produce synthetic imagery suitable for evaluating human rover drivers with navigation tasks, as well as providing test data for computer vision software development.In this paper, we describe the software used to construct the simulated Lunar environment and the components of the driving simulation. Our synthetic terrain generation software artificially increases the resolution of Lunar digital elevation maps by fractal synthesis and inserts craters and rocks based on Lunar size-frequency distribution models. We describe the necessary enhancements to import large scale, high resolution terrains into Gazebo, as well as our approach to modeling the visual environment of the Lunar surface. An overview of the mission software system is provided, along with how ROS was used to emulate flight software components that had not been developed yet. Finally, we discuss the effect of using the high-fidelity synthetic Lunar images for visual odometry. We also characterize the wheel slip model, and find some inconsistencies in the produced wheel slip behaviour.

Allan, Mark↗
NASA NTRS2019

State Predictor of Classification Cognitive Engine Applied to Channel Fading

This study presents the application of machine learning (ML) to a space-to-ground communication link, showing how ML can be used to detect the presence of detrimental channel fading. Using this channel state information, the communication link can be used more efficiently by reducing the amount of lost data during fading. The motivation for this work is based on channel fading observed during on-orbit operations with NASA's Space Communication and Navigation (SCaN) testbed on the International Space Station (ISS). This paper presents the process to extract a target concept (fading and not-fading) from the raw data. The pre-processing and data exploration effort is explained in detail, with a list of assumptions made for parsing and labelling the dataset. The model selection process is explained, specifically emphasizing the benefits of using an ensemble of algorithms with majority voting for binary classification of the channel state. Experimental results are shown, highlighting how an end-to-end communication system can utilize knowledge of the channel fading status to identity fading and take appropriate action. With a laboratory testbed to emulate channel fading, the overall performance is compared to standard adaptive methods without fading knowledge, such as adaptive coding and modulation.

Fading↗
NASA NTRS2015

Radiant Heating Tests on Meteoritic Material

A new effort geared toward modeling the physics of meteor entry and break-up is underway at NASA Ames Research Center. This is part of a broader interdisciplinary effort on providing physics-based risk assessment models for potentially hazardous objects. As part of the entry modeling task we are seeking to improve our understanding of, among other things, the ablation of meteoric material during high speed entry into earths atmosphere. Meteoroid entry differs greatly in some key respects from spacecraft entry modeling. First, the aerothermal environment at these high velocities (18 km/s) is dominated by radiation. Second, meteoroids less than, say, 50m in size, will likely lose a significant portion of their mass during the high-speed atmospheric entry process due to vaporization as well as melting and spallation. The mass of the object, in turn, directly affects the amount of energy deposited in the atmosphere, and therefore the amount of damage done. Thus it is important for us to understand and be able to model this process in greater detail in order to assess the hazard posed by these objects.In this presentation, we first give an overview of the simple ablation models that are typically used in meteor entry calculations. Additionally, we will present a new model which utilizes a similar approach to what is typically done for spacecraft TPS response modeling. This uses an equilibrium assumption near the surface to compute the ablation rate, as is done in heritage material response codes. Next we describe the radiant heating experiment which uses a high-powered laser to emulate the radiation dominated heating environment experienced by meteoroids during atmospheric entry. The facility the Laser Hardened Material Evaluation Laboratory (LHMEL) permits us to expose samples of meteoritic material to heating rates in excess of 100kW/sq.cm. An overview of the experimental set-up and test plan for the initial exploratory campaign at this facility will be given. Then we present both qualitative and quantitative results from this initial test series. Comparisons between predicted and measured ablation rates suggest that there may be significant blockage of the incident beam by the ablation plume. Furthermore, melt is shown to be a significant ablation mechanics, even at high heating rates (16 kW/sq.cm). Finally, comparisons between the phenomenology of the ablation of terrestrial rocks namely, basalt -- to that of meteorites show very different behavior. This is shown to likely be due in part to the effect of composition on the melt viscosity.

Stern, Eric↗