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NASA NTRS2021

Locating the Isolator Shock-Train Leading Edge with Limited Pressure Information

Real-time detection and control of the isolator shock-train leading edge (STLE) is important to the performance of high-speed air-breathing engines, such as dual-mode scramjets. Typically, the STLE location is determined using wall static-pressure measurements, but there are often restrictions on the placement and overall number of the pressure transducers, reducing the viability and accuracy of such approaches. To address these issues, we introduce the adaptive pressure profile (APP) method for estimating the STLE location. This method does not require extensive prior characterization of the isolator or engine model. Instead, it uses real-time pressure measurements from a small number of transducers to adaptively learn the isolator pressure profile and subsequently uses this deduced profile to estimate the STLE location in a data-driven manner. The APP method works well in situations with sparse transducer placement. It produces accurate estimates when the STLE location is 1) not bounded by two or more transducers or 2) between two transducers that are several isolator duct heights apart. We demonstrate the efficacy of the APP method using simulations and experimental data from direct-connect isolator models. This validation shows that the APP method is accurate and robust for different flow regimes, transducer configurations, and model geometries.

Gregory J. Hunt↗
NASA NTRS2010

Human Factors in Training: Space Medical Proficiency Training

The early Constellation space missions are expected to have medical capabilities very similar to those currently on the Space Shuttle and the International Space Station (ISS). For Crew Exploration Vehicle (CEV) missions to the ISS, medical equipment will be located on the ISS, and carried into CEV in the event of an emergency. Flight surgeons (FS) on the ground in Mission Control will be expected to direct the crew medical officer (CMO) during medical situations. If there is a loss of signal and the crew is unable to communicate with the ground, a CMO would be expected to carry out medical procedures without the aid of a FS. In these situations, performance support tools can be used to reduce errors and time to perform emergency medical tasks. The space medical training work is part of the Human Factors in Training Directed Research Project (DRP) of the Space Human Factors Engineering (SHFE) Project under the Space Human Factors and Habitability (SHFH) Element of the Human Research Program (HRP). This is a joint project consisting of human factors team from the Ames Research Center (ARC) with Immanuel Barshi as Principal Investigator and the Johnson Space Center (JSC). Human factors researchers at JSC have recently investigated medical performance support tools for CMOs on-orbit, and FSs on the ground, and researchers at the Ames Research Center performed a literature review on medical errors. Work on medical training has been conducted in collaboration with the Medical Training Group at the Johnson Space Center (JSC) and with Wyle Laboratories that provides medical training to crew members, biomedical engineers (BMEs), and to flight surgeons under the Bioastronautics contract. One area of research building on activities from FY08, involved the feasibility of just-in-time (JIT) training techniques and concepts for real-time medical procedures. A second area of research involves FS performance support tools. Information needed by the FS during the ISS mission support, especially for an emergency situation (e.g., fire onboard ISS), may be located in many different places around the FS s console. A performance support tool prototype is being developed to address this issue by bringing all of the relevant information together in one place. The tool is designed to include procedures and other information needed by a FS during an emergency, as well as procedures and information to be used after the emergency is resolved. Several walkthroughs of the prototype with FSs have been completed within a mockup of an ISS FS console. Feedback on the current tool design as well as recommendations for existing ISS FS displays were captured. The tool could have different uses depending on the situation and the skill of the user. An experienced flight surgeon could use it during an emergency situation as a decision and performance support tool, whereas a new flight surgeon could use it as JITT, or part of his/her regular training. The work proposed for FY10 continues to build on this strong collaboration with the Space Medical Training Group and previous research.

Byrne, Vicky E.↗
NASA NTRS2023

Research and Technology Challenges for Human Data Analysts in Future Safety Management Systems

Enabling new and novel concepts of operations for Advanced Air Mobility poses an important need to evolve current safety management systems (SMS) and is posited to be realized through advances in Machine Learning (ML) Data Sciences and Artificial Intelligence. The “In-time Aviation Safety Management System” (IASMS) concept of operations supports the need to evolve today’s SMS to become more tailorable, scalable, and interoperable in response to forecasted changes expected for the future airspace system. Key to IASMS is integration of proactive and predictive ML algorithms trained to provide “in time” detection and mitigation of hazards and emergent risks through new methods and novel data types. IASMS research and technology development includes human factors design considerations for these systems to include human-system teaming, innovations in human interfaces and management of complex digital data information, human-system interaction/model-based system engineering, and verification and validation for data assurance and trust.

