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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 NTRS1985

Development concerns for satellite-based air traffic control surveillance systems

Preliminary results of an investigation directed toward the configuration of a practical system design which can form the baseline for assessing the applications and value of a satellite based air traffic surveillance system for future use in the National Airspace System (NAS) are described. This work initially studied the characteristics and capabilities of a satellite configuration which would operate compatibly with the signal structure and avionics of the next generation air traffic control secondary surveillance radar system, the Mode S system. A compatible satellite surveillance system concept is described and an analysis is presented of the link budgets for the various transmission paths. From this, the satellite characteristics are established involving a large multiple feed L band antenna of approximately 50 meter aperture dimension. Trade offs involved in several of the alternative large aperture antennas considered are presented as well as the influence of various antenna configurations on the performance capabilities of the surveillance system. The features and limitations of the use of large aperture antenna systems for air traffic surveillance are discussed. Tentative results of this continuing effort are summarized with a brief description of follow on investigations involving other space based antenna systems concepts.

Mcdonald, K. D.↗

NASA NTRS2017

Real-Time Radar-Based Tracking and State Estimation of Multiple Non-Conformant Aircraft

In this study, a novel solution for automated tracking of multiple unknown aircraft is proposed. Many current methods use transponders to self-report state information and augment track identification. While conformant aircraft typically report transponder information to alert surrounding aircraft of its state, vehicles may exist in the airspace that are non-compliant and need to be accurately tracked using alternative methods. In this study, a multi-agent tracking solution is presented that solely utilizes primary surveillance radar data to estimate aircraft state information. Main research challenges include state estimation, track management, data association, and establishing persistent track validity. In an effort to realize these challenges, techniques such as Maximum a Posteriori estimation, Kalman filtering, degree of membership data association, and Nearest Neighbor Spanning Tree clustering are implemented for this application.

RADAR tracking↗

NASA NTRS2009

Flightspeed Integral Image Analysis Toolkit

The Flightspeed Integral Image Analysis Toolkit (FIIAT) is a C library that provides image analysis functions in a single, portable package. It provides basic low-level filtering, texture analysis, and subwindow descriptor for applications dealing with image interpretation and object recognition. Designed with spaceflight in mind, it addresses: Ease of integration (minimal external dependencies) Fast, real-time operation using integer arithmetic where possible (useful for platforms lacking a dedicated floatingpoint processor) Written entirely in C (easily modified) Mostly static memory allocation 8-bit image data The basic goal of the FIIAT library is to compute meaningful numerical descriptors for images or rectangular image regions. These n-vectors can then be used directly for novelty detection or pattern recognition, or as a feature space for higher-level pattern recognition tasks. The library provides routines for leveraging training data to derive descriptors that are most useful for a specific data set. Its runtime algorithms exploit a structure known as the "integral image." This is a caching method that permits fast summation of values within rectangular regions of an image. This integral frame facilitates a wide range of fast image-processing functions. This toolkit has applicability to a wide range of autonomous image analysis tasks in the space-flight domain, including novelty detection, object and scene classification, target detection for autonomous instrument placement, and science analysis of geomorphology. It makes real-time texture and pattern recognition possible for platforms with severe computational restraints. The software provides an order of magnitude speed increase over alternative software libraries currently in use by the research community. FIIAT can commercially support intelligent video cameras used in intelligent surveillance. It is also useful for object recognition by robots or other autonomous vehicles

Thompson, David R.↗

DOE OSTI2023

Long-term survival and second malignant tumor prediction in pediatric, adolescent, and young adult cancer survivors using Random Survival Forests: a SEER analysis

Abstract Survival and second malignancy prediction models can aid clinical decision making. Most commonly, survival analysis studies are performed using traditional proportional hazards models, which require strong assumptions and can lead to biased estimates if violated. Therefore, this study aims to implement an alternative, machine learning (ML) model for survival analysis: Random Survival Forest (RSF). In this study, RSFs were built using the U.S. Surveillance Epidemiology and End Results to (1) predict 30-year survival in pediatric, adolescent, and young adult cancer survivors; and (2) predict risk and site of a second tumor within 30 years of the first tumor diagnosis in these age groups. The final RSF model for pediatric, adolescent, and young adult survival has an average Concordance index (C-index) of 92.9%, 94.2%, and 94.4% and average time-dependent area under the receiver operating characteristic curve (AUC) at 30-years since first diagnosis of 90.8%, 93.6%, 96.1% respectively. The final RSF model for pediatric, adolescent, and young adult second malignancy has an average C-index of 86.8%, 85.2%, and 88.6% and average time-dependent AUC at 30-years since first diagnosis of 76.5%, 88.1%, and 99.0% respectively. This study suggests the robustness and potential clinical value of ML models to alleviate physician burden by quickly identifying highest risk individuals.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2017

