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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Process Development of the Vaporizing Foil Actuator Welding Technique

The industrial focus on continuous improvement for products is leading design engineers to consider multi-material design concepts more frequently. The design concepts use the advantages of each material while simultaneously minimizing the drawbacks. It also allows for the overall weight of the products to be reduced. Some traditional joining methods, though, struggle to join dissimilar material combinations. A technique known as vaporizing foil actuator welding, which was developed around ten years ago, is a solid-state joining method that has the potential to overcome the barriers to dissimilar material joining. The biggest drawbacks of vaporizing foil actuator welding are that it is still exclusively used in laboratory settings and early in the technology development process. As such, there is not enough confidence in the process for it to be transitioned to manufacturing environments yet. This work begins by analyzing the current state of the technology and identifying a roadmap to achieve a transition to manufacturing. The analysis found that increased confidence in the technology requires sample to sample repeatability to be improved. Beyond that, the process also needs to be fully automated before it can be considered as a viable technique for mass production. In order to further develop the technique, a fully automated work cell was constructed. By removing the human element from the process, this cell helped identify iii the aspects which contribute to process variability. The positioning of the vaporizing foil actuator was identified as the most important factor in process stability. A small set of samples were made in the automated cell with the addition of one component epoxy adhesive around the perimeter of the weld. These samples showed improved process repeatability compared with the samples made without adhesive. This indicates that small amounts of adhesive may be critical in using the process in industrial settings. Additionally, work was performed with manual positioning and adhesive dispensing. This work found that a hybrid joint behaves like traditional weld bonding, where the addition of adhesive makes the joint stronger than if either of the two joining methods were used on their own. To realize this performance with vaporizing foil actuator welding, though, the adhesive needs to be protected from burning during the rapid compression of air during the impact process.

36 MATERIALS SCIENCE↗
NASA NTRS2012

Suitport Feasibility: Development and Test of a Suitport and Space Suit for Human Pressurized Space Suit Donning Tests

The suitport concept has been recently implemented as part of the small pressurized lunar rover (Currently the Space Exploration vehicle, or SEV) and the Multi-Mission Space Exploration Vehicle (MMSEV) concept demonstrator vehicle. Suitport replaces or augments the traditional airlock function of a spacecraft by providing a bulkhead opening, capture mechanism, and sealing system to allow ingress and egress of a space suit while the space suit remains outside of the pressurized volume of the spacecraft. This presents significant new opportunities to EVA exploration in both microgravity and surface environments. The suitport concept will enable three main improvements in EVA by providing reductions in: pre-EVA time from hours to less than thirty minutes; airlock consumables; contamination returned to the cabin with the EVA crewmember. To date, the first generation suitport has been tested with mockup suits on the rover cabins and pressurized on a bench top engineering unit. The work on the rover cabin has helped define the operational concepts and timelines, and has demonstrated the potential of suitport to save significant amounts of crew time before and after EVAs. The work with the engineering unit has successfully demonstrated the pressurizable seal concept including the ability to seal after the introduction and removal of contamination to the sealing surfaces. Using this experience, a second generation suitport was designed. This second generation suitport has been tested with a space suit prototype on the second generation MMSEV cabin, and testing is planned using the pressure differentials of the spacecraft. Pressurized testing will be performed using the JSC B32 Chamber B, a human rated vacuum chamber. This test will include human rated suitports, a suitport compatible prototype suit, and chamber modifications. This test will bring these three elements together in the first ever pressurized donning of a rear entry suit through a suitport. This paper presents the design of a human rated second generation suitport, the design of a suit capable of supporting pressurized human donning through a suitport, ambient pressure testing of the suit with the suitport, and modifications to the JSC human rated chamber B to accept a suitport. Design challenges and solutions, as well as compromises required to develop the system, are presented. Initial human testing results are presented.

