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NASA NTRS2005

Measurement and Prediction of the Thermomechanical Response of Shape Memory Alloy Hybrid Composite Beams

Previous work at NASA Langley Research Center (LaRC) involved fabrication and testing of composite beams with embedded, pre-strained shape memory alloy (SMA) ribbons within the beam structures. That study also provided comparison of experimental results with numerical predictions from a research code making use of a new thermoelastic model for shape memory alloy hybrid composite (SMAHC) structures. The previous work showed qualitative validation of the numerical model. However, deficiencies in the experimental-numerical correlation were noted and hypotheses for the discrepancies were given for further investigation. The goal of this work is to refine the experimental measurement and numerical modeling approaches in order to better understand the discrepancies, improve the correlation between prediction and measurement, and provide rigorous quantitative validation of the numerical analysis/design tool. The experimental investigation is refined by a more thorough test procedure and incorporation of higher fidelity measurements such as infrared thermography and projection moire interferometry. The numerical results are produced by a recently commercialized version of the constitutive model as implemented in ABAQUS and are refined by incorporation of additional measured parameters such as geometric imperfection. Thermal buckling, post-buckling, and random responses to thermal and inertial (base acceleration) loads are studied. The results demonstrate the effectiveness of SMAHC structures in controlling static and dynamic responses by adaptive stiffening. Excellent agreement is achieved between the predicted and measured results of the static and dynamic thermomechanical response, thereby providing quantitative validation of the numerical tool.

Davis, Brian↗

NASA NTRS1978

Adaptation of time line analysis program to single pilot instrument flight research

A data base was developed for SPIFR operation and the program was run. The outputs indicated that further work was necessary on the workload models. In particular, the workload model for the cognitive channel should be modified as the output workload appears to be too small. Included in the needed refinements are models to show the workload when in turbulence, when overshooting a radial or glideslope, and when copying air traffic control clearances.

Hinton, D. A.↗

NASA NTRS1996

Adaptive Fuzzy Systems in Computational Intelligence

In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated.

Berenji, Hamid R.↗

NASA NTRS1990

Error detection and control for nonlinear shell analysis

A problem-adaptive solution procedure for improving the reliability of finite element solutions to geometrically nonlinear shell-type problem is presented. The strategy incorporates automatic error detection and control and includes an iterative procedure which utilizes the solution at the same load step on a more refined model. Representative nonlinear shell problem are solved.

Mccleary, Susan L.↗

NASA NTRS2008

Photorefraction Screens Millions for Vision Disorders

Who would have thought that stargazing in the 1980s would lead to hundreds of thousands of schoolchildren seeing more clearly today? Collaborating with research ophthalmologists and optometrists, Marshall Space Flight Center scientists Joe Kerr and the late John Richardson adapted optics technology for eye screening methods using a process called photorefraction. Photorefraction consists of delivering a light beam into the eyes where it bends in the ocular media, hits the retina, and then reflects as an image back to a camera. A series of refinements and formal clinical studies followed their highly successful initial tests in the 1980s. Evaluating over 5,000 subjects in field tests, Kerr and Richardson used a camera system prototype with a specifically angled telephoto lens and flash to photograph a subject s eye. They then analyzed the image, the cornea and pupil in particular, for irregular reflective patterns. Early tests of the system with 1,657 Alabama children revealed that, while only 111 failed the traditional chart test, Kerr and Richardson s screening system found 507 abnormalities.

Source record↗

NASA NTRS2007

Recommendations for Refinement and Validation of Intermittent Artificial Gravity

The IMAG Pilot Study, recently completed at the University of Texas Medical Branch, filled in the second major gap in knowledge standing in the way of development of a practical Short Radius Centrifuge (SRC) and the use of Artificial Gravity (AG) as a multi-system countermeasure to combat the deconditioning associated with extended weightlessness. (The first challenge, to adapt rapidly rotating subjects to permit unlimited head movements without excessive motion sickness, was achieved in a series of studies at MIT involving incremental increases in head and centrifuge velocity.) It remained to be demonstrated that intermittent exposure to AG, at only one hour per day for 21 days, would have any positive effect on slowing or eliminating of deconditioning. Bed-rested normal subjects were used as a ground analog for astronauts in weightlessness. The results are clearly positive for the key physiological systems of interest: cardiovascular, muscle, and bone. No functionally relevant changes were observed in immune, cognitive, or sensory-motor function. Furthermore, we found that our initial concerns about the inability of deconditioned subjects to withstand daily centrifugation without syncope were misplaced. These encouraging initial results clearly support the further development of AG protocols. We recommend, as the next steps, the integration of a controlled exercise device on the SRC to determine the synergy between AG and exercise. Coupled with appropriate exercise device(s) the AG protocol will be tuned to-ward an optimal prescription for minimum exposure duration and frequency, maximum AG level and SRC speed.. Performance of these next steps will require extensive use of bed-rest/centrifuge facilities and eventually validation using an SRC in space. A space SRC could be placed in the ISS or on a planetary surface.