Considerations for an Integrated UAS CNS Architecture

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) is investigating revolutionary and advanced universal, reliable, always available, cyber secure and affordable Communication, Navigation, Surveillance (CNS) options for all altitudes of UAS operations. In Spring 2015, NASA issued a Call for Proposals under NASA Research Announcements (NRA) NNH15ZEA001N, Amendment 7 Subtopic 2.4. Boeing was selected to conduct a study with the objective to determine the most promising candidate technologies for Unmanned Air Systems (UAS) air-to-air and air-to-ground data exchange and analyze their suitability in a post-NextGen NAS environment. The overall objectives are to develop UAS CNS requirements and then develop architectures that satisfy the requirements for UAS in both controlled and uncontrolled air space. This contract is funded under NASAs Aeronautics Research Mission Directorates (ARMD) Aviation Operations and Safety Program (AOSP) Safe Autonomous Systems Operations (SASO) project and proposes technologies for the Unmanned Air Systems Traffic Management (UTM) service.There is a need for accommodating large-scale populations of Unmanned Air Systems (UAS) in the national air space. Scale obviously impacts capacity planning for Communication, Navitation, and Surveillance (CNS) technologies. For example, can wireless communications data links provide the necessary capacity for accommodating millions of small UASs (sUAS) nationwide? Does the communications network provide sufficient Internet Protocol (IP) address space to allow air traffic control to securely address both UAS teams as a whole as well as individual UAS within each team? Can navigation and surveillance approaches assure safe route planning and safe separation of vehicles even in crowded skies?Our objective is to identify revolutionary and advanced CNS alternatives supporting UASs operating at all altitudes and in all airspace while accurately navigating in the absence of navigational aids. These CNS alternatives must be reliable, redundant, always available, cyber-secure, and affordable for all types of vehicles including small UAS to large transport category aircraft. The approach will identify CNS technology candidates that can meet the needs of the range of UAS missions to specific air traffic management applications where they will be most beneficial and cost effective.

Templin, Fred L.↗

NASA NTRS2017

Considerations for an Integrated UAS CNS Architecture

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) is investigating revolutionary and advanced universal, reliable, always available, cyber secure and affordable Communication, Navigation, Surveillance (CNS) options for all altitudes of UAS operations. In Spring 2015, NASA issued a Call for Proposals under NASA Research Announcements (NRA) NNH15ZEA001N, Amendment 7 Subtopic 2.4. Boeing was selected to conduct a study with the objective to determine the most promising candidate technologies for Unmanned Air Systems (UAS) air-to-air and air-to-ground data exchange and analyze their suitability in a post-NextGen NAS environment. The overall objectives are to develop UAS CNS requirements and then develop architectures that satisfy the requirements for UAS in both controlled and uncontrolled air space. This contract is funded under NASAs Aeronautics Research Mission Directorates (ARMD) Aviation Operations and Safety Program (AOSP) Safe Autonomous Systems Operations (SASO) project and proposes technologies for the Unmanned Air Systems Traffic Management (UTM) service.There is a need for accommodating large-scale populations of Unmanned Air Systems (UAS) in the national air space. Scale obviously impacts capacity planning for Communication, Navigation, and Surveillance (CNS) technologies. For example, can wireless communications data links provide the necessary capacity for accommodating millions of small UASs (sUAS) nationwide? Does the communications network provide sufficient Internet Protocol (IP) address space to allow air traffic control to securely address both UAS teams as a whole as well as individual UAS within each team? Can navigation and surveillance approaches assure safe route planning and safe separation of vehicles even in crowded skies?Our objective is to identify revolutionary and advanced CNS alternatives supporting UASs operating at all altitudes and in all airspace while accurately navigating in the absence of navigational aids. These CNS alternatives must be reliable, redundant, always available, cyber-secure, and affordable for all types of vehicles including small UAS to large transport category aircraft. The approach will identify CNS technology candidates that can meet the needs of the range of UAS missions to specific air traffic management applications where they will be most beneficial and cost effective.