Boyle, Robert M.↗
NASA NTRS2015

Human Research Program Advanced Exercise Concepts (AEC) Overview

Exercise countermeasures provide benefits that are crucial for successful human spaceflight, to mitigate the spaceflight physiological deconditioning which occurs during exposure to microgravity. The NASA Human Research Program (HRP) within the Human Exploration and Operations Mission Directorate (HEOMD) is managing next generation Advanced Exercise Concepts (AEC) requirements development and candidate technology maturation to Technology Readiness Level (TRL) 7 (ground prototyping and flight demonstration) for all exploration mission profiles from Multi Purpose Crew Vehicle (MPCV) Exploration Missions (up to 21 day duration) to Mars Transit (up to 1000 day duration) missions. These validated and optimized exercise countermeasures systems will be provided to the ISS Program and MPCV Program for subsequent flight development and operations. The International Space Station (ISS) currently has three major pieces of operational exercise countermeasures hardware: the Advanced Resistive Exercise Device (ARED), the second-generation (T2) treadmill, and the cycle ergometer with vibration isolation system (CEVIS). This suite of exercise countermeasures hardware serves as a benchmark and is a vast improvement over previous generations of countermeasures hardware, providing both aerobic and resistive exercise for the crew. However, vehicle and resource constraints for future exploration missions beyond low Earth orbit will require that the exercise countermeasures hardware mass, volume, and power be minimized, while preserving the current ISS capabilities or even enhancing these exercise capabilities directed at mission specific physiological functional performance and medical standards requirements. Further, mission-specific considerations such as preservation of sensorimotor function, autonomous and adaptable operation, integration with medical data systems, rehabilitation, and in-flight monitoring and feedback are being developed for integration with the exercise countermeasures systems. Numerous technologies have been considered and evaluated against HRP-approved functional device requirements for these extreme mission profiles, and include wearable sensors, exoskeletons, flywheel, pneumatic, and closed-loop microprocessor controlled motor driven systems. Each technology has unique advantages and disadvantages. The Advanced Exercise Concepts project oversees development of candidate next generation exercise countermeasures hardware, performs trade studies of current and state of the art exercise technologies, manages and supports candidate systems physiological evaluations with human test subjects on the ground, in flight analogs and flight. The near term goal is evaluation of candidate systems in flight, culminating in an integrated candidate next generation exercise countermeasures suite on the ISS which coalesces research findings from HRP disciplines in the areas of exercise performance for muscle, bone, cardiovascular, sensorimotor, behavioral health, and nutrition for optimal benefit to the crew.

Countermeasures↗
DOE OSTI2024

Self-Supervised Cloud Classification

Abstract Low-level marine clouds play a pivotal role in Earth’s weather and climate through their interactions with radiation, heat and moisture transport, and the hydrological cycle. These interactions depend on a range of dynamical and microphysical processes that result in a broad diversity of cloud types and spatial structures, and a comprehensive understanding of cloud morphology is critical for continued improvement of our atmospheric modeling and prediction capabilities moving forward. Deep learning has recently accelerated our ability to study clouds using satellite remote sensing, and machine learning classifiers have enabled detailed studies of cloud morphology. A major limitation of deep learning approaches to this problem, however, is the large number of hand-labeled samples that are required for training. This work applies a recently developed self-supervised learning scheme to train a deep convolutional neural network (CNN) to map marine cloud imagery to vector embeddings that capture information about mesoscale cloud morphology and can be used for satellite image classification. The model is evaluated against existing cloud classification datasets and several use cases are demonstrated, including training cloud classifiers with very few labeled samples, interrogation of the CNN’s learned internal feature representations, cross-instrument application, and resilience against sensor calibration drift and changing scene brightness. The self-supervised approach learns meaningful internal representations of cloud structures and achieves comparable classification accuracy to supervised deep learning methods without the expense of creating large hand-annotated training datasets. Significance Statement Marine clouds heavily influence Earth’s weather and climate, and improved understanding of marine clouds is required to improve our atmospheric modeling capabilities and physical understanding of the atmosphere. Recently, deep learning has emerged as a powerful research tool that can be used to identify and study specific marine cloud types in the vast number of images collected by Earth-observing satellites. While powerful, these approaches require hand-labeling of training data, which is prohibitively time intensive. This study evaluates a recently developed self-supervised deep learning method that does not require human-labeled training data for processing images of clouds. We show that the trained algorithm performs competitively with algorithms trained on hand-labeled data for image classification tasks. We also discuss potential downstream uses and demonstrate some exciting features of the approach including application to multiple satellite instruments, resilience against changing image brightness, and its learned internal representations of cloud types. The self-supervised technique removes one of the major hurdles for applying deep learning to very large atmospheric datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Efforts to enhance reproducibility in a human performance research project

