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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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In this paper, an analytical method for comparing the performance of an ideal forecasting algorithm to the best statistical methods in terms of average data return is derived.
The biomedical data of the astronauts on Skylab 3 were analyzed to evaluate the univariate statistical methods for comparing endocrine series experiments in relation to other medical experiments. It was found that an information storage and retrieval system was needed to facilitate statistical analyses.
Statistical methods for analyzing stochastic effects influencing design of space vehicle control systems
Distribution of noise spectrum obtained by Fourier transform of interferogram - use of sampling theory and statistical methods
A comparison of methods for selecting optimum TM band combinations for visual interpretation of data are examined. Two statistical methods and visual interpretation by experienced photointerpreters are compared using photographic chips of 1:250,000 scale prints and the digital equivalent of the chips. A mosaic of TM data of the Great Salt Lake area of Utah was done. The filtering process was reviewed since the results were not satisfactory.
The purpose of this study was to develop 3D digital boundary manikins that are representative of the anthropometry of a unique population. These digital manikins can be used by designers to verify and validate that the components of the spacesuit design satisfy the requirements specified in the Human Systems Integration Requirements (HSIR) document. Currently, the HSIR requires the suit to accommodate the 1st percentile American female to the 99th percentile American male. The manikin anthropometry was derived using two methods: Principal Component Analysis (PCA) and Whole Body Posture Based Analysis (WBPBA). PCA is a statistical method for reducing a multidimensional data set by using eigenvectors and eigenvalues. The goal is to create a reduced data set that encapsulates the majority of the variation in the population. WBPBA is a multivariate analytical approach that was developed by the Anthropometry and Biomechanics Facility (ABF) to identify the extremes of the population for a given body posture. WBPBA is a simulation-based method that finds extremes in a population based on anthropometry and posture whereas PCA is based solely on anthropometry. Both methods yield a list of subjects and their anthropometry from the target population; PCA resulted in 20 female and 22 male subjects anthropometry and WBPBA resulted in 7 subjects' anthropometry representing the extreme subjects in the target population. The subjects anthropometry is then used to 'morph' a baseline digital scan of a person with the same body type to create a 3D digital model that can be used as a tool for designers, the details of which will be discussed in subsequent papers.
One of the goals for NASA s Orbital Debris Program Office has been to accurately characterize the population of debris in the geosynchronous Earth orbit (GEO) environment. Most objects larger than about 1 meter in size are regularly tracked and catalogued by the US Space Surveillance System in the GEO regime. The consequence has been that most large intact GEO objects are tracked, but the vast majority of GEO debris fragments are not. Only in recent years have observations been dedicated to characterize the GEO debris population. NASA s efforts have concentrated on using wide field-of-view telescopes to make complete surveys of the GEO regime to better our statistical understanding of the GEO debris population. These telescopes operate in a staring mode, and only make limited short-arc measurements of the orbits. This information, while limited, allows the possibility of debiasing the observations and constructing statistical distributions of orbits in inclination and ascending node. Recent work suggests that we may be able to use statistical methods to estimate better orbit parameters despite the limited data. Both of these types of studies estimating statistical orbit distributions, and estimating accurate orbits using limited short-arc data have direct analogues in ongoing studies of near-Earth objects (NEO) such as asteroids and comets. This talk will describe the GEO study methods in use and being developed at NASA, and will discuss how such methods may or may not be applicable for NEO studies as well.
The study of circular-to-rectangular transition duct flows with and without inlet swirl is presented. A method was devised to create a swirling, solid body rotational flow with minimal associated disturbances. Details of the swirl generator design and construction are discussed. Coefficients based on velocities and total and static pressures measured in cross stream planes at four axial locations within the transition duct along with surface static pressures and surface oil film visualization are presented for both nonswirling and swirling incoming flows. A method was developed to acquire trace gas measurements within the transition duct at high flow velocities. Statistical methods are used to help interpret the trace gas results.
It is the purpose of this study to demonstrate the viability and economy of Design-of-Experiments methodologies to arrive at microscale secondary flow control array designs that maintain optimal inlet performance over a wide range of the mission variables and to explore how these statistical methods provide a better understanding of the management of flow in compact air vehicle inlets. These statistical design concepts were used to investigate the robustness properties of low unit strength micro-effector arrays. Low unit strength micro-effectors are micro-vanes set at very low angles-of-incidence with very long chord lengths. They were designed to influence the near wall inlet flow over an extended streamwise distance, and their advantage lies in low total pressure loss and high effectiveness in managing engine face distortion. The term robustness is used in this paper in the same sense as it is used in the industrial problem solving community. It refers to minimizing the effects of the hard-to-control factors that influence the development of a product or process. In Robustness Engineering, the effects of the hard-to-control factors are often called noise , and the hard-to-control factors themselves are referred to as the environmental variables or sometimes as the Taguchi noise variables. Hence Robust Optimization refers to minimizing the effects of the environmental or noise variables on the development (design) of a product or process. In the management of flow in compact inlets, the environmental or noise variables can be identified with the mission variables. Therefore this paper formulates a statistical design methodology that minimizes the impact of variations in the mission variables on inlet performance and demonstrates that these statistical design concepts can lead to simpler inlet flow management systems.
