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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2019

Cloud Giovanni: Reining in Costs and Improving Performance with Analytical Data Stores Using Scalable Serverless Architecture

Giovanni is the Geospatial Interactive Online Visualization ANd aNalysis Infrastructure developed at NASA GES DISC which provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science data. It receives large number of user requests each day for a variety of analysis and visualization services, which leads to the big data challenge of serving gradually increasing large data volumes with diverse statistical algorithms. We hereby propose a multi-dimensional accumulation method which provides fast and cost-efficient cloud analysis for diverse services including both area averaging and time averaging. This method involves the weighted volume integration over multiple variable dimensions (time and space), and is implemented in AWS using Athena providing serverless and highly scalable data analysis. Compared to the standard method, this approach dramatically reduced the computational time by order of magnitude with a minimal AWS cost incurred. For example, for a benchmark of 10-year area averaging over the 1x1 degree daily variable, the computational time was reduced from minutes to seconds, and the Athena cost is only $5 for 100,000 requests.

Zhang, Hailiang↗
NASA NTRS2020

Single-Photon Counting Detector Scalability for High Photon Efficiency Optical Communications Links

For high photon-efficiency deep space or low power optical communications links, such as the Orion Artemis-2 Optical Communications System (O2O) project, the received optical signal is attenuated to the extent that single- photon detectors are required. For direct-detection receivers operating at 1.55 µm wavelength, single-photon detectors including Geiger-mode InGaAs avalanche photon diodes (APDs), and in particular superconducting nanowire single-photon detectors (SNSPDs) offer the highest sensitivity and fastest detection speeds. However, these photon detectors exhibit a recovery time between registered input pulses, effectively reducing the detection efficiency over the recovery interval, resulting in missed photon detections, reduced count rate, and ultimately limiting the achievable data rate. A method to overcome this limitation is to divide the received optical signal into multiple detectors in parallel. Here we analyze this approach for a receiver designed to receive a high photon efficiency serially concatenated pulse position modulation (SCPPM) input waveform. From measured count rate and efficiency data using commercial SNSPDs, we apply a model from which we determine the effective detection efficiency, or blocking loss, for different input signal rates. We analyze the scalability of adding detectors in parallel for different modulation orders and background levels to achieve desired data rates. Finally we show tradeoffs between the number of detectors and the required received optical power, useful for real link design considerations.

Vyhnalek, Brian E.↗
NASA NTRS2019

Accuracy, Scalability, and Efficiency of Mixed-Element USM3D for Benchmark Three-Dimensional Flows

The unstructured, mixed-element, cell-centered, finite-volume flow solver USM3D is enhanced with new capabilities including parallelization, line generation for general unstructured grids, improved discretization scheme, and optimized iterative solver. The paper reports on the new developments to the flow solver and assesses the accuracy, scalability, and efficiency. The USM3D assessments are conducted using a baseline method and the recent hierarchical adaptive nonlinear iteration method framework. Two benchmark turbulent flows, namely, a subsonic separated flow around a three-dimensional hemisphere-cylinder configuration and a transonic flow around the ONERA M6 wing are considered.

Pandya, Mohagna J.↗
NASA NTRSDate not supplied

MESA: Scalable Runtime Verification Tool Using Actors

This work presents our runtime verification approach implemented by the tool MESA (MEssage-based System Analysis) which allows for using concurrent monitors to check for properties specified in linear temporal logic and finite state machines.We employ the actor programing model to implement MESA where monitors are captured by concurrent actors that communicate via messaging. The paper also presents a case study where MESA is used to monitor flights in National Airspace System of United States using live air traffic data stream. The case study which motivated this work in the first place shows that our approach is effective.We also perform empirical study by conducting experiments using monitoring systems with different numbers of concurrent monitors and different layers of indexing.This paper describes our experiments, evaluates our results,and discusses challenges faced during the study. The evaluation shows our approach is scalable.

