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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
DOE OSTI2022

Facilitated Direct Liquid Fuel Cells with High Temperature Membrane Electrode Assemblies

Dimethyl ether (DME) is a liquid fuel of great potential impact due to its exceptionally high energy density. However, it has received minimal prior investigation as an alternative to either purified hydrogen or other liquid fuels, including methanol (MeOH). In the limited published literature work on direct dimethyl ether fuel cells, regardless of operating temperature, PtRu (either supported or unsupported on carbon) has been established as the standard catalyst of choice. The majority of the work in this program also utilized a Johnson Matthey (JM) HiSPEC ® 12100 PtRu/C (nominally 50% Pt, 25% Ru) while looking at electrode optimizations, beginning of life (BoL) performance, pressure- and temperature-dependent studies to look at the effect of binding affinity of DME oxidation intermediates, mass transport effects, crossover studies, and durability. However, it does also investigate some promising alternatives to PtRu/C as well, which should be investigated in more detail in further work. Those catalysts include a pair of ternary PtRuPd/C catalysts (from Los Alamos National Laboratory (LANL) and Pajarito Powder, LLC. (PP)) as well as a Pt 2 Bi Black catalyst from Professor Anastasios Angelopoulos of the University of Cincinnati (UC). This work achieved several project objectives, including an optimization of the membrane electrode assembly (MEA) process using PtRu/C anode catalyst. Additionally, these direct dimethyl ether fuel cells (DDFCs) were able to match or exceed many performance metrics for the state-of-the-art (SOA) direct methanol fuel cells (DMFCs), a primary and more evolved competitor to direct dimethyl ether fuel cells. This included peak specific power, total platinum group metal (PGM) loading, crossover current, degradation rate, start/stop cycling losses, and anode specific current.

09 BIOMASS FUELS↗
NASA NTRSDate not supplied

Algorithmic Detection of Elemental Biosignatures

Machine learning models that classify a sample as indicative or non-indicative of life could play an important role in life-detection missions. Their predictions result from agnostic algorithms and thereby add redundancy to judgements resulting from human expertise. Additionally, their important features can reveal the most informative measurements within the operational constraints of a life-detection mission. The Ladder of Life Detection (Neveu 2018) identifies the need for an understanding of how combinations of multiple biosignatures affect overall confidence. The present work provides a starting point to answer this need, and future work will expand the data types to obtain even more predictive combinations of features. Elemental abundance was chosen as a starting set of features due to its availability in diverse sample types, which are needed to train a generalizable model. A standardized dataset was collected, including 35 non-indicative, e.g., lunar rock, basalt; 19 indicative mixed, e.g., seawater, agricultural soil; 46 indicative non-alive, e.g., coal, chalk; and 10 indicative alive, e.g., biofilm, bacteria. This dataset could be valuable for complementary biosignature research. The samples were standardized to the same limit of detection of a simulated mission scenario. Four classification models were used: k-nearest neighbors (KNN), logistic regression (LR), linear support vector machines (SVM), and Gaussian naïve Bayes (GNB). To obtain feature importances, KNN was run on three principal components of the training data and LR and SVM were run with L1 and L2 regularization. The performances and feature importances of the six model variants on 40:60 train to validation ratios were assessed with Monte Carlo simulations. ROC AUC and mean accuracy scores ranged between 82% - 94%, with sensitivity greater than specificity. For indicative of life predictors, all models had C and Ca as strong and Cl as medium; a majority of models had N, K, and P as medium. For non-indicative of life predictors, all models had Si as strong, and a majority of models had Mg, Al, and Ti as medium. Varied elements were Fe (slightly non-indicative), H (slightly indicative), O (widely varied), Na, Mn, and S. These results serve as a proof of concept and suggest important elemental signals beyond merely the CHNOPS of Earth-based life.

Algorithmic↗
DOE OSTI2022

Tallo: A global tree allometry and crown architecture database

Abstract Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research—from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non‐forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC‐BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599 . To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology—from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.

