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At least 1,261 records · Page 70

Computing rovibrational, vibrational, and cascade emission spectra for comparison to astronomical observations

Over the last several years, our group has been involved in developing approaches to compute highly accurate spectroscopic constants and vibrational frequencies for small transient molecules that may be used in the interpretation and assignment of high-resolution laboratory experiments as well as high-resolution astronomical spectra. Additionally, we have used the computed spectroscopic constants to simulate purely rotational and rovibrational spectra so that these may be compared directly with high-resolution astronomical observations, and we have worked on developing approaches that can be applied to much larger molecules, such as polycyclic aromatic hydrocarbon (PAH) molecules, where we can explicitly determine anharmonic corrections to vibrational frequencies as well as take into account intensity sharing due to resonances. In addition, for PAH molecules we have used the anharmonic data to compute a library of temperature-dependent anharmonic vibrational spectra, with proper inclusion of polyad resonances, which can then be used to model the cascade emission spectra of PAH molecules – the type of spectra directly observed by astronomers. Another part of our work in spectroscopic signatures involves computing highly accurate line lists for common molecules, such as CO2, SO2, and NH3, which occur in many astrophysical environments, including the atmospheres of exoplanents, and often need to have their lines identified in high-resolution observations in order to determine which lines are due to other molecules. In order to characterize the atmospheres of hot exoplanets, these line lists need to be very accurate and extend to very high energies. I will discuss our latest work in these areas of astrochemical spectroscopy research.

Timothy J Lee↗

An Efficient and Accurate Algorithm for Computing Grid-Averaged Solar Fluxes for Horizontally Inhomogeneous Clouds

A computationally efficient method is presented to account for the horizontal cloud inhomogeneity by using a radiatively equivalent plane parallel homogeneous (PPH) cloud. The algorithm can accurately match the calculations of the reference (rPPH) independent column approximation (ICA) results, but use only the same computational time required for a single plane parallel computation. The effective optical depth of this synthetic sPPH cloud is derived by exactly matching the direct transmission to that of the inhomogeneous ICA cloud. The ffective9 scattering asymmetry factor is found from a pre-calculated albedo inverse look-up-table that is allowed to vary over the range from -1.0 to 1.0. In the special cases of conservative scattering and total absorption, the synthetic method is exactly equivalent to the ICA, with only a small bias (about 0.2% in flux) relative to ICA due to imperfect interpolation in using the look-up tables. In principle, the ICA albedo can be approximated accurately regardless of cloud inhomogeneity. For a more complete comparison, the broadband shortwave albedo and transmission calculated from the synthetic sPPH cloud and averaged over all incident directions, have the RMS biases of 0.26% and 0.76%, respectively, for inhomogeneous clouds over a wide variation of particle size. The advantages of the synthetic PPH method are that (1) it is not required that all the cloud subcolumns have uniform microphysical characteristic, (2) it is applicable to any 1D radiative transfer scheme, and (3) it can handle arbitrary cloud optical depth distributions and an arbitrary number of cloud subcolumns with uniform computational efficiency.

cloud inhomogeneity↗

Computational Investigation of Retropropulsion Operating Environments with a GPU-Enabled Detached Eddy Simulation Approach

Human exploration of the surface of Mars will require an extended powered descent phase of flight, during which aerodynamic-propulsive interference effects can be significant. Characterization of these environments to enable implementation of this technology into a flight vehicle will rely heavily on computational simulation. This work advances the understanding of retropropulsion aerodynamics through application of a massively parallel detached eddy simulation approach on a GPU-accelerated computational framework, yielding data that are largely unachievable with conventional high-performance computing resources. This work includes time-dependent and time-averaged forces and moments on a conceptual, full-scale vehicle in environments and operating conditions relevant to human Mars exploration. Conditions are examined where the engine exhaust flow transitions between over-expanded and under-expanded flow structures, and flight operation will require the ability to maintain control of the vehicle during such a transition. These transitions occur as the vehicle decelerates, and as such, this investigation includes supersonic, transonic, and subsonic flight conditions. Options for vehicle control during powered flight include differential throttling of the engines. This paper provides an overview of the computational campaign, approach, and discussion of results in characterizing the resulting aerodynamics for differential throttling with retropropulsion in atmospheric environments.

