SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
- SAGE data have been used to estimate particle size distribution (PSD) parameters - Mode radius - Distribution width (σ) - Measurement error is often neglected - Wrana et al. 2021 included error - Bimodal distributions have not been evaluated
A method to estimate raindrop size distribution (DSD) parameters from a combined Zm profile and path-integrated attenuation is shown, and a test result of the method using the data from an aircraft experiment is presented. The 'semi' dual-parameter (SDP) measurement is employed to estimate DSD parameters using the data obtained from an aircraft experiment conducted by Communications Research Laboratory, Tokyo, in conjunction with NASA. The validity of estimated DSD parameters is examined using measured Ka-band radar reflectivities. The estimated path-averaged N(0) is consistent with the Ka/X Ze ratio, and the use of estimated DSD shows excellent agreement between the rain rates estimated from the X-band and K-band Zes. The feasibility of estimating DSD parameters from space is confirmed.
Distribution plots of electrical parameters as means of investigating potential reliability of integrated circuits
In-flight estimation of large structure model errors in order to detect inevitable deficiencies in large structure controller/estimator models is discussed. Such an estimation process is particularly applicable in the area of shape control system design required to maintain a prescribed static structural shape and, in addition, suppress dynamic disturbances due to the vehicle vibrational modes. The paper outlines a solution to the problem of static shape estimation where the vehicle shape must be reconstructed from a set of measurements discretely located throughout the structure. The estimation process is based on the principle of least-squares that inherently contains the definition and explicit computation of model error estimates that are optimal in some sense. Consequently, a solution is provided for the problem of estimation of static model errors (e.g., external loads). A generalized formulation applicable to distributed parameters systems is first worked out and then applied to a one-dimensional beam-like structural configuration.
A theoretical framework is presented that can be used to treat approximation techniques for very general classes of parameter estimation problems involving distributed systems that are either first or second order in time. Using the approach developed, one can obtain both convergence and stability (continuous dependence of parameter estimates with respect to the observations) under very weak regularity and compactness assumptions on the set of admissible parameters. This unified theory can be used for many problems found in the recent literature and in many cases offers significant improvements to existing results.
In this work, a novel approach for the determination of the particle size distribution (PSD) parameters of stratospheric sulfate aerosols is presented. For this, ratios of extinction coefficients obtained from SAGE III/ISS (Stratospheric Aerosol and Gas Experiment III on the International Space Station) solar occultation measurements at 449, 756 and 1544 nm were used to retrieve the mode width and median radius of a size distribution assumed to be monomodal lognormal. The estimated errors at the peak of the stratospheric aerosol layer, on average, lie between 20% and 25% for the median radius and 5% and 7% for the mode width. The results are consistent in magnitude with other retrieval results from the literature, but a robust comparison is difficult, mainly because of differences in temporal and spatial coverage. Other quantities like number density and effective radius were also calculated. A major advantage of the described method over other retrieval techniques is that both the median radius and the mode width can be retrieved simultaneously, without having to assume one of them. This is possible due to the broad wavelength spectrum covered by the SAGE III/ISS measurements. Also, the presented method – being based on the analysis of three wavelengths – allows unique solutions for the retrieval of PSD parameters for almost all of the observed extinction spectra, which is not the case when using only two spectral channels. In addition, the extinction coefficients from SAGE III/ISS solar occultation measurements, on which the retrieval is based, are calculated without a priori assumptions about the PSD. For those reasons, the data produced with the presented retrieval technique may be a valuable contribution for a better understanding of the variability of stratospheric aerosol size distributions, e.g. after volcanic eruptions. While this study focuses on describing the retrieval method, and a future study will discuss the PSD parameter data set produced in depth, some exemplary results for background conditions in June 2017 are shown.