Jucker, Tommaso↗
DOE OSTI2025

New NDA Methods for Thorium Fuel Cycle Safeguards (Final Report)

This project developed portable Neutron Resonance Transmission Analysis (pNRTA) as a new non-destructive assay (NDA) method for thorium fuel cycles safeguards and other applications where multiple isotopes must be measured when present together. pNRTA leverages epithermal neutron resonances to assay multiple safeguards-relevant isotopes (e.g., 233 U and 235 U) when they are present together in a sample. Existing techniques are challenged by this task, driving the need for new active interrogation methods. With selected detectors, pNRTA works in high gamma-ray backgrounds from fission and activation products and 232 U progeny expected in thorium fuel cycle samples. This project leveraged a pNRTA system developed at Pacific Northwest National Laboratory (PNNL) and collaboration with the Massachusetts Institute of Technology (MIT). The system uses a commercially available deuterium-tritium (DT) neutron generator at short standoff (2 m). Key achievements in this project included: first-of-a-kind pNRTA quantitative measurements of 233 U oxide samples, an assessment of neutron detector technologies suitable for pNRTA in high gamma-ray background environments, experimentally demonstrating quantitative assay of samples containing 233 U and 235 U, and modeling studies showing the applicability of pNRTA to a wide range of material forms. Further, a custom algorithm was developed at MIT, which provided mean bias of 9% and relative standard deviation of 36% in assaying 233 U, 235 U, 238 U, and 232 Th content in eight measured samples. These outcomes form a solid technical basis for pNRTA as a new promising capability for international safeguards verification that is portable, non-destructive, quantitative, and isotopic specific.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Stresses within rare-earth doped yttria-stabilized zirconia thermal barrier coatings from in-situ synchrotron X-ray diffraction at high temperatures

There is a growing interest for smart coatings that can be integrated into turbine engines for in-situ temperature measurements or health monitoring. The addition of rare-earth dopants into standard thermal barrier ceramic top coat materials is used to obtain luminescent coatings that enable spectral measurements, for real-time temperature or health monitoring. The thermomechanical performance and durability of such novel coating compositions in extreme environments still remains to be evaluated. Consequently, the ability to manufacture sensor coatings which present suitable thermal properties needs to be demonstrated. For this study, highly luminescent erbium and europium doped yttria-stabilized zirconia and state-of-the-art yttria-stabilized zirconia coatings manufactured by air plasma spray were characterized to determine the effects of the embedded rareearth dopants on coating internal strain and stress and to quantify and compare their high temperature response using synchrotron X-ray diffraction. In-situ depth-resolved strain measurements were performed at 15 μm intervals along the depth of the coatings to evaluate materials response at key locations, specifically at layer interfaces. In-plane stress was calculated for the coatings and a finite element model was implemented to supplement the results and enable further predictions. The results show that the sensor coatings that were manufactured in this work revealed only minor variations in the strain response of sensor coatings under a typical thermal cycle and especially at temperatures closer to that of gas turbine operating conditions, compared to state-of-the-art coatings. This work demonstrates the viability of manufacturing rare-earth doped yttria-stabilized zirconia coatings that provide beneficial spectroscopic monitoring capabilities while having minimal impact on the thermomechanical response of the thermal barrier coatings.

Air plasma spray↗
NASA NTRS2004

Fabrication of a novel gigabit/second free-space optical interconnect - photodetector characterization and testing and system development