EDL↗

Computational Fluid Dynamics Simulations of the Transonic Dynamics Tunnel Airstream Oscillator System

This paper presents computational fluid dynamics simulations of the flow in the NASA Langley Research Center Transonic Dynamics Tunnel (TDT) with the airstream oscillator system (AOS) in operation. Test section flow angle can be modulated using sinusoidally oscillating vanes which are located in pairs within the contraction section of the tunnel circuit. A compilation of previously published experimental data for validation has been reviewed that contains hot-wire anemometer and fast-response probe data acquired in an empty test section with the vanes oscillating in-phase and out-of-phase. These experimental data have not previously been compared with computational results. The present results provide an opportunity for validation of the simulations of the sinusoidal AOS flow field in the TDT test section. Computed tunnel centerline, lateral and cross-section contour data of flow angle and vorticity show that resolving the vane tip vortex is critical to accurately predicting the test section center flow angle. The computed results show, as do the experimental data, what appears to be a resonance at higher frequencies. It is currently not known what is the cause of that resonance. Further refinement of the mesh is expected to improve the comparisons with experimental data

Robert E Bartels↗

Dynamic Shared Computing Resources for Multi-Robot Mars Exploration

The NASA roadmap for 2020 and beyond includes several key technologies which will have a game-changing impact on planetary exploration. The first of these is High Performance Spaceflight Computing (HPSC), which will provide orders of magnitude increases in processing power for next-generation rovers and orbiters (Doyle et al. 2013). The second is Delay Tolerant Networking, which overlays the Deep Space Network, providing internet-like abstractions and store-forward to route data through intermittent delays in connectivity. The third is a trend toward small, co-dependent robots included in flagship missions (MarCO, PUFFER, and Mars Heli). Taken together, these imply an increasing amount of communication and computing heterogeneity on Mars in coming decades. Motivated by these technological trends, we study the concept of Mars on-site shared analysis, information, and communication (MOSAIC) for Mars exploration. The key algorithmic problem associated with MOSAIC networks is simultaneous scheduling of computation, communication, and caching of data, which we illustrate using the three scenarios. We present models, preliminary solutions, and simulation results for two scenarios, showing how mission efficiency relates to communication bandwidth, processing power, geography of the environment, and optimal scheduling of computation, communication, and data caching. The third scenario illustrates future directions of this work.

Chien, Steve↗

Atmospheric Boundary-Layer and Flutter Computations Using CFD Model of the Transonic Dynamics Tunnel

This paper presents two Computational Fluid Dynamics (CFD) models of the flow in the NASA Langley Research Center Transonic Dynamics Tunnel (TDT). The TDT is a continuous-flow, closed circuit wind tunnel with a 16- by 16-foot slotted test section with cropped corners. The tunnel was originally built as the 19-ft Pressure Tunnel in 1938, but it was converted to the current transonic tunnel in the 1950s, with capabilities to use either air or heavy gas as the test medium. The first computational model describes the generation of an atmospheric-boundary-layer (ABL) profile inside the tunnel. An ABL, which includes both a wind profile and turbulence content, is one of the aerodynamic characteristics affecting the occurrence of wind-induced oscillations for a launch vehicle sitting on a pad. The challenging part of this analysis was modeling the turbulent flow inside the tunnel. This is due to the special ABL-generating hardware that was installed at the entrance of the TDT test section in order to change the downstream velocity profile and to introduce velocity fluctuations into the flow. The second CFD model builds on the computational aeroelastic results that were generated in support of the second Aeroelastic Prediction Workshop (AePW) for the NASA Benchmark Supercritical Wing (BSCW) configuration. During the AePW, the wing-only configuration (classical free-air model) was analyzed. In the current study, the flutter computations were conducted on the configuration as it was mounted in the TDT during the experiment. This includes the wing attached to the splitter plate that was attached to the wind-tunnel walls. The preliminary results show that the wind-tunnel walls marginally affect flutter prediction.

