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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Information Theoretic Extraction of EEG Features for Monitoring Subject Attention

The goal of this project was to test the applicability of information theoretic learning (feasibility study) to develop new brain computer interfaces (BCI). The difficulty to BCI comes from several aspects: (1) the effective data collection of signals related to cognition; (2) the preprocessing of these signals to extract the relevant information; (3) the pattern recognition methodology to detect reliably the signals related to cognitive states. We only addressed the two last aspects in this research. We started by evaluating an information theoretic measure of distance (Bhattacharyya distance) for BCI performance with good predictive results. We also compared several features to detect the presence of event related desynchronization (ERD) and synchronization (ERS), and concluded that at least for now the bandpass filtering is the best compromise between simplicity and performance. Finally, we implemented several classifiers for temporal - pattern recognition. We found out that the performance of temporal classifiers is superior to static classifiers but not by much. We conclude by stating that the future of BCI should be found in alternate approaches to sense, collect and process the signals created by populations of neurons. Towards this goal, cross-disciplinary teams of neuroscientists and engineers should be funded to approach BCIs from a much more principled view point.

Principe, Jose C.↗
NASA NTRSDate not supplied

A New ML-Based Adaptive Thinning Methodology to Improve the Impact of AIRS and CrIS Assimilation on Global Tropical Cyclone Forecasts

This work builds on previous research performed by this team to improve the forecast of Tropical Cyclones (TCs) by assimilating AIRS and CrIS radiances into the NASA Global Earth Observing System (GEOS). Past published work demonstrated that the assimilation of radiances with variable density was beneficial to TC forecasting in the GEOS. In the previous setup, a fixed-size moving square named 'TC domain' was activated by the so-called TC-vitals, an international real-time message accessible to all NWP forecasting centers, that documents the existence of a TC, its estimated position, and its size. The information from TC-vitals activated a switch in the GEOS, which allowed to reduce the distance used for thinning AIRS and CrIS data inside a 15 degrees by 15 degrees moving TC domain centered on the storm, so that more data were assimilated in the vicinity of the TC during its lifetime. The methodology produced improved TC analyses and led to better forecasts, particularly related to intensity, without damaging the global forecast skill. In the new version, the adaptive thinning methodology is based on a machine-learning technique. The technique searches for TCs and creates TC masks by using cloud-top temperatures from all geostationary satellites without the need for additional information. It is being trained against the International Best Track Archive for Climate Stewardship (IBTrACS) data base. Once a TC mask is created, a switch identical to the one used in the previous adaptive thinning method is activated, allowing the GEOS to ingest more data in the TC-shaped size-changing domain that follows the storm. As of today, the team has been able to successfully assimilate data inside the ML-detected TC domains. Future work includes an improved capability of reducing false alarm rates (i.e., cloud systems that are erroneously labeled as TCs).

Oreste Reale↗
NASA NTRSDate not supplied

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↗
NASA NTRS2014

Visual Image Sensor Organ Replacement

This innovation is a system that augments human vision through a technique called "Sensing Super-position" using a Visual Instrument Sensory Organ Replacement (VISOR) device. The VISOR device translates visual and other sensors (i.e., thermal) into sounds to enable very difficult sensing tasks. Three-dimensional spatial brightness and multi-spectral maps of a sensed image are processed using real-time image processing techniques (e.g. histogram normalization) and transformed into a two-dimensional map of an audio signal as a function of frequency and time. Because the human hearing system is capable of learning to process and interpret extremely complicated and rapidly changing auditory patterns, the translation of images into sounds reduces the risk of accidentally filtering out important clues. The VISOR device was developed to augment the current state-of-the-art head-mounted (helmet) display systems. It provides the ability to sense beyond the human visible light range, to increase human sensing resolution, to use wider angle visual perception, and to improve the ability to sense distances. It also allows compensation for movement by the human or changes in the scene being viewed.