The time when computing power is limited by the copper wire inherent in the computer system and not the speed of the microprocessor is rapidly approaching. With constant advances in computer technology, many researchers believe that in only a few years, optical interconnects will begin to replace copper wires in your Central Processing Unit (CPU). On a more macroscopic scale, the telecommunications industry has already made the switch to optical data transmission as, to date, fiber optic technology is the only reasonable method of reliable, long range data transmission. Within the span of a decade, we will see optical technologies move from the macroscopic world of the telecommunications industry to the microscopic world of the computer chip. Already, the communications industry is marketing commercially available optical links to connect two personal computers, thereby eliminating the need for standard and comparatively slow wired and wireless Ethernet transfers and greatly increasing the distance the computers can be separated. As processing demands continue to increase, the realm of optical communications will continue to move closer to the microprocessor and quite possibly onto the microprocessor itself. A day may come when copper connections are used only to supply power, not transfer data. This summer s work marks some of the beginning stages of a 5 to 10 year, long-term research project to create and study a free-space, 1 Gigabit/sec optical interconnect. The research will result in a novel fabricated, chip-to-chip interconnect consisting of a Vertical Cavity Surface Emitting Laser (VCSEL) Diode linked through free space to a Metal- Semiconductor-Metal (MSM) Photodetector with the possible integration of microlenses for signal focusing and Micro-Electromechanical Systems (MEMS) devices for optical signal steering. The advantages, disadvantages, and practicality of incorporating flip-chip mounting technologies will also be addressed. My work began with the design and construction of a test setup for the experiment and then appropriate characterization of the test system. Specifically, I am involved in the characterization of a commercially available 1550nm wavelength, 5mW diode laser and a study of its modulation bandwidth. Commercially produced photodetectors as well as the incorporation of microwave technology, in the form of RF input and output, are used in the characterization procedure. The next stage involves the use of a probe station and network analyzer to characterize and test a series of photodetectors fabricated on a 2 inch, Indium Gallium Arsenide (InGaAs) wafer in the Branch s microlithography lab. Other project responsibilities include, but are not limited to the incorporation of a transimpedance amplifier to the photodetector circuit; a study of VCSEL technology; bit error rate analysis of an optical interconnect system; and analysis of free space divergence of the VCSEL, optical path length of the interconnect; and any other pertinent optical properties of the one gigabit per second interconnect for fabrication and testing.

Savich, Gregory R.↗
DOE OSTI2025

Overcoming the Conductance versus Crossover Trade-off in State-of-the-Art Proton Exchange Fuel-Cell Membranes by Incorporating Atomically Thin Chemical Vapor Deposition Graphene

Permeance–selectivity trade-offs are inherent to polymeric membranes. In fuel cells, thinner proton exchange membranes (PEMs) could enable higher proton conductance and increased power density with lower area-specific resistance (ASR), smaller ohmic losses, and lower ionomer cost. However, reducing thickness is accompanied by an increase in undesired species crossover harming performance and long-term efficiency. Here, we show that incorporating atomically thin monolayer graphene synthesized via scalable chemical vapor deposition (CVD) and tunable defect density into PEMs (Nafion, ~5–25 μm thick) can allow for reduced H 2 crossover (~34–78% of Nafion of a similar thickness) while maintaining adequate areal proton conductance for applications (>4 S cm –2 ). In contrast to most prior work using >50 μm symmetric Nafion sandwich structures, we elucidate the interplay of graphene defect density and Nafion proton transport resistance on the performance of Nafion|graphene composite membranes and find high-quality low-defect density CVD graphene (G) supported on Nafion 211 (~25 μm); i.e., N211|G has a high areal proton conductance (~6.1 S cm –2 ) and the lowest H 2 crossover (~0.7 mA cm –2 ). Fully functional centimeter-scale N211|G fuel-cell membranes demonstrate performance comparable to that of state-of-the-art Nafion N211 at room temperature as well as standard operating conditions (~80 °C, ~150–250 kPa-abs) with H 2 /air (power density ~0.57–0.63 W cm –2 ) and H 2 /O 2 feed (power density ~1.4–1.62 W cm –2 ) and markedly reduced H 2 crossover (~53–57%).

