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At least 487 records · Page 27

Medical/Scientific Illustration And Production Of Otological Health Awareness Materials

Over the past year, I have worked for my mentor, Beth Cooper, on a large variety of projects. Beth is the Manager of the Acoustical Testing Laboratory, which tests the acoustical emissions of payloads destined for the International Space Station. She is also responsible for educating, and developing new methods of educating, people of all occupational and educational backgrounds in hearing conservation. Beth spends much of her time developing new materials and strategies with which to train people and teach other people to train people in hearing conservation and noise emissions control. I have been helping Beth develop and market these materials by way of graphic design and scientific illustration. Last summer, I spent much of my time creating educational illustrations that visually explained particular concepts in Beth's presentations. Sometimes these illustrations were small "comics" while, at other times, they were an instructional series of illustrations. Since then, Beth and her lab have been developing and updating some materials which will be distributed free to hearing conservation and noise control professionals and others in related fields. I have helped with these projects by designing their packaging. In each instance, it was my responsibility to develop an aesthetically appealing package that would also, through its imagery, describe or summarize the contents of the product. I did this for 3 CD's (Auditory Demonstrations 11, MACSUG, and JeopEARdy) and saw them through their actual production and distribution. In addition to working with Beth, I work with the Imaging Technology Center on various imaging projects. Some of my activities include photo retouching and manipulation for videos and print. This summer, I also had the opportunity to develop a screen saver that would show of some of the photography contained on the soon-to-be-released "Highlights of the GRC Image Archives, vol. 2". I was also able to utilize my medical training to help several of ITC s videographers identify the best histological examples of cancerous cells for incorporation in one of their videos. Over the last part of this summer and then throughout the school year, I will be working with Beth to develop a "pre-packaged" lecture series about the physics of acoustics in the context of hearing conservation. These lectures will be used to teach people of all backgrounds the fundamental concepts involved in acoustical physics so they might be better aware of their own and others auditory health in and out of the work place, and, in the case of payload developers, to design and build more quiet science experiments for the ISS. Even though it may not seem as such, this project is precisely what I am learning to do as a student of the Cleveland Institute of Art's Medical Illustration Department. From my perspective, this project is about taking technical information and translating it into terms that anyone, regardless of background, can understand.

Hawes, Nicholas E.↗

Using Analog Field Tests To Link and Prepare Science and In-Situ Resource Utilization for Future Space Missions

A major goal of NASA s human exploration program is to learn how to use the resources of space, known as In-Situ Resource Utilization (ISRU), to lower the cost and risk of human space exploration. Successful implementation of ISRU requires detailed knowledge of surface and subsurface materials, minerals, and volatiles that may be present. This same information is required to better understand the physical and geologic composition, structure, origin, and evolution of the Moon, Mars, and other extraterrestrial bodies of interest. It is also important to recognize that while ISRU and science objectives may be similar, the desired method or hardware to achieve the information desired may be drastically different. One method to promote understanding, coordination, and joint development of instruments and operations between Science and ISRU is the use of analog field demonstrations.

Sanders, Gerald B.↗

Spacecraft 3D Augmented Reality Mobile App

The Spacecraft 3D application allows users to learn about and interact with iconic NASA missions in a new and immersive way using common mobile devices. Using Augmented Reality (AR) techniques to project 3D renditions of the mission spacecraft into real-world surroundings, users can interact with and learn about Curiosity, GRAIL, Cassini, and Voyager. Additional updates on future missions, animations, and information will be ongoing. Using a printed AR Target and camera on a mobile device, users can get up close with these robotic explorers, see how some move, and learn about these engineering feats, which are used to expand knowledge and understanding about space. The software receives input from the mobile device's camera to recognize the presence of an AR marker in the camera's field of view. It then displays a 3D rendition of the selected spacecraft in the user's physical surroundings, on the mobile device's screen, while it tracks the device's movement in relation to the physical position of the spacecraft's 3D image on the AR marker.

