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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 authors present a novel method called time-varying transfer function estimation (TTFE) in which time-domain parameters are computed through identification in the frequency domain. The method is particularly well suited for flexible structure control problems. An example of a flexible manipulator system is presented for which a self-tuning control law with frequency shaping is derived and demonstrated.
A report is presented on an ongoing effort for end-point position control of flexible-link manipulators under realistic conditions in laboratory setups consisting of one- and two-link manipulators. The authors treat modeling, identification, and control of flexible-link manipulators that are required to carry payloads, possibly unknown and varying, while undergoing disturbance effects from the environments and the workspace. The various identification and control techniques being investigated are summarized, with emphasis on the ability of the controller to adjust to changes in dynamics, payload, and working environment. It is found that time-domain methods offer identified model structures that are readily available for control design, whereas frequency-domain methods, particularly the time-varying transfer function estimation approach developed for this application, are more desirable when rapid controller tuning is required.
Mappings between fuzzy cubes are discussed. This level of abstraction provides a surprising and fruitful alternative to the propositional and predicate-calculas reasoning techniques used in expert systems. It allows one to reason with sets instead of propositions. Discussed here are fuzzy and neural function estimators, neural vs. fuzzy representation of structured knowledge, fuzzy vector-matrix multiplication, and fuzzy associative memory (FAM) system architecture.
The present adhesion model-based one-dimensional simulations of gravitational clustering have yielded bound-object catalogs applicable in tests of analytical approaches to cosmological structure formation. Attention is given to Press-Schechter (1974) type functions, as well as to their density peak-theory modifications and the two-point correlation function estimated from peak theory. The extent to which individual collapsed-object locations can be predicted by linear theory is significant only for objects of near-characteristic nonlinear mass.
The author develops a computational approach to multivariable frequency domain identification, based on 2-norm minimization. In particular, a Gauss-Newton (GN) iteration is developed to minimize the 2-norm of the error between frequency domain data and a matrix fraction transfer function estimate. To improve the global performance of the optimization algorithm, the GN iteration is initialized using the solution to a particular sequentially reweighted least squares problem, denoted as the SK iteration. The least squares problems which arise from both the SK and GN iterations are shown to involve sparse matrices with identical block structure. A sparse matrix QR factorization method is developed to exploit the special block structure, and to efficiently compute the least squares solution. A numerical example involving the identification of a multiple-input multiple-output (MIMO) plant having 286 unknown parameters is given to illustrate the effectiveness of the algorithm.
Early results from a recent experiment designed to directly evaluate the aeroacoustic/elastic spectral responses of a MOD-2 turbine blade to turbulence-induced unsteady blade loads are discussed. The experimental procedure consisted of flying a hot-film anemometer from a tethered balloon in the turbine inflow and simultaneously measuring the fluctuating airload and aeroelastic response at two blade span stations (65% and 87% spans) using surface-mounted, subminiature pressure transducers and standard strain gage instrumentation. The radiated acoustic pressure field was measured with a triad of very-low-frequency microphones placed at ground level, 1.5 rotor diameters upwind of the disk. Initial transfer function estimates for acoustic radiation, blade normal forces, flapwise acceleration/displacement, and chord/flapwise moments are presented.
In this paper we describe current research in tethered formations for interferometry, and a roadmap to demonstrating the required key technologies via on-ground and in-orbit testing. We propose an integrated kilometer-size tethered spacecraft formation flying concept which enables Far IR and Sub-mm astronomy observations from space. A rather general model is used to predict the dynamics, control, and estimation performance of formations of spacecraft connected by tethers in LEO and deep space. These models include the orbital and tethered formation dynamics, environmental models, and models of the formation estimator/controller/commander. Both centralized and decentralized control/sensing/estimation schemes are possible, and dynamic ranges of interest for sensing/control are described. Key component/subsystem technologies are described which need both ground-based and in-orbit demonstration prior to their utilization in precision space interferometry missions using tethered formations. Defining an orbiting formation as an ensemble of orbiting spacecraft performing a cooperative task, recent work has demonstrated the validity of the tethering the spacecraft to provide both the required formation rigidity and satisfy the formation reconfiguration needs such as interferometer baseline control. In our concept, several vehicles are connected and move along the tether, so that to reposition them the connecting tether links must vary in length. This feature enables variable and precise baseline control while the system spins around the boresight. The control architecture features an interferometer configuration composed of one central combiner spacecraft and two aligned collector spacecraft. The combiner spacecraft acts as the formation leader and is also where the centralized sensing and estimation functions reside. Some of the issues analyzed with the model are: dynamic modes of deformation of the distributed structure, architecture of the formation sensor, and sources of dynamical perturbation that need to be mitigated for precision operation in space. Examples from numerical simulation of an envisioned scenario in heliocentric orbit demonstrate the potential of the concept for space interferometry.
