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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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The objectives of the current program are to develop an analytical approach, based on boundary layer theory, for predicting the effects of airfoil cooling on downstream heat transfer rates and to verify the resulting analytical method by comparison of predictions with hot cascade data obtained under this program.
Stable crack growth in 50-mm-thick side-grooved three-point bend specimens of HY-130 steel is modeled numerically. A two-dimensional elastoplastic FEM formulation incorporating a CTOD fracture criterion is developed analytically, and the theoretical predictions are compared with experimental data in graphs. Good general agreement is obtained, except for the crack extension, which was typically overpredicted. The value of the J integral at crack initiation is found to be 83 kJ/sq m.
In this paper, the post impact compressive behavior of polymeric composites is studied both analytically and experimentally. In the analytical study, a closed-form solution is obtained for postbuckling of composites with a circular delamination. Using this solution, the reduced stiffness of the impact damaged region is calculated and the residual compressive strengths of quasi-isotropic laminates are predicted as a function of damage size. In the experimental study, in-plane displacements near the damaged region are determined by employing a micro moire interferometry technique. The strain concentrations as well as the compression-after-impact strength have been measured and compared with analytical predictions. Good agreement between the predicted results and experimental data was observed.
Over the operational lifetime of both military and civil aircraft, structural components are exposed to hundreds of thousands of low-stress repetitive load cycles and less frequent but higher-stress transient loads originating from maneuvering flight and atmospheric gusts. Micro-material imperfections in the structure, such as cracks and debonded laminates, expand and grow in this environment, reducing the structural integrity and shortening the life of the airframe. Extreme costs associated with refurbishment of critical load-bearing structural components in a large fleet, or altogether reinventoring the fleet with newer models, indicate alternative solutions for life extension of the airframe structure are highly desirable. Increased levels of operational safety and reliability are also important factors influencing the desirability of such solutions. One area having significant potential for impacting crack growth/fatigue damage reduction and structural life extension is flight control. To modify the airframe response dynamics arising from command inputs and gust disturbances, feedback loops are routinely applied to vehicles. A dexterous flight control system architecture senses key vehicle motions and generates critical forces/moments at multiple points distributed throughout the airframe to elicit the desired motion characteristics. In principle, these same control loops can be utilized to influence the level of exposure to harmful loads during flight on structural components. Project objectives are to investigate and/or assess the leverage control has on reducing fatigue damage and enhancing long-term structural integrity, without degrading attitude control and trajectory guidance performance levels. In particular, efforts have focused on the effects inner loop control parameters and architectures have on fatigue damage rate. To complete this research, an actively controlled flexible aircraft model and a new state space modeling procedure for crack growth have been utilized. Analysis of the analytical state space model for crack growth revealed the critical mathematical factors, and hence the physical mechanism they represent, that influenced high rates of airframe crack growth. The crack model was then exercised with simple load inputs to uncover and expose key crack growth behavior. To characterize crack growth behavior, both "short-term" laboratory specimen test type inputs and "long-term" operational flight type inputs were considered. Harmonic loading with a single overload revealed typical exponential crack growth behavior until the overload application, after which time the crack growth was retarded for a period of time depending on the overload strength. An optimum overload strength was identified which leads to maximum retardation of crack growth. Harmonic loading with a repeated overload of varying strength and frequency again revealed an optimum overload trait for maximizing growth retardation. The optimum overload strength ratio lies near the range of 2 to 3 with dependency on frequency. Experimental data was found to correlate well with the analytical predictions.
Spacecraft are being designed based on LS-DYNA simulations of water landing impacts. The Elemental Water Impact Test (EWIT) series was undertaken to assess the accuracy of LS-DYNA water impact simulations. EWIT Phase 2 featured a 36-inch aluminum tank head. The tank head was outfitted with one accelerometer, twelve pressure transducers, three string potentiometers, and four strain gages. The tank head was dropped from heights of 1 foot and 2 feet. The focus of this report is the correlation of analytical models against test data. As a measure of prediction accuracy, peak responses from the baseline LS-DYNA model were compared to peak responses from the tests.