Chad L Stephens↗
NASA NTRS2017

An Alternative Approach to Human Servicing of Crewed Earth Orbiting Spacecraft

As crewed spacecraft have grown larger and more complex, they have come to rely on spacewalks, or Extravehicular Activities (EVA), for assembly and to assure mission success. Typically, these spacecraft maintain all of the hardware and trained personnel needed to perform an EVA on-board at all times. Maintaining this capability requires up-mass, volume for storage of EVA hardware, crew time for ground and on-orbit training, and on-orbit maintenance of EVA hardware. This paper proposes an alternative methodology, utilizing either launch-on-need hardware and crew or regularly scheduled missions to provide EVA capability for space stations in low Earth orbit after assembly complete. Much the same way that one would call a repairman to fix something at their home these EVAs are dedicated to maintenance and upgrades of the orbiting station. For crew safety contingencies it is assumed the station would be designed such the crew could either solve those issues from inside the spacecraft or use the docked Earth to Orbit vehicles as a return lifeboat, in the same manner as the International Space Station (ISS) which does not rely on EVA for crew safety related contingencies. This approach would reduce ground training requirements for long duration crews, save Intravehicular Activity (IVA) crew time in the form of EVA hardware maintenance and on-orbit training, and lead to more efficient EVAs because they would be performed by specialists with detailed knowledge and training stemming from their direct involvement in the development of the EVA. The on-orbit crew would then be available to focus on the immediate response to any failures such as IVA systems reconfiguration or jumper installation as well as the day-to-day operations of the spacecraft and payloads. This paper will look at how current unplanned EVAs are conducted on ISS, including the time required for preparation, and offer an alternative for future spacecraft. As this methodology relies on the on-time and on-need launch of spacecraft, any space station that utilized this approach would need a robust transportation system, possibly including more than one launch vehicle capable of carrying crew. In addition, the fault tolerance of the future space station would be an important consideration in how much time was available for EVA preparation after the failure. Ideally the fault tolerance of the station would allow for the maintenance tasks to be grouped such that they could be handled by regularly scheduled maintenance visits and not contingency launches. Each future program would have to weigh the risk of on-time launch against the increase in available crew time for the main objective of the spacecraft. This is only one of several ideas that could be used to reduce or eliminate a station's reliance on rapid turnaround EVAs using on-board crew. Others could include having shirt-sleeve access to critical systems or utilizing low pressure temporarily pressurized equipment bays.

Mularski, John R.↗
NASA NTRS2013

An Onboard ISS Virtual Reality Trainer

Prior to the retirement of the Space Shuttle, many exterior repairs on the International Space Station (ISS) were carried out by shuttle astronauts, trained on the ground and flown to the Station to perform these specific repairs. With the retirement of the shuttle, this is no longer an available option. As such, the need for ISS crew members to review scenarios while on flight, either for tasks they already trained for on the ground or for contingency operations has become a very critical issue. NASA astronauts prepare for Extra-Vehicular Activities (EVA) or Spacewalks through numerous training media, such as: self-study, part task training, underwater training in the Neutral Buoyancy Laboratory (NBL), hands-on hardware reviews and training at the Virtual Reality Laboratory (VRLab). In many situations, the time between the last session of a training and an EVA task might be 6 to 8 months. EVA tasks are critical for a mission and as time passes the crew members may lose proficiency on previously trained tasks and their options to refresh or learn a new skill while on flight are limited to reading training materials and watching videos. In addition, there is an increased need for unplanned contingency repairs to fix problems arising as the Station ages. In order to help the ISS crew members maintain EVA proficiency or train for contingency repairs during their mission, the Johnson Space Center's VRLab designed an immersive ISS Virtual Reality Trainer (VRT). The VRT incorporates a unique optical system that makes use of the already successful Dynamic On-board Ubiquitous Graphics (DOUG) software to assist crew members with procedure reviews and contingency EVAs while on board the Station. The need to train and re-train crew members for EVAs and contingency scenarios is crucial and extremely demanding. ISS crew members are now asked to perform EVA tasks for which they have not been trained and potentially have never seen before. The Virtual Reality Trainer (VRT) provides an immersive 3D environment similar to the one experienced at the VRLab crew training facility at the NASA Johnson Space Center. VRT bridges the gap by allowing crew members to experience an interactive, 3D environment to reinforce skills already learned and to explore new work sites and repair procedures outside the Station.

