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Dimensionless characteristics of turbines are discussed. On that basis, a universal method of mathematical approximation is formulated. The approximation of the turbine characteristics in a TKR-10ST turbocompressor is considered, as an example.
The Atmospheric Radiation Measurement user facility (ARM) Southern Great Plains observatory (SGP) houses over 50 precipitation instruments, using approximately 10 different methods for measurement. Nine different types of sensors are deployed at the SGP Central Facility with duplicates of the video disdrometer, impact disdrometer, and laser disdrometer. These instruments are deployed relatively close to one another in the “Central Cluster” of the SGP Central Facility. This effort employed simple clustering techniques on these data to calculate a precipitation best estimate, which in turn was used to develop a weighting scheme for each instrument type across rain rates that can be reused in the future for different configurations of instrument deployments. Product should be considered as an evaluation data set.
In nuclear fuel cycle safeguards, special nuclear material "held up" in pipes, ducts, and glove boxes causes significant uncertainty in material-unaccounted-for estimates. Quantitative imaging is a proposed non-destructive assay technique with potential to estimate the holdup mass more accurately and reliably than current techniques. However, uncertainty analysis for quantitative imaging remains a significant challenge. In this work we demonstrate an analysis approach for data acquired with a fast-neutron coded aperture imager. The work includes a calibrated forward model of the imager. Cross-validation indicates that the forward model predicts the imager data typically within 23%; further improvements are forthcoming. A new algorithm based on the chi-squared goodness-of-fit metric then uses the forward model to calculate a holdup confidence interval. The new algorithm removes geometry approximations that previous methods require, making it a more reliable uncertainty estimator.
Discrete-optimal model-reduction techniques such as the Gauss{Newton with Approximated Tensors (GNAT) method have shown promise, as they have generated stable, accurate solutions for large-scale turbulent, compressible ow problems where standard Galerkin techniques have failed. However, there has been limited comparative analysis of the two approaches. This is due in part to difficulties arising from the fact that Galerkin techniques perform projection at the time-continuous level, while discrete-optimal techniques do so at the time-discrete level. This work provides a detailed theoretical and experimental comparison of the two techniques for two common classes of time integrators: linear multistep schemes and Runge{Kutta schemes. We present a number of new ndings, including conditions under which the discrete-optimal ROM has a time-continuous representation, conditions under which the two techniques are equivalent, and time-discrete error bounds for the two approaches. Perhaps most surprisingly, we demonstrate both theoretically and experimentally that decreasing the time step does not necessarily decrease the error for the discrete-optimal ROM; instead, the time step should be `matched' to the spectral content of the reduced basis. In numerical experiments carried out on a turbulent compressible- ow problem with over one million unknowns, we show that increasing the time step to an intermediate value decreases both the error and the simulation time of the discrete-optimal reduced-order model by an order of magnitude.
Identifying erratic or unstable time-series is an area of interest to many fields. Recently, there have been successful developments towards this goal. These newly developed methodologies however come from domains where it is typical to have several thousand or more temporal observations. This creates a challenge when attempting to apply these methodologies to time-series with much fewer temporal observations such as for spatio-temporal socio-cultural understanding, a domain where a typical time series of interest might only consist of 20-30 annual observations. Identifying instability in spatio-temporal trends is critical for understanding global dynamics and finding areas of potential concern or intervention. In the case of spatio-temporal socio-cultural understanding, instability is marked by two characteristics, 1) how widely varying the values are and 2) how predictable that variance is from one observation to the next. Approaches for simultaneously addressing both of these concerns have been limited. In this paper, we introduce an approximate entropy based method for characterizing the behaviour of a time series with limited temporal observations. For Geocomputation, this methodology represents a novel additional tool for researchers to use for exploring and understanding spatio-temporal data, specifically with limited temporal observations. As a case study, we look at national youth male unemployment across the world from 1991-2014.