The National Aeronautics and Space Administration Global Precipitation Measurement (GPM) mission ground validation program uses dual-polarization radar moments to estimate raindrop size distribution (DSD) parameters, the mass-weighted mean drop diameter Dmass, and normalized intercept parameter NW, to validate the GPM Core Observatory–derived DSD parameters. The disdrometer-based Dmass and NW are derived through empirical relationships between Dmass and differential reflectivity ZDR, and between NW, reflectivity ZH, and Dmass. This study employs large datasets collected from two-dimensional video disdrometers (2DVD) during six different field studies to derive the requisite empirical relationships. The uncertainty of the derived Dmass(ZDR) relationship is evaluated through comparisons of 2DVD-calculated and ZDR-estimated Dmass, where ZDR is calculated directly from 2DVD observations. Similarly, the uncertainty of the NW(ZH, Dmass) relationship is evaluated through 2DVD-calculated and Dmass and ZH-estimated NW, where Dmass and ZH are directly calculated from 2DVD observations. This study also presents the sensitivity of Dmass(ZDR) relationships to climate regime and to disdrometer type after developing three additional Dmass(ZDR) relationships from second-generation Particle Size Velocity (PARSIVEL2) disdrometer (P2) observations collected in the Pacific Northwest, in Iowa, and at Kwajalein Atoll in the tropical Pacific Ocean. The application of P2-derived Dmass(ZDR) relationship based on precipitation in the northwestern United States to P2 observations collected over the tropical ocean resulted in the highest error among comparisons of the three datasets.
Parameters of the normalized gamma particle size distribution (PSD) have been retrieved from the Precipitation Image Package (PIP) snowfall observations collected during the International Collaborative Experiment - PyeongChang Olympics and Paralympic (ICE-POP 2018). Two of the gamma PSD parameters, the mass weighted particle diameter (Dmass) and the normalized intercept parameter NW, have median values of 1.15-1.31 mm and 2.84-3.04 log(mm-1 m-3), respectively. This range arises from the choice of the relationship between the maximum versus equivalent diameter, Dmx-Deq, and the relationship between the Reynolds and Best numbers, Re-X. Normalization of snow water equivalent rate (SWER) and ice water content (W) by NW reduces the range in NW resulting in well fitted power law relationship, between SWER/NW and Dmass and between W/NW and Dmass. The bulk descriptors of snowfall are calculated from PIP observations and from the gamma PSD with values of the shape parameter (μ) ranging from -2 to 10. NASA’s Global Precipitation Measurement (GPM) mission, which adopted the normalized gamma PSD, assumes μ = 2 and μ = 3 in its two separate algorithms. The mean fractional bias (MFB) of the snowfall parameters changes with μ, where the functional dependence on μ depends on the specific snowfall parameter of interest. The MFB of the total concentration was underestimated by 0.23-0.34 when μ = 2 and by 0.29-0.40 when μ = 3, while the MFB of SWER had a much narrower range (-0.03 to 0.04) for the same μ values.
Stratospheric aerosols play key roles in the chemistry and radiation balance of the atmosphere and are a key input parameter for global chemistry and climate models. The degree to which aerosols impact chemistry and radiation balance depends primarily on their microphysical properties such as particle size distribution (PSD). The PSD is a mathematical description of the relative abundance of different sized particles within a sampling volume. If the PSD is accurately known then other key modeling parameters (e.g., surface area density) can be derived. Occultation observations from orbital instruments such as SAGE III/ISS have been used to infer these PSD parameters by inverting the extinction coefficient spectra. However, past efforts failed to address two key issues with this methodology: 1. The measurement uncertainty was not accounted for; 2. They assumed the PSDs to be single-mode only, while “real-world” PSDs are predominantly bi-modal. Accounting for both issues in the retrieval will yield an expanded solution space to the inferred PSD parameters; the question is “by how much?” To address this knowledge gap, we propose to carry out a series of simulations and, for every valid SAGE III/ISS data point, determine the range of PSD parameters that yield extinction spectra that are indistinguishable from the SAGE III/ISS data, within the limits of the reported uncertainty. Further, we will expand the solution space, for the first time, to include bimodal distributions. The results of this work will advance Earth system modeling/prediction capability through identifying the uncertainty of PSD parameter estimates using SAGE III/ISS data. The key benefits of this study over previous studies are twofold: 1. we will provide PSD estimates that include bimodal distributions in the solution space, 2. we will provide an uncertainty estimate for these parameters. The results of this study may be used directly in current and future climate and chemistry models.
