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A direct method for the solution of unsteady two-dimensional incompressible Navier-Stokes equations
The unsteady incompressible Navier-Stokes equations are formulated in terms of vorticity and stream function in generalized curvilinear orthogonal coordinates to facilitiate analysis of flow configurations with general geometries. The numerical method developed solves the conservative form of the transport equation using the alternating-direction implicit method, whereas the stream-function equation is solved by direct block Gaussian elimination. The method is applied to a model problem of flow over a back-step in a doubly infinite channel, using clustered conformal coordinates. One-dimensional stretching functions, dependent on the Reynolds number and the asymptotic behavior of the flow, are used to provide suitable grid distribution in the separation and reattachment regions, as well as in the inflow and outflow regions. The optimum grid distribution selected attempts to honor the multiple length scales of the separated-flow model problem. The asymptotic behavior of the finite-differenced transport equation near infinity is examined and the numerical method is carefully developed so as to lead to spatially second-order accurate wiggle-free solutions, i.e., with minimum dispersive error. Results have been obtained in the entire laminar range for the backstep channel and are in good agreement with the available experimental data for this flow problem.
The generation of a Gaussian random process in a position parameter
Analog computer method for approximating stationary Gaussian random process depending only on position parameter
An optimum decision-directed scheme for Gaussian mixtures.
Optimally weighted and moment method decision directed estimation algorithms for resolving Gaussian mixtures of signals in order to detect signals
CMB-PAInT: An inpainting tool for the cosmic microwave background
Abstract The presence of astrophysical emissions in microwave observations forces us to perform component separation to extract the Cosmic Microwave Background (CMB) signal. However, even in the most optimistic cases, there are still strongly contaminated regions, such as the Galactic plane or those with emission from extragalactic point sources, which require the use of a mask. Since many CMB analyses, especially the ones working in harmonic space, need the whole sky map, it is crucial to develop a reliable inpainting algorithm that replaces the values of the excluded pixels by others statistically compatible with the rest of the sky. This is especially important when working withQandUsky maps in order to obtainE- andB-mode maps which are free fromE-to-Bleakage. In this work we study a method based on Gaussian Constrained Realizations (GCR), that can deal with both intensity and polarization. Several tests have been performed to asses the validation of the method, including the study of the one-dimensional probability distribution function (1-PDF),E- andB-mode map reconstruction, and power spectra estimation. We have considered two scenarios for the input simulation: one case with only CMB signal and a second one including also Planck PR4 semi-realistic noise. Even if we are limited to low resolution maps, N side = 64 ifT,QandUare considered, we believe that this is a useful approach to be applied to future missions such as LiteBIRD, where the target are the largest scales.
A high level Ab initio study of the anionic hydrogen-bonded complexes FH-CN-, FH-NC-, H2O-CN- and H2O-NC-
HF, H2O, CN- and their hydrogen-bonded complexes were studied using state-of-the-art ab initio quantum mechanical methods. A large Gaussian one particle basis set consisting of triple zeta plus double polarization plus diffuse s and p functions (TZ2P + diffuse) was used. The theoretical methods employed include self consistent field, second order Moller-Plesset perturbation theory, singles and doubles configuration interaction theory and the singles and doubles coupled cluster approach. The FH-CN- and FH-NC- and H2O-CN-, H2O-NC- pairs of complexes are found to be essentially isoenergetic. The first pair of complexes are predicted to be bound by approx. 24 kcal/mole and the latter pair bound by approximately 15 kcal/mole. The ab initio binding energies are in good agreement with the experimental values. The two being shorter than the analogous C-N hydrogen bond. The infrared (IR) spectra of the two pairs of complexes are also very similar, though a severe perturbation of the potential energy surface by proton exchange means that the accurate prediction of the band center of the most intense IR mode requires a high level of electronic structure theory as well as a complete treatment of anharmonic effects. The bonding of anionic hydrogen-bonded complexes is discussed and contrasted with that of neutral hydrogen-bonded complexes.