Background: Ensuring the validity of results from funded programs is a critical concern for agencies that sponsor biological research. In recent years, the open science movement has sought to promote reproducibility by encouraging sharing not only of finished manuscripts but also of data and code supporting their findings. While these innovations have lent support to third-party efforts to replicate calculations underlying key results in the scientific literature, fields of inquiry where privacy considerations or other sensitivities preclude the broad distribution of raw data or analysis may require a more targeted approach to promote the quality of research output. Methods: We describe efforts oriented toward this goal that were implemented in one human performance research program, Measuring Biological Aptitude, organized by the Defense Advanced Research Project Agency's Biological Technologies Office. Our team implemented a four-pronged independent verification and validation (IV&V) strategy including 1) a centralized data storage and exchange platform, 2) quality assurance and quality control (QA/QC) of data collection, 3) test and evaluation of performer models, and 4) an archival software and data repository. Results: Our IV&V plan was carried out with assistance from both the funding agency and participating teams of researchers. QA/QC of data acquisition aided in process improvement and the flagging of experimental errors. Holdout validation set tests provided an independent gauge of model performance. Conclusions: In circumstances that do not support a fully open approach to scientific criticism, standing up independent teams to cross-check and validate the results generated by primary investigators can be an important tool to promote reproducibility of results.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Raman-based sorting of microbial cells to link functions to their genes

In our recent work, we developed an optofluidic platform that allows a direct link to be made between the phenotypes (functions) and the genotypes (genes) of microbial cells within natural communities. By combining stable isotope probing, optical tweezers, Raman microspectroscopy, and microfluidics, the platform performs automated Raman-based sorting of taxa from within a complex community in terms of their functional properties. In comparison with manual sorting approaches, our method provides high throughput (up to 500 cells per hour) and very high sorting accuracy (98.3 ± 1.7%), and significantly reduces the human labour required. The system provides an efficient manner to untangle the contributions of individual members within environmental and host-associated microbiomes. In this News and Thoughts, we provide an overview of our platform, describe potential applications, suggest ways in which the system could be improved, and discuss future directions in which Raman-based analysis of microbial populations might be developed.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1986

Highlights of contractor initiatives in quality enhancement and productivity improvement

The NASA/Contractor Team efforts are presented as part of NASA's continuing effort to facilitate the sharing of quality and productivity improvement ideas among its contractors. This complilation is not meant to be a comprehensive review of contractor initiative nor does it necessarily express NASA's views. The submissions represent samples from a general survey, and were not edited by NASA. The efforts are examples of quality and productivity programs in private industry, and as such, highlight company efforts in individual areas. Topics range from modernization of equipment, hardware, and technology to management of human resources. Of particular interest are contractor initiatives which deal with measurement and evaluation data pertaining to quality and productivity performance.

Source record↗
NASA NTRS2005

Pilot Preference, Compliance, and Performance With an Airborne Conflict Management Toolset

A human-in-the-loop experiment was conducted at the NASA Ames and Langley Research Centers, investigating the En Route Free Maneuvering component of a future air traffic management concept termed Distributed Air/Ground Traffic Management (DAG-TM). NASA Langley test subject pilots used the Autonomous Operations Planner (AOP) airborne toolset to detect and resolve traffic conflicts, interacting with subject pilots and air traffic controllers at NASA Ames. Experimental results are presented, focusing on conflict resolution maneuver choices, AOP resolution guidance acceptability, and performance metrics. Based on these results, suggestions are made to further improve the AOP interface and functionality.