Stratospheric aerosols play key roles in the chemistry and radiation balance of the atmosphere and are a key input parameter for global chemistry and climate models. The degree to which aerosols impact chemistry and radiation balance depends primarily on the relative abundance of different sized particles within the sample volume, often referred to as the particle size distribution (PSD). If the PSD is accurately known then other key modeling parameters (e.g., surface area density and effective radius) can be derived. Historically, occultation observations from orbital instruments such as SAGE III/ISS have been used to infer these PSD parameters by inverting the extinction coefficient spectra. However, past efforts routinely failed to account for measurement uncertainty and lacked a rigorous estimate of the inferred PSD uncertainty. We carried out a series of simulations to evaluate the accuracy of these inferences and, for every valid SAGE III/ISS extinction spectrum, determined the range of PSD parameters that fell withing the bounds of the extinction error bars. Special application of this method was applied to estimate the impact of the 2022 Hunga Tonga eruption had on particle size distributions.
The efficient numerical treatment of optimal control problems governed by elliptic partial differential equations (PDEs) and systems of elliptic PDEs, where the control is finite dimensional is discussed. Distributed control as well as boundary control cases are discussed. The main characteristic of the new methods is that they are designed to solve the full optimization problem directly, rather than accelerating a descent method by an efficient multigrid solver for the equations involved. The methods use the adjoint state in order to achieve efficient smoother and a robust coarsening strategy. The main idea is the treatment of the control variables on appropriate scales, i.e., control variables that correspond to smooth functions are solved for on coarse grids depending on the smoothness of these functions. Solution of the control problems is achieved with the cost of solving the constraint equations about two to three times (by a multigrid solver). Numerical examples demonstrate the effectiveness of the method proposed in distributed control case, pointwise control and boundary control problems.
This paper presents an analytical solution to the Riccati equation for self-adjoint systems such as beams, plates, strings and membranes moving in space, and shows how the optimal control law can be implemented using the given solution. It is then shown that there always exists a self-adjoint operator describing the distribution of potential energy if the state space is appropriately augmented. A beam-like gravity-stabilized satellite moving in a circular orbit around the earth is used to illustrate the main results in this paper.
A merged and highly reduced database of TRMM level 1 (precipitation radar, microwave imager, lightning) and NCEP reanalysis (basic state, radiative and surface flux) data has been assembled for three years of the TRMM mission. This allows direct examination of the dependence of convective spectra (as observed through radar reflectivity, microwave brightness temperature and lightning flash rate) on environmental basic states and anomalies. Such analysis may be more physically justified and instructive than traditional geographic and/or seasonal binning. The dependence of convective spectra on several environmental forcing parameters is presented, including surface Bowen ratio (sensible heat to total turbulent flux), net atmospheric radiative flux convergence and net atmospheric enthalpy flux convergence. The latter are basic drivers of net moisture convergence in simple quasi-equilibrium models of tropical atmospheric convection.
Critical excitation function used to estimate transient response of thin film devices at time intervals exceeding greatest time constant
Algorithmic and digital computer method for approximating irrational transfer functions by rational functions in control systems
The behavior of hyperbolic and parabolic partial differential equations is contrasted by studying the point-to-point time-optimal control problem for the equation of heat conduction and the equation of motion of a vibrating string. A maximal principle is obtained for the time-optimal control of the one-dimensional heat equation, and it is proven that time optimal controls are weakly bang-bang. The bang-bang principle is proven to be invalid for hyperbolic equations because of the finite speed of wave propagation. In the case of boundary value control of the vibrating spring, the latter is demonstrated by deriving an explicit formula for the time optimal control.
Two weighted adaptive algorithms are proposed for updating the estimates of the mean vector and the covariance matrix, respectively, in a multispectral pattern recognition system. To achieve computational efficiency, the auxiliary matrices have been utilized in the algorithm for covariance matrix updating. Enhancements in the performance accuracy of a multispectral processing system and extensions of the Gaussian maximum likelihood classification capabilities to larger scale surveys are the motivations in developing the algorithms presented herein.
The basic assumption that a large space structure can be decoupled preceding the application of reduced order active control was considered and alternative solutions to the control of such structures (in contrast to the strict modal control) were investigated. The transfer function matrix from the actuators to the sensors was deemed to be a reasonable candidate. More refined models from multivariable systems theory were studied and recent results in the multivariable control field were compared with respect to theoretical deficiencies and likely problems in application to large space structures.