Planetary entry vehicles employ ablative TPS materials to shield the aeroshell from entry aeroheating environments. To ensure mission success, it must be demonstrated that the heatshield system, including local features such as seams, does not fail at conditions that are suitably margined beyond those expected in flight. Furthermore, its thermal response must be predictable, with acceptable fidelity, by computational tools used in heatshield design. Mission assurance is accomplished through a combination of ground testing and material response modelling. A material's robustness to failure is verified through arcjet testing while its thermal response is predicted by analytical tools that are verified against experimental data. Due to limitations in flight-like ground testing capability and lack of validated high-fidelity computational models, qualification of heatshield materials is often achieved by piecing together evidence from multiple ground tests and analytical simulations, none of which fully bound the flight conditions and vehicle configuration. Extreme heating environments (>2000 W/cm2 heat flux and >2 atm pressure), experienced during entries at Venus, Saturn and Ice Giants, further stretch the current testing and modelling capabilities for applicable TPS materials. Fully-dense Carbon Phenolic was the material of choice for these applications; however, since heritage raw materials are no longer available, future uses of re-created Carbon Phenolic will require re-qualification. To address this sustainability challenge, NASA is developing a new dual-layer material based on 3D weaving technology called Heatshield for Extreme Entry Environments (HEEET) [1]. Regardless of TPS material, extreme environments pose additional certification challenges beyond what has been typical in recent NASA missions.Scope of this presentation: This presentation will give an overview of challenges faced in verifying TPS performance at extreme heating conditions.
This presentation covers industry input received for future work in NASA Aeronautics over the next 5 years. It is intended to present areas of significant imput and to stimulate further discussion.
Blunt body aerodynamics in re-entry conditions are characterized by oscillatory behavior that is often encapsulated by the pitch damping coefficient (Cmq). Magnetic Suspension Wind Tunnel (MSWT) testing with a Magnetic Suspension Balance System (MSBS) aims to improve blunt body wind tunnel testing by removing the sometimes unknown aerodynamic effects of traditional sting mounts, as well as generate more constrained outputs than vertical spin tunnel testing. Efforts to calculate the uncertainty of the pitch damping coefficient with a proposed Earth Entry Vehicle (EEV) capsule model have been made by fitting an analytic prediction of the attitude time history to video data of a free-to-oscillate MSBS test.
Two commercially available large area silicon avalanche photodiodes (APD) were characterized in the laboratory. Specifically, the response of the APD’s to a sequence of 8-bit pulse position modulated (256-PPM) laser pulses, with and without background noise, was recorded and stored for post analysis. Empirical probability distribution functions (pdf) were constructed from the signal and noise slot data and compared to pdf’s predicted by an analytical model based on Webb/Gaussian statistics. The limited pulse sequence was also used to generate bit-error rate (BER) versus signal photons per pulse values, albeit with large error bars. These BER measurements were also compared to results predicted using the Gaussian and Webb/Gaussian models for APD channel statistics. While the measurements qualitatively reflect features predicted by theory, significant quantitative deviations are displayed between the measurements and theory. The source of these discrepancies is not currently well understood, but it is surmised that inaccurate knowledge of detector parameters such as gain and noise equivalent temperature models may explain the discrepancies.