Doble, Nathan A.↗
NASA NTRS2015

Atmosphere Resource Recovery and Environmental Monitoring

Atmosphere Resource Recovery and Environmental Monitoring (ARREM) is a project focused on evolving existing and maturing emerging 'closed loop' atmosphere revitalization (AR) life support systems that produce clean, breathable air for crewmembers, and developing a suite of low mass, low power environmental monitors to detect and measure air- and waterborne constituents and contaminants. The objective is to improve reliability and efficiency, reduce mass and volume, and increase recovery of oxygen from carbon dioxide created by human metabolism from 43% to greater than 90%. The technology developments under ARREM are vital to extending human space missions from low-Earth orbit like the International Space Station to destinations deeper into space such as Mars where dependency on Earth for resupply of maintenance items and critical life support elements such as water and oxygen is not possible. The primary goal of the ARREM project is to demonstrate that systems meet the more stringent performance parameters for deep space exploration and are compatible with other systems within closed loop life support through a series of integrated tests performed in an environmental test chamber capable of simulating human metabolic activities and measuring systems outputs.

Roman, Monsi↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

NASA has employed high-throughput molecular assays to identify sub-cellular changes impacting human physiology during spaceflight. Machine learning (ML) methods hold the promise to improve our ability to identify important signals within highly dimensional molecular data. However, the inherent limitation of study subject numbers within a spaceflight mission minimizes the utility of ML approaches. To overcome the sample power limitations, data from multiple spaceflight missions must be aggregated while appropriately addressing intra- and inter-study variabilities. Here we describe an approach to log transform, scale and normalize data from six heterogeneous, mouse liver derived transcriptomics datasets (ntotal=137) which enabled ML-methods to perform well (AUC ≥ 0.87) in classifying spaceflown vs ground control animals rather than mission-of-origin. Concordance was found between liver-specific biological processes identified from harmonized ML-based analysis and study-by-study classical omics analysis. This work demonstrates the feasibility of applying ML methods on integrated, heterogeneous datasets of small sample size.

Machine Learning↗
DOE OSTI2022

Deep learning multiphysics network for imaging CO 2 saturation and estimating uncertainty in geological carbon storage

Multiphysics inversion exploits different types of geophysical data that often complement each other and aims to improve overall imaging resolution and reduce uncertainties in geophysical interpretation. Despite the advantages, traditional multiphysics inversion is challenging because it requires a large amount of computational time and intensive human interactions for preprocessing data and finding trade-off parameters. These issues make it nearly impossible for traditional multiphysics inversion to be applied as a real-time monitoring tool for geological carbon storage. In this paper, we present a deep learning (DL) multiphysics network for imaging CO 2 saturation in real time. The multiphysics network consists of three encoders for analysing seismic, electromagnetic and gravity data and shares one decoder for combining imaging capabilities of the different geophysical data for better predicting CO 2 saturation. The network is trained on pairs of CO 2 label models and multiphysics data so that it can directly image CO 2 saturation. Here we use the bootstrap aggregating method to enhance the imaging accuracy and estimate uncertainties associated with CO 2 saturation images. Using realistic CO 2 label models and multiphysics data derived from the Kimberlina CO 2 storage model, we evaluate the performance of the deep learning multiphysics network and compare its imaging results to those from the deep learning single-physics networks. Our modelling experiments show that the deep learning multiphysics network for seismic, electromagnetic, and gravity data not only improves the imaging accuracy but also reduces uncertainties associated with CO 2 saturation images. Our results also suggest that the deep learning multiphysics network for the non-seismic data (i.e., electromagnetic and gravity) can be used as an effective low-cost monitoring tool in between regular seismic monitoring.

58 GEOSCIENCES↗
DOE OSTI2024

Robustness of topological persistence in knowledge distillation for wearable sensor data

Topological data analysis (TDA) has shown great success in various applications involving wearable sensor data. However, there are difficulties in leveraging topological features in machine learning and wearable sensors because of the large time consumption and computational resources required to extract the features. To address this problem, knowledge distillation (KD) is utilized to generate a small model and accommodate topological features with persistence image (PI) representations from the raw time series data. Deploying topological knowledge in KD enables the student to achieve better performance compared to the one trained solely on raw time series data. However, it is not yet known if there are coherent characteristics for topological features in PI, which can aid in improving the performance during KD. In this paper, we investigate the suitability and challenges of utilizing topological features in KD for wearable sensor data, thereby contributing to the advancement of the field. Our study explores the impact of transferred topological features by comparing the Teacher-to-Student framework with Multiple Teachers-to-Student where teachers utilize both time series data and persistence images obtained by TDA as inputs. Additionally, we conduct a rigorous examination of topological knowledge effects by testing under various corruptions, knowledge types, and learning strategies in the context of human activity recognition tasks. Our analysis of topological features in KD presents the optimal strategy for incorporating these features. This study includes datasets of varying scales, window lengths, and activity classes, providing a comprehensive evaluation. Our results demonstrate that leveraging topological features in KD to enhance performance across databases.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

