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NASA NTRS2008

Facilitating Analysis of Multiple Partial Data Streams

Robotic Operations Automation: Mechanisms, Imaging, Navigation report Generation (ROAMING) is a set of computer programs that facilitates and accelerates both tactical and strategic analysis of time-sampled data especially the disparate and often incomplete streams of Mars Explorer Rover (MER) telemetry data described in the immediately preceding article. As used here, tactical refers to the activities over a relatively short time (one Martian day in the original MER application) and strategic refers to a longer time (the entire multi-year MER missions in the original application). Prior to installation, ROAMING must be configured with the types of data of interest, and parsers must be modified to understand the format of the input data (many example parsers are provided, including for general CSV files). Thereafter, new data from multiple disparate sources are automatically resampled into a single common annotated spreadsheet stored in a readable space-separated format, and these data can be processed or plotted at any time scale. Such processing or plotting makes it possible to study not only the details of a particular activity spanning only a few seconds, but also longer-term trends. ROAMING makes it possible to generate mission-wide plots of multiple engineering quantities [e.g., vehicle tilt as in Figure 1(a), motor current, numbers of images] that, heretofore could be found only in thousands of separate files. ROAMING also supports automatic annotation of both images and graphs. In the MER application, labels given to terrain features by rover scientists and engineers are automatically plotted in all received images based on their associated camera models (see Figure 2), times measured in seconds are mapped to Mars local time, and command names or arbitrary time-labeled events can be used to label engineering plots, as in Figure 1(b).

Maimone, Mark W.↗

NASA NTRS2010

Fisk-Gloeckler Suprathermal Proton Spectrum in the Heliosheath and the Local Interstellar Medium

Convergence of suprathermal keV-MeV proton and ion spectra approximately to the Fisk-Gloeckler (F-G) form j(E) = j(sub 0) E(sup -1.5) in Voyager land 2 heliosheath measurements is suggestive of distributed acceleration in Kolmogorov turbulence which may extend well beyond the heliopause into the local interstellar medium (LISM). Turbulence of this type is already indicated by interstellar radio scintillation measurements of electron density power spectra. Previously published extrapolations (Cooper et al., 2003, 2006) of the LISM proton spectrum from eV to GeV energies are highly consistent with the F-G power-law and further indicative of such turbulence and LISM effectiveness of the F-G cascade acceleration process. The LISM pressure computed from this spectrum well exceeds that from current estimates for the LISM magnetic field, so exchange of energy between the protons and the magnetic field would likely have a strong role in evolution of the turbulence as per the F-G theory and as long ago proposed for cosmic ray energies by Parker and others. Pressure-dependent estimates of the LISM field strength should not ignore this potentially strong and even dominant contribution from the plasma. Presence of high-beta suprathermal plasma on LISM field lines could significantly affect interactions with the heliospheric outer boundary region and might potentially account for distributed and more discrete features in ongoing measurements of energetic neutral emission from the Interstellar Boundary Explorer (IBEX) mission.

Cooper, John F.↗

NASA NTRS2010

A Study of Fluid Interface Configurations in Exploration Vehicle Propellant Tanks

The equilibrium shape and location of fluid interfaces in spacecraft propellant tanks while in low-gravity is of interest to system designers, but can be challenging to predict. The propellant position can affect many aspects of the spacecraft such as the spacecraft center of mass, response to thruster firing due to sloshing, liquid acquisition, propellant mass gauging, and thermal control systems. We use Surface Evolver, a fluid interface energy minimizing algorithm, to investigate theoretical equilibrium liquid-vapor interfaces for spacecraft propellant tanks similar to those that have been considered for NASA's new class of Exploration vehicles. The choice of tank design parameters we consider are derived from the NASA Exploration Systems Architecture Study report. The local acceleration vector employed in the computations is determined by estimating low-Earth orbit (LEO) atmospheric drag effects and centrifugal forces due to a fixed spacecraft orientation with respect to the Earth or Moon, and rotisserie-type spacecraft rotation. Propellant/vapor interface positions are computed for the Earth Departure Stage and Altair lunar lander descent and ascent stage tanks for propellant loads applicable to LEO and low-lunar orbit. In some of the cases investigated the vapor ullage bubble is located at the drain end of the tank, where propellant management device hardware is often located.