Diverse observational and simulation datasets are needed to understand and predict complex ecosystem behavior over seasonal to decadal and century time-scales. Integration of these datasets poses a major barrier towards advancing environmental science, particularly due to differences in the structure and formats of data provided by various sources. Here, we describe BASIN-3D (Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets), a data integration framework designed to dynamically retrieve and transform heterogeneous data from different sources into a common format to provide an integrated view. BASIN-3D enables users to adopt a standardized approach for data retrieval and avoid customizations for the data type or source. We demonstrate the value of BASIN-3D with two use cases that require integration of data from regional to watershed spatial scales. The first application uses the BASIN-3D Python library to integrate time-series hydrological and meteorological data to provide standardized inputs to analytical and machine learning codes in order to predict the impacts of hydrological disturbances on large river corridors of the United States. The second application uses the BASIN-3D Django framework to integrate diverse time-series data in a mountainous watershed in East River, Colorado, United States to enable scientific researchers to explore and download data through an interactive web portal. Thus, BASIN-3D can be used to support data integration for both web-based tools, as well as data analytics using Python scripting and extensions like Jupyter notebooks. The framework is expected to be transferable to and useful for many other field and modeling studies.
NASA is embarked upon research and development tasks aimed at providing the general aviation industry with a reliable crashworthy airframe design technology. The goals of the NASA program are: reliable analytical techniques for predicting the nonlinear behavior of structures; significant design improvements of airframes; and simulated full-scale crash test data. The analytical tools will include both simplified procedures for estimating energy absorption characteristics and more complex computer programs for analysis of general airframe structures under crash loading conditions. The analytical techniques being developed both in-house and under contract are described, and a comparison of some analytical predictions with experimental results is shown.
In order to reduce operation and maintenance (O&M) costs, nuclear power plants (NPPs) are moving from corrective and periodic maintenance to predictive maintenance strategies. Such transition requires changes on the data that needs to be retrieved and on the type of decision processes to be employed. Advanced monitoring and data analysis technologies are essential to support predictive strategies. They can in fact provide precise information about health of a component, track its degradation trends, and provide information of its expected failure time. With such information, maintenance operations for a component can be performed right before its expected failure time. This dynamic context of O&M operations requires new methods to analyze data, propagate component health information from the component to the system level, and optimize plant resources. In this respect, the risk informed asset management (RIAM) project has been tasked to develop and test this new class of methods into a risk analytics toolset. This toolset consists of data analytics tools coupled with reliability methods designed to manage plant assets and performances in a predictive maintenance context. This report shows the latest improvements on such development and the initial testing of our methods on the three main research areas that the RIAM project is focusing on. These areas are the following: equipment reliability data analytics, system reliability modeling, and plant resources optimization methods. We show how the methods developed in these areas can support predictive maintenance strategies by: 1) analyzing equipment reliability data (either in numeric and textual form), 2) assessing component and system health through an innovative margin-based reliability approach, and 3) identifying the most critical components and set optimal maintenance schedule based on plant economic and operational constraints.
Viscoseal performance as predicted by three different analytical models is compared with experimental data for a rarefied gas sealant. An experimental investigation has been conducted on two multiple grooved two-inch diameter viscoseals over a wide range of gas densities and shaft speeds up to 30,000 rpm. The two seal geometries considered differ significantly insofar as groove aspect ratio is concerned. Two types of seal performances are presented in the form of net leakage operation and sealing coefficient performance as functions of the degree of gas rarefication. Although the analytical models correctly predict sealing trends, all of the models are shown to have some deficiencies which prevent their indiscriminate application.