Evaluation of the Terminal Precision Scheduling and Spacing System for Near-Term NAS Application

NASA has developed a capability for terminal area precision scheduling and spacing (TAPSS) to provide higher capacity and more efficiently manage arrivals during peak demand periods. This advanced technology is NASA's vision for the NextGen terminal metering capability. A set of human-in-the-loop experiments was conducted to evaluate the performance of the TAPSS system for near-term implementation. The experiments evaluated the TAPSS system under the current terminal routing infrastructure to validate operational feasibility. A second goal of the study was to measure the benefit of the Center and TRACON advisory tools to help prioritize the requirements for controller radar display enhancements. Simulation results indicate that using the TAPSS system provides benefits under current operations, supporting a 10% increase in airport throughput. Enhancements to Center decision support tools had limited impact on improving the efficiency of terminal operations, but did provide more fuel-efficient advisories to achieve scheduling conformance within 20 seconds. The TRACON controller decision support tools were found to provide the most benefit, by improving the precision in schedule conformance to within 20 seconds, reducing the number of arrivals having lateral path deviations by 50% and lowering subjective controller workload. Overall, the TAPSS system was found to successfully develop an achievable terminal arrival metering plan that was sustainable under heavy traffic demand levels and reduce the complexity of terminal operations when coupled with the use of the terminal controller advisory tools.

Thipphavong, Jane↗
DOE OSTI2020

Modeling the Sources and Transport Processes During Extreme Ammonia Episodes in the U.S. Corn Belt

Abstract Atmospheric ammonia (NH 3 ) is the primary form of reactive nitrogen (N r ) and a precursor of ammonium (NH 4 + ) aerosols. Ammonia has been linked to adverse impacts on human health, the loss of ecosystem biodiversity, and plays a key role in aerosol radiative forcing. The midwestern United States is the major NH 3 source in North America because of dense livestock operations and the high use of synthetic nitrogen fertilizers. Here, we combine tall‐tower (100 m) observations in Minnesota and Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) modeling to investigate high and low NH 3 emission episodes within the U.S. Corn Belt to improve our understanding of the distribution of emission sources and transport processes. We examined observations and performed model simulations for cases in February through November of 2017 and 2018. The results showed the following: (1) Peak emissions in November 2017 were enhanced by above‐normal air temperatures, implying a Q 10 (i.e., the change in NH 3 emissions for a temperature increase of 10°C) of 2.5 for emissions. (2) The intensive livestock emissions rom northern Iowa, approximately 400 km away from the tall tower, accounted for 17.6% of the abundance in tall‐tower NH 3 mixing ratios. (3) Ammonia mixing ratios in the innermost domain 3 frequently (i.e., 336 hr, 48% of November 2017) exceeded 5.3 ppb, an important air quality health standard. (4) In November 2017, simulated NH 3 net ecosystem exchange (the difference between NH 3 emissions and dry deposition) accounted for 60–65% of gross NH 3 emissions for agricultural areas and was 2.8–3.1 times the emissions of forested areas. (5) We estimated a mean annual NH 3 net ecosystem exchange of 1.60 ± 0.06 nmol · m −2 · s −1 for agricultural lands and −0.07 ± 0.02 nmol · m −2 · s −1 for forested lands. These results imply that future warmer fall temperatures will enhance agricultural NH 3 emissions, increase the frequency of dangerous NH 3 episodes, and enhance dry NH 3 deposition in adjacent forested lands.