25 ENERGY STORAGE↗
NASA NTRS2007

Model-Based Systems

Engineers, who design systems using text specification documents, focus their work upon the completed system to meet Performance, time and budget goals. Consistency and integrity is difficult to maintain within text documents for a single complex system and more difficult to maintain as several systems are combined into higher-level systems, are maintained over decades, and evolve technically and in performance through updates. This system design approach frequently results in major changes during the system integration and test phase, and in time and budget overruns. Engineers who build system specification documents within a model-based systems environment go a step further and aggregate all of the data. They interrelate all of the data to insure consistency and integrity. After the model is constructed, the various system specification documents are prepared, all from the same database. The consistency and integrity of the model is assured, therefore the consistency and integrity of the various specification documents is insured. This article attempts to define model-based systems relative to such an environment. The intent is to expose the complexity of the enabling problem by outlining what is needed, why it is needed and how needs are being addressed by international standards writing teams.

Frisch, Harold P.↗
DOE OSTI2020

Indoor air quality in new and renovated low-income apartments with mechanical ventilation and natural gas cooking in California

This work presents pollutant concentrations and performance data for code-required mechanical ventilation equipment in 23 low-income apartments at 4 properties constructed or renovated 2013-2017. All apartments had natural gas cooking burners. Occupants pledged to not use windows for ventilation during the study but several did. Measured airflows of range hoods and bathroom exhaust fans were lower than product specifications. Only eight apartments operationally met all ventilation code requirements. Pollutants measured over one week in each apartment included time-resolved fine particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ), formaldehyde and carbon dioxide (CO 2 ) and time-integrated formaldehyde, NO 2 and nitrogen oxides (NO X ). Compared to a recent study of California houses with code-compliant ventilation, apartments were smaller, had fewer occupants, higher densities, and higher mechanical ventilation rates. Mean PM 2.5 , formaldehyde, NO 2 , and CO 2 were 7.7 µg/m 3 , 14.1 ppb, 18.8 ppb, and 741 ppm in apartments; these are 4% lower, 25% lower, 165% higher, and 18% higher compared to houses with similar cooking frequency. Four apartments had weekly PM 2.5 above the California annual outdoor standard of 12 µg/m 3 and also discrete days above the World Health Organization 24-h guideline of 25 µg/m 3 . Two apartments had weekly NO 2 above the California annual outdoor standard of 30 ppb.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2019

Machine Learning Prototype App For Recognition of Fruits

As the incidence of obesity and associated negative health consequences is rising, it becomes crucial to monitor the dietary choices of individuals. Unfortunately, traditional methods to collect this information involve collecting food frequency questionnaires from individuals using paper. Electronic food trackers have been developed to collect food data, but they require participants to manually label and describe the content of their meals, and which may be difficult for researchers to interpret in a standardized fashion. Machine learning, however, provides an easy and efficient method for both participants and researchers to label food items with standardized descriptions. This project aims to create a prototype phone application that can identify and label photos of apples. This is done by making a machine learning model through Turicreate, a python module, which is then implemented into an iOS app through Xcode and Swift. The modules used in Swift include CoreML and AVFoundation. This machine learning application will be incorporated with a MealLogger phone app that is also under development. The MealLogger app will be used to keep track of participants' calorie intake and other personal details throughout the sleep study. The machine learning model will present several potential identities of the foods found in the photo, and the user will only need to select the correct option. This will be a user-friendly method for participants to easily log their food consumption without the hard work of manually inputting each and every description. Some limitations to this project include the wide variety of food, including those within different cultures. To deal with this, the model will include the most generic food categories, which the participant may select, and produce a drop-down menu of more specific dishes under that specified category, with the option of self-input. Additional questionnaires may be implemented according to the food type selected This will allow the process to be quick and easy, but also specific for the purpose of analysis. The release of the application will require a much longer process, but the machine learning prototype presents a first step toward an application that may change data analysis for researchers interested in collecting food intake from individuals living in the real world.