Cosmological Boltzmann codes are often used by researchers for calculating the CMB angular power spectra from different theoretical models, for cosmological parameter estimation, etc. Therefore, the accuracy of a Boltzmann code is of utmost importance. Different Markov Chain Monte Carlo based parameter estimation algorithms typically require 10^3 - 10^4 iterations of Boltzmann code. This makes the time complexity of such codes another critical factor. In the last two decades, several Boltzmann packages, such as CMBFAST, CAMB, CMBEasy, CLASS etc., have been developed. In this paper, we present a new cosmological Boltzmann code, CMBAns, that can be used for accurate calculation of the CMB power spectrum. At present, CMBAns is developed for a flat background matrix. It is mostly written in the C language. However, we borrowed the concept of class from C++. This gives researchers the flexibility to develop their own independent package based on CMBAns, without an in-depth understanding of the source code. We also develop multiple stand-alone facilities which can be directly compiled and run on a given parameter set. In this paper, we discuss all the mathematical formulation, approximation schemes, integration methods etc., that are used in CMBAns. The package will be made available through github for public use in the near future.
Measurements of scalar flux distributions with fine spatial and energy resolutions are needed to remedy one of the validation shortcomings of the novel neutronics full core solvers, such as MPACT and nTRACER. While a very detailed resolution of the flux can be calculated with such codes, only a limited experimental data set is available to check their accuracy. Such type of measurements are on-going at the zero power reactor CROCUS, operated at the Laboratory for Reactor Physics and System Behaviours of the EPFL, thanks to the development of advanced miniature neutron detection systems. This kind of experimental data would provide the community with a suitable benchmark for the validation of high fidelity neutronics solvers. In parallel, a multi-physics solver for steady-state and transient analysis of nuclear reactors, named GeN-Foam, has been developed. Based on the finite-volume OpenFOAM library, GeN-Foam provides us with enough flexibility to analyze non-conventional reactor geometries such as that of CROCUS. While CROCUS heterogeneities cannot be modeled by MPACT and nTRACER for the moment, GeN-Foam offers a unique opportunity to build a high fidelity model which mimics these codes' method to reach sub-pin simulation resolution. This document aims at describing the work achieved to get from the existing GeN-Foam model of the CROCUS reactor based on a structured mesh and using the neutron diffusion, the first high-fidelity model using discrete ordinates method as an approximation to neutron transport and an unstructured mesh for inter lattice water gap description and sub-pin heterogeneous modeling. (authors)
The probability distribution describing the state of a Stochastic Reaction Network (SRN) evolves according to the Chemical Master Equation (CME). It is common to estimate its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm (SSA). In many cases, these simulations can take an impractical amount of computational time. Therefore, many methods have been developed that approximate sample paths of the underlying stochastic process and estimate the solution of the CME. A prominent class of these methods include hybrid methods that partition the set of species and the set of reactions into discrete and continuous subsets. Such a partition separates the dynamics into a discrete and a continuous part. Simulating such a stochastic process can be computationally much easier than simulating the exact discrete stochastic process with SSA. Moreover, the quasi-stationary assumption to approximate the dynamics of fast subnetworks can be applied for certain classes of networks. However, as the dynamics of a SRN evolves, these partitions may have to be adapted during the simulation. We develop a hybrid method that approximates the solution of a CME by automatically partitioning the reactions and species sets into discrete and continuous components and applying the quasi-stationary assumption on identifiable fast subnetworks. Our method does not require any user intervention and it adapts to exploit the changing timescale separation between reactions and/or changing magnitudes of copy-numbers of constituent species. We demonstrate the efficiency of the proposed method by considering examples from systems biology and showing that very good approximations to the exact probability distributions can be achieved in significantly less computational time. This is especially the case for systems with oscillatory dynamics, where the system dynamics change considerably throughout the time-period of interest.
In this research, we assess the viability of four different, publicly available algorithms for estimating the azimuth and tilt parameters of solar photovoltaic systems using only the associated AC power time series data and site latitude-longitude coordinates. In this work, we curated a benchmarking data set of 44 fixed-tilt systems, comprising 275 measured AC power inverter data streams, with known azimuth and tilt parameters. Additionally, we isolated test cases in the data set with real- world issues, including shading and clipping, to determine how algorithm performance varies based on the presence of these phenomena. Using this data set for benchmarking, we evaluated the estimated vs. actual system characteristics for each algorithm, as well as the associated algorithm execution time using a standardized benchmarking process. The two highest performing algorithms were the Solar Data Tools and the PVWatts 5- based methods, which both achieved a median absolute error of approximately 5 and 1 degrees for azimuth and tilt, respectively. During run time analysis, the SDT method was approximately 5 times faster than the PVWatts 5-based method, with the median execution time for a stream varying between 6 and 8 seconds vs. a median run time of 31 seconds for the PVWatts 5-based method.