Hussey, Kevin J.↗

A Path Towards Quantum Advantage in Training Deep Generative Models with Quantum Annealing

A class of quantum-classical hybrid machine-learning algorithms can be obtained by integrating classical deep generative models with quantum probability distributions as 'priors' over their latent variables. We introduce a hybrid implementation of variational autoencoders (QVAE) and also present a technique to hybridize flow-based invertible generative models. We demonstrate the use of D-Wave quantum annealers as physical simulators of quantum Boltzmann machines (QBM) to perform quantum-assisted training of QVAE. Latent-space QBM develop slowly mixing modes, opening a path to obtain quantum advantage in generative modeling with available quantum devices.

Vinci, Walter↗

Interpretable Machine Learning Models for Autonomous Characterization of Analogue Ocean World Seawater Chemistry and Biosignature Potential Using Isotope Ratio Data

Background: Future missions to ocean worlds, such as Enceladus and Europa, will attempt to characterize the subsurface seawater chemistry and assess the potential for life. Such missions will be equipped with capabilities to precisely measure volatile isotopes in plumes, atmospheres, and exospheres. Motivation: While large isotopic fractionations can indicate a biological source, there are signatures resulting from abiotic geochemical processes that mimic isotopic biosignatures. While machine learning (ML) has the potential to disentangle competing effects and biotic mimicry, high-dimensional isotope ratio mass spectrometry (IRMS) data is likely to contain noise/irrelevant features and involve complex statistical interactions that make human inference and interpretation difficult. Further, ML predictions with as far-reaching implications as an extraterrestrial biosignature on an ocean world requires the use of interpretable models (i.e., not “black box” models) with physically and mathematically meaningful feature spaces along with false positive diagnostics. Methods: We use volatile CO2 IRMS data of analogue ocean world seawaters to validate an ML approach to provide biogeochemical context for biosignature detection. We employ a feature selection method called nearest-neighbor projected distance regression (NPDR) that detects statistical interactions and helps elucidate the mechanisms of the Random Forest classification models. Results: We train and validate predictive ML models on volatile CO2 IRMS data of analogue ocean world seawaters to predict major salt components (e.g., MgSO4, NaHCO3), pH, ionic strength, and the presence of biosignatures. Features derived from IRMS measurements are augmented with extracted time-series features. Our results show high test accuracy and interpretability, which is increased by interaction network visualization, sample-wise variable importance scores, and single-sample class probability estimates. We demonstrate an ML mission software solution that triggers autonomous data transmission and biogeochemical sample prediction.

geochemistry↗

Accelerated Simulation of Air Pollution Using NVIDIA RAPIDS

Atmospheric chemistry models are a central tool to study and forecast the impact of air pollution on the environment, vegetation, and human health. However, the numerical simulation of chemical kinetics is computationally expensive due to the stiffness of the system of ordinary differential equations that describes atmospheric chemistry. Here we present an alternative approach to the computation of atmospheric chemistry based on machine learning. Our training data set is produced using the NASA Goddard Earth Observing System (GEOS) model with GEOS-Chem chemistry, run on the NASA Center for Climate Simulation (NCCS) Discover supercomputing cluster on 384 Intel Xeon Haswell cores. This model spends more than 50% of total run time on solving atmospheric chemistry. The data set contains as input features the air pollution concentrations before solving the differential equations, together with some key physical parameters such as temperature and sun intensity. As target variables we define the air pollution concentrations after solving the differential equations. Using Dask-cuDF and Dask-XGBoost on the NVIDIA RAPIDS platform on 8 Tesla V100 GPUs, we generate from this training set gradient boosted decision tree models that can reproduce the simulation of chemical kinetics. We do this on the NCCS Advanced Data Analytics Platform (ADAPT) science cloud environment. Our application takes full advantage of recent advances in Dask-XGBoost, such as multi-node and multi-GPU scaling for distributed training with large data sets. The increase in training data size enabled by this is critical to capture the full range of chemical environments encountered across the globe and all annual seasons.The boosted tree models offer good predictability and show many of the features of the full chemistry reference simulation. Further improvements can be achieved through mass balance considerations and by accounting for error correlations. We incorporate the boosted tree models into the GEOS reference model using XGBoost's C API. This enables a seamless integration of the GPU trained models into GEOS-Chem, which is written in Fortran and optimized for use in a massively parallel CPU environment. We show the benefits of this approach and discuss the potential speedup of this machine learning accelerated atmospheric chemistry model.

