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NASA NTRS2018

The Photochemistry of Purine in Ice Analogs Relevant to Dense Interstellar Clouds

The aromatic nitrogen heterocyclic compound purine is the core structural framework of many important biomolecules, particularly nucleobases. Purine and purine derivatives have been observed in carbonaceous chondrites, and it has been hypothesized that the exogenous delivery of these compounds, along with many other biologically relevant compounds, may have played a role in the emergence of life. Numerous experiments in our laboratory have demonstrated that the nucleobases used by life to encode genetic material could have been produced abiotically under astrophysically relevant conditions. Specically, the UV photoprocessing of pyrimidine and purine in simple ices of astrophysical interest has resulted in the production of all five biological nucleobases, namely, uracil (RNA), cytosine (RNA and DNA), thymine (DNA), adenine (RNA and DNA), and guanine (RNA and DNA). Additionally, follow-up work has examined the photochemistry of pyrimidine in more complex astrophysical ice mixtures to better understand the formation of these compounds under realistic conditions. In this work, we examine the photochemistry of purine in more complex ices of astrophysical interest and compare our results with those from simpler ice mixtures. We also examine the effects of competing parallel synthesis of organic compounds in the ices (unrelated to purine). Finally, we discuss the astrophysical and astrobiological implications of our findings.

Molecular Processes↗
NASA NTRS2018

EcAMSat: Small Satellite to Examine E. coli's Response in Microgravity to the Antibiotic Gentamicin

We have successfully flown the EcAMSat (Escherichia coli Antimicrobial Satellite) free-flyer mission. This was a 6U small satellite that autonomously conducted an experiment in low Earth orbit to explore the impact of the space environment on antibiotic resistance in uropathogenic E. coli (UPEC) and the role a particular sigma factor plays in the response. After being held in stasis during transport to orbit, two strains†a wildtype UPEC and an isogenic mutant with a deleted gene that encodes a sigma factor†were grown to stationary phase in a fluidic card inside EcAMSat's payload, then incubated with three concentrations of the antibiotic gentamicin. The payload then administered alamarBlue®, a redox indicator, into all wells of the fluidic card. The cells were then incubated for 144 hours and metabolic activity was measured optically using the payloads' LED and detector system. Data were then telemetered to the ground and compared to a control experiment conducted in an identical satellite in a lab. The results of this experiment will help us better understand important therapeutic targets for treating bacterial infections on Earth and in space. Such targets are particularly relevant to deep-space and long-duration missions where crew may be more susceptible to infection and treatments for them may work differently.

antibiotic↗
NASA NTRS2019

Energetic Particles of Cosmic Accelerators II: Active Galactic Nuclei and Gamma-ray Bursts

The high-energy universe has revealed that energetic particles are ubiquitous in the cosmos and play a vital role in the cultivation of cosmic environments on all scales. Our pursuit of more than a century to uncover the origins and fate of these cosmic energetic particles has given rise to some of the most interesting and challenging questions in astrophysics. Within our own galaxy, we have seen that energetic particles engage in a complex interplay with the galactic environment and even drive many of its key characteristics (for more information, see the first white paper in this series). On cosmological scales, the energetic particles supplied by the jets of active galactic nuclei (AGN) are an important source of energy for the intracluster and intergalactic media, providing a mechanism for regulating star formation and black hole growth and cultivating galaxy evolution (AGN feedback). Gamma-ray burst (GRB) afterglows encode information about their circumburst environment, which has implications for massive stellar winds during previous epochs over the stellar lifecycle. As such, GRB afterglows provide a means for studying very high-redshift galaxies since GRBs can be detected even if their host galaxy cannot. It has even been suggest that GRB could be used to measure cosmological distance scales if they could be shown to be standard candles. Though they play a key role in cultivating the cosmological environment and/or enabling our studies of it, there is still much we do not know about AGNs and GRBs, particularly the avenue in which and through which they supply radiation and energetic particles, namely their jets. Despite the enormous progress in particle-in-cell and magnetohydrodynamic simulations, we have yet to pinpoint the processes involved in jet formation and collimation and the conditions under which they can occur. For that matter, we have yet to identify the mechanism(s) through which the jet accelerates energetic particles – is it the commonly invoked diffusive shock acceleration process or is another mechanism, such as magnetic reconnection, required? Do AGNs and GRBs accelerate hadrons, and if so, do they accelerate them to ultra-high energies and are there high-energy neutrinos associated with them? MeV gamma-ray astronomy, enabled by technological advances that will be realized in the coming decade, will provide a unique and indispensable perspective on the persistent mysteries of the energetic universe. This White Paper is the second of a two-part series highlighting the most well-known high-energy cosmic accelerators and contributions that MeV gamma-ray astronomy will bring to understanding their energetic particle phenomena. Specifically, MeV astronomy will: 1. Determine whether AGNs accelerate CRs to ultra-high energies; 2. Provide the missing pieces for the physics of the GRB prompt emission; 3. Measure magnetization in cosmic accelerators and search for acceleration via reconnection.