Zimmerli, Gregory A.↗

NASA NTRS2018

Parallelization of a Six Degree of Freedom Entry Vehicle Trajectory Simulation Using OpenMP and OpenACC

The art and science of writing parallelized software, using methods such as Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC), is dominated by computer scientists. Engineers and non-computer scientists looking to apply these techniques to their project applications face a steep learning curve, especially when looking to adapt their original single threaded software to run multi-threaded on graphics processing units (GPUs). There are significant changes in mindset that must occur; such as how to manage memory, the organization of instructions, and the use of if statements (also known as branching). The purpose of this work is twofold: 1) to demonstrate the applicability of parallelized coding methodologies, OpenMP and OpenACC, to tasks outside of the typical large scale matrix mathematics; and 2) to discuss, from an engineer’s perspective, the lessons learned from parallelizing software using these computer science techniques. This work applies OpenMP, on both multi-core central processing units (CPUs) and Intel® Xeon Phi™ 7210, and OpenACC on GPUs. These parallelization techniques are used to tackle the simulation of thousands of entry vehicle trajectories through the integration of six degree of freedom (DoF) equations of motion (EoM). The forces and moments acting on the entry vehicle, and used by the EoM, are estimated using multiple models of varying levels of complexity. Several benchmark comparisons are made on the execution of six DoF trajectory simulation: single thread Intel® Xeon® E5-2670 CPU, multi-thread CPU using OpenMP, multi-thread Xeon Phi™ 7210 using OpenMP, and multi-thread NVIDIA® Tesla® K40 GPU using OpenACC. These benchmarks are run on the Pleiades Supercomputer Cluster at the National Aeronautics and Space Administration (NASA) Ames Research Center (ARC), and a Xeon Phi™ 7210 node at NASA Langley Research Center (LaRC).

Green, Justin S.↗

NASA NTRSDate not supplied

Trajectory Simulation for Air Traffic Management Employing a Multirotor Urban Air Mobility Aircraft Model

This paper describes a point-mass model of a multirotor electric vertical takeoff and landing aircraft designed to carry up to five passengers and a pilot. The model employs thrust, thrust vector angle and bank angle as controls, which are computed in part using the acceleration, heading-angle rate, and flight-path-angle rate generated by control systems in response to speed, heading angle, and flight-path angle commands. The equations of motion are integrated forward in time using the controls needed for following the desired vertical climb/descent and speed profiles, and horizontal path and cruise speed in the presence of wind to generate the flight trajectory.

trajectory simulation, urban air mobility aircraft↗

NASA NTRSDate not supplied

QuAIL Tools for Benchmarking, Analysis and Quantum Algorithm Development

HybridQ and PySA are open-source tools developed by NASA to support benchmarking, analysis and quantum algorithm development in areas such as simulation, optimization and machine learning. These tools leverage classical hardware acceleration via high-performance computing CPU and GPU architectures and support high-performance computing. HybridQ is a highly extensible platform designed to provide a common framework to integrate multiple state-of-the-art techniques to simulate large scale quantum circuits. PySA is an extensible platform to optimize a classical cost function. We provide an outline of each of these open-source tools and highlight projects using each of these tools in contexts of simulation, optimization and machine learning.

Quantum Computing↗

NASA NTRS1990

An improved Newton iteration for the generalized inverse of a matrix, with applications

The purpose here is to clarify and illustrate the potential for the use of variants of Newton's method of solving problems of practical interest on highly personal computers. The authors show how to accelerate the method substantially and how to modify it successfully to cope with ill-conditioned matrices. The authors conclude that Newton's method can be of value for some interesting computations, especially in parallel and other computing environments in which matrix products are especially easy to work with.