Food tracker↗
DOE OSTI2021

Bio-project “derisking” through development of systematic methodologies and frameworks for risk assessment

One of the primary hindrances to producing a viable, sustainable domestic biomass industry for renewable biofuels, bio-products and bio-power is the lack of understanding and quantification of the risks associated with both the biomass supply chain and preprocessing and conversion technologies. Currently a consistent method for assessing, comparing, and quantifying risks in biomass supply chains does not exist, creating a major investment barrier to bioenergy projects in the U.S. The lack of a standardized approach has resulted in bioenergy stakeholders independently using inconsistent approaches and evaluation criteria, leading to unreliable and incomparable assessments of risks and financing barriers to bio-project development. Along with the challenges of inconsistent risk assessment for supply chain risk, technology specific risks based on variability in biomass properties are not fully understood and can pose significant unforeseen challenges for bioenergy projects. In many cases these properties have not yet been identified and the impacts on the proposed technology and products unquantified. This is particularly challenging for emerging preprocessing and conversion technologies. Without a firm understanding of the preprocessing/conversion technology-specific critical properties, the risk of a proposed bio-project cannot be fully evaluated. To address inconsistent risk evaluation in the biomass supply chain supporting project financing, a Biomass Supply Chain Risk Standards (BSCRS) framework was developed. The BSCRS framework includes a comprehensive list of known and perceived risks (Risk Indicators) to the supply chain developed through 100’s of interviews with bioenergy industry experts spanning from feedstock growers and suppliers to representatives from the financial sector. These risks have been organized into a manageable hierarchy of Risk Categories and Risk Factors that can be practically assessed. This BSCRS framework also provides mitigation strategies for multiple Risk Indicators from best available industry practices and research findings. Additionally, a risk quantification methodology for each Risk Factor, Risk Category, and the bio-project as a whole was developed to enable capital markets to assess feedstock risk more efficiently and more accurately. Multiple case studies representing existing bio-projects have been used to evaluate and verify the BSCRS framework and scoring methodology. To address technological risk along with the supply chain risk captured in the developed BSCRS framework, this work also focuses on development of a systematic criticality assessment tool using well-accepted, quantitative risk analysis methods to evaluate bioenergy feedstock critical properties impacting system unit operations. The proposed Failure Mode and Effect Analysis (FMEA) approach uses a team of subject area experts (SAEs) for each targeted unit operation within a system. Collectively, the team will develop and use a quantitative scoring system to assess the material attributes, process parameters, and quality attributes for key unit operations that have already been identified. The FMEA process generates Risk Priority Numbers (RPNs) for the various failures and predominant causes for each material/process unit/product combination resulting in a semi-quantitative, standardized methodology for assessing technological risk and biomass properties contributing to that risk.

09 BIOMASS FUELS↗
DOE OSTI2025

A proposed high-intensity radiometer calibration method using concentrated solar radiation

Accurate calibration of irradiance measurement devices, or radiometers, is essential for ensuring the reliability of measurements in high heat applications such as concentrating solar power (CSP), aerospace, defense, and pulsed power systems. Despite the critical need, existing calibration standards and service providers are limited to irradiance levels below 100 kW/m 2 and specific radiation sources, which is insufficient for many applications. For instance, CSP technologies, particularly those under the Department of Energy’s Solar Energy Technologies Office (SETO) Gen 3 program, require accurate measurements of broadband irradiance at levels exceeding 2000 kW/m 2 . In even more extreme scenarios, such as re-entry vehicles, heat levels can surpass 10000 kW/m 2 . Current ISO standards, specifically ISO 14934–2 and ISO 14934–3, are constrained to lower irradiance levels and dependent on black body heat sources, limiting their applicability for high-intensity broadband irradiance measurements, particularly in concentrated solar applications. Here, to address this shortfall, the National Solar Thermal Test Facility (NSTTF) at Sandia National Laboratories (SNL) proposes a calibration method and facility capable of characterizing radiometers up to 2750 kW/m 2 using concentrated solar irradiance. Calibrating with concentrated sunlight is important for solar applications as it aligns the calibration process with the solar spectrum. This alignment is crucial for minimizing systematic errors and avoiding the need for additional corrections that may arise when radiometers designed for solar applications are calibrated using black-body or electrical sources. This paper presents the present day NSTTF characterization facility and procedure, detailing the proposed calibration method and uncertainty quantification. The presented method builds upon 1980′s NSTTF methodology and involves both theoretical and empirical methods to establish a robust relationship between gauge voltage output and irradiance intensity, quantifying both measurement and fitting errors. By addressing the limitations of existing standards and extending the characterization range, this work provides an advancement in the field of high-intensity irradiance measurement and instrumentation characterization.