Venters, Tonia M.↗
NASA NTRS2018

A Formally Verified Floating-Point Implementation of the Compact Position Reporting Algorithm

The Automatic Dependent Surveillance-Broadcast (ADS-B) system allows aircraft to communicate their current state, including position and velocity information, to other aircraft in their vicinity and to ground stations. The Compact Position Reporting (CPR) algorithm is the ADS-B module responsible for the encoding and decoding of aircraft positions. CPR is highly sensitive to computer arithmetic since it heavily relies on functions that are intrinsically unstable such as floor and modulo. In this paper, a formally-verified double-precision floating-point implementation of the CPR algorithm is presented. The verification proceeds in three steps. First, an alternative version of CPR, which reduces the floating-point rounding error is proposed. Then, the Prototype Verification System (PVS) is used to formally prove that the ideal real-number counterpart of the improved algorithm is mathematically equivalent to the standard CPR definition. Finally, the static analyzer Frama-C is used to verify that the double-precision implementation of the improved algorithm is correct with respect to its operational requirement. The alternative algorithm is currently being considered for inclusion in the revised version of the ADS-B standards document as the reference implementation of the CPR algorithm.

Laura Titolo↗
NASA NTRS2018

GEONEX: Progressive Conditional Generative Adversarial Training Using Transfer learning

Obtaining accurate segmentation on large scale images is an open problem in deep learning. The main problem is the amount of labeled data that exists for large scale images. Traditionally, the common solution to this problem is to crop the large images into smaller images to increase the amount of available data and train a Conditional Generative Adversarial Network (CGAN). CGANs are currently the state of the art in image to image translation and provide better accuracy than the traditional method of training a encoder based conv-net architecture to minimize the loss at each pixel. This method can produce noisy and discontinuous images with inaccurate results. We seek to solve this problem by utilizing the concepts of transfer learning and progressive training to create a CGAN that can segment large scale images with a limited amount of labeled data. In transfer learning we recognize that many learned features are applicable to many classes from multiple domains. This introduces the concept of feature reusability, which is the basis for finetuning. Progressive training got its start in training models on the same images at different resolutions. In this work we instead train a GAN on increasing image scales by transferring the weights from the smaller scales to the larger scales. The learned features at the smaller scales are continually reused and applied to larger scales to create a CGAN that can perform accurate segmentation on large scale images. We apply this method to detect building footprints on very high-resolution overhead imagery (e.g Digital Globe and high resolution airborne platforms).

