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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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At least 433 records · Page 24

Modular, Parallel Pulse-Shaping Filter Architectures

Novel architectures based on parallel subconvolution frequency-domain filtering methods have been developed for modular processing rate reduction of discrete-time pulse-shaping filters. Such pulse-shaping is desirable and often necessary to obtain bandwidth efficiency in very-high-rate wireless communications systems. In principle, this processing could be implemented in very-large-scale integrated (VLSI) circuits. Whereas other approaches to digital pulse-shaping are based primarily on time-domain processing concepts, the theory and design rules of the architectures presented here are founded on frequency-domain processing that has advantages in certain systems.

Gray, Andrew A.↗

Constraint-based scheduling

The GERRY scheduling system developed by NASA Ames with assistance from the Lockheed Space Operations Company, and the Lockheed Artificial Intelligence Center, uses a method called constraint-based iterative repair. Using this technique, one encodes both hard rules and preference criteria into data structures called constraints. GERRY repeatedly attempts to improve schedules by seeking repairs for violated constraints. The system provides a general scheduling framework which is being tested on two NASA applications. The larger of the two is the Space Shuttle Ground Processing problem which entails the scheduling of all the inspection, repair, and maintenance tasks required to prepare the orbiter for flight. The other application involves power allocation for the NASA Ames wind tunnels. Here the system will be used to schedule wind tunnel tests with the goal of minimizing power costs. In this paper, we describe the GERRY system and its application to the Space Shuttle problem. We also speculate as to how the system would be used for manufacturing, transportation, and military problems.

Zweben, Monte↗

Constraint-based scheduling

The GERRY scheduling system developed by NASA Ames with assistance from the Lockheed Space Operations Company, and the Lockheed Artificial Intelligence Center, uses a method called constraint-based iterative repair. Using this technique, one encodes both hard rules and preference criteria into data structures called constraints. GERRY repeatedly attempts to improve schedules by seeking repairs for violated constraints. The system provides a general scheduling framework which is being tested on two NASA applications. The larger of the two is the Space Shuttle Ground Processing problem which entails the scheduling of all the inspection, repair, and maintenance tasks required to prepare the orbiter for flight. The other application involves power allocation for the NASA Ames wind tunnels. Here the system will be used to schedule wind tunnel tests with the goal of minimizing power costs. In this paper, we describe the GERRY system and its application to the Space Shuttle problem. We also speculate as to how the system would be used for manufacturing, transportation, and military problems.

Zweben, Monte↗

Revisiting Nuclear Thermal Propulsion for Human Mars Exploration

Nuclear Thermal Propulsion (NTP) has long been considered as a viable in-space transportation alternative for delivering crew and cargo to the Martian system. While technology development work in nuclear propulsion has continued over the year, general interest in NTP propulsion applications has historically been tied directly to the ebb and flow of interest in sending humans to explore Mars. As far back as the 1960’s, plans for NTP-based human Mars exploration have been proposed and periodically revisited having most recently been considered as part of NASA Design Reference Architecture (DRA) 5.0. NASA has been investigating human Mars exploration strategies tied to its current Journey to Mars for the past few years however, NTP has only recently been added into the set of alternatives under consideration for in-space propulsion under the Mars Study Capability (MSC) team, formerly the Evolvable Mars Campaign (EMC) team. The original charter of the EMC was to find viable human Mars exploration approaches that relied heavily on technology investment work already underway, specifically related to the development of large Solar Electric Propulsion (SEP) systems. The EMC team baselined several departures from traditional Mars exploration ground rules to enable these types of architectures. These ground rule changes included lower energy conjunction class trajectories with corresponding longer flight times, aggregation of mission elements in cis-Lunar space rather than Low Earth Orbit (LEO) and, in some cases, the pre-deployment of Earth return propulsion systems to Mars. As the MSC team continues to refine the in-space transportation trades, an NTP-based architecture that takes advantage of some of these ground rule departures is being introduced.