Maluf, David A.↗
NASA NTRS2004

Planning for rover opportunistic science

The Mars Exploration Rover Spirit recently set a record for the furthest distance traveled in a single sol on Mars. Future planetary exploration missions are expected to use even longer drives to position rovers in areas of high scientific interest. This increase provides the potential for a large rise in the number of new science collection opportunities as the rover traverses the Martian surface. In this paper, we describe the OASIS system, which provides autonomous capabilities for dynamically identifying and pursuing these science opportunities during longrange traverses. OASIS uses machine learning and planning and scheduling techniques to address this goal. Machine learning techniques are applied to analyze data as it is collected and quickly determine new science gods and priorities on these goals. Planning and scheduling techniques are used to alter the behavior of the rover so that new science measurements can be performed while still obeying resource and other mission constraints. We will introduce OASIS and describe how planning and scheduling algorithms support opportunistic science.

artificial intelligence↗
NASA NTRS1993

Obstacle detection by recognizing binary expansion patterns

This paper describes a technique for obstacle detection, based on the expansion of the image-plane projection of a textured object, as its distance from the sensor decreases. Information is conveyed by vectors whose components represent first-order temporal and spatial derivatives of the image intensity, which are related to the time to collision through the local divergence. Such vectors may be characterized as patterns corresponding to 'safe' or 'dangerous' situations. We show that essential information is conveyed by single-bit vector components, representing the signs of the relevant derivatives. We use two recently developed, high capacity classifiers, employing neural learning techniques, to recognize the imminence of collision from such patterns.

Baram, Yoram↗
NASA NTRS2022

Discovery of CWISE J052306.42-015355.4, an Extreme T Subdwarf Candidate

We present the discovery of CWISE J052306.42−015355.4, which was found as a faint, significant proper-motion object (0(\farcs)52 ± 0(\farcs)08 per yr) using machine-learning tools on the unWISE re-processing of time series images from the Wide-field Infrared Survey Explorer. Using the CatWISE2020 W1 and W2 magnitudes along with a J-band detection from the VISTA Hemisphere Survey, the location of CWISE J052306.42−015355.4 on the W1 − W2 versus J − W2 diagram best matches that of other known, or suspected, extreme T subdwarfs. As there is currently very little knowledge concerning extreme T subdwarfs we estimate a rough distance of ≤68 pc, which results in a tangential velocity of ≤167 km/s, both of which are tentative. A measured parallax is greatly needed to test these values. We also estimate a metallicity of −1.5 < [M/H] < −0.5 using theoretical predictions.

Hunter Brooks↗
NASA NTRS2008

From the Big Bang to the Nobel Prize and on to James Webb Space Telescope

The Big Bang 13.7 billion years ago started the expansion of our piece of the universe, and portions of it stopped expanding and made stars, galaxies, planets, and people. I summarize the history of the universe, and explain how humans have learned about its size, its expansion, and its constituents. The COBE (Cosmic Background Explorer) mission measured the remnant heat radiation from the Big Bang, showed that its color (spectrum) matches the predictions perfectly, and discovered hot and cold spots in the radiation that reveal the primordial density variations that enabled us to exist. My current project, the James Webb Space Telescope (JWST), is the planned successor to the Hubble Space Telescope, and will extend its scientific discoveries to ever greater distances and ever closer to the Big Bang itself. Its infrared capabilities enable it to see inside dust clouds to study the formation of stars and planets, and it may reveal the atmospheric properties of planets around other stars. Planned for launch in 2013, it is an international project led by NASA along with the European and Canadian Space Agencies.

Mather, John C.↗
NASA NTRS2014

Imaging Transitional Disks with TMT: Lessons Learned from the SEEDS Survey

TMT studies of the early phases of giant planet formation will build on studies carried out in this decade using 8-meter class telescopes. One such study is the Strategic Exploration of Exoplanets and Disks with Subaru transitional disk survey. We have found a wealth of indirect signatures of giant planet presence, including spiral arms, pericenter offsets of the outer disk from the star, and changes in disk color at the inner edge of the outer disk in intermediate-mass PMS star disks. T Tauri star transitional disks are less flamboyant, but are also dynamically colder: any spiral arms in these diskswill be more tightly wound. Imaging such features at the distance of the nearest star-forming regions requires higher angular resolution than achieved with HiCIAO+ AO188. Imaging such disks with extreme AO systems requires use of laser guide stars, and are infeasible with the extreme AO systems currently commissioning on 8-meter class telescopes. Similarly, the JWST and AFTAWFIRST coronagraphs being considered have inner working angles 0.2, and will occult the inner 28 atomic units of systems at d140pc, a region where both high-contrast imagery and ALMA data indicate that giant planets are located in transitional disks. However, studies of transitional disks associated with solar-mass stars and their planet complement are feasible with TMT using NFIRAOS.

