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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 NTRS1987

Autonomous control procedures for shuttle rendezvous proximity operations

The results are presented of a study which uses fuzzy sets to model a Space Shuttle pilot's reasoning and actions while performing rendezvous proximity operation maneuvers. In this model fuzzy sets are used to simulate smooth and continuous actions as would be expected from an experienced pilot and to simulate common sense reasoning in the decision process. The present model assumes visual information available to the Shuttle pilot from the Shuttle Crew Optical Alignment Sighting (COAS) device and the overhead window and rendezvous radar sensor information available to him from an onboard display. This model will be used in a flight analysis simulator to perform studies requiring a large number of runs, each of which currently needs an engineer in the loop to supply the piloting decisions. This work has much broader implications in control of robots such as the Flight Telerobotic Servicer, in automated pilot control and attitude control, and in advisory and evaluation functions that could be used for flight data monitoring or for testing of various rule sets in flight preparation.

Lea, Robert N.↗
NASA NTRS2008

Reducing NPR 7120.5D to Practice: Preparing for a Life-Cycle Review

In March 2007, NASA issued revised rules for space flight project management, NPR 7120.5D, 'NASA Space Flight Program and Project Management Requirements.' Central to the new rules was the construct of Key Decision Points, maturity gates that the project team must pass in order to continue development. In order that the KDP decision be fully informed, the NPR required, as entrance criteria for the gate, the generation and delivery of specified planning, technical, and cost/schedule documents (gate products) and a life-cycle review, the Preliminary Design Review. Building on JPL experience on the Prometheus and Juno projects, the team successfully organized for and conducted these reviews on an aggressive schedule. Key actions were taken to proactively interact with the SRB, produce high-quality gate products with stakeholder review, generate review presentation materials, and handle a myriad of supporting logistical functions. A review preparation team was established, including a Review Captain and leads for documentation, information systems, and logistics, and their roles, responsibilities and task assignments were identified. Aids were produced, including a detailed review preparation schedule and a comprehensive gate products production table. Institutional support was leveraged early and often. Implementation strategy reflected the needs of a nationally-distributed team, as well as applicable export control and IT security requirements. This paper gives a brief overview of the GRAIL mission and its project management challenges, provides a detailed description of project PMSR and PDR preparation and execution activities, including positive and negative lessons learned, and identifies recommendations for future NASA (and non-NASA) project teams.

NPR 7120.5D↗
NASA NTRS2021

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↗
NASA NTRS2021

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↗
NASA NTRS2010

Autonomous Flight Safety System

The Autonomous Flight Safety System (AFSS) is an independent self-contained subsystem mounted onboard a launch vehicle. AFSS has been developed by and is owned by the US Government. Autonomously makes flight termination/destruct decisions using configurable software-based rules implemented on redundant flight processors using data from redundant GPS/IMU navigation sensors. AFSS implements rules determined by the appropriate Range Safety officials.

Simpson, James↗
NASA NTRS2005

Autonomous Flight Safety System Road Test

On February 3, 2005, Kennedy Space Center (KSC) conducted the first Autonomous Flight Safety System (AFSS) test on a moving vehicle -- a van driven around the KSC industrial area. A subset of the Phase III design was used consisting of a single computer, GPS receiver, and UPS antenna. The description and results of this road test are described in this report.AFSS is a joint KSC and Wallops Flight Facility project that is in its third phase of development. AFSS is an independent subsystem intended for use with Expendable Launch Vehicles that uses tracking data from redundant onboard sensors to autonomously make flight termination decisions using software-based rules implemented on redundant flight processors. The goals of this project are to increase capabilities by allowing launches from locations that do not have or cannot afford extensive ground-based range safety assets, to decrease range costs, and to decrease reaction time for special situations.