Percy, Thomas K.↗

A Survey of the Spacecraft Line-Of-Sight Jitter Problem

Predicting, managing, controlling, and testing spacecraft Line-of-Sight (LoS) jit- ter due to on-board internal disturbance sources is a challenging multi- disciplinary systems engineering problem, especially for those observatories hosting extremely sensitive optical sensor payloads with stringent requirements on allowable LoS jitter. Some specific spacecraft jitter engineering challenges will be introduced and described in this survey paper. Illustrative examples of missions where dynamic interactions have to be addressed to satisfy demanding payload instrument LoS jitter requirements will be provided. Some lessons learned and a set of recommended rules of thumb are also presented to provide guidance for analysts on where to initiate and how to approach a new spacecraft jitter design problem. These experience-based spacecraft jitter lessons learned and rules of thumb are provided in the hope they can be leveraged on new space system development projects to help overcome unfamiliarity with previously identified jitter technical pitfalls and challenges.

Dennehy, Cornelius J.↗

Evaluating Alerting and Guidance Performance of a UAS Detect-And-Avoid System

A key challenge to the routine, safe operation of unmanned aircraft systems (UAS) is the development of detect-and-avoid (DAA) systems to aid the UAS pilot in remaining "well clear" of nearby aircraft. The goal of this study is to investigate the effect of alerting criteria and pilot response delay on the safety and performance of UAS DAA systems in the context of routine civil UAS operations in the National Airspace System (NAS). A NAS-wide fast-time simulation study was conducted to assess UAS DAA system performance with a large number of encounters and a broad set of DAA alerting and guidance system parameters. Three attributes of the DAA system were controlled as independent variables in the study to conduct trade-off analyses: UAS trajectory prediction method (dead-reckoning vs. intent-based), alerting time threshold (related to predicted time to LoWC), and alerting distance threshold (related to predicted Horizontal Miss Distance, or HMD). A set of metrics, such as the percentage of true positive, false positive, and missed alerts, based on signal detection theory and analysis methods utilizing the Receiver Operating Characteristic (ROC) curves were proposed to evaluate the safety and performance of DAA alerting and guidance systems and aid development of DAA system performance standards. The effect of pilot response delay on the performance of DAA systems was evaluated using a DAA alerting and guidance model and a pilot model developed to support this study. A total of 18 fast-time simulations were conducted with nine different DAA alerting threshold settings and two different trajectory prediction methods, using recorded radar traffic from current Visual Flight Rules (VFR) operations, and supplemented with DAA-equipped UAS traffic based on mission profiles modeling future UAS operations. Results indicate DAA alerting distance threshold has a greater effect on DAA system performance than DAA alerting time threshold or ownship trajectory prediction method. Further analysis on the alert lead time (time in advance of predicted loss of well clear at which a DAA alert is first issued) indicated a strong positive correlation between alert lead time and DAA system performance (i.e. the ability of the UAS pilot to maneuver the unmanned aircraft to remain well clear). While bigger distance thresholds had beneficial effects on alert lead time and missed alert rate, it also generated a higher rate of false alerts. In the design and development of DAA alerting and guidance systems, therefore, the positive and negative effects of false alerts and missed alerts should be carefully considered to achieve acceptable alerting system performance by balancing false and missed alerts. The results and methodology presented in this study are expected to help stakeholders, policymakers and standards committees define the appropriate setting of DAA system parameter thresholds for UAS that ensure safety while minimizing operational impacts to the NAS and equipage requirements for its users before DAA operational performance standards can be finalized.

UAS Detect-and-Avoid (DAA) System↗

Trusted Communication: Utilizing Speech Communication to Enhance Human-Machine Teaming Success