star-forming regions↗
NASA NTRS2023

A Blind Convolutional Deep Autoencoder for Spectral Unmixing of Hyperspectral Images Over Waterbodies

Harmful algal blooms have dangerous repercussions for biodiversity, the ecosystem, and public health. Automatic identification based on remote sensing hyperspectral image analysis provides a valuable mechanism for extracting the spectral signatures of harmful algal blooms and their respective percentage in a region of interest. This paper proposes a new model called a non-symmetrical autoencoder for spectral unmixing to perform endmember extraction and fractional abundance estimation. The model is assessed in benchmark datasets, such as Jasper Ridge and Samson. Additionally, a case study of the HSI2 image acquired by NASA over Lake Erie in 2017 is conducted for extracting optical water types. The results using the proposed model for the benchmark datasets improve unmixing performance, as indicated by the spectral angle distance compared to five baseline algorithms. Improved results were obtained for various metrics. In the Samson dataset, the proposed model outperformed other methods for water (0.060) and soil (0.025) endmember extraction. Moreover, the proposed method exhibited superior performance in terms of mean spectral angle distance compared to the other five baseline algorithms. The non-symmetrical autoencoder for the spectral unmixing approach achieved better results for abundance map estimation, with a root mean square error of 0.091 for water and 0.187 for soil, compared to the ground truth. For the Jasper Ridge dataset, the non-symmetrical autoencoder for the spectral unmixing model excelled in the tree (0.039) and road (0.068) endmember extraction and also demonstrated improved results for water abundance maps (0.1121). The proposed model can identify the presence of chlorophyll-a in waterbodies. Chlorophyll-a is an essential indicator of the presence of the different concentrations of macrophytes and cyanobacteria. The non-symmetrical autoencoder for spectral unmixing achieves a value of 0.307 for the spectral angle distance metric compared to a reference ground truth spectral signature of chlorophyll-a. The source code for the proposed model, as implemented in this manuscript, can be found at https://github.com/EstefaniaAlfaro/autoencoder_owt_spectral.git.

hyperspectral imaging↗
NASA NTRS2005

The Diversity of Martian Volcanic features as Seen in the MOC, THEMIS, and MOM Data Sets

This one-year project (which included one-year no-cost tension) focused on the evolution of the summit areas of Martian volcanoes. It extended the studies conducted under an earlier MDAP project (Grant NAG5-9576, Principal Investigator P. Mouginis- Mark). By using data collected from the Mars Orbiter Camera (MOC), Thermal Emission Imaging System (THEMIS), and the Mars Orbiter Laser Altimeter (MOLA) instruments, we tried to better understand the diversity of constructional volcanism on Mars, and hence further understand the eruption processes. By inspecting THEMIS and MOC data, we explored the following four questions: (1) Where might near-surface volatiles have been released at the summits of the Tharsis volcanoes? Is the trapping and subsequent remobilization of degassed volatiles [Scott and Wilson, 19991 adequate to produce eruptions responsible for extensive deposits such as the ones identified on Arsia Mons [Mouginis-Mark, 2002]? To answer this question, we investigated the diversity of eruption styles by studying the summit areas of Arsia, Pavonis and Ascraeus Montes. (2) What are the geomorphic characteristics of the valley system on Hecates Tholus, a volcano that we have previously proposed experienced explosive activity [Mouginis-Murk et al., 1982]? Our inspection of THEMIS data suggests that water release on the volcano took place over an extended period of time, suggesting that hydrothermal activity may have taken place here. (3) How similar are the collapse processes observed at Martian and terrestrial calderas? New THEMIS data provide a more complete view of the entire Olympus Mons caldera, thereby enabling the comparison with the collapse features at Masaya volcano, Nicaragua, to be investigated. (4) What can we learn about the emplacement of long lava flows in the lava plains of Eastern Tharsis? The result of this work provided a greater understanding of the temporal and spatial variations in the eruptive history of volcanoes on Mars, and the influence of the volatiles within the top few kilometers of the volcanic edifice. This relationship in turn pertains to the availability of volatiles (both juvenile magmatic volatiles and ground water contained within the near-surface rocks) and to magma supply rates at appreciable distances (tens to hundreds of kilometers) from the centers of volcanoes. Explosive volcanism on Mars, a major factor in the release of water at the surface, may have been driven not only by volatiles within the parental melt, but also by magma encountering water or ice at shallow depth within the volcano [Mouginis-Mark et al., 1982, 1988; Crown and Greeley, 1993; Robinson et al., 19931.