Hu, Cheng↗
DOE OSTI2023

Federated benchmarking of medical artificial intelligence with MedPerf

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1994

Constraints on Neural Mechanisms Underlying the Spatial Integration of Speed Information

We measured human ability to integrate speed information presented simultaneously at multiple locations in the visual field. Observers did a two-interval forced-choice speed discrimination task with n grating patches in each interval at 4' eccentricity from fixation. In the first (integration) paradigm, all gratings in each interval moved at the same speed and observers were asked to pick the interval containing the faster gratings. Speed discrimination improved as the number of gratings in each interval increased. The observed decrease in threshold is not due simply to an increase in the effective area because performance with a single grating two, four, or six times the area of the original grating showed no such improvement. Furthermore, the improvement with n was still observed even when the speeds of the individual gratings in each interval were independent samples from a Gaussian distribution and the grating directions were balanced (i.e. equal numbers of gratings moved to the left and right). These results indicate that the neural mechanisms responsible for the integration of speed information act as if each patch provides an independent sample of speed, independent of grating direction. The results are not consistent with simple summation across space by a directionally selective mechanism with a large receptive field. In the second (search) paradigm, only one of the gratings in an interval moved faster and observers were asked to pick this interval. In this case, thresholds increased with the number of distractor gratings. The decrease in performance with increased n is common in search tasks and is often attributed to a bottleneck in the information that observers can process simultaneously. However, the results in the integration paradigm show that subjects indeed have access to and can use additional information with increasing n, at least up to 4 patches. Finally, simple detection models predict the trends in both the search and integration paradigms without invoking an input bottleneck.

Verghese, Preeti↗
NASA NTRS1994

Constraints on Neural Mechanisms Underlying the Spatial Integration of Speed Information

We measured human ability to integrate speed information presented simultaneously at multiple locations in the visual field. Observers did a two-interval forced-choice speed discrimination task with n grating patches in each interval at 4deg eccentricity from fixation. In the first (integration) paradigm, all gratings in each interval moved at the same speed and observers were asked to pick the interval containing the faster gratings. Speed discrimination improved as the number of gratings in each interval increased. The observed decrease in threshold is not due simply to an increase in the effective area because performance with a single grating two, four, or six times the area of the original grating showed no such improvement. Furthermore, the improvement with n was still observed even when the speeds of the individual gratings in each interval were independent samples from a Gaussian distribution and the grating directions were balanced (i.e. equal numbers of gratings moved to the left and right). These results indicate that the neural mechanisms responsible for the integration of speed information act as if each patch provides an independent sample of speed, independent of grating direction. The results are not consistent with simple summation across space by a directionally selective mechanism with a large receptive field. in the second (search) paradigm, only one of the gratings in an interval moved faster and observers were asked to pick this interval. In this case, thresholds increased with the number of distractor gratings. The decrease in performance with increased n is common in search tasks and is often attributed to a bottleneck in the information that observers can process simultaneously. However, the results in the integration paradigm show that subjects indeed have access to and can use additional information with increasing n, at least up to 4 patches. Finally, simple detection models predict the trends in both the search and integration paradigms without invoking an input bottleneck.

Verghese, Preeti↗
NASA NTRS2011

Space Shuttle GN and C Development History and Evolution

Completion of the final Space Shuttle flight marks the end of a significant era in Human Spaceflight. Developed in the 1970 s, first launched in 1981, the Space Shuttle embodies many significant engineering achievements. One of these is the development and operation of the first extensive fly-by-wire human space transportation Guidance, Navigation and Control (GN&C) System. Development of the Space Shuttle GN&C represented first time inclusions of modern techniques for electronics, software, algorithms, systems and management in a complex system. Numerous technical design trades and lessons learned continue to drive current vehicle development. For example, the Space Shuttle GN&C system incorporated redundant systems, complex algorithms and flight software rigorously verified through integrated vehicle simulations and avionics integration testing techniques. Over the past thirty years, the Shuttle GN&C continued to go through a series of upgrades to improve safety, performance and to enable the complex flight operations required for assembly of the international space station. Upgrades to the GN&C ranged from the addition of nose wheel steering to modifications that extend capabilities to control of the large flexible configurations while being docked to the Space Station. This paper provides a history of the development and evolution of the Space Shuttle GN&C system. Emphasis is placed on key architecture decisions, design trades and the lessons learned for future complex space transportation system developments. Finally, some of the interesting flight operations experience is provided to inform future developers of flight experiences.

Zimpfer, Douglas↗