Pan, Victor↗

NASA NTRS2012

Lattice Thermal Conductivity from Atomistic Simulations: ZrB2 and HfB2

Ultra high temperature ceramics (UHTC) including ZrB2 and HfB2 have a number of properties that make them attractive for applications in extreme environments. One such property is their high thermal conductivity. Computational modeling of these materials will facilitate understanding of fundamental mechanisms, elucidate structure-property relationships, and ultimately accelerate the materials design cycle. Progress in computational modeling of UHTCs however has been limited in part due to the absence of suitable interatomic potentials. Recently, we developed Tersoff style parameterizations of such potentials for both ZrB2 and HfB2 appropriate for atomistic simulations. As an application, Green-Kubo molecular dynamics simulations were performed to evaluate the lattice thermal conductivity for single crystals of ZrB2 and HfB2. The atomic mass difference in these binary compounds leads to oscillations in the time correlation function of the heat current, in contrast to the more typical monotonic decay seen in monoatomic materials such as Silicon, for example. Results at room temperature and at elevated temperatures will be reported.

Lawson, John W.↗

NASA NTRS2012

Lattice Thermal Conductivity of Ultra High Temperature Ceramics (UHTC) ZrB2 and HfB2 from Atomistic Simulations

Lawson, John W.↗

NASA NTRS1971

Development and initial operating characteristics of the 20 megawatt linear plasma accelerator facility

A 20-megawatt linear plasma accelerator facility, a steady flow, Faraday-type plasma accelerator facility for high velocity aerodynamic testing, was constructed, developed, and brought to an operational status. The accelerator has a 63.5-mm-square and 0.5-meter-long channel and utilizes nitrogen-seeded with 2 % mole fraction of cesium vapor. Modification of the original accelerator design characteristics and the improvements necessary to make the arc heater a suitable plasma source are described. The measured accelerator electrode current distribution and the electrode-wall potential distributions are given. The computed and the measured values are in good agreement. Measured pitot pressure indicates that an accelerator exit velocity of 9.2 km/sec, is obtained with 30 of the 36 electrode pairs powered and corresponds to a velocity increase to about 2 1/4 times the computed entrance velocity. The computed stagnation enthalpy at the accelerator exit is 92 MJ/kg, and the mass density corresponds to an altitude of about 58 km. The 92 MJ/kg stagnation enthalpy corresponds to a kinetic energy content at low temperature equivalent to a velocity of 13.6 km/sec.

Carter, A. F.↗

NASA NTRSDate not supplied

Edge-Based Viscous Method for Mixed-Element Node-Centered Finite-Volume Solvers

A novel, efficient, edge-based viscous (EBV) discretization method has been recently developed, implemented in a practical, unstructured-grid, node-centered, finite-volume flow solver, and applied to viscous-kernel computations that include evaluations of meanflow viscous fluxes, turbulence-model and chemistry-model diffusion terms, and the corresponding Jacobian contributions. Initially, the EBV method had been implemented for tetrahedral grids and demonstrated multifold acceleration of all viscous-kernel computations. This paper presents an extension of the EBV method for mixed-element grids. In addition to the primal edges of a given mixed-element grid, virtual edges are introduced to connect cell nodes that are not connected by a primal edge. The EBV method uses an efficient loop over all (primal and virtual) edges and features a compact discretization stencil based on the nearest neighbors. This study verifies the EBV method and assesses its efficiency on mixed-element grids by comparing the EBV solution accuracy and iterative convergence with those of well-established solutions obtained using a cell-based viscous (CBV) discretization method. The EBV solver’s memory footprint is optimized and often smaller than the memory footprint of the CBV solver. A multifold speedup is demonstrated for all viscous-kernel computations resulting in significant reduction of the time to solutions for several benchmark mixed-element-grid computations, including simulations of a flow around NASA’s juncture-flow model and a hypersonic, chemically reacting flow around a blunt body.