Simpson, James C.↗
NASA NTRS2010

Range Safety for an Autonomous Flight Safety System

The Range Safety Algorithm software encapsulates the various constructs and algorithms required to accomplish Time Space Position Information (TSPI) data management from multiple tracking sources, autonomous mission mode detection and management, and flight-termination mission rule evaluation. The software evaluates various user-configurable rule sets that govern the qualification of TSPI data sources, provides a prelaunch autonomous hold-launch function, performs the flight-monitoring-and-termination functions, and performs end-of-mission safing

Lanzi, Raymond J.↗
NASA NTRS1987

RB-ARD: A proof of concept rule-based abort

The Abort Region Determinator (ARD) is a console program in the space shuttle mission control center. During shuttle ascent, the Flight Dynamics Officer (FDO) uses the ARD to determine the possible abort modes and make abort calls for the crew. The goal of the Rule-based Abort region Determinator (RB/ARD) project was to test the concept of providing an onboard ARD for the shuttle or an automated ARD for the mission control center (MCC). A proof of concept rule-based system was developed on a LMI Lambda computer using PICON, a knowdedge-based system shell. Knowdedge derived from documented flight rules and ARD operation procedures was coded in PICON rules. These rules, in conjunction with modules of conventional code, enable the RB-ARD to carry out key parts of the ARD task. Current capabilities of the RB-ARD include: continuous updating of the available abort mode, recognition of a limited number of main engine faults and recommendation of safing actions. Safing actions recommended by the RB-ARD concern the Space Shuttle Main Engine (SSME) limit shutdown system and powerdown of the SSME Ac buses.

Smith, Richard↗
NASA NTRSDate not supplied

One-Carbon Metabolism and SANS 2022 Update

Spaceflight Associated Neuro-ocular Syndrome, or SANS, affects a subset of astronauts (1, 2), and biochemical evidence has documented differences in those astronauts (3). Specifically, they had higher circulating concentrations of metabolites of the one-carbon metabolic pathway (1C), including homocysteine, and these concentrations were higher before flight (3). After ruling out many potential confounding factors in these otherwise healthy individuals (e.g., sex, kidney function, vitamin status, coffee consumption), a study of genetics was warranted. In an initial pilot effort, we documented a genetic predisposition to develop ophthalmic changes after long-duration space flight (4). That is, from a limited study of 5 single-nucleotide polymorphisms (SNPs), we found that the G allele for the MTRR A66G SNP was associated with a greater risk of choroidal folds and cotton-wool spots after flight, and the C allele for SHMT1 C1420T was protective against optic disc edema (4). These data provide a potential pathway for understanding why some individuals develop SANS, while others do not. The initial pilot study of 5 SNPs yielded striking findings, but the 1C pathway is far more complex. An effort was undertaken to examine more than 500 1C SNPs to see if a broader examination could help illuminate this association. That work is ongoing. The astronaut findings led us to advocate for the inclusion of 1C pathway genetic and biochemistry testing on other SANS-related projects, noting that genetics might help identify responders, non-responders, or outliers. The first such effort yielded evidence of an association of specific forms of the MTRR and SHMT-1 SNPs and vitamin B12 status with end-tidal CO2 after acute carbon dioxide exposure (5). The second such effort led to the identification that individuals exposed to strict head-down tilt and CO2 for 30-d who developed optic disc edema also had risk alleles for the two SNPs described above (6). Additionally, we identified a clinical population with many characteristics either attributed or purported to be involved in the ocular changes seen in affected astronauts: women with polycystic ovary syndrome (PCOS). PCOS is a condition of androgen excess and anovulatory menstrual cycles. The shared characteristics and clinical findings between SANS and PCOS generally include higher circulating homocysteine concentrations, increased retinal nerve fiber layer thickness, increased androgen concentrations (or responses), and altered carbohydrate metabolism. To our knowledge, no study has examined whether women with PCOS have asymptomatic ophthalmic anomalies observed in astronauts with SANS. While researchers have evaluated the one-carbon metabolism pathway polymorphisms of PCOS patients, and initial studies show an association with certain one-carbon polymorphisms, none have looked at the set of SNPs identified in our studies that are associated with ophthalmic changes in astronauts. Accordingly, we designed a study to evaluate the association of one-carbon pathway SNPs and ophthalmic findings in patients with PCOS and/or IIH compared to controls. Subjects provided blood samples for vitamin and one carbon biochemistry analyses, an extensive analysis of >500 SNPs associated with one carbon metabolism and had eye examinations and ocular imaging. Data analysis are underway. The data collected to date have shown associations between one carbon pathway biochemistry and genetics and incidence of SANS. The mechanisms for SANS has yet to be identified, although many hypotheses exist. Based on our data, we have developed (8, 9) and expanded (6) a multi-hit hypothesis for how these seemingly disparate findings could be linked. While intriguing, the hypothesis represents the starting point for further research. We aim to clarify the relationship between B-vitamin status and genetics with regard to the risk of SANS. Ultimately, understanding the mechanism(s) behind this will provide a means to predict, prevent, or treat these ophthalmologic pathologies in astronauts, and terrestrial populations.