Keller, Christoph A.↗

Grain Spectroscopy

Our fundamental knowledge of interstellar grain composition has grown substantially during the past two decades thanks to significant advances in two areas: astronomical infrared spectroscopy and laboratory astrophysics. The opening of the mid-infrared, the spectral range from 4000-400 cm(sup -1) (2.5-25 microns), to spectroscopic study has been critical to this progress because spectroscopy in this region reveals more about a materials molecular composition and structure than any other physical property. Infrared spectra which are diagnostic of interstellar grain composition fall into two categories: absorption spectra of the dense and diffuse interstellar media, and emission spectra from UV-Vis rich dusty regions. The former will be presented in some detail, with the latter only very briefly mentioned. This paper summarized what we have learned from these spectra and presents 'doorway' references into the literature. Detailed reviews of many aspects of interstellar dust are given.

Allamandola, L. J.↗

Physics of Colloids in Space (PCS) Flight Hardware Developed

investigation that will be located in an Expedite the Process of Experiments to Space Station (EXPRESS) Rack. The investigation will be conducted in the International Space Station U.S. laboratory, Destiny, over a period of approximately 10 months during the station assembly period from flight 6A through flight UF-2. This experiment will gather data on the basic physical properties of colloids by studying three different colloid systems with the objective of understanding how they grow and what structures they form. A colloidal suspension consists of fine particles (micrometer to submicrometer) suspended in a fluid for example, paints, milk, salad dressings, and aerosols. The long-term goal of this investigation is to learn how to steer the growth of colloidal suspensions to create new materials and new structures. This experiment is part of a two-stage investigation conceived by Professor David Weitz of Harvard University along with Professor Peter Pusey of the University of Edinburgh. The experiment hardware was developed by the NASA Glenn Research Center through contracts with Dynacs, Inc., and ZIN Technologies.

Koudelka, John M.↗

Introduction: Photons and Ground-Based

A Conference on Measurement Techniques for Solar and Space Physics was held on 20-24 April 2015 in Boulder, Colorado, at the National Center for Atmospheric Research Center Green Campus. The present volume collects together the conference papers for photons and ground-based categories. This gathering of over 200 scientists and instrumentalists was born out of the desire to collect in one place the latest experiment and instrument technologies required for advancement of scientific knowledge in the disciplines of solar and space physics. The two goals for this conference and the subsequent publication of its content are (a) to describe measurement techniques and technology development needed to advance high priority science in the fields of solar and space physics; and (b) to provide a survey or reference of techniques for in situ measurement and remote sensing of space plasmas. Towards this end, our goal has always been inspired by the two 1998 Geophysical Monographs (Nos. 102 and 103) entitled, "Measurement Techniques in Space Plasmas" (particles and fields) [Pfaff et al., 1998a, 1998b], which have served as a reference and resource for advanced students, engineers, and scientists who wish to learn the fundamentals of measurement techniques and technology in this field. Those monographs were the product of an American Geophysical Union Chapman Conference that took place in Santa Fe, NM, in 1995: "Measurement Techniques in Space Plasmas-What Works, What Doesn't." Two decades later, we believe that it is appropriate to revisit this subject, in light of recent advances in technology, research platforms, and analysis techniques. Moreover, we now include direct measurements of neutral gases in the upper atmosphere, optical imaging techniques, and remote observations in space and on the ground. Accordingly, the workshop was organized among four areas of measurement techniques: particles, fields, photons, and ground-based. This two-set volume is largely composed of the content of that workshop. Special attention is given to those techniques and technologies that demonstrate promise of significant advancement in measurements that will enable the highest priority science as described in the 2012 National Research Council Decadal Survey [Baker and Zurbuchen et al., 2013]. Additionally, a broad tutorial survey of the current technologies is provided to serve as reference material and as a basis from which advanced and innovative ideas can be discussed and pursued. Included are instrumentation and techniques to observe the solar environment from its interior to its outer atmosphere, the heliosphere out to the interstellar regions, in geospace, and other planetary magnetospheres and atmospheres. To make significant progress in priority science as expressed in the National Research Council solar and space physics decadal survey and recent NASA Heliophysics roadmaps, identification of enabling new measurement techniques and technologies to be developed is required. Also, it is valuable to the community and future scientists and engineers to have a complete survey of the techniques and technologies used by the practitioners of solar and space physics. As with the 1995 conference and subsequent 1998 publication, it is incumbent on the community to identify those measurements that are particularly challenging and still require new techniques to be identified and tested to enable the necessary accuracy and resolution of certain parameters to be achieved. The following is a partial list of the measurement technique categories that are featured in these special publications: Particles; Thermal plasma to MeV energetic particles, neutral gas properties including winds, density, temperature, and composition, and enhanced neutral atom imaging; Fields; DC electric and magnetic fields, plasma waves, and electron drift instruments from which the plasma velocity information provides a measure of the DC electric field; Photons; Instruments sensitive from the near-infrared to X-rays; Contributions of techniques and technology for optical design, optical components, sensors, material selection for cameras, telescopes, and spectrographs; Ground based; Remote sensing methods for solar and geospace activity and space weather. The focus includes solar observatories, all-sky cameras, lidars, and ionosphere thermosphere mesosphere observatory systems such as radars, ionosondes, GPS receivers, magnetometers, conjugate observations, and airborne campaigns. The present volume collects together the papers for photons and ground-based categories. The companion volume collects together the papers for particles and fields categories. It is recognized that there are measurement techniques that overlap among the four categories. For example, use of microchannel plate detectors is used in photon and particle measurement techniques or the observation of visible photons and magnetic fields in space and on the ground share common technologies. Therefore, the reader should consider the entire collection of papers as they seek to understand particular applications. We hope that these volumes will be as valuable as a reference for our community as the earlier 1998 volumes have been.