Mouginis-Mark, Peter J.↗
NASA NTRSDate not supplied

Using DIC for Long Slender Structures

High-strain composite deployable structures have been developed for systems such as solar arrays, camera masts or solar sailing propulsion elements. Composite booms in such applications are often flattened and then rolled into a small footprint for low-packaged volume and are then deployed in space. There is a need to obtain deformation for long, slender composite booms on earth through gravity offloading by suspending them vertically and applying distal end (tip) loads. Three-dimensional digital image correlation (3D-DIC), along with other measurement techniques, were used to obtain strain and displacement along the length of a 7.5 m subscale composite Triangular, Rollable, and Collapsible (TRAC) boom in preparation for full scale testing of a 30 m boom. However, incorporating 3D-DIC as a primary measurement tool on long, slender, high-aspect-ratio boom structures presents significant challenges. Challenges include small correlated area due to high aspect ratio, limited standoff distance due to size of test area, coordinate system alignment of multiple camera systems along the length of the boom, nodal mesh extraction for adequate test/analysis correlation, as well as measurement comparison between DIC and other instrumentation used such as fiber optic strain sensing (FOSS) and laser displacement tracking. The contents of the proposed paper will focus on techniques and methods for overcoming the previously mentioned challenges associated with applying 3D-DIC to long, slender boom structures. Results from subscale test along with lessons learned will be discussed.

Deployable Boom↗
NASA NTRS2010

Orion Abort Flight Test

The purpose of NASA's Constellation project is to create the new generation of spacecraft for human flight to the International Space Station in low-earth orbit, the lunar surface, as well as for use in future deep-space exploration. One portion of the Constellation program was the development of the Orion crew exploration vehicle (CEV) to be used in spaceflight. The Orion spacecraft consists of a crew module, service module, space adapter and launch abort system. The crew module was designed to hold as many as six crew members. The Orion crew exploration vehicle is similar in design to the Apollo space capsules, although larger and more massive. The Flight Test Office is the responsible flight test organization for the launch abort system on the Orion crew exploration vehicle. The Flight Test Office originally proposed six tests that would demonstrate the use of the launch abort system. These flight tests were to be performed at the White Sands Missile Range in New Mexico and were similar in nature to the Apollo Little Joe II tests performed in the 1960s. The first flight test of the launch abort system was a pad abort (PA-1), that took place on 6 May 2010 at the White Sands Missile Range in New Mexico. Primary flight test objectives were to demonstrate the capability of the launch abort system to propel the crew module a safe distance away from a launch vehicle during a pad abort, to demonstrate the stability and control characteristics of the vehicle, and to determine the performance of the motors contained within the launch abort system. The focus of the PA-1 flight test was engineering development and data acquisition, not certification. In this presentation, a high level overview of the PA-1 vehicle is given, along with an overview of the Mobile Operations Facility and information on the White Sands tracking sites for radar & optics. Several lessons learned are presented, including detailed information on the lessons learned in the development of wind placards for flight. PA-1 flight data is shown, as well as a comparison of PA-1 flight data to nonlinear simulation Monte Carlo data.