runtime verification, concurrency, actor programin↗
NASA NTRSDate not supplied

Scalable Traffic Management for Emergency Response Operations (STEReO)Airspace Operations Laboratory (AOL) Unmanned Aircraft Systems (UAS)Traffic Management (UTM) (AOLUSS)Tabletop Overview

The Scalable Traffic Management for Emergency Response Operations (STEReO) project requires prototyping software to extend existing Airspace Operations Laboratory (AOL) software capabilities to support research in integrating drone flight activities within an emergency response airspace environment. A presentation encompassing project AOLUSS details, communications and network design will be presented at a Workshop.

STEReO software↗
NASA NTRSDate not supplied

Scalability of Cohesive Fatigue Analyses Using Explicit Solvers

A cohesive fatigue law has been integrated into a constitutive material model compatible with an explicit finite element solver. The cohesive fatigue model response is based on engineering approximations of the endurance limit and the Goodman diagram. This approach can predict stress-life diagrams for crack initiation, the Paris law regime, and transient effects of crack initiation and stable tearing. Simplified cyclic loading is utilized so that the applied load(or displacement) corresponds to the peak load of a fatigue cycle. Loads are held constant during fatigue while damage develops with increasing solution increments. An automatically-calculated ratio of fatigue cycles per solution increment controls the rate of damage growth, ensuring that damage growth is modeled with a sufficient minimum number of increments and damage growth advances to a minimum desired extent within the explicit analysis step time. The compatibility with an explicit finite element solver enables the analysis of structures that are computationally intractable for implicit finite element solvers. Scalability studies are conducted for geometrically nonlinear problems involving fiber-reinforced composite structures that exhibit fatigue damage growth of interacting matrix cracks and delaminations

Frank A Leone↗
NASA NTRS2020

Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA): A Scalable Open Source Method for Land Cover Monitoring Using Data Fusion

The increasing availability of very-high resolution (VHR; <2 m) imagery has the potential to enable agricultural monitoring at increased resolution and cadence, particularly when used in combination with widely available moderate-resolution imagery. However, scaling limitations exist at the regional level due to big data volumes and processing constraints. Here, we demonstrate the Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA), using a suite of open source software capable of efficiently characterizing time-series field-scale statistics across large geographical areas at VHR resolution. We provide distinct implementation examples in Vietnam and Senegal to demonstrate the approach using WorldView VHR optical, Sentinel-1 Synthetic Aperture Radar, and Sentinel-2 and Sentinel-3 optical imagery. This distributed software is open source and entirely scalable, enabling large area mapping even with modest computing power. FARMA provides the ability to extract and monitor sub-hectare fields with multisensor raster signals, which previously could only be achieved at scale with large computational resources. Implementing FARMA could enhance predictive yield models by delineating boundaries and tracking productivity of smallholder fields, enabling more precise food security observations in low and lower-middle income countries.

fusion↗
NASA NTRS2021

Scalability and Design of Six Rod Tensegrity Soft Robotic Structure

Robots based on tensegrity structures (interconnected rods and cables) offer many advantages such as low weight, small volume when packed, and have high impact resistance. Unfortunately tensegrity robots can be difficult to make and scale due a fundamental design trade-off: they need to have enough tension in the structure to maintain its integrity, while not having so much tension that it is difficult to actuate,change shape and move. This paper addresses this issue with three approaches:1) Traction based pulley actuation that is less sensitive to the tensioning of the structure, 2) A mix of elastic and inelastic cables allowing for a better balance between tensioning and actuation and 3) Using flexible rods allowing for actuation with inelastic cables. We test configurations and show that these approaches can indeed increase the scalability and usefulness of tensegrity robots.