Spann, James↗

Learning for Autonomous Navigation

Robotic ground vehicles for outdoor applications have achieved some remarkable successes, notably in autonomous highway following (Dickmanns, 1987), planetary exploration (1), and off-road navigation on Earth (1). Nevertheless, major challenges remain to enable reliable, high-speed, autonomous navigation in a wide variety of complex, off-road terrain. 3-D perception of terrain geometry with imaging range sensors is the mainstay of off-road driving systems. However, the stopping distance at high speed exceeds the effective lookahead distance of existing range sensors. Prospects for extending the range of 3-D sensors is strongly limited by sensor physics, eye safety of lasers, and related issues. Range sensor limitations also allow vehicles to enter large cul-de-sacs even at low speed, leading to long detours. Moreover, sensing only terrain geometry fails to reveal mechanical properties of terrain that are critical to assessing its traversability, such as potential for slippage, sinkage, and the degree of compliance of potential obstacles. Rovers in the Mars Exploration Rover (MER) mission have got stuck in sand dunes and experienced significant downhill slippage in the vicinity of large rock hazards. Earth-based off-road robots today have very limited ability to discriminate traversable vegetation from non-traversable vegetation or rough ground. It is impossible today to preprogram a system with knowledge of these properties for all types of terrain and weather conditions that might be encountered.

mixture of Gaussians↗

POINTER: Portable Intelligent Trainer for External Robotics

Intelligent tutoring systems (ITS's) play an increasing role in training and education of people with different levels of skill and knowledge. As compared to conventional computer based training (CBT) an ITS provides more tailored instruction by trying to mimic the teaching behavior of a human instructor as much as possible and is therefore much more flexible. This paper starts with an introduction to ITS's, followed by the description of an ITS for training of an (astronaut) operator in monitoring and controlling robotic arm procedures. The robotic arm will be used for exchange of equipment between a space station and a space plane involving critical and accurate movements of the robotic arm. The ITS for this application, called Pointer, is developed by TNO Physics and Electronics Laboratory and is based upon an existing ITS that includes procedural training. Pointer has been developed on a workstation whereas the target platform was a portable computer. Therefore, a lot of attention had to be paid to scaling effects and keeping up with user friendliness of the much smaller user interface. Although the learning domain was the control of a robotic arm, it is clear that use of intelligent training technologies on a portable computer has many other applications (payload operations, operation control rooms, etc.). Training can occur at any time and place in an attractive and cost effective way.