GEONEX↗
NASA NTRS2016

A FDTD Solution of Scattering of Laser Beam with Orbital Angular Momentum by Dielectric Particles: Far-Field Characteristics

Electromagnetic (EM) beams with orbital angular momentum (OAM) may have great potential applications in communication technology and in remote sensing of the Earth-atmosphere system and outer planets. Study of their interaction with optical lenses and dielectric or metallic objects, or scattering of them by particles in the Earth-atmosphere system, is a necessary step to explore the advantage of the OAM EM beams. In this study, the 3-dimensional (3D) scattered-field (SF) finite-difference time domain (FDTD) technique with the convolutional perfectly matched layer (CPML) absorbing boundary conditions (ABC) is applied to calculate the scattering of the purely azimuthal (the radial mode number is assumed to be zero) Laguerre–Gaussian (LG) beams with the OAM by dielectric particles. We found that for OAM beam׳s interaction with dielectric particles, the forward-scattering peak in the conventional phase function (P11) disappears, and light scattering peak occurs at a scattering angle of ~15° to 45°. The disappearance of forward-scattering peak means that, in laser communications most of the particle-scattered noise cannot enter the receiver, thus the received light is optimally the original OAM-encoded signal. This feature of the OAM beam also implies that in lidar remote sensing of the atmospheric particulates, most of the multiple-scattering energy will be off lidar sensors, and this may result in an accurate profiling of particle layers in the atmosphere or in the oceans by lidar, or even in the ground when a ground penetration radar (GPR) with the OAM is applied. This far-field characteristics of the scattered OAM light also imply that the optical theorem, which is derived from plane-parallel wave scattering case and relates the forward scattering amplitude to the total cross section of the scatterer, is invalid for the scattering of OAM beams by dielectric particles.

Electromagnetic beams↗
NASA NTRS2020

CoCoSim, a Code Generation Framework for Control/command Applications: An Overview of CoCoSim for Multi-Periodic Discrete Simulink Models

We present CoCoSim, a framework to support the design, code generation and analysis of discrete dataflow model expressed in Simulink. In this work, we specifically focus on the analysis and code generation of multi-periodic systems. For that CoCoSim provides two complementary approaches: the first amounts to encode the multiperiodic semantics in a pure-synchronous one – à la Lustre–, enabling the use of model checker for verifying properties. The second provides a faithful code generation into multiple communicating (mono)synchronous components – à la Prelude– that can be then simulated or embedded in the final platform with any real-time scheduler. These approaches have been experimented in various settings.

Bourbouh, Hamza↗
NASA NTRS2020

Probing the Evolutionary History of Comets: an Investigation of the Hypervolatiles CO, CH4, and C2H6 in the Jupiter-Family Comet 21P/Giacobini–Zinner

Understanding the cosmogonic record encoded in the parent volatiles stored in cometary nuclei requires investigating whether evolution (thermal or otherwise) has modified the composition of short-period comets during successive perihelion passages. As the most volatile molecules systematically observed in comets, the abundances of CO, CH4, and C2H6 in short-period comets may serve to elucidate the interplay between natal conditions and post-formative evolution in setting present-day composition, yet secure measurements of CO and CH4 in Jupiter-family comets (JFCs) are especially sparse. The highly favorable 2018 apparition of JFC 21P/Giacobini–Zinner enabled a sensitive search for these "hypervolatiles" in a prototypical carbon-chain depleted comet. We observed 21P/Giacobini–Zinner with the iSHELL spectrograph at the NASA Infrared Telescope Facility on four pre-perihelion dates, two dates near-perihelion, and one post-perihelion date. We obtained detections of CO, CH4, and C2H6 simultaneously with H2O on multiple dates. We present rotational temperatures, production rates, and mixing ratios. Combined with previous work, our results may indicate that the hypervolatile coma composition of 21P/Giacobini–Zinner was variable across apparitions as well as within a particular perihelion passage, yet the spread in these measurements is a relatively small fraction of the variation in each molecule from comet to comet. We discuss the implications of our measured hypervolatile content of 21P/Giacobini–Zinner for the evolution of JFCs, and place our results in the context of findings from the Rosetta mission and ground-based studies of comets.