In this research, we assess the viability of four different, publicly available algorithms for estimating the azimuth and tilt parameters of solar photovoltaic systems using only the associated AC power time series data and site latitude-longitude coordinates. In this work, we curated a benchmarking data set of 44 fixed-tilt systems, comprising 275 measured AC power inverter data streams, with known azimuth and tilt parameters. Additionally, we isolated test cases in the data set with real-world issues, including shading and clipping, to determine how algorithm performance varies based on the presence of these phenomena. Using this data set for benchmarking, we evaluated the estimated vs. actual system characteristics for each algorithm, as well as the associated algorithm execution time using a standardized benchmarking process. The two highest performing algorithms were the Solar Data Tools and the PVWatts 5-based methods, which both achieved a median absolute error of approximately 5 and 1 degrees for azimuth and tilt, respectively. During run time analysis, the SDT method was approximately 5 times faster than the PVWatts 5-based method, with the median execution time for a stream varying between 6 and 8 seconds vs. a median run time of 31 seconds for the PVWatts 5-based method.
This paper studies the shallow Ritz method for solving the one-dimensional diffusion problem. It is shown that the shallow Ritz method improves the order of approximation dramatically for non-smooth problems. To realize this optimal or nearly optimal order of the shallow Ritz approximation, we develop a damped block Newton (dBN) method that alternates between updates of the linear and non-linear parameters. Per each iteration, the linear and the non-linear parameters are updated by exact inversion and one step of a modified, damped Newton method applied to a reduced non-linear system, respectively. The computational cost of each dBN iteration is $\mathcal{O}$(n). Starting with the non-linear parameters as a uniform partition of the interval, numerical experiments show that the dBN is capable of efficiently moving mesh points to nearly optimal locations. In conclusion, to improve the efficiency of the dBN further, we propose an adaptive damped block Newton (AdBN) method by combining the dBN with the adaptive neuron enhancement (ANE) method [28].
In this work, we present the Sequential Ensemble Transform (SET) method, an approach for generating approximate samples from a Bayesian posterior distribution. The method explores the posterior distribution by solving a sequence of discrete optimal transport problems to produce a series of transport plans which map prior samples to posterior samples. We prove that the sequence of Dirac mixture distributions produced by the SET method converges weakly to the true posterior as the sample size approaches infinity. Furthermore, our numerical results indicate that, when compared to standard Sequential Monte Carlo (SMC) methods, the SET approach is more robust to the choice of Markov mutation kernels and requires less computational efforts to reach a similar accuracy when used to explore complex posterior distributions. Finally, we describe adaptive schemes that allow to completely automate the use of the SET method.
We approach the problem of parallelizing state-of-the-art nonlinear programming optimization algorithms.Specifically, we focus on parallelizing quasi-Newton interior-point methods that use limited-memory secant Hessian approximations. Such interior-point methods are known to have better convergence properties and to be more effective on large-scale problems than gradient-based and derivative-free optimization algorithms. We target nonlinear and potentially nonconvex optimization problems with an arbitrary number of bound constraints and a small number of general equality and inequality constraints on the optimization variables. These problems occur for example in the form of optimal control, optimal design, and inverse problems governed by ordinary or partial differential equations, whenever they are expressed in a “reduced-space” optimization approach. We introduce and analyze the time and space complexity of a decomposition method for solving the quasi-Newton linear systems that leverages the fact that the quasi-Newton Hessian matrix has a small number of dense blocks that border a low-rank update of a diagonal matrix. This enables an efficient parallelization on memory-distributed computers of the iterations of the optimization algorithm, a state-of-the-art filter line-search interior-point algorithm by Wächter et. al. We illustrate the efficiency of the proposed method by solving structural topology optimization problems on up to 4608 cores on a parallel machine.