Young, Lauren R.↗

NASA NTRS2011

Low Boom Configuration Analysis with FUN3D Adjoint Simulation Framework

Off-body pressure, forces, and moments for the Gulfstream Low Boom Model are computed with a Reynolds Averaged Navier Stokes solver coupled with the Spalart-Allmaras (SA) turbulence model. This is the first application of viscous output-based adaptation to reduce estimated discretization errors in off-body pressure for a wing body configuration. The output adaptation approach is compared to an a priori grid adaptation technique designed to resolve the signature on the centerline by stretching and aligning the grid to the freestream Mach angle. The output-based approach produced good predictions of centerline and off-centerline measurements. Eddy viscosity predicted by the SA turbulence model increased significantly with grid adaptation. Computed lift as a function of drag compares well with wind tunnel measurements for positive lift, but predicted lift, drag, and pitching moment as a function of angle of attack has significant differences from the measured data. The sensitivity of longitudinal forces and moment to grid refinement is much smaller than the differences between the computed and measured data.

Park, Michael A.↗

NASA NTRS2024

Artificial Intelligence Medical Support for Long-Duration Space Missions

We envision an artificial intelligence (AI) based system that will provide support and recommendations to the crew medical officer (CMO) and ground flight surgeon during long-duration space missions. Such a system would be pretrained on the knowledgebase of clinical knowledge on Earth, minimizing the amount of Earth data that needs to be transferred into space. Then during deployment, the system would be constantly refined through active learning from diverse streams of data from sensors in the spacecraft, data collected daily from individual astronauts, and human-in-the-loop feedback from the crew. The model could be interrogated for predictions and recommendations on personalized crew health based on the overall status of the spacecraft, medicinal stores, and status of other crew members. Adaptation techniques would be used to incorporate spaceflight data that have very different distributions from the training data due to the extreme environment. Edge computing and the most advanced neuromorphic processing would enable computation in scenarios with low power and bandwidth, while dimensionality reduction would be employed to ensure that the input data streams from spaceflight are as small as possible. In order to realize this long-term vision, several hardware and software aspects need to be developed and assembled. First, models pretrained on Earth biomedical data would need to be evaluated for predictive accuracy, and the best one selected. That model would need to be adapted to learn from diverse, sparse, and inconsistently measured data streams, as well as human-in-the-loop feedback. A data integration, standardization, and dimensionality reduction methodology would need to be developed to handle all data types and feed them into the model. Once the software and data infrastructure is developed, it would need to be integrated with small footprint compute processors and tested in high-radiation, high-vibration, unregulated temperature situations. As a short-term goal, we recommend to focus on the development of the data and model software structure. Several large language models (LLM) already exist that have been trained on Earth biomedical and clinical knowledgebases, including BioMedLLM, Med-PaLM, SPOKE LLM, and Foresight. These models need to be evaluated for accuracy and the best one chosen for a proof-of-concept structure, while maintaining awareness of the accelerating AI field and incorporating any newly improved model architectures as needed. Then, we recommend to develop a database of synthetic data types to mimic the diverse data streams that are expected in a long-duration space mission. This should include environmental and microbial data from the spacecraft, non-invasive data from wearables and point-of-care devices employed by astronauts, and more invasive molecular and physiological monitoring of clinical and biomarker data from astronauts. The data standardization methodology should be developed, and these data streams used to refine the clinical LLM. Several scenarios should be developed that could plausibly come up in a long-duration space mission, and changes or aberrations introduced to the data at specific times to mimic these scenarios. Then, question and answer tasks should be designed to interrogate the model for predictions and recommendations, with acceptable answers already identified.