A high-level ab initio study of the anionic hydrogen-bonded complexes FH-CN(-), FH-NC(-), H2O-CN(-), and H2O-NC(-)
HF, H2O, CN- and their hydrogen-bonded complexes were studied using state-of-the-art ab initio quantum mechanical methods. A large Gaussian one particle basis set consisting of triple zeta plus double polarization plus diffuse s and p functions (TZ2P + diffuse) was used. The theoretical methods employed include self consistent field, second order Moller-Plesset perturbation theory, singles and doubles configuration interaction theory and the singles and doubles coupled cluster approach. The FH-CN- and FH-NC- and H2O-CN-, H2O-NC- pairs of complexes are found to be essentially isoenergetic. The first pair of complexes are predicted to be bound by approx. 24 kcal/mole and the latter pair bound by approximately 15 kcal/mole. The ab initio binding energies are in good agreement with the experimental values. The two being shorter than the analogous C-N hydrogen bond. The infrared (IR) spectra of the two pairs of complexes are also very similar, though a severe perturbation of the potential energy surface by proton exchange means that the accurate prediction of the band center of the most intense IR mode requires a high level of electronic structure theory as well as a complete treatment of anharmonic effects. The bonding of anionic hydrogen-bonded complexes is discussed and contrasted with that of neutral hydrogen-bonded complexes.
The Three-Dimensional Expansion of the Ejecta from Tycho's Supernova Remnant
We present the first 3D measurements of the velocity of various ejecta knots in Tycho's supernova remnant, known to result from a Type Ia explosion. Chandra X-ray observations over a 12 yr baseline from 2003 to 2015 allow us to measure the proper motion of nearly 60 tufts of Si-rich ejecta, giving us the velocity in the plane of the sky. For the line-of-sight velocity, we use two different methods: a nonequilibrium ionization model fit to the strong Si and S lines in the 1.22.8 keV regime, and a fit consisting of a series of Gaussian lines. These methods give consistent results, allowing us to determine the redshift or blueshift of each of the knots. Assuming a distance of 3.5 kpc, we find total velocities that range from 2400 to 6600 km/s, with a mean of 4430 km/s. We find several regions where the ejecta knots have overtaken the forward shock. These regions have proper motions in excess of 6000 km/s. Some SN Ia explosion models predict a velocity asymmetry in the ejecta. We find no such velocity asymmetries in Tycho, and we discuss our findings in light of various explosion models, favoring those delayed-detonation models with relatively vigorous and symmetrical deflagrations. Finally, we compare measurements with models of the remnant's evolution that include both smooth and clumpy ejecta profiles, finding that both ejecta profiles can be accommodated by the observations.
Gaussian-process generative model for the QCD equation of state
We develop a generative model for the nuclear matter equation of state at zero net baryon density using the Gaussian process regression method. We impose first-principles theoretical constraints from lattice quantum chromodynamics and hadron resonance gas at high- and low-temperature regions, respectively. By allowing the trained Gaussian process regression model to vary freely near the phase transition region, we generate random smooth crossover equations of state with different speeds of sound that do not rely on specific parametrizations. Here, we explore a collection of experimental observable dependencies on the generated equations of state, which paves the groundwork for future Bayesian inference studies to use experimental measurements from relativistic heavy-ion collisions to constrain the nuclear matter equation of state.
Layer-by-layer design method for soft-X-ray multilayers
A new design method effective for a nontransparent system has been developed for soft-X-ray multilayers with the aid of graphic representation of the complex amplitude reflectance in a Gaussian plane. The method provides an effective means of attaining the absolute maximum reflectance on a layer-by-layer basis and also gives clear insight into the evolution of the amplitude reflectance on a multilayer as it builds up. An optical criterion is derived for the selection of a proper pair of materials needed for designing a high-reflectance multilayer. Some examples are given to illustrate the usefulness of this design method.
Improved Gaussian Beam-Scattering Algorithm
The localized model of the beam-shape coefficients for Gaussian beam-scattering theory by a spherical particle provides a great simplification in the numerical implementation of the theory. We derive an alternative form for the localized coefficients that is more convenient for computer computations and that provides physical insight into the details of the scattering process. We construct a FORTRAN program for Gaussian beam scattering with the localized model and compare its computer run time on a personal computer with that of a traditional Mie scattering program and with three other published methods for computing Gaussian beam scattering. We show that the analytical form of the beam-shape coefficients makes evident the fact that the excitation rate of morphology-dependent resonances is greatly enhanced for far off-axis incidence of the Gaussian beam.