Roth, Nathan X.↗
NASA NTRS2019

Exploring Network-Related Optimization Problems Using Quantum Heuristics

Network-related connectivity optimization problems are underlying a wide range of applications and are also of high computational complexity. We consider studying network optimization problems using two types of quantum heuristics.One is quantum annealing, and the other Quantum Alternating Operator Ansatz, an extension of the Quantum Approximate Optimization Algorithms for gate-model quantum computation, in which a cost-function based unitary and a non-commuting mixing unitary are applied alternately. We present problem mappings for problems of finding the spanning-tree or spanning-graph of a graph that optimizes certain costs, and a variant that further requires the spanning-tree be degree-bounded. With quantum annealing, all constraints are cast into penalty terms in the cost Hamiltonian, and the solution is encoded as the ground state of the Hamiltonian. We provide three mappings to the quadratic unconstrained binary optimization (QUBO) form, compare the resource requirements, and analyze the tradeoffs. For QAOA, we give special focus on the design of mixers based on the constraints presented in the problem, such that the system evolution remains in a subspace of the full Hilbert space where all constraints are satisfied. In the spanning-tree problem, one such hard constraint is that a mixer applied to a spanning-tree needs also be a spanning tree. This involves checking the connectivity of a subgraph, which is a global condition common for most network-related problems. We show how this feature can be efficiently represented in the mixer in a quantum coherent way, based on manipulation of a descendant-matrix and an adjacent matrix. We further develop a mixer for the spanning-graphs based on the spanning-tree mixer.

Wang, Zhihui↗
NASA NTRS2020

Constraining Impact Melt Volumes in Young Simple Lunar Craters

Impact cratering is the most ubiquitous geomorphological phenomenon in the solar system. Ponds of once molten material often occupy the bottoms of lunar craters. The melt was originally produced during the intense heat and pressure of impact, and has the potential to give us insight into the physics of impact cratering, as well as potentially encoding information about the materials comprising impacted surfaces. Here, we seek to define a methodology capable of placing an upper bound on simple lunar impact crater melt volumes using Lunar Reconnaissance Orbiter NAC and Kaguya Terrain Camera digital terrain models (DTMs). One prediction we seek to test is that melt production increases with porosity of impacted materials. Previous research suggests impact craters in the more porous, anorthositic highlands contain impact melt ponds of greater volume than those in less porous basaltic maria in craters of comparable diameters.

Chandra, R.↗
NASA NTRS2019

A Learning-Based Guidance Selection Mechanism for a Formally Verified Sense and Avoid Algorithm

This paper describes a learning-based strategy for selecting conflict avoidance maneuvers for autonomous unmanned aircraft systems. The selected maneuvers are provided by a formally verified algorithm and they are guaranteed to solve any impending conflict under general assumptions about aircraft dynamics. The decision-making logic that selects the appropriate maneuvers is encoded in a stochastic policy encapsulated as a neural network. The network’s parameters are optimized to maximize a reward function. The reward function penalizes loss of separation with other aircraft while rewarding resolutions that result in minimum excursions from the nominal flight plan. This paper provides a description of the technique and presents preliminary simulation results.

Balachandran, Swee↗
NASA NTRS2019

Study network-related optimization problems using quantum alternating optimization ansatz

Network-related connectivity optimization problems are underlying a wide range of applications and are also of high computational complexity. We consider studying network optimization problems using two types of quantum heuristics. One is quantum annealing, and the other Quantum Alternating Operator Ansatz, an extension of the Quantum Approximate Optimization Algorithms for gate-model quantum computation, in which a cost-function based unitary and a non-commuting mixing unitary are applied alternately. We present problem mappings for problems of finding the spanning-tree or spanning-graph of a graph that optimizes certain costs, and a variant that further requires the spanning-tree be degree-bounded. With quantum annealing, all constraints are cast into penalty terms in the cost Hamiltonian, and the solution is encoded as the ground state of the Hamiltonian. We provide three mappings to the quadratic unconstrained binary optimization (QUBO) form, compare the resource requirements, and analyze the tradeoffs. For QAOA, we give special focus on the design of mixers based on the constraints presented in the problem, such that the system evolution remains in a subspace of the full Hilbert space where all constraints are satisfied. In the spanning-tree problem, one such hard constraint is that a mixer applied to a spanning-tree needs also be a spanning tree. This involves checking the connectivity of a subgraph, which is a global condition common for most network-related problems. We show how this feature can be efficiently represented in the mixer in a quantum coherent way, based on manipulation of a descendant-matrix and an adjacent matrix. We further develop a mixer for the spanning-graphs based on the spanning-tree mixer.