Wavelet transforms are used for on-line detection of aircraft loss of control. Wavelet transforms are compared with Fourier transform methods and shown to more rapidly detect changes in the vehicle dynamics. This faster response is due to a time window that decreases in length as the frequency increases. New wavelets are defined that further decrease the detection time by skewing the shape of the envelope. The wavelets are used for power spectrum and transfer function estimation. Smoothing is used to tradeoff the variance of the estimate with detection time. Wavelets are also used as front-end to the eigensystem reconstruction algorithm. Stability metrics are estimated from the frequency response and models, and it is these metrics that are used for loss of control detection. A Matlab toolbox was developed for post-processing simulation and flight data using the wavelet analysis methods. A subset of these methods was implemented in real time and named the Loss of Control Analysis Tool Set or LOCATS. A manual control experiment was conducted using a hardware-in-the-loop simulator for a large transport aircraft, in which the real time performance of LOCATS was demonstrated. The next step is to use these wavelet analysis tools for flight test support.
During the initial period of the work we concentrated on Saharan dust storms and published a sequence of papers (Colarco et a1 2002,2003a,b, Toon, 2004). The U.S. Air Force liked the dust model so well that they appropriated it for operational dust storm forecasting (Barnum et al., 2004). The Air Force has used it for about 5 yrs in the Middle East where dust storms cause significant operational problems. The student working on this project, Peter Colarco, has graduated and is now a civil servant at Goddard where he continues to interact with the TOMS team. This work helped constrain the optical properties of dust at TOMS wavelengths, which is useful for climate simulations and for TOMS retrievals of dust properties such as optical depth. We also used TOMS data to constrain the sources of dust in Africa and the Middle East, to determine the actual paths taken by Saharan dust storms, to learn more about the mechanics of variations in the optical depths, and to learn more about the mechanisms controlling the altitudes of the dust. During the last two years we have been working on smoke from fires. Black carbon aerosols are one of the leading factors in radiative forcing. The US Climate Change Science Program calls this area out for specific study. It has been suggested by Jim Hansen, and Mark Jacobsen among others, that by controlling emissions of black carbon we might reduce greenhouse radiative forcing in a relatively painless manner. However, we need a greatly improved understanding of the amount of black carbon in the atmosphere, where it is located, where it comes from, how it is mixed with other particles, what its actual optical properties are, and how it evolves. In order to learn about these issues we are using a numerical model of smoke. We have applied this model to the SAFARI field program data, and used the TOMS satellite observations in that period (Sept. 2000). Our goal is to constrain source function estimates for black carbon, and smoke optical properties.
The existing ban on commercial supersonic flight overland is largely due to the effects of loud and startling sonic booms on communities. NASA is planning a nationwide campaign of community response surveys using the experimental X-59 Quiet SuperSonic Technology (X-59 QueSST) aircraft to understand how communities perceive the sounds of quiet supersonic flight. The X-59 community response survey data will be presented to noise regulators, who are considering replacing the ban with a noise-based certification limit so quiet supersonic vehicles can fly over land. In this document, we use pilot community response survey data to explore and assess multiple approaches to statistically model the dose-response relationship between single-event sonic boom sound exposure and human annoyance. The models have two primary functions—estimating two types of quantities that support setting regulations and experimental design of future surveys.
The increasing complexity of the interconnected power system makes high-fidelity dynamic simulation models computationally more intensive. To improve computation efficiency, model reduction techniques have been investigated to only preserve the dynamics in a limited area of interest (study area), while deriving equivalent representation for the external area. For this purpose, a measurement-based reduction approach using system identification techniques has been previously proposed. In that case, external areas are represented by dynamic equivalent loads using transfer function estimation. In this paper, in order to enhance the accuracy of the reduced model in preserving dynamics of the study area, multiple “grid” events of different types and at different locations are considered for identifying the parameters of the equivalent loads. Case studies are carried out in the NPCC 140-bus system. Comparison are made between multiple events training and single event training, highlighting the advantages of the proposed method in providing a better representation of the grid dynamics under different operating conditions.