Templin, Fred L.↗

NASA NTRS2016

Piloted "Well Clear" Performance Evaluation of Detect-and-Avoid Systems with Suggestive Guidance

This study evaluated the performance of four prototype unmanned aircraft detect-and-avoid (DAA) display configurations, each with different informational elements driven by alerting and guidance algorithms. Sixteen unmanned aircraft pilots flew each combination of the display configurations, with half being given zero DAA surveillance sensor uncertainty and the other half experiencing errors that were comparable, and in some cases slightly better than, errors that were measured in DAA system flight tests. The displays that showed intruder alert information in altitude and heading bands had significantly fewer losses of well clear compared with alternative displays that lacked that information. This difference was significant from a statistical and practical perspective: those losses that did occur lasted for shorter periods and did not penetrate as far into the geometric "separation cylinder" as those in the non-banded displays. A modest level of DAA surveillance sensor uncertainty did not affect the proportion of losses of well clear or their severity. It is recommended that DAA traffic displays implement a band-type display in order to improve the safety of UAS operations in the National Airspace System. Finally, this report provides pilot response time distributions for responding to DAA alerts.

unmanned aircraft system↗

NASA NTRS2012

The Effects of Limited Intent Information Availability on Self-Separation in Mixed Operations

This paper presents the results of a computer simulation of the NASA Autonomous Flight Rules (AFR) concept for airborne self-separation in airspace shared with conventional Instrument Flight Rules (IFR) traffic. This study was designed to determine the impact of varying levels of intent information from IFR aircraft on the performance of AFR conflict detection and resolution. The study used Automatic Dependent Surveillance-Broadcast (ADS-B) to supply IFR intent, but other methods such as an uplink from a ground-based System Wide Information Management (SWIM) network could alternatively supply this information. The independent variables of the study consist of the number of ADS-B trajectory change reports broadcast by IFR aircraft and the time interval between those reports. The conflict detection and resolution metrics include: the number of conflicts and losses of separation, the average conflict warning time, and the amount of time spent in strategic vs. tactical flight modes (i.e., whether the autoflight system was decoupled from the planned route in the Flight Management System in order to respond to a short-notice traffic conflict). The results show a measurable benefit of broadcasting IFR intent vs. relying on state-only broadcasts. The results of this study will inform ongoing separation assurance research and FAA NextGen design decisions for the sharing of trajectory intent information in the National Airspace System.

Lewis, Timothy A.↗

DOE OSTI2022

Los Alamos National Laboratory Wetland Assessment for the Replacement Water Lines from Technical Area 48 to Technical Area 55 Project

The National Nuclear Security Administration (NNSA), a semi-autonomous agency within the U.S. Department of Energy (DOE), is proposing to take action at Los Alamos National Laboratory (LANL) near a wetland at Technical Area (TA) 48 and under a wetland at TA-55. The project proposes to install two 12-inch water lines along an alignment from TA-48 to Pecos Road on the east side of TA-55 (Figure 1). The proposed water lines will replace aging cast iron pipes that provide water for fire suppression to buildings within TA-55, and potable water to other areas in the Pajarito Corridor. NNSA has prepared this wetland assessment in accordance with 10 Code of Federal Regulations (CFR) Part 1022 Compliance with Wetland and Wetland Environmental Review Requirements (10 CFR Part 1022) (CFR 2003) which was promulgated to implement DOE requirements under Executive Order 11988 Wetland Management (EO 1977). A wetland is defined in 10 CFR 1022 as “the lowlands adjoining inland and coastal waters and relatively flat areas and flood prone areas of offshore islands,” and a base wetland as “the 100-year wetland, that is, a wetland with a 1.0 percent chance of flooding in any given year (CFR 2003).” This wetland assessment evaluates potential impacts to wetland values and functions from implementation of the proposed action, identifies alternatives to the Proposed Action, and allows for meaningful public comment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Los Alamos National Laboratory Floodplain Assessment for the Technical Area 8 to Technical Area 22 Natural Gas Line Replacement Project