Miralles, Evelyn↗
NASA NTRS2009

Human Factors in Training - Space Medicine Proficiency Training

The early Constellation space missions are expected to have medical capabilities very similar to those currently on the Space Shuttle and International Space Station (ISS). For Crew Exploration Vehicle (CEV) missions to ISS, medical equipment will be located on ISS, and carried into CEV in the event of an emergency. Flight Surgeons (FS) on the ground in Mission Control will be expected to direct the Crew Medical Officer (CMO) during medical situations. If there is a loss of signal and the crew is unable to communicate with the ground, a CMO would be expected to carry out medical procedures without the aid of a FS. In these situations, performance support tools can be used to reduce errors and time to perform emergency medical tasks. Work on medical training has been conducted in collaboration with the Medical Training Group at the Space Life Sciences Directorate and with Wyle Lab which provides medical training to crew members, Biomedical Engineers (BMEs), and to flight surgeons under the JSC Space Life Sciences Directorate s Bioastronautics contract. The space medical training work is part of the Human Factors in Training Directed Research Project (DRP) of the Space Human Factors Engineering (SHFE) Project under the Space Human Factors and Habitability (SHFH) Element of the Human Research Program (HRP). Human factors researchers at Johnson Space Center have recently investigated medical performance support tools for CMOs on-orbit, and FSs on the ground, and researchers at the Ames Research Center performed a literature review on medical errors. The work proposed for FY10 continues to build on this strong collaboration with the Space Medical Training Group and previous research. This abstract focuses on two areas of work involving Performance Support Tools for Space Medical Operations. One area of research building on activities from FY08, involved the feasibility of just-in-time (JIT) training techniques and concepts for real-time medical procedures. In Phase 1, preliminary feasibility data was gathered for two types of prototype display technologies: a hand-held PDA, and a Head Mounted Display (HMD). The PDA and HMD were compared while performing a simulated medical procedure using ISS flight-like medical equipment. Based on the outcome of Phase 1, including data on user preferences, further testing was completed using the PDA only. Phase 2 explored a wrist-mounted PDA, and compared it to a paper cue card. For each phase, time to complete procedures, errors, and user satisfaction ratings were captured.

Connell, Erin↗
NASA NTRS2018

Augmented Reality Data Generation for Training Deep Learning Neural Network

One of the major challenges in deep learning is retrieving sufficiently large labeled training datasets, which can become expensive and time consuming to collect. A unique approach to training segmentation is to use Deep Neural Network (DNN) models with a minimal amount of initial labeled training samples. The procedure involves creating synthetic data and using image registration to calculate affine transformations to apply to the synthetic data. The method takes a small dataset and generates a highquality augmented reality synthetic dataset with strong variance while maintaining consistency with real cases. Results illustrate segmentation improvements in various target features and increased average target confidence.

Torres, Gil↗
NASA NTRS1990

Statistical methods and neural network approaches for classification of data from multiple sources

Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.

Benediktsson, Jon Atli↗
NASA NTRS1989

Payloads simulation in the Shuttle Mission Training Facility

The Shuttle Mission Training Facility (SMFT) at the NASA Johnson Space Center is the primary facility for full mission training of astronauts. The training provided in the SMTF includes payload operations that are performed by the astronauts while the Space Shuttle is in orbit. This training requires real-time simulation of the operational aspects of Shuttle payloads as well as simulation of the interaction between payloads and the Orbiter. For this purpose the SMTF contains Payloads Simulators (PLS) that are coupled to and operated in synchronization with the Shuttle Vehicle Simulators. The PLS are used for simulating all payloads with the exception of the European Space Agency's Spacelab. The SMTF contains a separate Spacelab Simulator for training Spacelab astronauts. This paper provides a synopsis of actual payload operations, the methodology for payload simulation in the SMTF, and the system architecture of the PLS.

