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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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At least 217 records · Page 12

Microsample Characterization of Coatings for Grcop-84 for High Heat Flux Applications

A multidisciplinary Johns Hopkins University-NASA Glenn team is undertaking a collaborative research program to elucidate and model the thermal stability and mechanical integrity of candidate coatings for GRCop-84. GRCop-84 is a high conductivity, high strength copper alloy that was recently developed at NASA Glenn for use in high temperature, high heat flux applications. With potential applications in rocket motor combustion chamber liners, nozzle ramps and other actively cooled structures, this new material offers great potential for decreasing weight and increasing reliability of third generation reusable launch vehicles. Current emphasis has turned toward the development of environmentally resistant and thermal barrier coatings for this alloy. Metallic coatings such as NiCrAlY and Cu-8-30%Cr have shown promise in: prohibiting blanching, reducing dog-house failures, increasing operating temperatures and decreasing cooling requirements. The focus of this research program is to develop a fundamental understanding of the substrate-coating interactions that occur during thermal cycling (inter-diffusion, viscoplasticity, morphological evolution, crack formation, etc.) and to derive a science-based protocol for future coating selection, optimization and reliability assurance. The microsample tensile testing approach adopted for this study allows us to characterize small-scale and highly scale-specific coatings and properties in a way not possible by conventional means. In addition to providing much needed design data, the integration of microsample testing with detailed microstructural observations provides a mechanistic foundation for coating optimization and life prediction modeling.

Hemker, Kevin↗

The Dangers of Parametrics

Building a parametric cost model is hard work. The data is noisy and often does not behave like we want it to. We need statistics to give us an indication of the goodness of our models, but; statistics can be manipulated and mislead. On top of all of that, our own very human biases can lead us astray; causing us to see patterns in the noise and draw false conclusions from the data. Yet, it is the data itself that is the foundation for making better cost estimates and cost models. I believe the mistake we often make is we believe that our models are representative of the data; that our models summarize the experiences, the knowledge, and the stories contained in the data. However, it is the opposite that is true. Our models are but imitations of reality. They give us trends, but not truth. The experiences, the knowledge, and the stories that we need in order to make good cost estimates is bound up in the data. You cannot separate good cost estimating from a knowledge of the historical data. One final thought. It is our attempts to make sense out of the randomness that leads us astray. In order to make progress as cost modelers and cost estimators, we must accept that there are real limitations on our ability to model the past and predict the future. I do not believe we should throw up our hands and say this is the best we can do. Rather, to see real improvement we must first recognize these limitations, avoid the easy but misleading solutions, and seek to find ways to better model the world we live in. I don't have any simple solutions. Perhaps the answers lie in better data or in a totally different approach to simulating how the world works. All I know is that we must do our best to speak truth to ourselves and our customers. Misleading ourselves and our customers will, in the end, result in an inability to have a positive impact on those we serve.

Prince, Frank A.↗

The Dangers of Parametrics

Building a parametric cost model is hard work. The data is noisy and often does not behave like we want it to. We need statistics to give us an indication of the goodness of our models, but; statistics can be manipulated and mislead. On top of all of that, our own very human biases can lead us astray; causing us to see patterns in the noise and draw false conclusions from the data. Yet, it is the data itself that is the foundation for making better cost estimates and cost models. I believe the mistake we often make is we believe that our models are representative of the data; that our models summarize the experiences, the knowledge, and the stories contained in the data. However, it is the opposite that is true. Our models are but imitations of reality. They give us trends, but not truth. The experiences, the knowledge, and the stories that we need in order to make good cost estimates is bound up in the data. You cannot separate good cost estimating from a knowledge of the historical data. One final thought. It is our attempts to make sense out of the randomness that leads us astray. In order to make progress as cost modelers and cost estimators, we must accept that there are real limitations on our ability to model the past and predict the future. I do not believe we should throw up our hands and say this is the best we can do. Rather, to see real improvement we must first recognize these limitations, avoid the easy but misleading solutions, and seek to find ways to better model the world we live in. I don't have any simple solutions. Perhaps the answers lie in better data or in a totally different approach to simulating how the world works. All I know is that we must do our best to speak truth to ourselves and our customers. Misleading ourselves and our customers will, in the end, result in an inability to have a positive impact on those we serve.