Tensegrity↗
NASA NTRSDate not supplied

Aircraft Classification Using Radar from Small Unmanned Aerial Systems for Scalable Traffic Management Emergency Response Operations

This work investigates two machine learning techniques: Support Vector Machine (SVM) and Autoencoders (AE)with SVM layer for classification of radar trajectories as General Aviation (GA), fixed-wing small Unmanned Aerial System (sUAS), or not-an-aircraft using radar data recorded from sUAS. Onboard identification of intruder aircraft type is useful for planning avoidance maneuvers and is necessary to provide autonomous systems to meet or exceed the avoidance capability of a human pilot. Aircraft classification can identify intruder aircraft that are not part of the team and may be violating a Temporary Flight Restriction. Aircraft classification is needed in monitoring an airspace where multiple aircraft are teaming on a shared task. Scalable Traffic Management for Emergency Response Operations (STEReO) is a NASA project aimed at improving disaster response by enabling large scale aircraft operations through the teaming of manned aircraft with sUAS to maximize emergency response resources. To this end, this work uses trajectories and radar derived features to classify aircraft from a multirotor sUAS. The AE + SVM generated the strongest classification overall accuracy of 93.5% using the first 4 seconds of radar track data for tracks that activated the avoidance system. Subsampling the available track data increased the available training data with the maximum aircraft recall of 0.94 achieved using the SVM with 1 second track data.

Chester V. Dolph↗
NASA NTRSDate not supplied

Demonstration of a Modular, Scalable, Laser Communication Terminal for Manned Spaceflight Missions

Free-space laser communication systems are increasingly implemented on state of the art satellites for their high-speed connectivity. This work outlines a demonstration of the Modular, Agile, Scalable Optical Terminal(MAScOT) we have developed to support Low-Earth Orbit (LEO) to deep-space communication links. In LEO, the MAScOT will be implemented on the International Space Station to support the Integrated Laser Communications Relay Demonstration (LCRD) LEO User Modem and Amplifier Terminal (ILLUMA-T) program. ILLUMA-Ts overarching objective is to demonstrate high bandwidth data transfer between LEO and a ground station via a geosynchronous (GEO) relay satellite. Outside of GEO, MAScOT will also be implemented on the Artemis-II mission to demonstrate high data rate optical communications to and from the moon as part of theOrion EM-2 Optical Communications (O2O) program. Both missions leverage the same modular architecture despite varying structural, thermal, and optical requirements. To achieve sufficient performance, the optical terminal relies on a nested tracking loop to realize sub-arcsecond pointing across±120◦elevation and±175◦azimuth field of regard.

optical communications↗
NASA NTRSDate not supplied

Initial Exploration of STEReO (Scalable Traffic management for Emergency Response Operations) system user requirements for safe integration of small UAS

Environment-based disasters, such as wildfire, can cause substantial loss of life and loss of property and are costing billions of dollars annually. Existing disaster response operations are complex, with many challenges. The aim of the Scalable Traffic management for Emergency Response Operations (STEReO) system is to demonstrate the application of technologies that will enable small, low-altitude Unmanned Aerial System operations to safely take place in the airspace above a disaster event, increasing the effectiveness of the response. As a first step, the project aims to build a small prototype to demonstrate and test the core functions of this system based on user needs and requirements that were gathered from experts in the field during discussions and walkthroughs.

STEReO↗
NASA NTRS2022

A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data

Wildfire is a global phenomenon that has dramatic effects on erosion and flood potential. On steep slopes, burned areas are more likely to experience significant overland flow during heavy rainfall leading to post fire debris flows (PFDFs). Previous work establishes methods for PFDF hazard assessment, often relying on regional-scale parameterizations with in-situ rainfall measurements to categorize hazard as a function of meteorological and surface properties. We present a globally scalable approach to extend the benefit these models provide to new areas. Our new model relies on publicly available satellite-based inputs with a global extent to provide first order hazard assessments of recently burned areas. Our results show it is possible to identify the conditions relevant for PFDF-initiation processes across a variety of physiographic settings. Improvements to satellite borne rainfall intensity data and increased availability of PFDF occurrence data worldwide are expected to enhance model skill and applicability further.