Gardon gauge↗
NASA NTRS2007

Multi-System Effects of Daily Artificial Gravity Exposures in Humans Deconditioned by Bed Rest

We have begun to explore the utility of intermittent artificial gravity (AG) as a multi-system countermeasure to the untoward health and performance effects of adaptation to decreased gravity during prolonged space flight. The first study in this exploration was jointly designed by an international, multi-disciplinary team of scientists interested in standardizing an approach so that comparable data could be obtained from follow-on studies performed in multiple international locations. Fifteen rigorously screened male volunteers participated in the study after providing written informed consent. All were subjected to 21 days of 6deg head-down-tilt (HDT) bed rest. Eight were treated with daily 1hr AG exposures (2.5g at the feet decreasing to 1.0g at the heart) aboard a short radius (3m) centrifuge, while the other seven served as controls. Multiple observations were made of dependent measures in the bone, muscle, cardiovascular, sensory-motor, immune, and behavioral systems during a 10 day acclimatization period prior to HDT bed rest and again during an 8 day recovery period after the bed rest period. Comparisons between the treatment and control subjects demonstrated salutary effects of the AG exposure on aspects of the muscle and cardiovascular systems, with no untoward effects on the vestibular system, the immune system, or cognitive function. Bone deconditioning was similar between the treatment and control groups, suggesting that the loading provided by this specific AG paradigm was insufficient to protect that system from deconditioning. Future work will be devoted to varying the loading duty cycle and/or coupling the AG loading with exercise to provide maximum physiological protection across all systems. Testing will also be extended to female subjects. The results of this study suggest that intermittent AG could be an effective multi-system countermeasure.

Paloski, William H.↗
DOE OSTI2022

Do Machine Learning Approaches Offer Skill Improvement for Short-Term Forecasting of Wind Gust Occurrence and Magnitude?

Abstract Wind gusts, and in particular intense gusts, are societally relevant but extremely challenging to forecast. This study systematically assesses the skill enhancement that can be achieved using artificial neural networks (ANNs) for forecasting of wind gust occurrence and magnitude. Geophysical predictors from the ERA5 reanalysis are used in conjunction with an autoregressive term in regression and ANN models with different predictors, and varying model complexity. Models are derived and assessed for the warm (April–September) and cold (October–March) seasons for three high passenger volume airports in the United States. Model uncertainty is assessed by deriving models for 1000 different randomly selected training (70%) and testing (30%) subsets. Gust prediction fidelity in independent test samples is critically dependent on inclusion of an autoregressive term. Gust occurrence probabilities derived using five-layer ANNs exhibit consistently higher fidelity than those from regression models and shallower ANNs. Inclusion of the autoregressive term and increasing the number of hidden layers in ANNs from 1 to 5 also improve the model performance for gust magnitudes (lower RMSE, increased correlation, and model standard deviations that more closely approximate observed values). Deeper ANNs (e.g., 20 hidden layers) exhibit higher skill in forecasting strong (17–25.7 m s −1 ) and damaging (≥25.7 m s −1 ) wind gusts. However, such deep networks exhibit evidence of overfitting and still substantially underestimate (by 50%) the frequency of strong and damaging wind gusts at the three airports considered herein. Significance Statement Improved short-term forecasting of wind gusts will enhance aviation safety and logistics and may offer other societal benefits. Here we present a rigorous investigation of the relative skill of models of wind gust occurrence and magnitude that employ different statistical methods. It is shown that artificial neural networks (ANNs) offer considerable skill enhancement over regression methods, particularly for strong and damaging wind gusts. For wind gust magnitudes in particular, application of deeper learning networks (e.g., five or more hidden layers) offers tangible improvements in forecast accuracy. However, deeper networks are vulnerable to overfitting and exhibit substantial variability with the specific training and testing data subset used. Also, even deep ANNs reproduce only half of strong and damaging wind gusts. These results indicate the need for future work to elucidate the dynamical mechanisms of intense wind gusts and advance solutions to their prediction.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