Artificial Intelligence↗

NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or aerial wildfire operations reports to better understand the risks present. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement. These applications can benefit from the use of state-of-the-art natural language processing techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of adaptation of NLP tools to the aviation domain by fine-tuning transformer based models using aviation data sets. In 2018, a novel language model based on neural units (also called transformers) was created and became known as “Bidirectional Encoder Representations from Transformers” or BERT. This architecture combined with large amounts of English training data and innovative semi-supervised training tasks set the standard for what would later emerge as Large Language Models. The performance of these models was further improved by hyperparameter tuning and refinement of the semi-supervised training task and resulted in “Robustly Optimized BERT Pre-training Approach through hyperparameter tuning” or RoBERTa models. These pre-trained Large Language Models proved to be useful for a wide variety of natural language processing tasks such as text classification and question answering through a process called fine-tuning. The transformer architecture with pre-trained weights served as the basis with the last few layers replaced with layers fine-tuned to perform a new task e.g., a layer that provides a label for the entire input text. This process of fine-tuning can also be used to adapt the models to new domains; e.g., BioBERT started with the pre-trained BERT model and was completed by additional fine-tuning and training on biomedical documents. Transformer-based architectures can also be used to create rich representations of text called embeddings which can serve as the input to other machine learning models. This allows simpler algorithms such as logistic regression to use context-rich representations of the text while still remaining quick to train and evaluate. In the world of aviation, there is a growing demand for natural language processing and understanding but the domain presents unique challenges. Due to the technical content (and specialized language) of most aviation documents, fine-tuning pre-trained Large Language Models to specific tasks has not met the benchmark on natural language processing tasks set by simpler models trained from scratch on the data. To address this deficiency, this paper evaluates the improvements from fine-tuning a Large Language Model on a large set of aviation documents using the original semi-supervised training tasks before performing specific natural language tasks. In fine-tuning, a domain-specific dataset is used on the original training task but with the pre-trained Large Language Model instead of starting from a random initialization. This approach allows the model to be adapted to the specific domain language without discarding the information gained from training on general English data. This paper utilized two major dataset types to train and assess the RoBERTa fine-tuning performance. The first are 7,057 Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the semi-supervised fine-tuning. The second type is the 494 document classification labels to be used for evaluation. This down-stream evaluation aims to show the performance of the fine-tuned model, better understand how much data is needed for an effective fine-tuning, and how fine-tuning can be adapted for different applications in-the domain. After semi-supervised training, evaluation begins by encoding the documents for classification using the fine-tuned RoBERTa model. Then a logistic regression classifier is trained to label the document type and compared against our ground truth labels. This currently leads to a 82.8% accuracy on 10-fold cross validation showing improvement over baseline RoBERTa which achieved 81.0%. We plan to measure the improvements on additional tasks and it is expected that these improvements will lead to more robust models that can tackle the natural language processing challenges present in aviation datasets.

ATM↗

NASA NTRS2004

Subsurface Stress Fields in FCC Single Crystal Anisotropic Contacts

Single crystal superalloy turbine blades used in high pressure turbomachinery are subject to conditions of high temperature, triaxial steady and alternating stresses, fretting stresses in the blade attachment and damper contact locations, and exposure to high-pressure hydrogen. The blades are also subjected to extreme variations in temperature during start-up and shutdown transients. The most prevalent high cycle fatigue (HCF) failure modes observed in these blades during operation include crystallographic crack initiation/propagation on octahedral planes, and non-crystallographic initiation with crystallographic growth. Numerous cases of crack initiation and crack propagation at the blade leading edge tip, blade attachment regions, and damper contact locations have been documented. Understanding crack initiation/propagation under mixed-mode loading conditions is critical for establishing a systematic procedure for evaluating HCF life of single crystal turbine blades. This paper presents analytical and numerical techniques for evaluating two and three dimensional subsurface stress fields in anisotropic contacts. The subsurface stress results are required for evaluating contact fatigue life at damper contacts and dovetail attachment regions in single crystal nickel-base superalloy turbine blades. An analytical procedure is presented for evaluating the subsurface stresses in the elastic half-space, based on the adaptation of a stress function method outlined by Lekhnitskii. Numerical results are presented for cylindrical and spherical anisotropic contacts, using finite element analysis (FEA). Effects of crystal orientation on stress response and fatigue life are examined. Obtaining accurate subsurface stress results for anisotropic single crystal contact problems require extremely refined three-dimensional (3-D) finite element grids, especially in the edge of contact region. Obtaining resolved shear stresses (RSS) on the principal slip planes also involves considerable post-processing work. For these reasons it is very advantageous to develop analytical solution schemes for subsurface stresses, whenever possible.