The National Nuclear Security Administration (NNSA), a semi-autonomous agency within the U.S. Department of Energy (DOE), is proposing new construction that crosses through upper Pajarito Canyon and upper Starmer Canyon from Technical Area (TA) 8 to TA-22 at Los Alamos National Laboratory (LANL). The project proposes to install a new 12 inch natural gas line to replace the existing 70+ year old natural gas line. Replacement will prevent loss of service to several LANL TAs and Bandelier National Monument. The project activities within the 100- year floodplains include 1) new natural gas line installation to replace and existing aging gas line, 2) relocating a section of fence and 3) three construction equipment and materials storage areas. NNSA has prepared this floodplain assessment in accordance with 10 Code of Federal Regulations (CFR) Part 1022 Compliance with Floodplain and Wetland Environmental Review Requirements (10 CFR Part 1022) (CFR 2003) which was promulgated to implement DOE requirements under Executive Order 11988 Floodplain Management (EO 1977). A floodplain is defined in 10 CFR 1022 as “the lowlands adjoining inland and coastal waters and relatively flat areas and flood prone areas of offshore islands,” and a base floodplain as “the 100-year floodplain, that is, a floodplain with a 1.0 percent chance of flooding in any given year (CFR 2003).” This floodplain assessment evaluates potential impacts to floodplain values and functions from implementation of the proposed action, identifies alternatives to the Proposed Action, and allows for meaningful public comment.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2017

Transforming Science Data for GIS: How to Find and Use NASA Earth Observation Data Without Being a Rocket Scientist

NASAs Earth Observing System Data Information System (EOSDIS) manages Earth Observation satellites and the Distributed Active Archive Centers (DAACs), where the data is stored and processed. The challenge is that Earth Observation data is complicated. There is plenty of data available, however, the science teams have had a top-down approach: define what it is you are trying to study -select a set of satellite(s) and sensor(s), and drill down for the data.Our alternative is to take a bottom-up approach using eight environmental fields of interest as defined by the Group on Earth Observations (GEO) called Societal Benefit Areas (SBAs): Disaster Resilience (DR) Public Health Surveillance (PHS) Energy and Mineral Resource Management (EMRM) Water Resources Management (WRM) Infrastructure and Transport Management (ITM) Sustainable Urban Development (SUD) Food Security and Sustainable Agriculture (FSSA) Biodiversity and Ecosystems Sustainability (BES).

DAAC↗

NASA NTRS1995

Design of a GaAs/Ge Solar Array for Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAV) are being proposed for many applications including surveillance, mapping and atmospheric studies. These applications require a lightweight, low speed, medium to long duration airplane. Due to the weight, speed, and altitude constraints imposed on such aircraft, solar array generated electric power is a viable alternative to air-breathing engines. Development of such aircraft is currently being funded under the Environmental Research Aircraft and Sensor Technology (ERAST) program. NASA Lewis Research Center (LeRC) is currently building a Solar Electric Airplane to demonstrate UAV technology. This aircraft utilizes high efficiency Applied Solar Energy Corporation (ASEC) GaAs/Ge space solar cells. The cells have been provided by the Air Force through the ManTech Office. Expected completion of the plane is early 1995, with the airplane currently undergoing flight testing using battery power.

Scheiman, David A.↗

NASA NTRS2018

Reliable and Secure Surveillance, Communications and Navigation (RSCAN) for Unmanned Air Systems (UAS) in Controlled Airspace

