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NASA NTRS2017

Developing Model Benchtop Systems for Microbial Experimental Evolution

Understanding how microbes impact an ecosystem has improved through advances of molecular and genetic tools, but creating complex systems that emulate natural biology goes beyond current technology. In fact, many chemical, biological, and metabolic pathways of even model organisms are still poorly characterized. Even then, standard laboratory techniques for testing microbial impact on environmental change can have many drawbacks; they are time-consuming, labor intensive, and are at risk of contamination. By having an automated process, many of these problems can be reduced or even eliminated. We are developing a benchtop system that can run for long periods of time without the need for human intervention, involve multiple environmental stressors at once, perform real-time adjustments of stressor exposure based on current state of the population, and minimize contamination risks. Our prototype device allows operators to generate an analogue of real world micro-scale ecosystems that can be used to model the effects of disruptive environmental change on microbial ecosystems. It comprises of electronics, mechatronics, and fluidics based systems to control, measure, and evaluate the before and after state of microbial cultures from exposure to environmental stressors. Currently, it uses four parallel growth chambers to perform tests on liquid cultures. To measure the population state, optical sensors (LED/photodiode) are used. Its primary selection pressure is UV-C radiation, a well-studied stressor known for its cell- and DNA-damaging effects and as a mutagen. Future work will involve improving the current growth chambers, as well as implementing additional sensors and environmental stressors into the system. Full integration of multiple culture testing will allow inter-culture comparisons. Besides the temperature and OD sensors, other types of sensors can be integrated such as conductivity, biomass, pH, and dissolved gasses such as CO and O. Additional environmental stressor systems like temperature (extreme heat or cold), metal toxicity, and other forms of radiation will increase the scale and testing range.

Developing↗
NASA NTRS2018

SLS Navigation Model-Based Design Approach

The SLS Program chose to implement a Model-based Design and Model-based Requirements approach for managing component design information and system requirements. This approach differs from previous large-scale design efforts at Marshall Space Flight Center where design documentation alone conveyed information required for vehicle design and analysis and where extensive requirements sets were used to scope and constrain the design. The SLS Navigation Team has been responsible for the Program-controlled Design Math Models (DMMs) which describe and represent the performance of the Inertial Navigation System (INS) and the Rate Gyro Assemblies (RGAs) used by Guidance, Navigation, and Controls (GN&C). The SLS Navigation Team is also responsible for the navigation algorithms. The navigation algorithms are delivered for implementation on the flight hardware as a DMM. For the SLS Block 1-B design, the additional GPS Receiver hardware is managed as a DMM at the vehicle design level. This paper provides a discussion of the processes and methods used to engineer, design, and coordinate engineering trades and performance assessments using SLS practices as applied to the GN&C system, with a particular focus on the Navigation components. These include composing system requirements, requirements verification, model development, model verification and validation, and modeling and analysis approaches. The Model-based Design and Requirements approach does not reduce the effort associated with the design process versus previous processes used at Marshall Space Flight Center. Instead, the approach takes advantage of overlap between the requirements development and management process, and the design and analysis process by efficiently combining the control (i.e. the requirement) and the design mechanisms. The design mechanism is the representation of the component behavior and performance in design and analysis tools. The focus in the early design process shifts from the development and management of design requirements to the development of usable models, model requirements, and model verification and validation efforts. The models themselves are represented in C/C++ code and accompanying data files. Under the idealized process, potential ambiguity in specification is reduced because the model must be implementable versus a requirement which is not necessarily subject to this constraint. Further, the models are shown to emulate the hardware during validation. For models developed by the Navigation Team, a common interface/standalone environment was developed. The common environment allows for easy implementation in design and analysis tools. Mechanisms such as unit test cases ensure implementation as the developer intended. The model verification and validation process provides a very high level of component design insight. The origin and implementation of the SLS variant of Model-based Design is described from the perspective of the SLS Navigation Team. The format of the models and the requirements are described. The Model-based Design approach has many benefits but is not without potential complications. Key lessons learned associated with the implementation of the Model Based Design approach and process from infancy to verification and certification are discussed

Oliver, T. Emerson↗
NASA NTRS2018

Remote Sensing, Uncertainty Quantification, and a Theory of Data Systems; Workshop Report

The purpose of the workshop was to invite statisticians, applied mathematicians, computer scientists, data system architects, experts in remote sensing technology, and Climate and Earth System scientists to review, discuss, and plan research on issues related to large-scale, efficient analysis of distributed data using spatial statistical methods. Our motivation in organizing this event was to catalyze interchange among experts on the fast-emerging problem of analysis of distributed data. As part of SAMSI's 2017-2018 Program on Mathematical and Statistical Methods for Climate and the Earth System, a Working Group on Remote Sensing was established to address statistical and mathematical research problems in the analysis of remote sensing data. The Working Group has five subgroups: 1) Spatial Retrieval Methodology (the so-called \Spatial-X" subgroup); 2) Spatial Analysis for Hyperspectral Data (the so-called \Spatial-Y" subgroup); 3) Emulators for Complex Forward Models; 4) Optimization for Remote Sensing Retrievals; and 5) Theory of Data Systems (ToDS). The ToDS subgroup spent the first half of this academic year formulating a framework in which to consider the joint problem of a) optimizing statistical methods for environments where data are distributed and too large to move to a central location, and b) the design of data system infrastructures within which to implement those statistical methods. To x ideas, the Workshop focused on spatial statistical methods. To date there are many new spatial statistical methods designed with massive data sets in mind, in the literature. However, very few have been implemented for remote sensing data, and none have been implemented in operational settings like those used by NASA and NOAA. A major impediment to their use in these cases is that the data are not only massive, but are stored in different physical locations. These data must be brought together in some way in order to estimate spatial covariance functions, but moving data to a central location for analysis is tedious at best and impossible at worst. Some remote data reduction is almost certainly necessary, but how much? What are the consequences for inference? The fundamental issue underlying these questions is how to navigate the trade-space between costs and uncertainty in the estimates or inferences that are ultimately produced.

Braverman, Amy↗
NASA NTRS2018

Independent Component Analysis for Preprocessing Optical Signals in Support of Multi-User Communication

In an effort to construct an optical transceiver for supporting multi-point communication, researchers developed large field-of-view FSO transceivers with wide apertures for facilitating rapid acquisition and optical signal tracking. The design is constructed from fiber-bundle(s) intelligently arranged to provide multiple optical pathways within a single transceiver, with each pathway limited to a particular optical directionality. Because designs with wide apertures are susceptible to receiving several optical signals simultaneously from multiple transmitters, accurately decoding individual signals and achieving true multi-user communication can be difficult. The work detailed in this paper investigated the use of blind source separation techniques, mainly independent component analysis (ICA), to estimate transmitted signals from their observed, combined mixtures sans information. Effects of signal power, format, data rate, wavelength, and turbulence severity on signal separation, as well as signal demodulation accuracy was also analyzed using experimental optical signals and corresponding mixtures. Signal generators, laser sources, turbulence emulator, beam profiler, photodiodes, and oscilloscopes comprised the experiment setup. Optical signals with varying power, format, and wavelength were generated and propagated through turbulence with varying degree severity, and finally received by the fiber-bundle-based optical transceiver. Results demonstrate that an ICA technique can accurately process the combined signals into corresponding individual components. The investigation further identified signal characteristics (e.g., power levels, format, data rate) and turbulence severity under which ICA is unable to accurately perform signal separations.

Optics↗
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↗