Pawel Chwalowski↗

NASA and Blue Origin’s Flight Assessment of Precision Landing Algorithms Computing Performance

NASA’s Safe and Precise Landing - Integrated Capabilities Evolution (SPLICE) project continues NASA’s work in the development and testing of technologies for Precision Landing and Hazard Avoidance (PL&HA). This paper presents results characterizing how SPLICE flight software utilizes the shared computing resources of the Descent Landing Computer (DLC), one of the PL&HA technologies under development. The SPLICE technologies are being tested as an integrated payload on Blue Origin’s New Shephard suborbital vehicle. The results presented in this paper are measured by applications running in and with the flight software both in flight, and in a high-fidelity Hardware-in-the-Loop (HWIL) simulation environment. Linux utilities to measure performance are also executed from the command line in the HWIL configuration. Performance measurements of the SPLICE workloads executing on the DLC provide insight on how efficiently the software is utilizing the DLC resources. Examples of how these measurements have guided improvements in the flight code are presented. In addition, the DLC uses a commercial processor as a surrogate for NASA’s High Performance Spaceflight Computing (HPSC) processor. This work provides insight on how an HPSC system may perform delivering PL&HA capabilities on a future mission. The measurements also can be used to infer architectural requirements for PL&HA capabilities, informing the HPSC project and other flight computer development efforts. Examples of the measurements collected include processor utilization, I/O bandwidth, cache and branch misses, and application profiles.

Precision Landing and Hazard Avoidance↗

Signal and Power Integrity Design Methodology for High-Performance Flight Computing Systems

Computing capabilities of space systems have in-creased onboard performance by orders of magnitude with the use of radiation-tolerant field-programmable gate arrays (FPGA)and processors. The incorporation of signal and power integrity analysis with printed circuit board (PCB) design in reliable computing architectures for space systems has become critical to enable future mission capabilities. Developers launch high-performance processors into a breadth of orbits and missions, running varying applications that create challenges for designing reliable computing hardware. Specifically, for these designs, academic and industry research has focused on component radiation performance, fault mitigation, and reliable architectures. How-ever, other design parameters including electromagnetic interference (EMI), PCB stackup, signal integrity (SI), voltage regulator module (VRM) design, and power distribution network (PDN)are often deprioritized or disregarded as the design matures. Since these characteristics are becoming more significant in high-performance processor designs, this research presents a hardware design and analysis methodology for high-performance, space-computing systems that focuses on a holistic design approach and PDN reliability. While these challenges exist across all space hardware, the reduced PCB dimensions imposed by SmallSats and CubeSats introduce additional hurdles, specifically to VRM and decoupling design. By examining the relationship between the PDN and radiation performance, an analytical relationship is developed that incorporates Total Ionizing Dose and Single-Event Transients to ensure reliability throughout the mission duration. The presented design methodology is applied to the SpaceCube v3.0 Mini, an FPGA-based on-board science data processing system developed at NASA Goddard Space Flight Center.

Advanced avionics↗

Efficient Implementation for Unitary Coupled Cluster State Preparation for Near-Term Quantum Computers

Unitary coupled cluster theory (UCC) is a common wave function ansatz for quantum simulation of molecular electronic structure using the variational quantum eigenvalue solver (VQE). Even for small molecules using a double-ζ basis, the number of variational parameters required to minimize the electronic energy (i.e., optimize the circuit) is large and beyond the reach of current quantum computers. For example, a circuit simulating C2 using the UCCSD ansatz and the cc-pVDZ basis set with frozen-core will require over 10,000 variational parameters and a Hilbert space of over 10^8 determinants. To make progress on simulating such molecular systems on near-term quantum computers, we explore how much of the optimization can be approximately prepared with classical simulation while reducing the number of optimization steps performed on a quantum device. Recently, Chen, Cheng, and Freericks [J. Chem. Theory Comput. 2021, 17, 841-847] presented an algorithm for the factorized form of the UCC ansatz that allows for efficient UCC optimizations on classical hardware. We flip the algorithm around and use it to prepare approximate quantum circuits for systems that require a large number of qubits to represent. We will present results from our implementation and discuss strategies for incorporating this implementation for algorithms involving near-term quantum computers.