Arakere, Nagaraj K.↗

NASA NTRS2007

Reusable, Extensible High-Level Data-Distribution Concept

A framework for high-level specification of data distributions in data-parallel application programs has been conceived. [As used here, distributions signifies means to express locality (more specifically, locations of specified pieces of data) in a computing system composed of many processor and memory components connected by a network.] Inasmuch as distributions exert a great effect on the performances of application programs, it is important that a distribution strategy be flexible, so that distributions can be adapted to the requirements of those programs. At the same time, for the sake of productivity in programming and execution, it is desirable that users be shielded from such error-prone, tedious details as those of communication and synchronization. As desired, the present framework enables a user to refine a distribution type and adjust it to optimize the performance of an application program and conceals, from the user, the low-level details of communication and synchronization. The framework provides for a reusable, extensible, data-distribution design, denoted the design pattern, that is independent of a concrete implementation. The design pattern abstracts over coding patterns that have been found to be commonly encountered in both manually and automatically generated distributed parallel programs. The following description of the present framework is necessarily oversimplified to fit within the space available for this article. Distributions are among the elements of a conceptual data-distribution machinery, some of the other elements being denoted domains, index sets, and data collections (see figure). Associated with each domain is one index set and one distribution. A distribution class interface (where "class" is used in the object-oriented-programming sense) includes operations that enable specification of the mapping of an index to a unit of locality. Thus, "Map(Index)" specifies a unit, while "LocalLayout(Index)" specifies the local address within that unit. The distribution class can be extended to enable specification of commonly used distributions or novel user-defined distributions. A data collection can be defined over a domain. The term "data collection" in this context signifies, more specifically, an abstraction of mappings from index sets to variables. Since the index set is distributed, the addresses of the variables are also distributed.

James, Mark↗

NASA NTRS1991

Finite-volume application of high-order ENO schemes to two-dimensional boundary-value problems

Finite-volume applications of high-order accurate ENO schemes to two-dimensional boundary-value problems are studied. These schemes achieve high-order spatial accuracy, in smooth regions, by a piecewise polynomial approximation of the solution from cell averages. In addition, this spatial operation involves an adaptive stencil algorithm in order to avoid the oscillatory behavior that is associated with interpolation across steep gradients. High-order TVD Runge-Kutta methods are employed for time integration, thus making these schemes best suited for unsteady problems. Fifth- and sixth-order accurate applications are validated through a grid refinement study involving the solutions of scalar hyperbolic equations. A previously proposed extension for the Euler equations of gas dynamics is tested, including its application to solutions of boundary-value problems involving solid walls and curvilinear coordinates.

Casper, Jay↗

NASA NTRS2023

Performance Modeling of Urban Air Mobility Vehicles to Support Air Traffic Management Research

The recent emergence of Urban Air Mobility (UAM) vehicles has resulted in a need for flight performance models that enable comprehensive simulation-based research on air traffic management topics such as route structure, scheduling, and separation standards. Successful performance modeling methods exist for a wide range of traditional aircraft designs. However, comparable modeling methods appropriate for UAM vehicles that combine fixed-wing and rotorcraft performance have not yet been established. One challenge to progress has been the lack of available data capturing the performance characteristics and unique flight profiles of these aircraft. This paper describes methods used to generate the required performance data and the development of performance models for UAM vehicles. Included is a review of the energy and power equations often used in developing performance models for traditional aircraft as well as a discussion of their applicability to UAM vehicles. The challenge of generating realistic performance data in over-actuated vehicles transitioning from hover to cruise flight is also addressed through an approach based on objective function optimization. A table-based performance model format adapted to UAM configurations is described, as well as parametric models intended to accompany the performance table to allow detailed modeling of power and fuel consumption during accelerated flight, turning flight, or flight at an arbitrary climb or descent rate. A discussion of future work is also provided, including the need for refinement of UAM performance modeling methods and formats, especially in conjunction with improvements to aerodynamic modeling of vehicles with complex designs where strong interaction effects may dominate important regions of the flight envelope.

Performance Modeling↗

NASA NTRS2023

Performance Modeling of Urban Air Mobility Vehicles to Support Air Traffic Management Research

Performance Modeling↗

NASA NTRS2016

Using Powder Cored Tubular Wire Technology to Enhance Electron Beam Freeform Fabricated Structures

Electron Beam Freeform Fabrication (EBF3) is an additive manufacturing technique, developed at NASA Langley Research Center, capable of fabricating large scale aerospace parts. Advantages of using EBF3 as opposed to conventional manufacturing methods include, decreased design-to-product time, decreased wasted material, and the ability to adapt controls to produce geometrically complex parts with properties comparable to wrought products. However, to fully exploit the potential of the EBF3 process development of materials tailored for the process is required. Powder cored tubular wire (PCTW) technology was used to modify Ti-6Al-4V and Al 6061 feedstock to enhance alloy content, refine grain size, and create a metal matrix composite in the as-solidified structures, respectively.