An area of increasing interest for the next generation of aircraft is autonomy and the integration of increasingly autonomous systems into the national airspace. Such an integration requires humans to work closely with autonomous systems, forming teams. Our hypothesis is that a team composed of both humans and autonomous systems will operate better than either entity alone. We have existing procedures for certifying pilots to operate in the national airspace and are currently working on methods for validating the function of autonomous systems, however we have no method in place for assessing the interaction of these two disparate systems. Communication is one avenue. This paper will examine the use of language as a metric for ascertaining human-machine teaming effectiveness. A proof-of-concept of the application of two communication-based analysis techniques, Linguistic Inquiry and Word Count (LIWC) and Latent Semantic Analysis (LSA), for the prediction of success in human/chatbot teaming was conducted. By running these analyses over data from the 2014 and 2015 Loebner Prize competitions of human/chatbot teaming, numerical scores were obtained that can be associated with scores provided by human judges during the competition. Correlating their LIWC and LSA data with the scores provided by the judges, and using linear regression over this correlation, formulae were obtained that predict the score of human/chatbot interaction. These formulae were tested over the 2013 Loebner Prize transcripts, determining that, though there was strong correlation between predicted and actual scores, the predictive success of this method was not strong. However, with specialized topic spaces and lexica, as well as larger data sets, the predictive power of these metrics will improve. Given the importance of providing metrics for human-machine system team success and given the promise shown by the communication-basedLIWCand LSAmethods, continuing research in this area is necessary. After examining the potential for using communication and spoken language as a metric for the success of human/autonomous system teaming, this paper then examines aspects inherent to communication systems that may contribute to unreliability and reduced trust. Modern natural language processing tools rely on deep learning algorithms to create language rules that produce accurate results, but these rules are uninterpretable. The resulting blackbox system lacks transparency necessary for full validation and complete trust. Additionally, speech-based interfaces pose other difficulties to developing coordinated teamwork between humans and autonomous systems. Human communication is infrequently limited to speech only, instead usually relying on a combination of verbal, gestural, and general body language communication. Reducing an analysis of team effectiveness to a study of spoken language alone is problematic as it leaves these other equally important forms of communication out. This paper will examine these problems and the general deficiencies in speech-based metrics for human-machine teaming.

E L Meszaros↗

Spitzer Observations of OGLE-2015-BLG-1212 Reveal a New Path toward Breaking Strong Microlens Degeneracies

Spitzer microlensing parallax observations of OGLE-2015-BLG-1212 decisively break a degeneracy between planetary and binary solutions that is somewhat ambiguous when only ground-based data are considered. Only eight viable models survive out of an initial set of 32 local minima in the parameter space. These models clearly indicate that the lens is a stellar binary system possibly located within the bulge of our Galaxy, ruling out the planetary alternative. We argue that several types of discrete degeneracies can be broken via such space-based parallax observations.

Spitzer microlensing parallax observations↗

Simulator for concurrent processing data flow architectures

A software simulator capability of simulating execution of an algorithm graph on a given system under the Algorithm to Architecture Mapping Model (ATAMM) rules is presented. ATAMM is capable of modeling the execution of large-grained algorithms on distributed data flow architectures. Investigating the behavior and determining the performance of an ATAMM based system requires the aid of software tools. The ATAMM Simulator presented is capable of determining the performance of a system without having to build a hardware prototype. Case studies are performed on four algorithms to demonstrate the capabilities of the ATAMM Simulator. Simulated results are shown to be comparable to the experimental results of the Advanced Development Model System.

Malekpour, Mahyar R.↗

Knowledge acquisition from natural language for expert systems based on classification problem-solving methods

It is shown how certain kinds of domain independent expert systems based on classification problem-solving methods can be constructed directly from natural language descriptions by a human expert. The expert knowledge is not translated into production rules. Rather, it is mapped into conceptual structures which are integrated into long-term memory (LTM). The resulting system is one in which problem-solving, retrieval and memory organization are integrated processes. In other words, the same algorithm and knowledge representation structures are shared by these processes. As a result of this, the system can answer questions, solve problems or reorganize LTM.

Gomez, Fernando↗

Flight Deck-Based Delegated Separation: Evaluation of an On-Board Interval Management System with Synthetic and Enhanced Vision Technology

An emerging Next Generation Air Transportation System concept - Equivalent Visual Operations (EVO) - can be achieved using an electronic means to provide sufficient visibility of the external world and other required flight references on flight deck displays that enable the safety, operational tempos, and visual flight rules (VFR)-like procedures for all weather conditions. Synthetic and enhanced flight vision system technologies are critical enabling technologies to EVO. Current research evaluated concepts for flight deck-based interval management (FIM) operations, integrated with Synthetic Vision and Enhanced Vision flight-deck displays and technologies. One concept involves delegated flight deck-based separation, in which the flight crews were paired with another aircraft and responsible for spacing and maintaining separation from the paired aircraft, termed, "equivalent visual separation." The operation required the flight crews to acquire and maintain an "equivalent visual contact" as well as to conduct manual landings in low-visibility conditions. The paper describes results that evaluated the concept of EVO delegated separation, including an off-nominal scenario in which the lead aircraft was not able to conform to the assigned spacing resulting in a loss of separation.