J Wayne Mullinax↗

A High-Performance Computing GNSS-aware Path Planning Algorithm for Safe Urban Flight Operations

The emergence and development of advanced technologies and vehicle types have created a growing demand for new forms of flight operations. These new and increasingly complex operational paradigms, such as Advanced and Urban Air Mobility (AAM/UAM), present regulatory authorities and the aviation community with several design-and-implementation challenges – particularly for highly autonomous vehicles. An overarching and daunting task is to develop protocols that can integrate these operations without compromising safety or disrupting traditional airspace operations. A shift toward a more predictive, autonomous, risk mitigation capability becomes critical to meet this challenge. This paper proposes and evaluates a computationally-efficient path planning approach to perform pre-flight planning and autonomous in-flight re-routing to minimize exposures to selected hazards. In our evaluation, hazards associated with degraded and missing critical GPS navigation data are considered. In this paper, we first present a high-performance computing path planning approach based on an adapted Bellman-Ford algorithm, developed in the CUDA programming language. Using the adapted path planning algorithm, we test this algorithm when encountering issues with GPS quality, and deliver an implementation that can produce flight paths that minimize exposure to risks, while maintaining a low computational burden. In our evaluation, the computation of periodic and aperiodic path updates are evaluated, prioritizing specific events as triggers for updates, based on changes to satellite availability. These critical events can lead to significant exposure to navigational hazards if not dealt with correctly.

GNSS↗

A High-Performance Computing GNSS-aware Path Planning Algorithm for Safe Urban Flight Operations

The emergence and development of advanced technologies and vehicle types have created a growing demand for new forms of flight operations. These new and increasingly complex operational paradigms, such as Advanced and Urban Air Mobility (AAM/UAM), present regulatory authorities and the aviation community with several design-and-implementation challenges – particularly for highly autonomous vehicles. An overarching and daunting task is to develop protocols that can integrate these operations without compromising safety or disrupting traditional airspace operations. A shift toward a more predictive, autonomous, risk mitigation capability becomes critical to meet this challenge. This paper proposes and evaluates a computationally-efficient path planning approach to perform pre-flight planning and autonomous in-flight re-routing to minimize exposures to selected hazards. In our evaluation, hazards associated with degraded and missing critical GPS navigation data are considered. In this paper, we first present a high-performance computing path planning approach based on an adapted Bellman-Ford algorithm, developed in the CUDA programming language. Using the adapted path planning algorithm, we test this algorithm when encountering issues with GPS quality, and deliver an implementation that can produce flight paths that minimize exposure to risks, while maintaining a low computational burden. In our evaluation, the computation of periodic and aperiodic path updates are evaluated, prioritizing specific events as triggers for updates, based on changes to satellite availability. These critical events can lead to significant exposure to navigational hazards if not dealt with correctly.

GNSS↗

Computational Analysis of a Quiet Single-Main Rotor Helicopter for Air Taxi Operations

A computational study has been done on NASA’s quiet single-main rotor helicopter concept for urban air mobility using high-fidelity computational fluid dynamics, rotorcraft comprehensive analysis tools, and computational aeroacoustics. High-order accurate schemes, dual-time stepping, and the delayed detached-eddy simulation model have been employed. A loose-coupling approach between the flow solver and the rotorcraft comprehensive code is implemented to include vehicle trim and blade motions. Acoustic simulations based on the Ffowcs-Williams and Hawkings equations have been performed to compute the rotor noise. Different blade geometries and tip speeds are analyzed, and performance and acoustics results are compared. The vehicle has been simulated in hover and cruise, with flight conditions representative of an air taxi mission. The quiet single-main rotor helicopter is one of the conceptual designs intended to focus and guide NASA’s research activities in support of aircraft development for vertical take-off and landing air taxi operations.