Establishing a Distance Learning Plan for International Space Station (ISS) Interactive Video Education Events (IVEE)

Educational outreach is an integral part of the International Space Station (ISS) mandate. In a few scant years, the International Space Station has already established a tradition of successful, general outreach activities. However, as the number of outreach events increased and began to reach school classrooms, those events came under greater scrutiny by the education community. Some of the ISS electronic field trips, while informative and helpful, did not meet the generally accepted criteria for education events, especially within the context of the classroom. To make classroom outreach events more acceptable to educators, the ISS outreach program must differentiate between communication events (meant to disseminate information to the general public) and education events (designed to facilitate student learning). In contrast to communication events, education events: are directed toward a relatively homogeneous audience who are gathered together for the purpose of learning, have specific performance objectives which the students are expected to master, include a method of assessing student performance, and include a series of structured activities that will help the students to master the desired skill(s). The core of the ISS education events is an interactive videoconference between students and ISS representatives. This interactive videoconference is to be preceded by and followed by classroom activities which help the students aftain the specified learning objectives. Using the interactive videoconference as the centerpiece of the education event lends a special excitement and allows students to ask questions about what they are learning and about the International Space Station and NASA. Whenever possible, the ISS outreach education events should be congruent with national guidelines for student achievement. ISS outreach staff should recognize that there are a number of different groups that will review the events, and that each group has different criteria for acceptance. For example, school administrators are more likely to be concerned about an event meeting national standards and the cost of the event. In contrast, a teacher's acceptance of an education event may be directly related to the amount of extra work the event imposes upon that teacher. ISS education events must be marketed differently to the different groups of educators, and must never increase the workload of the average teacher.

Wallington, Clint↗
NASA NTRS2004

Surface Modeling and Grid Generation for Iced Airfoils (SmaggIce)

Many of the troubles associated with problem solving are alleviated when there is a model that can be used to represent the problem. Through the Advanced Graphics and Visualization (G-VIS) Laboratory and other facilities located within the Research Analysis Center, the Computer Services Division (CSD) is able to develop and maintain programs and software that allow for the modeling of various situations. For example, the Icing Research Branch is devoted to investigating the effect of ice that forms on the wings and other airfoils of airplanes while in flight. While running tests that physically generate ice and wind on airfoils within the laboratories and wind tunnels on site are done, it would be beneficial if most of the preliminary work could be done outside of the lab. Therefore, individuals from within CSD have collaborated with Icing Research in order to create SmaggIce. This software allows users to create ice patterns on clean airfoils or open files containing a variety of icing situations, manipulate and measure these forms, generate, divide, and merge grids around these elements for more explicit analysis, and specify and rediscretize subcurves. With the projected completion date of Summer 2005, the majority of the focus of the Smagglce team is user-functionality and error handling. My primary responsibility is to test the Graphical User Interface (GUI) in SmaggIce in order to ensure the usability and verify the expected results of the events (buttons, menus, etc.) within the program. However, there is no standardized, systematic way in which to test all the possible combinations or permutations of events, not to mention unsolicited events such as errors. Moreover, scripting tests, if not done properly and with a view towards inevitable revision, can result in more apparent errors within the software and in effect become useless whenever the developers of the program make a slight change in the way a specific process is executed. My task therefore requires a brief yet intense study into GUI coverage criteria and creating algorithms for GUI implementation. Nevertheless, there are still heavily graphical features of SmaggIceSmaggIce that must be either corrected or redesigned before its release. A particular feature of SmaggIce is the ability to smooth out curves created by control points that form an arbitrary shape into something more acquiescent to gridding (while maintaining the integrity of the data). This is done by a mathematical model known as Non-Uniform Rational B-Spline (NURBS) curves. Existing NURBS code is written in FORTRAN-77 with static arrays for holding information. My new assignment is to allow for dynamic memory allocation within the code and to make it possible for the developers to call out functions from the NURBS code using C.