Wildfire↗
NASA NTRSDate not supplied

Transitioning a Flexible and Scalable Satellite Ground Station Observation Network (GSON) Framework to an Operational Environment

Obtaining accurate and timely satellite observations is of paramount importance in fields like disaster management, weather diagnoses/forecasting, and Earth Sciences remote sensing. Stored mission data (SMD), from low Earth orbiting (LEO) satellite sensors, provides important observations for these fields and applications, however data access to SMD can be delayed from one and half hours to three hours from the time the observations were made. This data latency poses a significant impact on data product optimal use. We developed a Ground Station Observation Network (GSON) that utilizes commercial ground station as a service (GSaaS) providers to acquire low latency direct broadcast (DB) data from AQUA, SNPP, and JPSS-1 satellites using antennas located in strategic locations around the world. We will discuss techniques to improve the deployment efficiency and code reliability and quality of the GSON framework. Topics include right-sizing and containerization of the code to facilitate integration and adaptation with continuous delivery (CD) pipeline, locating non-code assets in referenceable repositories separated from code, adaptation of pipelines as code and simplification of CD, intersecting with code quality tests and checks as part of the pipeline execution and deployment, and establishing distributed repositories, registries, and system identities in a way that mitigates compromise to the CD pipeline. These techniques enable deployment of processing systems that are both highly specific but also dynamically modifiable. This new class of system allows for a flexible and scalable deployment while avoiding the “black box” issues that can plague large system deployments.

cluster↗
NASA NTRSDate not supplied

Scalability of GlennICE in a Parallel Environment

GlennICE (Glenn Icing Computational Environment) is a comptational tool designed to calculate ice growth on complex three- dimensional geometries using the input from a user-supplied computational fluid dynamics (CFD) solution for the geometry of interest. The most significant developments in the advancement of GlennICE have been investigating the convergence of the collection efficiency, efficiently finding trajectories, and improving the refinement methodology. Such developments have increased the efficiency of GlennICE for tractability in a practical engineering application. Although studies have demonstrated a reduction in the amount of work (memory footprint) required, research has yet to systematically investigate the effects of scaling GlennICE. This paper sets out to benchmark the scalability of GlennICE within a parallel environment and investigate if an increase in the number of processors result in a linear speed up.

Computational Icing↗
NASA NTRSDate not supplied

Airspace Performance Observations of Scalable Autonomous Operations in a High Density Vertiplex Simulation

The National Aeronautics and Space Administration’s (NASA’s) High Density Vertiplex (HDV) subproject aims to develop and demonstrate progressive automation technologies that contribute to the Advanced Air Mobility (AAM) concept. Using Human-and-Hardware-In-TheLoop (HHITL) techniques, HDV demonstrates initial vertiport automation services at vertiports with increased air traffic volume in both simulated and live test environments. In 2023, the Scalable Autonomous Operations (SAO) simulation was conducted in which prototype vertiport, airspace, and ground control station technologies were assessed on technical performance. During the SAO simulation, an observational study captured an initial impression of the HDV airspace performance, potential disruptions to the airspace, and highlighted some capability and procedural gaps. Observations took place in two parts. In the first part, five scenario use cases (Nominal, Missed Approach, Speed Change, Divert, and MultiAircraft Divert) were conducted with three human operator roles (Vertiport Manager, Fleet Manager, and Ground Control Station Operator). Researchers collected metrics on throughput, closest point of approach, and airborne delay. In the second part of the study, the Missed Approach scenario was observed under three traffic density levels (20, 40, and 60 operations per hour) to challenge the automation to correctly identify slots in the vertiport arrival schedule. The results showed that the automation successfully found a slot for the Missed Approach vehicle in the 20 operations per hour condition, after some delay it found one in the 40 condition, and it did not find one in the 60 condition. The observations of technical and human performance throughout the five scenario use cases and the Missed Approach case study indicated that for HDV to increase traffic density and maintain or increase throughput, airspace monitoring services should be able to detect and resolve conflicts between aircraft. Furthermore, the roles and responsibilities of human operators need additional definition when it comes to responding to vehicle conflicts.