Recovery of streamwater acid neutralizing capacity (ANC) resulting from declines in regional acid deposition was examined using contemporary (1990-2005) data from two long-term monitoring stations located on the Appalachian Plateau in western Maryland, U.S. Two computational methods were used to estimate daily, monthly, and annual fluxes and discharge-weighted concentrations of ANC, sulfate, nitrate, and base cations over the period of record, and two statistical methods were used to evaluate long-term trends in fluxes and concentrations. The methods used to estimate concentrations, as well as the, statistical techniques, produced very similar results, underlining the robustness of the identified trends. We found clear evidence that streamwater sulfate concentrations have declined at an average rate of about 3 (microns)eq L(exp -1) yr(exp -1) at the two sites due to a 34% reduction in wet atmospheric sulfur deposition. Trends in nitrate concentrations appear to be related to other watershed factors, especially forest disturbance. The best evidence of recovery is based on a doubling of ANC (from 21 to 42 (microns)eq L(exp -1) at the more acid-sensitive site over the 16-year period. A slowing, or possible reversal, in the sulfate, nitrate, and SBC trends is evident in our data and may portend a decline in the rate of--or end to--further recovery.
NASA speech on use of statistical methods in governmental decision making
Liapunov method for estimating statistical averages of randomly perturbed system
An introductory treatment of the fundamentals of diffusion theory is presented, starting with molecular diffusion and leading up to the statistical methods of turbulent diffusion. A multilayer diffusion model, designed to permit concentration and dosage calculations downwind of toxic clouds from rocket vehicles, is described. The concepts and equations of diffusion are developed on an elementary level, with emphasis on atmospheric applications.
This report describes a three-month research program undertook jointly by the Robotics Institute at Carnegie Mellon University and Ames Research Center as part of the Ames' Joint Research Initiative (JRI.) The work was conducted at the Ames Research Center by Mr. Liam Pedersen, a graduate student in the CMU Ph.D. program in Robotics under the supervision Dr. Ted Roush at the Space Science Division of the Ames Research Center from May 15 1999 to August 15, 1999. Dr. Martial Hebert is Mr. Pedersen's research adviser at CMU and is Principal Investigator of this Grant. The goal of this project is to investigate and implement methods suitable for a robotic rover to autonomously identify rocks and minerals in its vicinity, and to statistically characterize the local geological environment. Although primary sensors for these tasks are a reflection spectrometer and color camera, the goal is to create a framework under which data from multiple sensors, and multiple readings on the same object, can be combined in a principled manner. Furthermore, it is envisioned that knowledge of the local area, either a priori or gathered by the robot, will be used to improve classification accuracy. The key results obtained during this project are: The continuation of the development of a rock classifier; development of theoretical statistical methods; development of methods for evaluating and selecting sensors; and experimentation with data mining techniques on the Ames spectral library. The results of this work are being applied at CMU, in particular in the context of the Winter 99 Antarctica expedition in which the classification techniques will be used on the Nomad robot. Conversely, the software developed based on those techniques will continue to be made available to NASA Ames and the data collected from the Nomad experiments will also be made available.
The discipline of statistics has gained recognition within NASA by spurring innovation and efficiency, and it has demonstrated significant impact and value. In aerospace research and development, it accelerates learning, maximizes knowledge, ensures strategic resource investment, and informs rigorous data-driven decisions. In practice, it requires immersive multidisciplinary collaboration to develop solution strategies that integrate statistical methods with subject-matter expertise to address challenging research objectives. This presentation provides an overview of statistical case studies in aeronautics, space exploration, and atmospheric science, and it highlights statistical research motivated by NASA’s challenging applications.
Crystallographic textures are a major determinant of the macroscale anisotropic properties of polycrystalline metallic alloys produced in a wide range of additive manufacturing (AM) processes. Here, we introduce a statistical method that can accurately quantify the degree of orientational order of textures despite the large random fluctuations in the orientation of individual grains inherent in AM processes. The method, demonstrated for laser and resolidification of AlSi thin films, extends Z-scoring to a dynamical regime to assess the statistical significance of observed textures compared to randomly generated ones at different stages of solidification. We further show that, combined with phase-field modeling, this method can be used to infer fundamental anisotropic properties of the solid-liquid interface that are essential for texture prediction, and are compared here to the results of atomistic simulations. In addition, phase-field modeling reveals that, even at rapid AM solidification rates, the observed 〈110〉-dominated textures in the AlSi thin films are controlled predominantly by the anisotropy of the interface free-energy and sheds light on the physical mechanism of grain competition. These results significantly enhance both the existing tools for the quantification and prediction of AM crystallographic textures and our basic understanding of their formation.
Assessing renewable energy resources under future climate scenarios has been highlighted to understand potential impacts of future climate change in renewable generation on the power sector. Climate model projection has been recognized by the renewable energy community as a useful data set to analyze the impacts of future climate change on renewable resources. However, future climate projections generated from general circulation models (GCMs) contain inherent biases that need to be corrected for accurate analysis of future projections of climate variables. In addition, the coarse spatiotemporal resolution of GCMs needs to be improved for regional climate studies. In this work, we develop statistical methods to downscale future projections of global horizontal irradiance (GHI) in a computationally efficient way. Our approach builds statistical downscaling models that correct bias of climate projection of GHI and downscale the future GHI projection from daily-scale to hourly-scale. The National Solar Radiation Database (NSRDB) is used to calibrate the statistical models and validate the downscaled GHI projections across the contiguous United State (CONUS). Preliminary results show that the statistical approach efficiently downscales climate projections of GHI with a nBIAS of 3%, nMAE of 34 % and nRMSE of 46% calculated against NSRDB for CONUS. This study describes the implemented methodology and initial results as well as future research to create high-resolution climate data sets for solar energy applications.
The neural networks method is applied to investigate the feasibility in detecting anomalies in turbopump vibration of SSME to supplement the statistical method utilized in the prototype system. The investigation of neural networks analysis is conducted using SSME vibration data from a NASA developed numerical simulator. The limited application of neural networks to the HPFTP has also shown the effectiveness in diagnosing the anomalies of turbopump vibrations.