Dominant balance-based adaptive mesh refinement for incompressible fluid flows
This work introduces a novel adaptive mesh refinement (AMR) method that utilizes dominant balance analysis (DBA) for efficient and accurate grid adaptation in computational fluid dynamics (CFD) simulations. The proposed method leverages a Gaussian mixture model (GMM) to classify grid cells into active and passive regions based on the dominant physical interactions within the equation space. By modeling truncation error probabilistically from discretized terms, the method identifies regions of high interaction where numerical accuracy is most sensitive to resolution. Unlike traditional AMR strategies, this approach does not rely on heuristic-based sensors or user-defined thresholds, providing a fully automated and problem-independent framework for AMR. Applied to the incompressible Navier-Stokes equations for steady and unsteady flow past a cylinder, the DBA-based AMR method achieves comparable accuracy to high-resolution grids while reducing computational costs by up to 70 %. The validation highlights the method’s effectiveness in capturing complex flow features while minimizing grid cells, directing computational resources toward regions with the most critical dynamics. This modular and scalable strategy is adaptable to a wide range of applications, presenting a promising tool for efficient high-fidelity simulations in CFD and other multiphysics domains.
Efficient Implementation of an Optimal Interpolator for Large Spatial Data Sets
Scattered data interpolation is a problem of interest in numerous areas such as electronic imaging, smooth surface modeling, and computational geometry. Our motivation arises from applications in geology and mining, which often involve large scattered data sets and a demand for high accuracy. The method of choice is ordinary kriging. This is because it is a best unbiased estimator. Unfortunately, this interpolant is computationally very expensive to compute exactly. For n scattered data points, computing the value of a single interpolant involves solving a dense linear system of size roughly n x n. This is infeasible for large n. In practice, kriging is solved approximately by local approaches that are based on considering only a relatively small'number of points that lie close to the query point. There are many problems with this local approach, however. The first is that determining the proper neighborhood size is tricky, and is usually solved by ad hoc methods such as selecting a fixed number of nearest neighbors or all the points lying within a fixed radius. Such fixed neighborhood sizes may not work well for all query points, depending on local density of the point distribution. Local methods also suffer from the problem that the resulting interpolant is not continuous. Meyer showed that while kriging produces smooth continues surfaces, it has zero order continuity along its borders. Thus, at interface boundaries where the neighborhood changes, the interpolant behaves discontinuously. Therefore, it is important to consider and solve the global system for each interpolant. However, solving such large dense systems for each query point is impractical. Recently a more principled approach to approximating kriging has been proposed based on a technique called covariance tapering. The problems arise from the fact that the covariance functions that are used in kriging have global support. Our implementations combine, utilize, and enhance a number of different approaches that have been introduced in literature for solving large linear systems for interpolation of scattered data points. For very large systems, exact methods such as Gaussian elimination are impractical since they require 0(n(exp 3)) time and 0(n(exp 2)) storage. As Billings et al. suggested, we use an iterative approach. In particular, we use the SYMMLQ method, for solving the large but sparse ordinary kriging systems that result from tapering. The main technical issue that need to be overcome in our algorithmic solution is that the points' covariance matrix for kriging should be symmetric positive definite. The goal of tapering is to obtain a sparse approximate representation of the covariance matrix while maintaining its positive definiteness. Furrer et al. used tapering to obtain a sparse linear system of the form Ax = b, where A is the tapered symmetric positive definite covariance matrix. Thus, Cholesky factorization could be used to solve their linear systems. They implemented an efficient sparse Cholesky decomposition method. They also showed if these tapers are used for a limited class of covariance models, the solution of the system converges to the solution of the original system. Matrix A in the ordinary kriging system, while symmetric, is not positive definite. Thus, their approach is not applicable to the ordinary kriging system. Therefore, we use tapering only to obtain a sparse linear system. Then, we use SYMMLQ to solve the ordinary kriging system. We show that solving large kriging systems becomes practical via tapering and iterative methods, and results in lower estimation errors compared to traditional local approaches, and significant memory savings compared to the original global system. We also developed a more efficient variant of the sparse SYMMLQ method for large ordinary kriging systems. This approach adaptively finds the correct local neighborhood for each query point in the interpolation process.