CFD↗

NASA NTRSDate not supplied

Edge-Based Viscous Method for Mixed-Element Node-Centered Finite-Volume Solvers

Edge-based viscous method↗

NASA NTRS1987

Decoupled dynamic analysis of combined systems by iterative determination of interface accelerations

A dynamic analysis technique is presented that can be used to determine the response of a discrete model of a large linear structural system composed of multiple substructures. The technique circumvents the costly computation of the modal characteristics of the combined system. This is accomplished by relying on a predictor-corrector scheme to converge iteratively to the interface accelerations of the combined system, while the equations of motions of the individual structures are integrated separately. In this regard, the temporal slopes of the interface accelerations (jerks) are computed at each time point of integration to predict the interface accelerations at the next time point. The proposed technique is exemplified by conducting a Space Shuttle landing loads analysis; the obtained numerical data demonstrate its reliability and efficiency.

Spanos, P. D.↗

NASA NTRSDate not supplied

Machine Learning Approaches for Rare-Earth Silicate Environmental Barrier Coating Thermochemical and Thermomechanical Property Predictions

Environmental barrier coatings (EBCs) are a necessary enabling technology for the transition from superalloys to silicon carbide (SiC) ceramic matrix composites (CMCs) in gas turbine engines for increased efficiency and decreased fuel costs. SiC-based CMCs are prone to oxidation-based degradation in the engine hot section, and rare-earth (RE) silicates are promising candidates for EBCs due to their close thermal expansion match to the composite substrate and oxidation resistance. However, the design of EBCs is hindered by the large chemical space of candidate materials and the difficulty in obtaining material properties for engineering optimization. This is especially difficult as research continues into mixed-cation or “high-entropy” RE silicates. First-principles computational methods such as density functional theory (DFT) are highly effective at calculating material properties to guide coating design but are limited by their computational cost. Atomistic simulations have the potential to both accelerate property calculations and expand the properties able to be calculated due to their lower computational compared to DFT. However, they require interatomic potentials (IAPs) specific to the material system of interest, and, to our knowledge, there are no suitable IAPs for RE silicates. Machine learning (ML) is a promising technique to accelerate material property predictions indirectly by generating IAPs for atomistic simulations or via direct prediction. In this work, we present two ML approaches to accelerate the calculation of RE silicate properties relevant to EBC design: 1) a ML-derived interatomic potential (IAP) for atomistic simulations of yttrium disilicate (Y2Si2O7) from DFT training data, and 2) a neural network (NN) model to directly predict thermochemical properties of RE silicates and oxides directly from easily obtainable unit cell parameters. Classical MD simulations using the IAP yield lattice properties and bond lengths in good agreement with both DFT and experimental results from x-ray diffraction. Thermodynamic properties calculated using the finite-displacement phonon method and quasi-harmonic approximation were orders of magnitude faster than DFT with good agreement to the DFT results. The IAP was also used to calculate properties such as coefficient of thermal expansion (CTE) that require large simulation supercells and are therefore difficult with DFT. The IAP correctly predicted the anisotropic nature of the CTE in three different phases of Y2Si2O7. The NN model predicts constant pressure heat capacity, Cp, orders of magnitude faster than DFT calculations, which can enable its use as a surrogate model for multiscale simulations. The two methods presented in this work demonstrate the utility of ML for accelerating the prediction of RE silicate properties, which can in turn accelerate EBC design and optimization.

machine learning↗

NASA NTRS2009

Enhanced Video-Oculography System

A previously developed video-oculography system has been enhanced for use in measuring vestibulo-ocular reflexes of a human subject in a centrifuge, motor vehicle, or other setting. The system as previously developed included a lightweight digital video camera mounted on goggles. The left eye was illuminated by an infrared light-emitting diode via a dichroic mirror, and the camera captured images of the left eye in infrared light. To extract eye-movement data, the digitized video images were processed by software running in a laptop computer. Eye movements were calibrated by having the subject view a target pattern, fixed with respect to the subject s head, generated by a goggle-mounted laser with a diffraction grating. The system as enhanced includes a second camera for imaging the scene from the subject s perspective, and two inertial measurement units (IMUs) for measuring linear accelerations and rates of rotation for computing head movements. One IMU is mounted on the goggles, the other on the centrifuge or vehicle frame. All eye-movement and head-motion data are time-stamped. In addition, the subject s point of regard is superimposed on each scene image to enable analysis of patterns of gaze in real time.