Infectious diseases (IDs) have a significant detrimental impact on global health. Timely and accurate ID forecasting can result in more informed implementation of control measures and prevention policies. To meet the operational decision-making needs of real-world circumstances, we aimed to build a standardized, reliable, and trustworthy ID forecasting pipeline and visualization dashboard that is generalizable across a wide range of modeling techniques, IDs, and global locations. We forecasted 6 diverse, zoonotic diseases (brucellosis, campylobacteriosis, Middle East respiratory syndrome, Q fever, tick-borne encephalitis, and tularemia) across 4 continents and 8 countries. We included a wide range of statistical, machine learning, and deep learning models (n=9) and trained them on a multitude of features (average n=2326) within the One Health landscape, including demography, landscape, climate, and socioeconomic factors. The pipeline and dashboard were created in consideration of crucial operational metrics—prediction accuracy, computational efficiency, spatiotemporal generalizability, uncertainty quantification, and interpretability—which are essential to strategic data-driven decisions. While no single best model was suitable for all disease, region, and country combinations, our ensemble technique selects the best-performing model for each given scenario to achieve the closest prediction. For new or emerging diseases in a region, the ensemble model can predict how the disease may behave in the new region using a pretrained model from a similar region with a history of that disease. The data visualization dashboard provides a clean interface of important analytical metrics, such as ID temporal patterns, forecasts, prediction uncertainties, and model feature importance across all geographic locations and disease combinations. As the need for real-time, operational ID forecasting capabilities increases, this standardized and automated platform for data collection, analysis, and reporting is a major step forward in enabling evidence-based public health decisions and policies for the prevention and mitigation of future ID outbreaks.
VAPEPS (VibroAcoustic Payload Environment Prediction System) is a computer program used to predict the vibroacoustic response of a structure. An alternate VAPEPS modeling technique, the Modified NASA Lewis Method, is an improvement for modeling unreinforced mass loaded honeycomb panels. The Modified NASA Lewis Method prediction is compared to the standard ASMS VAPEPS prediction, and the acoustic test data for three spacecraft panels. An analytical method of computing variance is presented and used to compute 95 percent confidence levels. These levels are compared to the standard VAPEPS confidence levels and to the envelope of the test data. As a result of using the new methodology suggested in the paper, both the mean prediction and the 95 percent confidence level prediction agree well with the test data in both spectral shape and magnitude. Therefore, the Modified NASA Lewis Method prediction methodology may be used to define more realistic random vibration test levels.
VAPEPS (vibroAcoustic Payload Environment Prediction System) is a computer program used to predict the vibroacoustic response of a structure. An alternate VAPEPS modeling technique, the Modified NASA Lewis Method, is an improvement for modeling unreinforced mass loaded honeycomb panels. The Modified NASA Lewis Method prediction is compared to the standard ASMS VAPEPS prediction, and the acoustic test data for three spacecraft panels. An analytical method of computing variance is presented and used to compute 95 percent confidence levels. These levels are compared to the standard VAPEPS confidence levels and to the envelope of the test data. As a result of using the new methodology suggested in the paper, both the mean prediction and the 95 percent confidence level prediction agree well with the test data in both spectral shape and magnitude. Therefore, the Modified NASA Lewis Method prediction methodology may be used to define more realistic random vibration test levels.
A dynamic analysis of multimesh-gear helicopter transmission systems was performed by correlating analytical simulations with experimental investigations. The two computer programs used in this study, GRDYNMLT and PGT, were developed under NASA/Army sponsorship. Parametric studies of the numerical model with variations on mesh damping ratios, operating speeds, tip-relief tooth modifications, and tooth-spacing errors were performed to investigate the accuracy, application, and limitations of the two computer programs. Although similar levels of dynamic loading were predicted by both programs, the computer code GRDYNMLT was found to be superior and broader in scope. Results from analytical work were also compared with experimental data obtained from the U.S. Army's UH-60A Black Hawk 2240-kW (3000-hp) class, twin-engine helicopter transmission tested at the NASA Lewis Research Center. Good correlation in gear stresses was obtained between the analytical model simulated by GRDYNMLT and the experimental measurements. More realistic mesh damping can be predicted through experimental data correlation.
The primary basis for heat transfer analysis of turbine airfoils is experimental data obtained in linear cascades. A detailed set of heat transfer coefficients was obtained along the midspan of a stator and a rotor in a rotating turbine stage. The data are to be compared to standard analyses of blade boundary layer heat transfer. A detailed set of heat transfer coefficients was obtained along the midspan of a stator located in the wake of a full upstream turbine stage. Two levels of inlet turbulence (1 and 10 percent) were used. The analytical capability will be examined to improve prediction of the experimental data.