Hajare, Ankur R.↗
NASA NTRS2007

Air Quality Research and Applications Using AURA OMi Data

The Ozone Monitoring Instrument (OMI) on EOS Aura is a new generation of satellite remote sensing instrument designed to measure trace gas and aerosol absorption at the UV and blue wavelengths. These measurements are made globally at urban scale resolution with no inter-orbital gaps that make them potentially very useful for air quality research, such as the determination of the sources and processes that affect global and regional air quality, and to develop applications such as air quality forecast. However, the use of satellite data for such applications is not as straight forward as satellite data have been for stratospheric research. There is a need for close interaction between the satellite product developers, in-situ measurement programs, and the air quality research community to overcome some of the inherent difficulties in interpreting data from satellite-based remote sensing instruments. In this talk we will discuss the challenges and opportunities in using OMI products for air quality research and applications. A key conclusion of this work is that to realize the full potential of OMI measurements it will be necessary to combine OMI data with data from instruments such as MLS, MODIS, AIRS, and CALIPSO that are currently flying in the "A-train" satellite constellation. In addition similar data taken by satellites crossing the earth at different local times than the A-train (e.g., the recently MetOp satellite) would need to be processed in a consistent manner to study diurnal variability, and to capture the effects on air quality of rapidly changing events such as wild fires.

Bhartia, P.K.↗
NASA NTRS1992

Results of an International Space Crew Debrief

In order to identify potential multi-cultural and multinational problems for future International Space Station Freedom crew, a crew debrief questionnaire was developed for U.S. astronauts who flew on shuttle missions with one or more crew members from other countries. Methods: From 1981-90, a total of 20 U.S. astronauts flew on international space missions. Debriefs were mailed to all 20 with instructions not to identify themselves or their specific mission. The debrief focused primarily on preflight training and post flight incidents of misunderstanding, miscommunication, and interpersonal friction among crewmembers. Astronauts were also asked to rate the impact of the incident to the mission (low, medium, high). Results: Ten astronauts responded, but only nine responses were able to be scored, for a return rate of 45 percent. 42 incidents were reported, 9 in the preflight period, 26 inflight, and 7 in the postflight period. Most of the incidents were rated at a low or medium impact, but 5 of the inflight incidents were rated at a 'high' mission impact. A number of causes for the problems were listed, and are discussed. Conclusions: The debrief respondents provide useful and timely recommendations on preflight training which might help facilitate the integration of multinational crews and prevent multi-cultural or multinational factors from interfering with mission operations.

Santy, P. A.↗
NASA NTRS2018

Training for Mars

Spaceflight training for Apollo era and Space Shuttle missions was designed to train crew on specific tasks for their short duration missions. The training mottos included "practice, practice, practice" and "hammering it in." However, training for longer duration ISS (International Space Station) missions has been more challenging. There is clear documentation that tasks and skills trained pre-flight are not all retained throughout the mission, and this lack of training retention is augmented with real-time ground support. NASA does not currently have a training design that supports semi-autonomous to autonomous mission operations. The current design of a manned Mars mission calls for a 32-month long mission and asynchronous communication involving long delays.

Barshi, Immanuel↗
NASA NTRS2022

Can Machine Learning Reveal Precursors of Reversals of the Geomagnetic Axial Dipole Field?

It is well known that the axial dipole part of Earth’s magnetic field reverses polarity, so that the magnetic North Pole becomes the South Pole and vice versa. The timing of reversals is well documented for the past 160 Myr, but the conditions that lead to a reversal are still not well understood. It is not known if there are reliable ‘precursors’ of reversals (events that indicate that a reversal is upcoming) or what they might be. We investigate if machine learning (ML) techniques can reliably identify precursors of reversals based on time-series of the axial magnetic dipole field. The basic idea is to train a classifier using segments of time-series of the axial magnetic dipole. This training step requires modification of standard ML techniques to account for the fact that we are interested in rare events—a reversal is unusual, while a non-reversing field is the norm. Without our tweak, the ML classifiers lead to useless predictions. Perhaps even more importantly, the usable observational record is limited to 0–2 Ma and contains only five reversals, necessitating that we determine if the data are even sufficient to reliably train and validate an ML algorithm. To answer these questions we use several ML classifiers (linear/non-linear support vector machines and long short-term memory networks), invoke a hierarchy of numerical models (from simplified models to 3-D geodynamo simulations), and two palaeomagnetic reconstructions (PADM2M and Sint-2000). The performance of the ML classifiers varies across the models and the observational record and we provide evidence that this is not an artefact of the numerics, but rather reflects how ‘predictable’ a model or observational record is. Studying models of Earth’s magnetic field via ML classifiers thus can help with identifying shortcomings or advantages of the various models. For Earth’s magnetic field, we conclude that the ability of ML to identify precursors of reversals is limited, largely due to the small amount and low frequency resolution of data, which makes training and subsequent validation nearly impossible. Put simply: the ML techniques we tried are not currently capable of reliably identifying an axial dipole moment (ADM) precursor for geomagnetic reversals. This does not necessarily imply that such a precursor does not exist, and improvements in temporal resolution and length of ADM records may well offer better prospects in the future.