Hammond, Brandy M.↗
NASA NTRS2019

GeneLab: A Systems Biology Platform for Omics Analysis

NASA's GeneLab includes an open-access repository of some 200+ omics datasets generated by biological experiments relevant to spaceflight (including simulated cosmic radiation and microgravity). In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics knowledge, GeneLab is now transforming the data in the repository into actual biological and physiological knowledge of the genetic and proteomic signatures found in these samples. This processed data is being derived by establishing standard data analysis workflows vetted by 114 scientists who are members of the four GeneLab Analysis Working Groups (Animal AWG, Plant AWG, Microbe AWG, Multi-Omics AWG). AWG members from institutes spanning the U.S. and four other countries participate on a voluntary basis. The AWGs meet monthly to discuss data mining, compare results and interpretations, and test forthcoming releases of the GeneLab Data Systems (GLDS). GLDS version 3.0 has been available to the general public since October 1st 2018, and has been providing a professional state-of-the-art bioinformatics platform for everyone in the space biology community to upload their data into a space biology omics data commons, to process their data with vetted standard workflows and to compare to existing analyses. The user interface for the platform is being designed to be accessible to a broad variety of users including those with limited bioinformatics experience, including high school and college students who can use it to learn about omics data analysis and space biology. As such, Genelab will constitute a powerful general public outreach capability of NASA and the Space Biology community at large. Data mining of the GeneLab database by the AWG has already started generating very interesting findings, including reports linking specific spaceflight conditions such as radiation, microgravity or carbon dioxide levels to molecular changes seen across various species. In this presentation, we will report on the current and future objectives for GeneLab, and review recent studies reported by the various AWGs relating molecular changes observed in various animal models and tissue with microgravity, radiation, circadian rhythm, hydration and carbon dioxide conditions.

Omics↗
NASA NTRS2019

GeneLab: A Systems Biology Platform for Omics Analysis: Disseminate and Reuse Data, Tools, and Samples Post-Project

NASA's GeneLab includes an open-access repository of some 200 plus omics datasets generated by biological experiments relevant to spaceflight (including simulated cosmic radiation and microgravity). In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics knowledge, GeneLab is now transforming the data in the repository into actual biological and physiological knowledge of the genetic and proteomic signatures found in these samples. This processed data is being derived by establishing standard data analysis workflows vetted by 114 scientists who are members of the four GeneLab Analysis Working Groups (Animal AWG, Plant AWG, Microbe AWG, Multi-Omics AWG). AWG members from institutes spanning the U.S. and four other countries participate on a voluntary basis. The AWGs meet monthly to discuss data mining, compare results and interpretations, and test forthcoming releases of the GeneLab Data Systems (GLDS). GLDS version 3.0 has been available to the general public since October 1st 2018, and has been providing a professional state-of-the-art bioinformatics platform for everyone in the space biology community to upload their data into a space biology omics data commons, to process their data with vetted standard workflows and to compare to existing analyses. The user interface for the platform is being designed to be accessible to a broad variety of users including those with limited bioinformatics experience, including high school and college students who can use it to learn about omics data analysis and space biology. As such, Genelab will constitute a powerful general public outreach capability of NASA and the Space Biology community at large. Data mining of the GeneLab database by the AWG has already started generating very interesting findings, including reports linking specific spaceflight conditions such as radiation, microgravity or carbon dioxide levels to molecular changes seen across various species. In this presentation, we will report on the current and future objectives for GeneLab, and review recent studies reported by the various AWGs relating molecular changes observed in various animal models and tissue with microgravity, radiation, circadian rhythm, hydration and carbon dioxide conditions.

Omics↗