S M Smith↗
NASA NTRS1990

Observations of severe turbulence near thunderstorm tops

Data derived from the flight tapes of two airliners that experienced severe turbulence near thunderstorm tops are used to produce quantitative descriptions of the turbulence and its environment. The likely turbulence-producing processes include a three-dimensional turbulent wake in the lee of a squall line and an updraft in the top of a thunderstorm. Results suggest that current procedures for using surface and airborne weather radar for routing aircraft near thunderstorm tops should be reexamined. Also, although useful rules for safe flight near thunderstorm tops already exist, there is evidence that they are not universally applied.

Pantley, K. C.↗
NASA NTRSDate not supplied

Use of Design of Experiments and Rule-Based Inference in Determining Neural Network Architectures for Loss of Control Detection

In this work, we describe methods for selecting the neural network architectures and input spaces to implement belief state inference on generic commercial transport aircraft. First, we highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). We present a structured method that can be used in a number of aerospace applications, to optimize the structure and training parameters of the CVAE for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from a NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications. While theoretically, we could create a single CVAE with an input space the size of all measurable flight variables and environmental dynamics, it becomes intractable to use such a neural network in an in-situ intelligent multi-agent system. Using the recommendations from our case study, we introduce a technical approach for feasibly describing the belief space by (1) identifying significant statistical relationships among flight variables using rule induction, (2) using a set of rules that cover all features to define the input space of multiple CVAEs, and (3) forming a belief space based on the joint probability density of their collective latent spaces. This results in a series of relatively small matrix multiplications that can be performed in real time, as opposed to large matrix computations in a single CVAE. We demonstrate the application of this approach on the T-2 flight loss-of control experiments, using the architecture and hyperparameter recommendations from the case study. We compare the utilities of an individual CVAE trained on all flight variables and multiple CVAEs defined on subsets of flight variables for detecting qualitative changes in flight. We demonstrate that the use of multiple CVAEs with smaller input spaces permits the CVAE to capture more granular relationships in the latent space, permitting better state space characterization and loss-of-control detection.

Design of experiments↗
NASA NTRS1994

SEQ-GEN: A comprehensive multimission sequencing system

SEQ-GEN is a user-interactive computer program used to plan and generate a sequence of commands for spacecraft. Desired activities are specified by the user of SEQ-GEN; SEQ-GEN in turn expands these activities, deriving the spacecraft commands necessary to accomplish the desired activities. SEQ-GEN models the effects on the spacecraft of the commands, predicting the state as a function of time, flagging any conflicts and rule violations. These states, conflicts, and violations are viewable both graphically and textually at the user's request. SEQ-GEN also displays the entire sequence graphically, showing each requested activity as a bar on its graphical timeline. SEQ-GEN immediately revalidates the sequence, updating its models and calculations along with its displays based on these changes. Because it has the ability to recalculate spacecraft states immediately, the user is able to perform 'what-if' sessions easily. SEQ-GEN, a multimission tool, is adaptable to any flight project. A flight project writes its adaptation files containing project unique information including in its simplest form, only spacecraft commands. For more involved projects the adaptation files may also contain flight and mission rules, description of the spacecraft and ground models, and the definition of activities. SEQ-GEN operates at whatever level of detail the adaptation files imply. Simple adaptations are straight forward to do. There is, however, no limit to the complexity of activity definitions or of spacecraft models: both may involve unlimited logical decision points. Commands and activities may involve any number of parameters of a wide variety of data types, including integer, float, time, boolean, and character strings. SEQ-GEN will be used by the Mars Pathfinder, Cassini, and VIM (Voyager Interstellar Mission) project in an effort to speed up adaptation time and to keep sequence generation costs down. SEQ-GEN is hosted on UNIX workstations. It uses MOTIF and X for windowing, and was designed and coded in an object-oriented style in the language C++.

Salcedo, Jose↗