Prince, Frank A.↗

The Market Demand for Air Transportation

Although the presentation will touch upon the areas of market for air transportation, the theoretical foundations of the demand function, the demand models, and model selection and evaluation, the emphasis of the presentation will be on a qualitative description of the factors affecting the demand for air transportation. The presentation will rely heavily on the results of market surveys carried out by the Port of New York Authority, the University of Michigan, and Census of Transportation.

Taneja, N.↗

Understanding climate: A strategy for climate modeling and predictability research, 1985-1995

The emphasis of the NASA strategy for climate modeling and predictability research is on the utilization of space technology to understand the processes which control the Earth's climate system and it's sensitivity to natural and man-induced changes and to assess the possibilities for climate prediction on time scales of from about two weeks to several decades. Because the climate is a complex multi-phenomena system, which interacts on a wide range of space and time scales, the diversity of scientific problems addressed requires a hierarchy of models along with the application of modern empirical and statistical techniques which exploit the extensive current and potential future global data sets afforded by space observations. Observing system simulation experiments, exploiting these models and data, will also provide the foundation for the future climate space observing system, e.g., Earth observing system (EOS), 1985; Tropical Rainfall Measuring Mission (TRMM) North, et al. NASA, 1984.

Thiele, O.↗

Expert system development for commonality analysis in space programs

This report is a combination of foundational mathematics and software design. A mathematical model of the Commonality Analysis problem was developed and some important properties discovered. The complexity of the problem is described herein and techniques, both deterministic and heuristic, for reducing that complexity are presented. Weaknesses are pointed out in the existing software (System Commonality Analysis Tool) and several improvements are recommended. It is recommended that: (1) an expert system for guiding the design of new databases be developed; (2) a distributed knowledge base be created and maintained for the purpose of encoding the commonality relationships between design items in commonality databases; (3) a software module be produced which automatically generates commonality alternative sets from commonality databases using the knowledge associated with those databases; and (4) a more complete commonality analysis module be written which is capable of generating any type of feasible solution.

Yeager, Dorian P.↗

The role of damage-softened material behavior in the fracture of composites and adhesives

The failure mechanism of polymer composites and adhesives with high strain in the zone near and ahead of the crack tip is investigated analytically, summarizing the findings of Ungsuwarungsri (1986). A double-cantilever-beam specimen with the nonlinear material confined to a thin strip between two wedge-loaded elastic beams is modeled as a beam on a nonlinear foundation; the two-point boundary-value problem for stationary and propagating cracks is solved numerically; and an FEM approach is applied to study the behavior of the nonlinear strip in detail. Typical results are presented in extensive graphs, and the accuracy and efficiency of the present method are shown to be superior to those of the Berry (1963) procedure.

Ungsuwarungsri, T.↗

NASA Balloon Technology Developments

The National Aeronautics and Space Administration (NASA) Balloon Program has been, and will continue to be, committed to improving the capabilities of balloons to support science missions. Fundamental to vehicle improvement is a program of technology development that will enable improved flight performance throughout the next decade. The program s technology thrust areas include: materials, vehicle design & development, structural analysis, operations & support systems, performance modeling and planetary balloons. Building on the foundations of the 18-year research and development program, a technology roadmap has been generated which identifies specific areas of interest to NASA and the vision of future developments. The major components of the roadmap are: vehicle systems, balloon-craft systems, operational and safety support systems, and planetary vehicles. Current technology activities include nanocomposite balloon films, a new balloon designed to lift 3600 kgs to 36 km, a balloon rotation rate study and Mars pumpkin balloon investigations. The technology roadmap, as well as specific projects and recent advancements, will be presented.