Prinzel, Lawrence J., III↗

Translating expert system rules into Ada code with validation and verification

The purpose of this ongoing research and development program is to develop software tools which enable the rapid development, upgrading, and maintenance of embedded real-time artificial intelligence systems. The goals of this phase of the research were to investigate the feasibility of developing software tools which automatically translate expert system rules into Ada code and develop methods for performing validation and verification testing of the resultant expert system. A prototype system was demonstrated which automatically translated rules from an Air Force expert system was demonstrated which detected errors in the execution of the resultant system. The method and prototype tools for converting AI representations into Ada code by converting the rules into Ada code modules and then linking them with an Activation Framework based run-time environment to form an executable load module are discussed. This method is based upon the use of Evidence Flow Graphs which are a data flow representation for intelligent systems. The development of prototype test generation and evaluation software which was used to test the resultant code is discussed. This testing was performed automatically using Monte-Carlo techniques based upon a constraint based description of the required performance for the system.

Becker, Lee↗

Pilot Interactions in an Over-Constrained Conflict Scenario as Studied in a Piloted Simulation of Autonomous Aircraft Operations

Feasibility and safety of autonomous aircraft operations were studied in a multi-piloted simulation of overconstrained traffic conflicts to determine the need for, and utility of, priority flight rules to maintain safety in this extraordinary and potentially hazardous situation. An overconstrained traffic conflict is one in which the separation assurance objective is incompatible with other objectives. In addition, a proposed scheme for implementing priority flight rules by staggering the alerting time between the two aircraft in conflict was tested for effectiveness. The feasibility study was conducted through a simulation in the Air Traffic Operations Laboratory at the NASA Langley Research Center. This research activity is a continuation of the Distributed Air-Ground Traffic Management feasibility analysis reported in the 4th USA/Europe Air Traffic Management R&D Seminar in December 2001 (paper #48). The over-constrained conflict scenario studied here consisted of two piloted aircraft that were assigned an identical en-route waypoint arrival time and altitude crossing restriction. The simulation results indicated that the pilots safely resolved the conflict without the need for a priority flight rule system. Occurrences of unnecessary maneuvering near the common waypoint were traced to false conflict alerts, generated as the result of including waypoint constraint information in the broadcast data link message issued from each aircraft. This result suggests that, in the conservative interests of safety, broadcast intent information should be based on the commanded trajectory and not on the Flight Management System flight plan, to which the aircraft may not actually adhere. The use of priority flight rules had no effect on the percentage of the aircraft population meeting completely predictable which aircraft in a given pair would meet the constraints and which aircraft would make the first maneuver to yield right-of-way. Therefore, the proposed scheme for implementing priority flight rules through staggering the alerting time between the two aircraft was completely effective. The data and observations from this experiment, together with results from the previously reported study, support the feasibility of autonomous aircraft operations.

Wing, David J.↗

Electron lithography STAR design guidelines. Part 2: The design of a STAR for space applications

The STAR design system developed by NASA enables any user with a logic diagram to design a semicustom digital MOS integrated circuit. The system is comprised of a library of standard logic cells and computr programs to place, route, and display designs implemented with cells from the library. Also described is the development of a radiation-hard array designed for the STAR system. The design is based on the CMOS silicon gate technology developed by SANDIA National Laboratories. The design rules used are given as well as the model parameters developed for the basic array element. Library cells of the CMOS metal gate and CMOS silicon gate technologies were simulated using SPICE, and the results are shown and compared.