Zhihui Wang↗
NASA NTRS2020

Convolutional Neural Network for Transition Modeling Based on Linear Stability Theory

Transition prediction is an important aspect of aerodynamic design because of its impact on skin friction and potential coupling with flow separation characteristics. Traditionally, the modeling of transition has relied on correlation-based empirical formulas based on integral quantities such as the shape factor of the boundary layer. However, in many applications of computational fluid dynamics, the shape factor is not straightforwardly available or not well-defined. We propose using the complete velocity profile along with other quantities (e.g., frequency, Reynolds number) to predict the perturbation amplification factor. While this can be achieved with regression models based on a classical fully connected neural network, such a model can be computationally more demanding. We propose a novel convolutional neural network inspired by the underlying physics as described by the stability equations. Specifically, convolutional layers are first used to extract integral quantities from the velocity profiles, and then fully connected layers are used to map the extracted integral quantities, along with frequency and Reynolds number, to the output (amplification ratio). Numerical tests on classical boundary layers clearly demonstrate the merits of the proposed method. More importantly, we demonstrate that, for Tollmien-Schlichting instabilities in two-dimensional, low-speed boundary layers, the proposed network encodes information in the boundary layer profiles into an integral quantity that is strongly correlated to a well-known, physically defined parameter – the shape factor.

Laminar-turbulent transition↗
NASA NTRSDate not supplied

Urban Air Mobility Airspace Dynamic Density Safety Metric

NASA’s Advanced Air Mobility (AAM) project focuses on enabling emerging aviation markets by accelerating development of safe, high-volume flight operations.[1] It involves development and validation of vehicles, airspace, and automation changes required to support concepts such as Urban Air Mobility, a vision for electric or hybrid electric, vertical or short take-off and landing vehicles that can transport passengers and cargo over an urban environment.[2] NASA’s System Wide Safety (SWS) project is coordinating with AAM by understanding how safety could be affected by these emerging operations. We approach the assessment of airspace safety by identifying threats to operations and then monitoring and predicting the evolution of those threats encoded in a set of safety metrics.[3] Toward this end, we have been developing a dynamic density metric to predict the likelihood of vehicle conflicts in the airspace.

Dynamic Density↗
NASA NTRSDate not supplied

The Pheno-Evo Model: Evolution of Microbial Phenotypic Diversity in 2D Space

Biologists appreciate microbes for their simplicity and predictability: we assume that a population of genetically identical cells in a uniform environment will all behave the same way. While this assumption is often useful, it is also often wrong. Not only might microbes in a clonal population act differently from one another, the differences may be categorical (growing v. non-growing; toxin-sensitive v. tolerant), and this diversity might be an evolved trait conferring increased fitness on the population. How does such phenotypic heterogeneity evolve? And how does a population find the optimal distribution of phenotypes for a given environment? Efforts at modeling microbial phenotypic heterogeneity often focus on populations with two discrete phenotypic types; phenotypes in continuous distributions remain poorly explored. To address this gap and to explore the role of spatial relationships, we use agent-based modeling to simulate a phenotypically diverse population of microbial cells evolving in the presence of periodic toxic stress. Cells on patches in a 2D grid may degrade toxin, suffer damage from toxin, switch phenotype, and reproduce. An individual's phenotype its toxin degradation rate-- for which there is a tradeoff with reproduction-- and the genotype encodes the distribution of phenotypic values in the population. The rate of toxin diffusion determines how individuals influence their neighbors' environments. We examine the effects of toxin concentration, diffusion rate, and environmental predictability on the survival success of populations with different phenotype distributions, and populations' evolutionary trajectories when phenotype distribution is allowed to evolve. We conduct all simulations on the platform NetLogo, which provides a friendly interface allowing users of any experience level to tweak parameters and run their own simulations. We have also created custom tools in R for analyzing and visualizing the results of multiple runs.