Fairbrother, D. A.↗

Modeling Landslide Susceptibility Over Large Regions with Fuzzy Overlay

Landslide susceptibility mapping is most effective if detailed surface and subsurface information can be combined with authoritative landslide catalogs or a deep understanding of local conditions. However, these types of homogeneous input data and catalogs are frequently not available over large areas. In this study, we model landslide susceptibility in Central America and the Caribbean islands by combining three globally available datasets and one regional dataset with fuzzy overlay. This primarily heuristic model provides the flexibility to test a range of different contributing variables and the capability to compare landslide inventories within the model framework that vary greatly in their size, spatiotemporal scope, and collection methods. We create a regional susceptibility map and evaluate its performance using receiver operating characteristics for both continuous and binned susceptibility values. This susceptibility map forms the basis for a near-real-time landslide hazard assessment system that couples susceptibility with rainfall and soil moisture triggers to estimate potential landslide activity at a regional scale. The application of this susceptibility model at the regional scale provides a foundation for transferring the methodology to other geographic areas.

Landslide↗

Three Generations of Tracking and Data Relay Satellite (TDRS) Spacecraft

The current Tracking and Data Relay Satellite configuration consists of nine in-orbit satellites (four first generation, three second generation and two third generation satellites) globally distributed in geosynchronous orbit to provide near continuous data relay service to missions like Hubble Space Telescope and the International Space Station. The 1st generation spacecraft were designed by TRW/Northrop Grumman with their launches of the five spacecraft ranging from 1983 through 1995. The 2nd and 3rd generation spacecraft were designed by Boeing with their launches ranging 2000 - 2002 and 2013 - 2017 respectively. TDRS-3 is now 27 years on orbit, continues to be a capable asset for the TDRS constellation. Lack of need for inclination control combined with large fuel reserves and redundancy on critical elements provides spacecraft that operate well past design life, all of which contributes to expanded TDRS constellation support capabilities. All spacecraft generations have issues. Significant issues will be summarized with the focus on the Boeing related problems. Degradations and failures are continually assessed and provide the foundation for yearly updates to spacecraft reliability models, constellation service projections and deorbit plans (in order to meet NASAs mandate of limiting orbital debris). Even when accounting for degradations and failures, the life expectancy for the Boeing delivered 2nd generation TDRS-8, 9 10 TDRS are anticipated to be 25+ years.

TDRS↗

Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska

Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has heightened local wildfire risk as canopy loss facilitates the conversion from bare to fire-prone grassland. We collected images from NASA satellite-based Earth observations to visualize land cover succession at roughly five-year intervals following a severe, mid-1990's beetle infestation to the present. We classified these data by vegetation cover type to quantify grassland encroachment patterns over time. Raster band math provided a change detection analysis on the land cover classifications. Results indicate the highest wildfire risk is linked to herbaceous and black spruce land cover types, The resulting land cover change image will give the Kenai National Wildlife Refuge (KENWR) ecologists a better understanding of where forests have converted to grassland since the 1990s. These classifications provided a foundation for us to integrate digital elevation models (DEMs), temperature, and historical fire data into a model using Python for assessing and mapping changes in wildfire risk. Spatial representations of this risk will contribute to a better understanding of ecological trajectories of beetle-affected landscapes, thereby informing management decisions at KENWR.

Risk modeling↗

Cryogenic Flow Boiling Parabolic Flight Experiment and Universal Correlations for Cryogens