Gonzales, Devon↗

NASA NTRS2021

Reviews and syntheses: Arctic fire regimes and emissions in the 21st century

In recent years, the pan-Arctic region has experienced increasingly extreme fire seasons. Fires in the northern high latitudes are driven by current and future climate change, lightning, fuel conditions, and human activity. In this context, conceptualizing and parameterizing current and future Arctic fire regimes will be important for fire and land management as well as understanding current and predicting future fire emissions. The objectives of this review were driven by policy questions identified by the Arctic Monitoring and Assessment Programme (AMAP) Working Group and posed to its Expert Group on Short-Lived Climate Forcers. This review synthesizes current understanding of the changing Arctic and boreal fire regimes, particularly as fire activity and its response to future climate change in the pan-Arctic have consequences for Arctic Council states aiming to mitigate and adapt to climate change in the north. The conclusions from our synthesis are the following. (1) Current and future Arctic fires, and the adjacent boreal region, are driven by natural (i.e. lightning) and human-caused ignition sources, including fires caused by timber and energy extraction, prescribed burning for landscape management, and tourism activities. Little is published in the scientific literature about cultural burning by Indigenous populations across the pan-Arctic, and questions remain on the source of ignitions above 70_ N in Arctic Russia. (2) Climate change is expected to make Arctic fires more likely by increasing the likelihood of extreme fire weather, increased lightning activity, and drier vegetative and ground fuel conditions. (3) To some extent, shifting agricultural land use and forest transitions from forest–steppe to steppe, tundra to taiga, and coniferous to deciduous in a warmer climate may increase and decrease open biomass burning, depending on land use in addition to climate-driven biome shifts. However, at the country and landscape scales, these relationships are not well established. (4) Current black carbon and PM2:5 emissions from wildfires above 50 and 65_ N are larger than emissions from heanthropogenic sectors of residential combustion, transportation, and flaring. Wildfire emissions have increased from 2010 to 2020, particularly above 60_ N, with 56% of black carbon emissions above 65_ N in 2020 attributed to open biomass burning – indicating how extreme the 2020 wildfire season was and how severe future Arctic wildfire seasons can potentially be. (5) What works in the boreal zones to prevent and fight wildfires may not work in the Arctic. Fire management will need to adapt to a changing climate, economic development, the Indigenous and local communities, and fragile northern ecosystems, including permafrost and peatlands. (6) Factors contributing to the uncertainty of predicting and quantifying future Arctic fire regimes include underestimation of Arctic fires by satellite systems, lack of agreement between Earth observations and official statistics, and still needed refinements of location, conditions, and previous fire return intervals on peat and permafrost landscapes. This review highlights that much research is needed in order to understand the local and regional impacts of the changing Arctic fire regime on emissions and the global climate, ecosystems, and pan-Arctic communities.

Arctic↗

NASA NTRS1977

F-8C adaptive flight control laws

Three candidate digital adaptive control laws were designed for NASA's F-8C digital flyby wire aircraft. Each design used the same control laws but adjusted the gains with a different adaptative algorithm. The three adaptive concepts were: high-gain limit cycle, Liapunov-stable model tracking, and maximum likelihood estimation. Sensors were restricted to conventional inertial instruments (rate gyros and accelerometers) without use of air-data measurements. Performance, growth potential, and computer requirements were used as criteria for selecting the most promising of these candidates for further refinement. The maximum likelihood concept was selected primarily because it offers the greatest potential for identifying several aircraft parameters and hence for improved control performance in future aircraft application. In terms of identification and gain adjustment accuracy, the MLE design is slightly superior to the other two, but this has no significant effects on the control performance achievable with the F-8C aircraft. The maximum likelihood design is recommended for flight test, and several refinements to that design are proposed.

Hartmann, G. L.↗

NASA NTRS1971

Apollo contamination analysis

The Apollo 12 photographic observations of clouds produced by venting liquids into space were reduced and analyzed. The number of Baker-Nunn photographs required that film measurement and data processing be automated, and the microdensitometer was adapted to automatic digital operation. Computer programs were prepared to process the image-density measurements. A substantial selection of Apollo 12 data was processed; both hydrogen and oxygen clouds are represented. Some limited data were obtained from Apollo 13 and 14 missions. During the Apollo 12 data analysis, the theory and models of cloud phenomena were refined because the models were initially applied to inferior oxygen data from earlier missions. The reported observations of water dumps from Apollo 12 are also discussed, along with the use of the oxygen and hydrogen data from Apollo 12 photographs with the cloud models.

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