ABSTRACT We present a new method to estimate redshift distributions and galaxy-dark matter bias parameters using correlation functions in a fully data driven and self-consistent manner. Unlike other machine learning, template, or correlation redshift methods, this approach does not require a reference sample with known redshifts. By measuring the projected cross- and auto-correlations of different galaxy sub-samples, e.g. as chosen by simple cells in colour–magnitude space, we are able to estimate the galaxy-dark matter bias model parameters, and the shape of the redshift distributions of each sub-sample. This method fully marginalizes over a flexible parametrization of the redshift distribution and galaxy-dark matter bias parameters of sub-samples of galaxies, and thus provides a general Bayesian framework to incorporate redshift uncertainty into the cosmological analysis in a data-driven, consistent, and reproducible manner. This result is improved by an order of magnitude by including cross-correlations with the cosmic microwave background and with galaxy–galaxy lensing. We showcase how this method could be applied to real galaxies. By using idealized data vectors, in which all galaxy-dark matter model parameters and redshift distributions are known, this method is demonstrated to recover unbiased estimates on important quantities, such as the offset Δz between the mean of the true and estimated redshift distribution and the 68 per cent, 95 per cent, and 99.5 per cent widths of the redshift distribution to an accuracy required by current and future surveys.
Examples of bad MCNP estimates resulting from exceeding the MCNP random number stride are given for a simple infinite medium problem.
One important source of systematics in galaxy redshift surveys comes from the estimation of the galaxy window function. Up until now, the impact of the uncertainty in estimating the galaxy window function on parameter inference has not been properly studied. In this paper, we show that the uncertainty and the bias in estimating the galaxy window function will be salient for ongoing and next-generation galaxy surveys using a simulation-based approach. With a specific case study of cross-correlating emission-line galaxies from the DESI Legacy Imaging Surveys and the Planck cosmic microwave background lensing map, we show that neural network-based regression approaches to modelling the window function are superior in comparison to linear regression-based models. Here, we additionally show that the definition of the galaxy overdensity estimator can impact the overall signal-to-noise of observed power spectra. Finally, we show that the additive biases coming from the window functions can significantly bias the modes of the inferred parameters and also degrade their precision. Thus, a careful understanding of the window functions will be essential to conduct cosmological experiments.
Heat transport in highly porous fiber networks is analyzed via two-point correlation functions. Fibers are assumed to be long and thin to allow a large number of crossing points per fiber. The network is characterized by three parameters: the fiber aspect ratio, the porosity and the anisotropy of the structure. We show that the effective thermal conductivity of the system can be estimated from knowledge of the porosity and the correlation lengths of the correlation functions obtained from a fiber structure image. As an application, the effects of the fiber aspect ratio and the network anisotropy on the thermal conductivity is studied.
Nitrogen is one of the most important nutrients for plant growth and a major constituent of proteins that regulate photosynthetic and respiratory processes. However, a comprehensive global analysis of nitrogen allocation in leaves for major processes with respect to different plant functional types is currently lacking. This study integrated observations from global databases with photosynthesis and respiration models to determine plant-functional-type-specific allocation patterns of leaf nitrogen for photosynthesis (Rubisco, electron transport, light absorption) and respiration (growth and maintenance), and by difference from observed total leaf nitrogen, an unexplained “residual” nitrogen pool. Based on our analysis, crops partition the largest fraction of nitrogen to photosynthesis (57%) and respiration (5%) followed by herbaceous plants (44% and 4%). Tropical broadleaf evergreen trees partition the least to photosynthesis (25%) and respiration (2%) followed by needle-leaved evergreen trees (28% and 3%). In trees (especially needle-leaved evergreen and tropical broadleaf evergreen trees) a large fraction (70% and 73% respectively) of nitrogen was not explained by photosynthetic or respiratory functions. Compared to crops and herbaceous plants, this large residual pool is hypothesized to emerge from larger investments in cell wall proteins, lipids, amino acids, nucleic acid, CO2 fixation proteins (other than Rubisco), secondary compounds, and other proteins. Our estimates are different from previous studies due to differences in methodology and assumptions used in deriving nitrogen allocation estimates. Unlike previous studies, we integrate and infer nitrogen allocation estimates across multiple plant functional types, and report substantial differences in nitrogen allocation across different plant functional types. Furthermore, the resulting pattern of nitrogen allocation provides insights on mechanisms that operate at a cellular scale within leaves, and can be integrated with ecosystem models to derive emergent properties of ecosystem productivity at local, regional, and global scales.
Problem solving - nonparametric estimate of probability density function
Application of polynomial-smoothing formulas and related derivative-estimating formulas simplifies certain linear least-squares problems. These problems can then be solved by a computer method.