Cryogenic fluids are employed in many industries and play crucial roles in space applications, including nuclear thermal propulsion systems, LOX/LCH4 ascent and descent stage feedlines, LOX/LH2 transfer lines for in-space fuel depots, and LHe transfer lines that cool space experiments. Due to the ultra-low boiling point of cryogens, phase change is inevitable in these systems, which leads to complex boiling behavior in reduced gravity. For cost-effective design and sizing of the aforementioned systems, accurate knowledge of two-phase parameters such as pressure drop, heat transfer coefficient (HTC), and critical heat flux (CHF) is desired. Since 2018, researchers from Purdue University and NASA Glenn Research Center (GRC) have been collaborating to investigate the effects of reduced gravity on cryogenic pool boiling, flow boiling ins tubes, and spray cooling. Using a variety of both terrestrial and parabolic flight experimental rigs, extensive LN2 data have been amassed and combined with cryogenic data from World literature to develop both ‘universal’ empirical correlations and CFD models, which are expected to serve as foundation for design of future space systems involving cryogenic fluids. This presentation will provide an overview of both the experimental work and predictive tools that have resulted from the joined Purdue-Glenn studies.

Flow Boiling↗

NASA Aerosciences Activities to Support Human Space Flight

The Lyndon B. Johnson Space Center (JSC) has been a critical element of the United State's human space flight program for over 50 years. It is the home to NASA s Mission Control Center, the astronaut corps, and many major programs and projects including the Space Shuttle Program, International Space Station Program, and the Orion Project. As part of JSC's Engineering Directorate, the Applied Aeroscience and Computational Fluid Dynamics Branch is charted to provide aerosciences support to all human spacecraft designs and missions for all phases of flight, including ascent, exo-atmospheric, and entry. The presentation will review past and current aeroscience applications and how NASA works to apply a balanced philosophy that leverages ground testing, computational modeling and simulation, and flight testing, to develop and validate related products. The speaker will address associated aspects of aerodynamics, aerothermodynamics, rarefied gas dynamics, and decelerator systems, involving both spacecraft vehicle design and analysis, and operational mission support. From these examples some of NASA leading aerosciences challenges will be identified. These challenges will be used to provide foundational motivation for the development of specific advanced modeling and simulation capabilities, and will also be used to highlight how development activities are increasing becoming more aligned with flight projects. NASA s efforts to apply principles of innovation and inclusion towards improving its ability to support the myriad of vehicle design and operational challenges will also be briefly reviewed.

LeBeau, Gerald J.↗

Understanding the Impact of Unobservable Variables on the Performance of Predictive Models: The Need for Feature Space Partitioning and Fusion

When developing predictive models over a dataset, the model is globally optimized across the entire feature space to learn a decision boundary. However, when unobservable variables—which cannot be measured or estimated—interact with the observable variables, this can negatively impact the optimization applied to the decision boundary since the data samples introduced by unobservable variables may have little to no association with the applied global optimization. This, consequently, penalizes the entire decision boundary and model performance. This paper examines some of the detrimental effects of unobservable variables, particularly their role in creating new modes in the distribution of observable variables and reducing the separability of class distributions. Such challenges result in skewed or warped decision boundaries and decreased accuracy of model predictions, particularly for interpretable models like logistic regression and decision trees. Through two illustrative case examples, we highlight the need to address the challenges imposed by unobservable variables. We propose a strategy to mitigate these challenges by creating local regions within the feature space through partitioning. This enables the optimization of local models within the regions to overcome the impact of unobservability in different feature space localities. Research into a more sophisticated partitioning strategy and where the partition should be relative to the sample of interest is left as future work. Through the analysis of the impact of unobservability and the development of a partitioning method, we demonstrate the clear need for a partitioning strategy that integrates knowledge from multiple local models to estimate risk factors using information fusion. Thus, we establish the foundation and motivation for using partitioning and information fusion to overcome the effects of unobservability in predictive models. Formal fusion methods, such as Dempster-Shafer theory, can better leverage the information from local regions to improve the performance of interpretable predictive models in the presence of unobservable variables.