ARMD↗

Zero-Trust Architecture for Autonomous Edge Computing

We are at the apex of an aviation revolution where autonomy will play a central role in enabling complex, multi-agent systems to communicate, interact, and collaborate on a myriad of applications spanning autonomous swarms to wild-fire management. Autonomy is not an absolute but rather a spectrum ranging from a system requiring significant human intervention to one requiring little to none [1]. For example, the extreme, in the case of an autonomous aircraft, is one that operates independently in the airspace interacting with all other elements (air traffic controllers, other pilots) as if it were a human pilot. Critical to this vision is an architecture that enables autonomous agents to interact with minimal latency. Edge computing is an emerging architecture where compute and storage is pushed to the ‘edge’ of the network in order to minimize the round-trip time from agent to resource thereby mitigating the latency associated with cloud-only based approaches. Additionally, services can generate massive amounts of data (e.g., video feeds), which may require analysis in near real-time. Moving this data to the cloud for further processing may not be feasible due to latency, bandwidth, and cost. Privacy, security, and reliability can also be improved by edge computing architectures. However, this geo-distributed and dynamic* architecture complicates the establishment of unambiguous network security boundaries and can lead to vulnerabilities including man in the middle attacks, replay attacks, physical security breaches of edge nodes, signal interception, etc. This motivates the need for zero-trust architectures [2–4] which de-emphasize the notion of static network perimeters and, as the name implies, do not instill any innate trust in any particular agent. It is required that all agents must be authorized and approved in every transaction. In this paper, we present a zero-trust architecture suitable for edge-computing applications that demand significant low-latency, security, privacy, and reliability.

zero trust↗

Cloud Computing Option for Modeling the Debris Environment

NASA’s Digital Transformation Initiative aims to promote the agency’s adoption of current and evolving digital technologies. Through agency-wide collaboration with other NASA teams, the Office of Safety and Mission Assurance (OSMA) has directed the Orbital Debris Program Office and the Meteoroid Environment Office to integrate cloud computing technologies in their publicly released software models: the Orbital Debris Engineering Model (ORDEM) and the Meteoroid Engineering Model (MEM). Decoupling the user interface from the backend processor was key for the software packages to run on a cloud computing framework. Benefits to this design include horizontal scaling of computing resources, user authentication and authorization, and automated deployment. Both models are hosted on a cloud computing platform supported by the NASA authorized IT security and compliance framework. This paper focuses on the new ORDEM web application, which includes the current features of the publicly released ORDEM software with an upgraded frontend design. The underlying ORDEM processor is run on a cloud container, allowing the user to run multiple spacecraft and telescope/radar mode simulations. Featuresexclusive to the ORDEM web application, such as importing multiple TLEs, auto-generated plotting, and the ability to check runtime progress are discussed. Comparisons between the current ORDEM software and the web application are summarized.

Andrew Vavrin↗

Efficient Calibration of Expensive Computational Models

Accounting for uncertainty when calibrating expensive computational models is a common challenge faced by scientists and engineers. Often Bayesian techniques are adopted to estimate a probability density function over the model parameters given noisy empirical data. The methods used to perform this type of probabilistic calibration are computationally prohibitive in that they require a large number of evaluations of the expensive model. In these cases, surrogate modeling -- that is, using a fast-to-evaluate, lower fidelity stand-in for the original computational model -- may be the only option to alleviate this computational burden. However, the upfront cost of generating training data to build a surrogate model can itself be expensive. As such, it is important to be judicious when selecting training points at which the full-fidelity model is evaluated. Here, an active learning approach is proposed that enables efficient selection of training points using approximate samples of the calibrated parameter probability density function. In this way, the training points can be concentrated in regions where the calibration algorithm requires high model accuracy.