microbial evolution↗
NASA NTRS2021

Waypoint Following Dynamics of a Quaternion Error Feedback Attitude Control System

Closed-loop attitude steering can be used to implement alternative attitude maneuvers by using a conventional attitude control system to track a non-standard attitude profile sampled as a series of sequential commands. The idea has been employed in practice to perform zero-propellant maneuvers on the International Space Station and minimum time maneuvers on NASA’s TRACE space telescope. A challenge for operational implementation of the idea is the finite capacity of a space vehicle’s command storage buffer. One approach to mitigate the problem is to downsample-and-hold the attitude commands so that the attitude control system to follows a set of waypoints. This paper explores the waypoint following dynamics of a quaternion error feedback control law for downsample-and-hold. It is shown that downsample-and-hold induces a ripple between downsamples that causes the satellite angular rate to significantly overshoot the desired limit. Analysis in the z-domain is carried out in order to understand the phenomenon. An interpolating Chebyshev-type filter is proposed that allows attitude commands to be encoded in terms of a small set of filter coefficients. Using the interpolating filter, commands can be issued at the ACS rate but with significantly reduced memory requirements. The attitude control system of NASA’s Lunar Reconnaissance Orbiter is used as an example to illustrate the behavior of a practical attitude control system.

Mark Karpenko↗
NASA NTRS2020

Charged Particle Dynamics in the Lunar Environment

As the Artemis Program streams forward, organizations of scientists and engineers across the country have been coming together to solve the complex network of problems to once again achieve the milestone of successfully touching down on the moon. This project is no exception, and it has been an honor to work with the Electrostatics and Surface Physics Laboratory (ESPL), a lab within the Exploration Research and Technology Programs’ Spaceport Technologies Office (UB-G) located at the National Aeronautics and Space Administration at Kennedy Space Center (NASA KSC). The authors and their mentor James R. Phillips III3, in tandem with researchers from the Astrodynamics and Space Robotics Laboratory (ASRL) at the University of Central Florida (UCF), have been working on creating a state-of-the-art (SOA) granular gas dynamics model for particulate contamination prevention and mitigation purposes. In essence, the underlying objective for this project is to more accurately model the resulting electrodynamic interactions between lunar regolith grains with applications to dust mitigation and rocket engine plume surface interactions. To achieve this goal, the team has been expanding upon existing open source classical molecular dynamics code developed by Sandia National Laboratories (SNL). The following reports on the details of the problem at hand as well as the contributions that the authors have made towards resolution, including but not limited to the encoding of physical attributes and interactions for non-spherical polydisperse particle distributions within various bed geometries and preemptive data analysis implementations. Significant data analysis processes were utilized, and several original algorithms were created to perform critical evaluations, resulting in only a 0.006% error in discrepancy.

Annelisa B Esparza↗
NASA NTRSDate not supplied

Machine Learning Explainability and Transferability for Path Navigation

Deep neural networks are powerful tools for machine perception. Unfortunately their decisions are difficult to explain due to the complexity and size of the networks. Previously we have alleviated this issue by using the representational portion of a deep neural network and combining it with a k-nearest neighbor (KNN) classifier. Through inspection of the decisions made by the KNN, we can directly see the training data responsible for the decisions, allowing us to determine the quality of the overall decision and the quality of the representational layer of the deep NN. While the technique worked well, it requires tens of thousands of latent vectors to be stored for classification. In addition, it lacks the ability to show how parts of an image influence the classification decision. Here we address these issues by 1) Using a radial basis function network (RBFN) in place of the KNN allowing far fewer images to be used in deployment and 2) Using an auto encoder network for explainability. In addition to these techniques, we examine the effects of transfer learning to determine that results are robust. All results are tested on a domain where an unmanned aerial vehicle (UAV) navigates a forest trail through a single camera.

explainable machine learning↗