Hayes, Peggy Sue↗
NASA NTRS2016

Large Payload Ground Transportation and Test Considerations

During test and verification planning for the Altair lunar lander project, a National Aeronautics and Space Administration (NASA) study team identified several ground transportation and test issues related to the large payload diameter. Although the entire Constellation Program-including Altair-has since been canceled, issues identified by the Altair project serve as important lessons learned for payloads greater than 7 m diameter being considered for NASA's new Space Launch System (SLS). A transportation feasibility study found that Altair's 8.97 m diameter Descent Module would not fit inside available aircraft. Although the Ascent Module cabin was only 2.35 m diameter, the long reaction control system booms extended nearly to the Descent Module diameter, making it equally unsuitable for air transportation without removing the booms and invalidating assembly workmanship screens or acceptance testing that had already been performed. Ground transportation of very large payloads over extended distances is not generally permitted by most states, so overland transportation alone would not be an option. Limited ground transportation to the nearest waterway may be possible, but water transportation could take as long as 66 days per production unit, depending on point of origin and acceptance test facility; transportation from the western United States would require transit through the Panama Canal to access the Kennedy Space Center launch site. Large payloads also pose acceptance test and ground processing challenges. Although propulsion, mechanical vibration, and reverberant acoustic test facilities at NASA's Plum Brook Station have been designed to accommodate large spacecraft, special handling and test work-arounds may be necessary, which could increase cost, schedule, and technical risk. Once at the launch site, there are no facilities currently capable of accommodating the combination of large payload size and hazardous processing such as hypergolic fuels, pyrotechnic devices, and high pressure gasses. Ironically, the limiting factor to a national heavy lift strategy may not be the rocket technology needed to throw a heavy payload, but rather the terrestrial infrastructure-roads, bridges, airframes, and buildings-necessary to transport, acceptance test, and process large spacecraft. Failure to carefully consider where and how large spacecraft are manufactured, tested, and launched could result in unforeseen cost to modify existing (or develop new) infrastructure, or incur additional risk due to increased handling operations or eliminating key verifications.

Rucker, Michelle A.↗
NASA NTRS1994

Fuzzy logic, neural networks, and soft computing

The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial intelligence. In the years ahead, this may well become a widely held position.

Zadeh, Lofti A.↗
NASA NTRS2013

Improved CPAS Photogrammetric Capabilities for Engineering Development Unit (EDU) Testing

This paper focuses on two key improvements to the photogrammetric analysis capabilities of the Capsule Parachute Assembly System (CPAS) for the Orion vehicle. The Engineering Development Unit (EDU) system deploys Drogue and Pilot parachutes via mortar, where an important metric is the muzzle velocity. This can be estimated using a high speed camera pointed along the mortar trajectory. The distance to the camera is computed from the apparent size of features of known dimension. This method was validated with a ground test and compares favorably with simulations. The second major photogrammetric product is measuring the geometry of the Main parachute cluster during steady-state descent using onboard cameras. This is challenging as the current test vehicles are suspended by a single-point attachment unlike earlier stable platforms suspended under a confluence fitting. The mathematical modeling of fly-out angles and projected areas has undergone significant revision. As the test program continues, several lessons were learned about optimizing the camera usage, installation, and settings to obtain the highest quality imagery possible.