Time Series Data↗

Magneto-thermo-elastokinetics of Geometrically Nonlinear Laminated Composite Plates. Part 1: Foundation of the Theory

A fully coupled magneto-thermo-elastokinetic model of laminated composite, finitely electroconductive plates incorporating geometrical nonlinearities and subjected to a combination of magnetic and thermal fields, as well as carrying an electrical current is developed, In this context. the first-order transversely shearable plate theory in conjunction with von-Karman geometrically nonlinear strain concept is adopted. Related to the distribution of electric and magnetic field disturbances within the plate, the assumptions proposed by Ambartsumyan and his collaborators are adopted. Based on the electromagnetic equations (i.e. the ones by Faraday, Ampere, Ohm, Maxwell and Lorentz), the modified Fourier's law of heat conduction and on the elastokinetic field equations, the 3-D coupled problem is reduced to an equivalent 2- D one. The theory developed herein provides a foundation for the investigation, both analytical and numerical, of the interacting effects among the magnetic, thermal and elastic fields in multi-layered thin plates made of anisotropic materials.

Hasanyan, Davresh↗

Autoclass: An automatic classification system

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass System searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters through a class hierarchy. The mathematical foundations of AutoClass are summarized.

Stutz, John↗

SOFIA: Flying the Telescope

The primary focus of this paper is how the flight test team for the Stratospheric Observatory For Infrared Astronomy (SOFIA) re-cast an extensive developmental test program to meet key milestones while simultaneously ensuring safe certification of the airframe and delivery of an operationally relevant platform, ultimately saving the overall program from financial demise. Following a brief introduction to the observatory and what it is designed to do, SOFIAs planned developmental test program is summarized, including analysis and design philosophy, envelope expansion, model validation and airframe certification. How NASA used lessons learned from other aircraft that employed open cavities in flight is explained as well as how and why the chosen design was selected. The approach to aerodynamic analysis, including bare airframe testing, wind tunnel testing, computational fluid dynamics and finite element modeling proved absolutely critical. Despite a solid analytical foundation, many unknowns remained. History provides several examples of disastrous effects on both systems and flight safety if cavity design is not approached properly. For these reasons, an extensive test plan was developed to ensure a safe and thorough build-up for envelope expansion, airframe certification and early science missions. Unfortunately, as is often the case, because of chronic delays in overall program execution, severe schedule and funding pressures were present. If critical milestones were not met, domestic as well as international funding was in serious jeopardy, and the demise of the entire program loomed large. Concentrating on rigorous model validation, the test team challenged certification requirements, increased test efficiency and streamlined engineering analysis. This resulted in the safe reduction of test point count by 72%, meeting all program milestones and a platform that soundly satisfied all operational science requirements. Results from early science missions are shown and a proof of concept mission for which SOFIA was opportunely positioned is showcased. Success on this time-critical mission to observe a rare astronomical event proved the usefulness of an airborne observatory and the value in waiting for the capability provided by SOFIA. Finally, lessons learned in the test program are presented with emphasis on how lessons from previous aircraft and successful test programs were applied to SOFIA. Effective application of these lessons was crucial to the success of the SOFIA flight test program. SOFIA is an international cooperative program between NASA and the German Space Agency, DLR. It is a 2.5 meter (100-inch) telescope mounted in a Boeing 747SP aircraft used for astronomical observations at altitudes above 35,000 feet. SOFIA will accommodate a host of scientific instruments from the international science community and has a planned operational lifespan of more than 20 years.

Asher, Troy A.↗

Foundations for the numerical solution of the Euler equations

The Navier-Stokes equations represent an extremely good model of the physical phenomena encountered in most aeronautical problems. However, the computational resource needed to solve the Navier-Stokes equations are so large that even with today's supercomputers, it is necessary to make use of simpler models. A large number of external aerodynamic problems can be accurately described by a simpler model. This model consists of an outer inviscid flow plus a boundary-layer thickness correction for the vehicle shape. The outer inviscid model may be represented by the potential equation or by the Euler equation. The present paper provides the foundations for the numerical solution of the Euler equations. The governing equations are considered, taking into account conservation laws, the medium, the differential form of the conservation laws, generalized solutions, shock-fitting, and characteristics. Attention is also given to initial and boundary conditions, existence and uniqueness, and rotational phenomena.

Salas, M. D.↗