active learning↗

Low-Speed Space Launch System Computational Fluid Dynamics: A Comprehensive Overview

In this review paper, low-speed computational work from NASA Langley in support of the Space Launch System (SLS) is discussed. This information includes both historic and present efforts with the Kestrel CFD solver. The low-speed aerodynamics of SLS is highly complex and analysis of the unsteady flowfield requires significant computational efforts. The SLS mission profile varies from the vehicle static on the launch pad through high-speed ascent, and this paper focuses on the prelaunch as well as liftoff and transition portions of the flight both in proximity to the launch tower and in isolation. High-alpha conditions, as large as 90 deg, result in a flowfield dominated by massive, large-scale flow separation and asymmetric vortices. High-fidelity solutions require an unsteady computational formulation to accurately capture the aerodynamics of the vehicle. A detailed discussion of the computational approach is presented, followed by key efforts to support the program, both historic and present, including information which has been either previously published or that has never before published external to NASA.

Brent Pomeroy↗

Cloud Computing Option for Modeling the Debris Environment

NASA’s Digital Transformation Initiative aims to promote the agency’s adoption of current and evolving digital technologies. Through agency-wide collaboration with other NASA teams, the Office of Safety and Mission Assurance (OSMA) has directed the Orbital Debris Program Office and the Meteoroid Environment Office to integrate cloud computing technologies into their publicly released software models: the Orbital Debris Engineering Model (ORDEM) and the Meteoroid Engineering Model (MEM). Decoupling the user interface from the backend processor was key for the software packages to run on a cloud computing framework. Benefits to this design include horizontal scaling of computing resources, user authentication and authorization, and automated deployment. Both models are hosted on a cloud computing platform supported by the NASA authorized IT security and compliance framework. This paper focuses on the new ORDEM web application, which includes the current features of the publicly released ORDEM software with an upgraded frontend design, although parallels between ORDEM and MEM are also discussed. The underlying ORDEM processor is run on a cloud container, allowing the user to run multiple spacecraft and telescope/radar mode simulations. Features exclusive to the ORDEM web application, such as importing multiple TLEs, auto-generated plotting, and the ability to check runtime progress are discussed. Comparisons between the current ORDEM software and the web application are summarized.

Andrew Vavrin↗

Ab initio ground states of strongly-correlated materials on quantum computers

The accurate first-principles description of strongly-correlated materials is an important and challenging problem in condensed matter physics. Ab initio downfolding has emerged as a way of deriving accurate many-body Hamiltonians including strong correlations, representing a subspace of interest of a material, using density functional theory calculations as a starting point. However, the solution of these material-specific models can scale exponentially on classical computers, constituting a challenge. Here we propose that utilizing quantum computers for obtaining the properties of downfolded Hamiltonians yields an accurate description of the ground state properties of strongly-correlated systems, while circumventing the exponential scaling problem. We benchmark the solution of Hubbard-like models obtained through downfolding by utilizing a classical tensor network implementation of variational quantum eigensolvers (VQE), and we reveal a strategy for driving the optimization through a hybrid minimization of the energy and maximization of the overlap with an approximate solution obtained through low-cost computational methods. This results in a reduction of the energy error by orders of magnitude compared to conventional VQE approaches, and allows us to reproduce long-range correlations for the first time. We demonstrate our first-principles approach for diverse strongly-correlated materials, correctly predicting the antiferromagnetic state of one-dimensional cuprate Ca 2 CuO 3 , the excitonic ground state of monolayer WTe2, and the charge-ordered state of correlated metal SrVO 3 . Our efficient computational implementation allows us to simulate large systems with up to 54 qubits and encompassing up to four correlated bands, which is indicative of the complexity that our framework can address.

Antonios M Alvertis↗