The aviation industry faces a rapidly-emerging need for integrating Unmanned Air Systems (UAS) into the national airspace (NAS). This trend will present challenging questions for the safe operation of UAS in controlled and uncontrolled airspaces based on new Communications, Navigation and Surveillance (CNS) technologies. For example, can wireless communications data links provide the necessary capacity for accommodating ever increasing numbers of UAS worldwide? Does the communications network provide ample Internet Protocol (IP) address space to allow Air Traffic Control (ATC) to securely address each UAS? Can navigation and surveillance approaches assure safe route planning and safe separation of vehicles even in crowded skies?Under NASA contract NNA16BD84C, Boeing is developing an integrated CNS architecture to enable UAS operations in the NAS. Revolutionary and advanced CNS alternatives are needed to support UAS operations at all altitudes and in all airspaces, including both controlled and uncontrolled. These CNS alternatives must be reliable, redundant, always available, cyber-secure, and affordable for all types of vehicles including small UAS to large transport category aircraft. Our approach considers CNS requirements that address the range of UAS missions where they will be most beneficial and cost-effective.A cybersecure future UAS CNS architecture is needed to support the NASA vision for an Unmanned Air Traffic Management (UTM) system in uncontrolled airspace and a cooperative operation of manned and unmanned aircraft in the controlled global Air Traffic Management (ATM) system. The architecture must, therefore, support always-available and cyber secure operations. This paper presents UAS CNS architecture concepts for large UAS operating in the ATM system in controlled airspace. Future companion works will consider small UAS operating in the UTM system in uncontrolled airspace.

Ponchak, Denise S.↗

NASA NTRS1998

GaAs/Ge Solar Powered Aircraft

Unmanned Aerial Vehicles (UAV) are being proposed for many applications for many applications including surveillance, mapping and atmospheric studies. These applications require a lightweight, low speed, medium to long duration aircraft. Due to the weight, speed, and altitude constraints imposed on such an aircraft, solar array generated electric power can be a viable alternative to air-breathing engines for certain missions. Development of such an aircraft is currently being funded under the Environmental Research Aircraft and Sensor Technology (ERAST) program. NASA Lewis Research Center (LeRC) has built a Solar Electric Airplane to demonstrate UAV technology. This aircraft utilizes high efficiency Applied Solar Energy Corporation (ASEC) GaAs/Ge space solar cells. The cells have been provided by the Air Force through the ManTech Office.

Colozza, Anthony J.↗

NASA NTRS1986

Tailorable Infrared Sensing Devices

Alternating layers of GexSi1.x and Si deposited by molecular beam epitaxy. When electric field applied across stack, acts as detector for infrared photons. Device fabricated on silicon substrate together with sophisticated very-large-scale integrated circuitry. Useful for wide range of applications related to robotics, space exploration, and terrestrial surveillance.

Cheng, L. J.↗

DOE OSTI2023

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

A Phage Foundry Framework to Systematically Develop Viral Countermeasures to Combat Antibiotic-Resistant Bacterial Pathogens

At its current rate, the rise of antimicrobial-resistant (AMR) infections is predicted to paralyze our industries and healthcare facilities while becoming the leading global cause of loss of human life. With limited new antibiotics on the horizon, we need to invest in alternative solutions. Bacteriophages (phages)–viruses targeting bacteria–offer a powerful alternative approach to tackle bacterial infections. Despite recent advances in using phages to treat recalcitrant AMR infections, the field lacks systematic development of phage therapies scalable to different applications. We propose a Phage Foundry framework to establish metrics for phage characterization and to fill the knowledge and technological gaps in phage therapeutics. Coordinated investment in AMR surveillance, sampling, characterization, and data sharing procedures will enable rational exploitation of phages for treatments. A fully realized Phage Foundry will enhance the sharing of knowledge, technology, and viral reagents in an equitable manner and will accelerate the biobased economy.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Enabling Efficient Surveillance, Control, and Automation of Geothermal Operations with Advanced Predictive Analytics

Automation and control of geothermal energy production and operations require reliable and efficient predictive tools. While physics-based simulation offers a comprehensive tool for predicting energy production performance in geothermal systems, predicting the behavior of geothermal reservoirs involves complex multi-physics processes with coupling effects, highly uncertain input parameters and subsurface descriptions. Moreover, building, running, and integrating simulation models into standard model calibration and optimization workflows entail significant technical and computational efforts. An emerging alternative to physics-based simulation is data-driven predictive analytics models that have gained popularity in energy industry. In this report, we develop novel predictive models for integration into real-time fault diagnosis and model predictive control algorithms to improve the efficiency of energy production operations in geothermal reservoirs. The report includes two major research Thrust Areas, that is, the surface power plant and the subsurface reservoir.

15 GEOTHERMAL ENERGY↗