Advanced Air Mobility↗
NASA NTRSDate not supplied

Airspace Performance Observations of Scalable Autonomous Operations in a High Density Vertiplex Simulation

The National Aeronautics and Space Administration’s (NASA’s) High Density Vertiplex (HDV) sub- project aims to develop and demonstrate progressive automation technologies that contribute to the Advanced Air Mobility (AAM) concept. Using Human-and-Hardware-In-The- Loop (HHITL) techniques, HDV demonstrates initial vertiport automation services at vertiports with increased air traffic volume in both simulated and live test environments. In 2023, the Scalable Autonomous Operations (SAO) simulation was conducted in which prototype vertiport, airspace, and ground control station technologies were assessed on technical performance. During the SAO simulation, an observational study captured an initial impression of the HDV airspace performance, potential disruptions to the airspace, and highlighted some capability and procedural gaps. Observations took place in two parts. In the first part, five scenario use cases (Nominal, Missed Approach, Speed Change, Divert, and Multi- Aircraft Divert) were conducted with three human operator roles (Vertiport Manager, Fleet Manager, and Ground Control Station Operator). Researchers collected metrics on throughput, closest point of approach, and airborne delay. In the second part of the study, the Missed Approach scenario was observed under three traffic density levels (20, 40, and 60 operations per hour) to challenge the automation to correctly identify slots in the vertiport arrival schedule. The results showed that the automation successfully found a slot for the Missed Approach vehicle in the 20 operations per hour condition, after some delay it found one in the 40 condition, and it did not find one in the 60 condition. The observations of technical and human performance throughout the five scenario use cases and the Missed Approach case study indicated that for HDV to increase traffic density and maintain or increase throughput, airspace monitoring services should be able to detect and resolve conflicts between aircraft. Furthermore, the roles and responsibilities of human operators need additional definition when it comes to responding to vehicle conflicts.

advanced air mobility↗
NASA NTRS2023

Easy, Scalable Subsetting of GEDI Point Clouds

The GEDI Subsetter, a Python tool developed for NASA’s Multi-mission Algorithm and Analysis Platform (MAAP), optimizes the accessibility and visualization of GEDI point clouds by enabling users to efficiently subset data in a convenient, scalable manner. Complex science data often requires users to learn new software skills and handle many large files. Handling and cleaning large data sets is tedious and error-prone. These challenges significantly impede analysis. One of the goals of NASA's MAAP is to provide a platform that lowers the barrier to conducting research and analysis at scale. When a group of MAAP users wanted to conduct above-ground biomass estimation using GEDI data, we found that their existing workflow for leveraging GEDI data suffered from the barriers mentioned above. Furthermore, their workflow did not scale easily beyond a small number of granules. We found that existing tools related to GEDI data retrieval and subsetting were too limiting, so the GEDI Subsetter was written to support MAAP users’ needs. Being able to run many subsetting jobs simultaneously in the MAAP, and parallelizing the code itself, has led to significant speed improvements in obtaining relevant data, reducing subsetting time from hours to minutes. MAAP users can now more quickly and easily obtain only the data relevant to their research, by choosing which GEDI collection they want to work with (L1A, L2A, L2B, or L4A), and how they want to subset it, by specifying an area of interest, a temporal range, and relevant attributes. This has significantly reduced the feedback loop for users, allowing them to much more quickly subset GEDI data and begin their analysis. Although the GEDI Subsetter originally targeted users of the MAAP, it is generalized such that it can also be used outside of the MAAP and includes a command-line interface for convenience. Furthermore, with minor modifications, it should be possible to use it with non-GEDI data as the general pattern should be applicable to other sparse/track-based sensors.

Charles Daniels↗