Trotter, J. D.↗

LogScope

LogScope is a software package for analyzing log files. The intended use is for offline post-processing of such logs, after the execution of the system under test. LogScope can, however, in principle, also be used to monitor systems online during their execution. Logs are checked against requirements formulated as monitors expressed in a rule-based specification language. This language has similarities to a state machine language, but is more expressive, for example, in its handling of data parameters. The specification language is user friendly, simple, and yet expressive enough for many practical scenarios. The LogScope software was initially developed to specifically assist in testing JPL s Mars Science Laboratory (MSL) flight software, but it is very generic in nature and can be applied to any application that produces some form of logging information (which almost any software does).

Havelund, Klaus↗

UAS Service Supplier Network Performance

This report summarizes the performance of Unmanned Aircraft System (UAS) Service Suppliers (USS) in the Technical Capability Level 4 (TCL4) flight test performed by NASA and its partners in support of the UAS Traffic Management (UTM) concept. TCL4 is the final in a series of TCL demonstrations of a traffic management system for small UAS (sUAS). All demonstrations have been executed in collaboration with industry partners. The [FAA 2018] UTM Concept of Operations document describes UTM as:...the manner in which the FAA will support operations for predominantly sUAS operating in low altitude airspace. UTM utilizes industry’s ability to supply services under FAA’s regulatory authority where these services do not currently exist. It is a community-based traffic management system, where the Operators are responsible for the coordination, execution, and management of operations, with rules of the road established by FAA. UTM is designed to support the demand and expectations for a broad spectrum of operations with ever-increasing complexity and risk. UTM should be considered a collection of services rather than a monolithic application. The following section describes the architecture at a high level. For further insight, the FAA’s concept document [FAA 2018] or NASA’s earlier concept publication [NASA 2016] should be consulted.

Rios, Joseph L.↗

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

Airport configuration selection is a complex decision-making process that involves several operational and human factors. In this paper we propose a novel recursive multi-step machine learning (ML) approach to predict airport configuration. The multi-step approach guarantees stability of the predicted configuration by taking as input the configuration predicted at the previous time step. The features of the proposed model include weather data, future arrival and departure counts and current configuration. Due to the importance of arrival and departure counts in predicting the airport configuration, arrival counts are calculated using landing time predictions selected from physics-based landing time predictions available in FAA System Wide Information Management data feeds for each flight. The selection rules were developed and refined to select the most accurate time for different phases of flight. The proposed model predicts the airport configurations up to 6 hours ahead. In this paper we show the predictive performance of the proposed model for six major US airports, including Charlotte Douglas International Airport (CLT), Dallas/Fort Worth International Airport (DFW), John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA) and Dallas Love Field Airport (DAL). We trained and evaluated models on 2019 and 2020 data in order to study the effect of the pandemic and how changes in traffic patterns affected the performance of the proposed model. Results are compared with a baseline assuming no airport configuration changes. In our results for DFW, we obtained a prediction accuracy of 89.3% for 3 hours ahead prediction, and 82.8% for 6 hours ahead when applied on 2019 data.

machine learning↗

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

Airport configuration selection is a complex decision-making process that involves several operational and human factors. In this paper we propose a novel recursive multi-step machine learning (ML) approach to predict airport configuration. The multi-step approach guarantees stability of the predicted configuration by taking as input the configuration predicted at the previous time step. The features of the proposed model include weather data, future arrival and departure counts and current configuration. Due to the importance of arrival and departure counts in predicting the airport configuration, arrival counts are calculated using landing time predictions selected from physics-based landing time predictions available in FAA System Wide Information Management data feeds for each flight. The selection rules were developed and refined to select the most accurate time for different phases of flight. The proposed model predicts the airport configurations up to 6 hours ahead. In this paper we show the predictive performance of the proposed model for six major US airports, including Charlotte Douglas International Airport (CLT), Dallas/Fort Worth International Airport (DFW), John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA) and Dallas Love Field Airport (DAL). We trained and evaluated models on 2019 and 2020 data in order to study the effect of the pandemic and how changes in traffic patterns affected the performance of the proposed model. Results are compared with a baseline assuming no airport configuration changes. In our results for DFW, we obtained a prediction accuracy of 89.3% for 3 hours ahead prediction, and 82.8% for 6 hours ahead when applied on 2019 data.

machine learning↗