Kuiper, Hilbert↗

Biomedical support of man in space

In its broadest sense, biomedical support of man in space must not be limited to assisting spacecraft crew during the mission; such support should also ensure that flight personnel be able to perform properly during landing and after leaving the craft. Man has developed mechanisms that allow him to cope with specific stresses in his normal habitat; there is indisputable evidence that, in some cases, the space environment, by relieving these stresses, has also allowed the adaptive mechanisms to lapse, causing serious problems after re-entry. Inflight biomedical support must therefore include means to simulate some of the normal stresses of the Earth environment. In the area of cardiovascular performance, we have come to rely heavily on complex feedback mechanisms to cope with two stresses, often combined: postural changes, which alter the body axis along which gravitational acceleration acts, and physical exercise, which increases the total load on the system. Unless the appropriate responses are reinforced continuously during flight, crew members may be incapacitated upon return. The first step in the support process must be a study of the way in which changes in g, even of short duration, affect these responses. In particular we should learn more about effects of g on the "on" and "off" dynamics, using a variety of approaches: increased acceleration on one hand at recumbency, immersion, lower body positive pressure, and other means of simulating some of the effects of low g, on the other. Once we understand this, we will have to determine the minimal exposure dose required to maintain the response mechanisms. Finally, we shall have to design stresses that simulate Earth environment and can be imposed in the space vehicle. Some of the information is already at hand; we know that several aspects of the response to exercise are affected by posture. Results from a current series of studies on the kinetics of tilt and on the dynamics of readjustment to exercise in different postures will be presented and discussed.

NASA Discipline Cardiopulmonary↗

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↗

The Interstellar Conspiracy

If we were designing a human-carrying starship that could be launched in the not-too-distant future, it would almost certainly not use a warp drive to instantaneously bounce around the universe, as is done in Isaac Asimov's classic Foundation series or in episodes of Star Trek or Star Wars. Sadly, those starships that seem to be within technological reach could not even travel at high relativistic speeds, as does the interstellar ramjet in Poul Anderson's Tau Zero. Warp-speeds seem to be well outside the realm of currently understood physical law; proton-fusing ramjets may never be technologically feasible. Perhaps fortunately in our terrorist-plagued world, the economics of antimatter may never be attractive for large-scale starship propulsion. But interstellar travel will be possible within a few centuries, although it will certainly not be as fast as we might prefer. If humans learn how to hibernate, perhaps we will sleep our way to the stars, as do the crew in A. E. van Vogt's Far Centaurus. However, as discussed in a landmark paper in The Journal of the British Interplanetary Society, the most feasible approach to transporting a small human population to the planets (if any) of Alpha Centauri is the worldship. Such craft have often been featured in science fiction. See for example Arthur C. Clarke's Rendezvous with Rama, and Robert A. Heinlein's Orphans of the Sky. Worldships are essentially mobile versions of the O Neill free-space habitats. Constructed mostly from lunar and/or asteroidal materials, these solar-powered, multi-kilometer-dimension structures could house 10,000 to 100,000 humans in Earth-approximating environments. Artificial gravity would be provided by habitat rotation, and cosmic ray shielding would be provided by passive methods, such as habitat atmosphere and mass shielding, or magnetic fields. A late 21st century space-habitat venture might support itself economically by constructing large solar-powered satellites to beam energy back to Earth.