Moore, Steven T.↗

NASA NTRS1986

Gravity gradient torque for an arbitrary potential function

To permit investigation of the relative effects of higher-order terms in the potential function and allow computation of the gravity gradient torques consistent with the gravitational acceleration for a given potential function, a simple formula is derived for computing the gravity gradient torque on a vehicle in terms of the eigenvalues and eigenvectors of the gravity gradient matrix and the inertia properties of the vehicle. The formulation is applicable to any potential function for which the gravity matrix can be computed for a given point in the potential field.

Glandorf, D. R.↗

NASA NTRS2011

Introduction of Parallel GPGPU Acceleration Algorithms for the Solution of Radiative Transfer

General-purpose computing on graphics processing units (GPGPU) is a recent technique that allows the parallel graphics processing unit (GPU) to accelerate calculations performed sequentially by the central processing unit (CPU). To introduce GPGPU to radiative transfer, the Gauss-Seidel solution of the well-known expressions for 1-D and 3-D homogeneous, isotropic media is selected as a test case. Different algorithms are introduced to balance memory and GPU-CPU communication, critical aspects of GPGPU. Results show that speed-ups of one to two orders of magnitude are obtained when compared to sequential solutions. The underlying value of GPGPU is its potential extension in radiative solvers (e.g., Monte Carlo, discrete ordinates) at a minimal learning curve.

Godoy, William F.↗

NASA NTRS1995

Sensitivity analysis in multipole-accelerated panel methods for potential flow

In the design of an airframe, the effect of changing the geometry on resulting computations is necessary for design optimization. The geometry is defined in terms of a series of design variables, including design variables to define the wing planform, tail, canard, pylon, and nacelle. Design optimization in this research is based on how these design variable affect the potential flow. The potential flow is computed as a function of the geometry and location of a series of panels describing the airframe, which are in turn a function of the design variables. Multipole accelerated panel methods improve the computational complexity of the problem and thus are an attractive approach. To utilize the methods in design optimization, it was necessary to define the appropriate sensitivity derivatives. The overhead incurred from finding the sensitivity derivatives in conjunction with the original computation should be small. This research developed the background for multipole-accelerated panel methods and the framework for finding sensitivity derivatives in the methods. Potential flow panel codes are commonly used for powered-lift aerodynamic predictions for three dimensional geometries. Given an airframe which has been discretized into a series of panels to define the airframe geometry, potential is computed as a function of the influence of all panels on all other panels. This is a computationally intensive problem for which efficient solutions are desired to improve the computational time and to allow greater resolution by use of more panels. One such solution is the use of hierarchical multipole methods which entail approximations of the effects of far-field terms. Hierarchical multipole methods have become prevalent in molecular dynamics and gravitational physics, and have been introduced into the fields of capacitance calculations, computational fluid dynamics, and electromagnetics. The methods utilize multipole expansions to describe the effect of bodies (i.e. particles, astrophysical bodies, panels, etc.) within a sphere on points distant from the sphere, where the influence diminishes as a function of distance. The expansions are exact with infinite series, however, for practical computations, the series are truncated and accuracy is selected based on the number of terms retained in the expansions. A hierarchical tree structure groups bodies together based on proximity to allow definition of multipole expansions for each group. The multipole expansions are then used to compute the effect of the bodies in a group on distant bodies.

Leathrum, James F., Jr.↗