By coupling structure prediction methods with first-principles total energy calculations we find lattice constant and bulk modulus of hcp LiBe{sub 2}. Structural and electronic properties are investigated using Linear Combination of Atomic Orbital’s method. The generalized gradient approximation based density functional calculations are attempted to look for the ground state. Thereafter ground state electronic band structure, total density of states and anisotropies in directional Compton profiles are computed. Step like behavior seen broadly in density of states (DOS) is appreciated by band structure suggesting 2D features. Anisotropic behavior of electronic states around Fermi level is also reflected in anisotropies of directional Compton profiles.
Significance Quantum mechanics (QM) simulations increasingly help researchers understand phenomena in chemistry, geoscience, material science, and biology. Unfortunately, the exact solution of the relevant QM equations is not available; consequently approximate yet accurate QM methods must be developed. In many situations, e.g., heterogeneous catalysis and enzymatic reactions, although the entire system is too large to treat with high-level QM, the relevant phenomena happen in a confined space. Quantum embedding theories can study exactly such cases by using highly accurate but expensive QM methods to simulate only the region of interest, while using fast but less accurate approximations to treat the surroundings. In this article, we present a unique, accurate quantum embedding theory applicable to covalently bonded systems.
In this paper, we present the nonconforming virtual element method (VEM) for the numerical approximation of velocity and pressure in the steady Stokes problem. The pressure is approximated using discontinuous piecewise polynomials, while each component of the velocity is approximated using the nonconforming virtual element space. On each mesh element the local virtual space contains the space of polynomials of up to a given degree, plus suitable nonpolynomial functions. The virtual element functions are implicitly defined as the solution of local Poisson problems with polynomial Neumann boundary conditions. As typical in VEM approaches, the explicit evaluation of the non-polynomial functions is not required. This approach makes it possible to construct nonconforming (virtual) spaces for any polynomial degree regardless of the parity, for two- and three-dimensional problems, and for meshes with very general polygonal and polyhedral elements. We show that the nonconforming VEM is inf-sup stable and establish optimal a priori error estimates for the velocity and pressure approximations. Finally, numerical examples confirm the convergence analysis and the effectiveness of the method in providing high-order accurate approximations.
Here, we derive the equations of quasiparticle random-phase approximation (QRPA) based on the finite amplitude method with the Hartree-Fock+Bardeen–Cooper–Schrieffer (HF+BCS) single-particle states, and calculate the magnetic dipole (M1) transition for deformed gadolinium isotopes. Our QRPA calculation shows both large spin-flip transitions in the 5 to 10 MeV excitation energy and the low energy orbital transition that would correspond to the M1 scissors mode observed in nuclear experiments. Then, we calculate neutron capture reactions based on the statistical Hauser-Feshbach theory with the photoabsorption cross sections of even-even nuclei given by QRPA. We find that the capture cross section is enhanced due to the contribution from the low energy M1 transition although the calculated capture cross section still underestimates the experimental data. This issue in the calculated capture cross section could be improved by uncertainties of the low energy E1 transition neglected in our QRPA calculation.
Equation-of-motion coupled-cluster singles and doubles (EOM-CCSD) is a reliable and popular approach to the determination of electronic excitation energies. Recently, we have developed a rank-reduced CCSD (RR-CCSD) method that allows the ground-state coupled-cluster energy to be determined with low-rank cluster amplitudes. Here, we extend this approach to excited-state energies through a RR-EOM-CCSD method. We start from the EOM-CCSD energy functional and insert low-rank approximations to the doubles amplitudes. The result is an approximate EOM-CCSD method with only a quadratic number (in the molecular size) of free parameters in the wavefunction. Importantly, our formulation of RR-EOM-CCSD preserves the size intensivity of the excitation energy and size extensivity of the total energy. Numerical tests of the method suggest that accuracy on the order of 0.05–0.01 eV in the excitation energy is possible with 1% or less of the original number of wavefunction coefficients; accuracy of better than 0.01 eV can be achieved with about 4% or less of the free parameters. The amount of compression at a given accuracy level is expected to increase with the size of the molecule. The RR-EOM-CCSD method is a new path toward the efficient determination of accurate electronic excitation energies.