Johnson, Les↗

Complexity for Survival of Living Systems

A logical connection between the survivability of living systems and the complexity of their behavior (equivalently, mental complexity) has been established. This connection is an important intermediate result of continuing research on mathematical models that could constitute a unified representation of the evolution of both living and non-living systems. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the two most relevant being Characteristics of Dynamics of Intelligent Systems (NPO- 21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; and Self-Supervised Dynamical Systems (NPO- 30634) NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72. As used here, living systems is synonymous with active systems and intelligent systems. The quoted terms can signify artificial agents (e.g., suitably programmed computers) or natural biological systems ranging from single-cell organisms at one extreme to the whole of human society at the other extreme. One of the requirements that must be satisfied in mathematical modeling of living systems is reconciliation of evolution of life with the second law of thermodynamics. In the approach followed in this research, this reconciliation is effected by means of a model, inspired partly by quantum mechanics, in which the quantum potential is replaced with an information potential. The model captures the most fundamental property of life - the ability to evolve from disorder to order without any external interference. The model incorporates the equations of classical dynamics, including Newton s equations of motion and equations for random components caused by uncertainties in initial conditions and by Langevin forces. The equations of classical dynamics are coupled with corresponding Liouville or Fokker-Planck equations that describe the evolutions of probability densities that represent the uncertainties. The coupling is effected by fictitious information-based forces that are gradients of the information potential, which, in turn, is a function of the probability densities. The probability densities are associated with mental images both self-image and nonself images (images of external objects that can include other agents). The evolution of the probability densities represents mental dynamics. Then the interaction between the physical and metal aspects of behavior is implemented by feedback from mental to motor dynamics, as represented by the aforementioned fictitious forces. The interaction of a system with its self and nonself images affords unlimited capacity for increase of complexity. There is a biological basis for this model of mental dynamics in the discovery of mirror neurons that learn by imitation. The levels of complexity attained by use of this model match those observed in living systems. To establish a mechanism for increasing the complexity of dynamics of an active system, the model enables exploitation of a chain of reflections exemplified by questions of the form, "What do you think that I think that you think...?" Mathematically, each level of reflection is represented in the form of an attractor performing the corresponding level of abstraction with more details removed from higher levels. The model can be used to describe the behaviors, not only of biological systems, but also of ecological, social, and economics ones.

Zak, Michail↗

X-Ray Backscatter Machine Support Frame

This summer at Kennedy Space Center, I spent 10 weeks as an intern working at the Prototype Development Lab. During this time I learned about the design and machining done here at NASA. I became familiar with the process from where a design begins in Pro/Engineer and finishes at the hands of the machinists. As an intern I was given various small jobs to do and then one project of my own. My personal project was a job for the Applied Physics Lab; in their work they use an X-Ray Backscatter machine. Previously it was resting atop a temporary frame that limited the use of the machine. My job was to design a frame for the machine to rest upon that would allow a full range of sample sizes. The frame was required to support the machine and provide a strain relief for the cords attached to the machine as it moved in the x and y directions. Calculations also had to be done to be sure the design would be able to withstand any loads or outside sources of stress. After the calculations proved the design to be ready to withstand the requirements, the parts were ordered or fabricated, as required. This helped me understand the full process of jobs sent to the Prototype Development Lab.

Cannon, Brooke↗

NASA Applications and Lessons Learned in Reliability Engineering

Since the Shuttle Challenger accident in 1986, communities across NASA have been developing and extensively using quantitative reliability and risk assessment methods in their decision making process. This paper discusses several reliability engineering applications that NASA has used over the year to support the design, development, and operation of critical space flight hardware. Specifically, the paper discusses several reliability engineering applications used by NASA in areas such as risk management, inspection policies, components upgrades, reliability growth, integrated failure analysis, and physics based probabilistic engineering analysis. In each of these areas, the paper provides a brief discussion of a case study to demonstrate the value added and the criticality of reliability engineering in supporting NASA project and program decisions to fly safely. Examples of these case studies discussed are reliability based life limit extension of Shuttle Space Main Engine (SSME) hardware, Reliability based inspection policies for Auxiliary Power Unit (APU) turbine disc, probabilistic structural engineering analysis for reliability prediction of the SSME alternate turbo-pump development, impact of ET foam reliability on the Space Shuttle System risk, and reliability based Space Shuttle upgrade for safety. Special attention is given in this paper to the physics based probabilistic engineering analysis applications and their critical role in evaluating the reliability of NASA development hardware including their potential use in a research and technology development environment.

Safie, Fayssal M.↗