Ray, Eric S.↗
NASA NTRSDate not supplied

Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions

From May 8th to June 9th, 2023, I had the opportunity to participate in an experiential learning experience at Johnson Space Center in Houston, TX with Exploration Medical Capability (ExMC), an element of the NASA Human Research Program. During this research experience, I was not only able to work on the above titled research project, but also gain an immense exposure to the field of aerospace medicine, make numerous connections within the field, tour NASA facilities, as well as travel to the Aerospace Medical Association Annual Conference (AsMA) in New Orleans. To briefly introduce my project, it is well understood that the medical capabilities available to crew medical officers (CMOs) on the International Space Station will be different than the capabilities available and needed during deep space exploration missions to the Moon, Mars, and beyond. Ground support is particularly limited due to distance, communication delays (or lack of communication), and lack of resupply. Therefore, to support medical care by CMOs on these missions, robust clinical decision support systems (CDSSs) must be designed. The recent publication and public launch of generative artificial intelligence (AI) tools based upon large language models (LLM) such as ChatGPT provides the opportunity to create a smart assistant for onboard triage, diagnosis, and treatment of medical conditions. Ultimately, the overall purpose of the project was to research what AI tools currently exist or are in development, and to see how they might be implemented onboard during exploration class spaceflights of the future. The ExMC element is actively developing several tools to be used in preparation for and during deep space exploration missions. One of those tools, known as IMPACT, is a probabilistic risk assessment model which can be used to propose a desired medical system (based on mass and volume) and suggest the clinical outcomes likely to occur for a design reference mission (DRM). The group recently presented the IMPACT model and a DRM of interest titled “Modified Long Duration Lunar Orbital and Lunar Surface” (mLDLOLS) at the recent AsMA conference. The mLDLOLS mock mission is a 9 month and 6-day deep space exploration mission consisting of time in Moon’s orbit (3 months on the Gateway space station), on the lunar surface (3 months within habitat), and another 3 months on Gateway before return to Earth. For this DRM, IMPACT ultimately outlined a preferred medical system that was then associated with medical conditions considered to be most likely based on frequency, most likely to cause astronaut task time loss (TTL), most likely to cause return to definitive care (RTDC), and most likely cause loss of crew life (LOCL). IMPACT also highlighted the medical capabilities/skills that would be required to care for those medical conditions, such as performing a history of present illness or musculoskeletal exam with ultrasound. The primary objective of the project was to perform a survey of the AI tools and systems applicable to the conditions outlined for the proposed mLDLOLS mission. Using PubMed (including most relevant MeSH terms) and Google Scholar, we then created a robust annotated bibliography organized by condition. The 56-page and over 500 reference annotated bibliography was subsequently used to create a review outline that would become the basis for drafting of a future publication. For the review outline, we took those medical conditions researched within the annotated bibliography (condition-based approach) and deployed a systems-based approach, combining those medical conditions and related tools into ten categories. These categories included general/all-purpose CDSSs, tools to diagnose or manage respiratory, dermatologic, neurologic, auditory and vestibular, ophthalmic, musculoskeletal, infection-associated, and gynecologic conditions, as well as tools that could be deployed in the setting of trauma/emergency. With the completion of the 30-page outline, we then began drafting the review paper. To conclude the research experience, I presented the findings from our survey to the ExMC Clinical and Science team. With these objectives, I ultimately learned about the number of AI tools that exist today to assist medical professionals with the triage, diagnosis, and management of several medical conditions. These tools can span from chatbot assistants to help triage knee pain to vision transformer models that can identify ophthalmic conditions based on ocular surface images captured with a cell phone. We also highlighted the current gaps that exist in the literature alongside the advancements that are needed to make the desired CDSS for deep space exploration missions. With this experience, I certainly confirmed an existing career goal and identified several additional skills needed to become an aerospace medical doctor including knowledge of critical care in an extreme medicine setting, aerospace engineering and human integration systems, artificial intelligence, machine learning, and risk models. I also identified numerous transferable skills for this career goal including the basic knowledge of medicine (MD), deployment of the scientific method for critical thought about new scientific questions (PhD), review of published literature, including creating an annotated bibliography (PhD), as well as detailed scientific writing (PhD). The results of my research will likely guide the design of an all-encompassing onboard medical assistant for use during deep space exploration missions of the future. I plan on sharing the outcomes from this experience with my peers at a student seminar in the Fall semester on August 30th. During the seminar, I will detail the project, my experience at NASA and AsMA, as well as offer best practice guidelines for students entertaining similar experiences or careers. In conclusion, I would like to thank the WVU School of Medicine, Research and Graduate Education office, as well as NASA ExMC for the unwavering support of this life-changing experience.

Ryan A. Lacinski↗
NASA NTRSDate not supplied

Exploring data-driven modeling of boundary layer transition

Prediction of laminar-turbulent transition in boundary layer flows is an important component of predicting the aerodynamic performance of a number of aerospace configurations. According to the CFD Vision 2030 [1], transition modeling represents acriticalarea in CFD simulation capability that will remain a pacing item for the foreseeable future. The fact thattransition can take placevia either one of a myriad possible paths adds to the challenges inreliable transition predictions, despite a limited knowledge of the relevant input parameters. In the low disturbance environments typical of flight applications, transition is often initiated by small amplitude disturbances in the form of linear instability waves of the laminar boundary layer. These disturbances amplify linearly at first and eventually undergo a sequence of nonlinear interactions that result in transition to turbulence. Because the nonlinear phase is rather rapid, the amplification of boundary layer instabilities is governed by the linearstability theory over a majority of the distance leading up to the onset of transition. Semi-empirical transition correlations based on the linear stability theory have been successful in explaining the observed trends in transition location within a broad class of flows. However, the application of stability theory is highly non-robust and often requires a significant domain expertise. Recent work at the NASA Langley Research Center has beenaimed at bridging the gap between physics based transition analyses such as those based on linear stability theory and practical applications that require transition prediction by users that may not be well versed in transition physics. The applications of deep learning have been at the center of these efforts. This presentation will focus on the progress achieved thus far, highlighting the applications of neural networks to selectedtransition scenarios across a range of Mach numbers and flow configuration, as well as the lessons learnedand remaining challengeswithrespect to the selection of training data and neural networks architectures, hyperparameter tuning, and the physical insights distilled from the otherwise black-box models.

M. R. Malik↗