A study of methods to predict and measure the transmission of sound through the walls of light aircraft. Integration of certain singular boundary element integrals for applications in linear acoustics
An alternative method for performing singular boundary element integrals for applications in linear acoustics is discussed. The method separates the integral of the characteristic solution into a singular and nonsingular part. The singular portion is integrated with a combination of analytic and numerical techniques while the nonsingular portion is integrated with standard Gaussian quadrature. The method may be generalized to many types of subparametric elements. The integrals over elements containing the root node are considered, and the characteristic solution for linear acoustic problems are examined. The method may be generalized to most characteristic solutions.
Representation of Probability Density Functions from Orbit Determination using the Particle Filter
Statistical orbit determination enables us to obtain estimates of the state and the statistical information of its region of uncertainty. In order to obtain an accurate representation of the probability density function (PDF) that incorporates higher order statistical information, we propose the use of nonlinear estimation methods such as the Particle Filter. The Particle Filter (PF) is capable of providing a PDF representation of the state estimates whose accuracy is dependent on the number of particles or samples used. For this method to be applicable to real case scenarios, we need a way of accurately representing the PDF in a compressed manner with little information loss. Hence we propose using the Independent Component Analysis (ICA) as a non-Gaussian dimensional reduction method that is capable of maintaining higher order statistical information obtained using the PF. Methods such as the Principal Component Analysis (PCA) are based on utilizing up to second order statistics, hence will not suffice in maintaining maximum information content. Both the PCA and the ICA are applied to two scenarios that involve a highly eccentric orbit with a lower apriori uncertainty covariance and a less eccentric orbit with a higher a priori uncertainty covariance, to illustrate the capability of the ICA in relation to the PCA.
Physics-Based Machine Learning Methods for U-235 Forensics Signatures
Signatures of low-intensity U-235 sources have been recently studied by utilizing a variety of machine learning (ML) classifiers using features derived from gamma spectral measurements collectedunder structured campaigns. Several ML classifiers, such as ensemble of tress and classification trees, revealed misleadingly-optimistic training error due to over-fitting, and furthermore,their performance is not directly relatable to the physical properties due to their data-driven, opaque designs. We present a regression-based ML method that first estimates the inverse distanceto the source and then utilizes a threshold to infer its presence, by representing the background as a source located at an infinite distance. For the inverse distance estimation, we study the ensembleof trees and Gaussian process regression methods, and a hyper parameter auto-tuning and selection method that employs five regression estimators. These methods avoid the over-fittingobserved in several ML classifiers, while providing the classification error nearly comparable to them based on independent test data. Their error is directly related to estimates of the inversephysical distance to source, and the precision of error determines the seperability property that determines the false alarm and missed detection rates. The property of monotonic decrease of thesource strength with increasing detector distance combined with Poisson distribution of measurements is utilized to analytically validate these methods by deriving the generalization equations ofunderlying regression methods.
A high-order computational framework for particle-resolved simulations of disperse multiphase flows
This work presents a high-order numerical approach for particle-resolved simulations of disperse multiphase flows, where the Navier-Stokes equations for fluid flow are solved using a high-order spectral element method in the Eulerian framework, and the particle phase is directly simulated with a discrete element method. The coupling between particles and fluids is explicitly handled using an adapted direct-forcing immersed boundary method. Unlike the conventional schemes, a high-order barycentric Lagrange interpolation method and a Gaussian projection kernel are used to ensure accurate momentum exchange between local boundary points and surrounding fluid nodes in the framework of high-order fluid solver. Benchmark tests of increasing complexity are conducted to demonstrate the accuracy and efficiency of our method. Here, it is found that our approach exhibits an excellent convergence performance, as the fluid element/grid is refined and the number of boundary points increases. Compared to conventional low-order methods, the proposed high-order framework enables the use of substantially larger fluid elements while maintaining high accuracy in modeling fluid-particle interactions, owing to the enhanced resolution of high-order basis functions. Moreover, since the primary unknowns are stored at element or grid nodes, the high-order approach offers improved efficiency in both CPU memory usage and total computational cost.
Saturation of fluorescence in flames with a Gaussian laser beam
The method of saturated fluorescence for measuring species concentrations in flames is usually performed with laser beams that do not provide a constant intensity distribution across the focal volume. Because of the intensity distribution across the beam, the fluorescence signal does not depend on laser power or intensity in the same manner as for uniform illumination. This leads to anomolous apparent saturation intensities. In the following, the effect is considered for atomic fluorescence. Relations for the fluorescence signal under two common excitation geometries are derived and uncertainty relations used to consider the benefits of high laser intensity.