Kyle Gwirtz↗
NASA NTRS1985

Fatigue and contraction of slow and fast muscles in hypokinetic/hypodynamic rats

The effects of hypokinesia/hypodynamia (H/H) on the fatigability and contractile properties of the rat soleus (S) and gastrocnemius (G) muscles have been investigated experimentally. Whole body suspension for one week was used to induce H/H, and fatigue was brought on by train stimulation for periods of 45 and 16 minutes. Following stimulation, rapid rates of fatigue were observed in the G-muscles of the suspended rats, while minimal fatigue was observed in the S-muscles. The twitch and tetanic contractile properties of the muscles were measured before and after train stimulation. It is found that H/H suspension increased twitch tension in the G-muscles, but did not change any contractile properties in the S-muscles. The peak twitch, train, tetanic tensions and time to peak were unchanged in the S-muscles of the suspended rats. On the basis of the experimental results, it is concluded that 1 wk of muscle atropy induced by H/H significantly increases fatigability in G-muscles, but does not affect the contractile properties of fast-twitch and slow-twitch muscles.

Fell, R. D.↗
NASA NTRS1991

Maintenance of Space Station Freedom - The role of mission controllers

The key roles played in the on-orbit maintenance of Space Station Freedom by mission controllers working in the Space Station Control Center are discussed. Responsibilities ranging from planning and procedure development to training and real-time support are addressed. The organization of the Mission Operations Directorate is described.

Watson, J. K.↗
NASA NTRS1992

Neural network training by integration of adjoint systems of equations forward in time

A method and apparatus for supervised neural learning of time dependent trajectories exploits the concepts of adjoint operators to enable computation of the gradient of an objective functional with respect to the various parameters of the network architecture in a highly efficient manner. Specifically, it combines the advantage of dramatic reductions in computational complexity inherent in adjoint methods with the ability to solve two adjoint systems of equations together forward in time. Not only is a large amount of computation and storage saved, but the handling of real-time applications becomes also possible. The invention has been applied it to two examples of representative complexity which have recently been analyzed in the open literature and demonstrated that a circular trajectory can be learned in approximately 200 iterations compared to the 12000 reported in the literature. A figure eight trajectory was achieved in under 500 iterations compared to 20000 previously required. The trajectories computed using our new method are much closer to the target trajectories than was reported in previous studies.

Toomarian, Nikzad↗
NASA NTRS1993

Multicultural factors in the space environment - Results of an international shuttle crew debrief

There is increasing interest and concern about the multicultural and multinational factors which might negatively affect adjustment and performance of Space Station Freedom (SSF) crews, living and working for long periods of time in the space environment. To begin identifying potential problem areas, a crew debrief questionnaire (called an 'International Crew Debrief') was mailed to U.S. astronauts who flew on Shuttle missions between 1981-1990 with one or more crewmembers from other countries. There were 20 U.S. astronauts who flew on international space missions; 9 of these responded to the questionnaire, for a return rate of 45 percent. There were 42 incidents reported: 9 in the preflight period; 26 inflight; and 7 in the postflight period. Most of these incidents were rated as having a low or medium impact, but five of the inflight incidents were rated to have a 'high' mission impact. A number of causes for the problems were listed, and are discussed. Debrief respondents provided useful and timely recommendations on preflight training which may help facilitate the integration of multinational crews, and prevent multicultural or multinational factors from interfering with mission operations.

Santy, Patricia A.↗