3,424 research outputs found

    Optimum electrode configurations for fast ion separation in microfabricated surface ion traps

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    For many quantum information implementations with trapped ions, effective shuttling operations are important. Here we discuss the efficient separation and recombination of ions in surface ion trap geometries. The maximum speed of separation and recombination of trapped ions for adiabatic shuttling operations depends on the secular frequencies the trapped ion experiences in the process. Higher secular frequencies during the transportation processes can be achieved by optimising trap geometries. We show how two different arrangements of segmented static potential electrodes in surface ion traps can be optimised for fast ion separation or recombination processes. We also solve the equations of motion for the ion dynamics during the separation process and illustrate important considerations that need to be taken into account to make the process adiabatic

    Surrogate-based optimisation using adaptively scaled radial basis functions

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    Aerodynamic shape optimisation is widely used in several applications, such as road vehicles, aircraft and trains. This paper investigates the performance of two surrogate-based optimisation methods; a Proper Orthogonal Decomposition-based method and a force-based surrogate model. The generic passenger vehicle DrivAer is used as a test case where the predictive capability of the surrogate in terms of aerodynamic drag is presented. The Proper Orthogonal Decomposition-based method uses simulation results from topologically different meshes by interpolating all solutions to a common mesh for which the decomposition is calculated. Both the Proper Orthogonal Decomposition- and force-based approaches make use of Radial Basis Function interpolation. The Radial Basis Function hyperparameters are optimised using differential evolution. Additionally, the axis scaling is treated as a hyperparameter, which reduces the interpolation error by more than 50% for the investigated test case. It is shown that the force-based approach performs better than the Proper Orthogonal Decomposition method, especially at low sample counts, both with and without adaptive scaling. The sample points, from which the surrogate model is built, are determined using an optimised Latin Hypercube sampling plan. The Latin Hypercube sampling plan is extended to include both continuous and categorical values, which further improve the surrogate\u27s predictive capability when categorical design parameters, such as on/off parameters, are included in the design space. The performance of the force-based surrogate model is compared with four other gradient-free optimisation techniques: Random Sample, Differential Evolution, Nelder–Mead and Bayesian Optimisation. The surrogate model performed as good as, or better than these algorithms, for 17 out of the 18 investigated benchmark problems

    Partial wave analysiss of pbar-p -> piminus-piplus, pizero-pizero, eta-eta and eta-etaprime

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    A partial wave analysis is presented of Crystal Barrel data on pbar-p -> pizero-pizero, eta-eta and eta-etaprime from 600 to 1940 MeV/c, combined with earlier data on d\sigma /d\Omega and P for pbar-p->piminus-piplus. The following s-channel I=0 resonances are identified: (i) J^{PC} = 5^{--} with mass and width (M,\Gamma) at (2295+-30,235^{+65}_{-40}) MeV, (ii) J^{PC} = 4^{++} at (2020+-12, 170+-15) MeV and (2300+-25, 270+-50) MeV, (iii) 3D3 JPC = 3^{--} at (1960+-15, 150+-25) MeV and (2210+-4$, 360+-55) MeV, and a 3G3 state at (2300 ^{+50}_{-80}, 340+-150) MeV, (iv) JPC = 2^{++} at (1910+-30, 260+-40) MeV, (2020+-30, 275+-35) MeV, (2230+-30, 245+-45) MeV, and (2300+-35, 290+-50) MeV, (v) JPC = 1^{--} at (2005+-40, 275+-75) MeV, and (2165+-40, 160 ^{+140}_{-70}) MeV, and (vi) JPC = 0^{++} at (2005+-30, 305+-50) MeV, (2105+-15, 200+-25) MeV, and (2320+-30, 175+-45) MeV. In addition, there is a less well defined 6^{++} resonance at 2485+-40 MeV, with Gamma = 410+-90 MeV. For every JP, almost all these resonances lie on well defined linear trajectories of mass squared v. excitation number. The slope is 1.10+-0.03 Gev^2 per excitation. The f_0(2105) has strong coupling to eta-\eta, but much weaker coupling to pizero-pizero. Its flavour mixing angle between q-qbar and s-sbar is (59-71.6)deg, i.e. dominant decays to s-sbar. Such decays and its strong production in pbar-p interactions strongly suggest exotic character.Comment: Makes available the combined fit to Crystal Barrel data on pbar-p -> 2-body final states. 29 pages, 11 figures. Typo corrected in version

    Radial Basis Function Neural Networks : A Review

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    Radial Basis Function neural networks (RBFNNs) represent an attractive alternative to other neural network models. One reason is that they form a unifying link between function approximation, regularization, noisy interpolation, classification and density estimation. It is also the case that training RBF neural networks is faster than training multi-layer perceptron networks. RBFNN learning is usually split into an unsupervised part, where center and widths of the Gaussian basis functions are set, and a linear supervised part for weight computation. This paper reviews various learning methods for determining centers, widths, and synaptic weights of RBFNN. In addition, we will point to some applications of RBFNN in various fields. In the end, we name software that can be used for implementing RBFNNs

    Evolving generalized euclidean distances for training RBNN

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    In Radial Basis Neural Networks (RBNN), the activation of each neuron depends on the Euclidean distance between a pattern and the neuron center. Such a symmetrical activation assumes that all attributes are equally relevant, which might not be true. Non-symmetrical distances like Mahalanobis can be used. However, this distance is computed directly from the data covariance matrix and therefore the accuracy of the learning algorithm is not taken into account. In this paper, we propose to use a Genetic Algorithm to search for a generalized Euclidean distance matrix, that minimizes the error produced by a RBNN.Publicad

    Wall Adhesion and Constitutive Modelling of Strong Colloidal Gels

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    Wall adhesion effects during batch sedimentation of strongly flocculated colloidal gels are commonly assumed to be negligible. In this study in-situ measurements of colloidal gel rheology and solids volume fraction distribution suggest the contrary, where significant wall adhesion effects are observed in a 110mm diameter settling column. We develop and validate a mathematical model for the equilibrium stress state in the presence of wall adhesion under both viscoplastic and viscoelastic constitutive models. These formulations highlight fundamental issues regarding the constitutive modeling of colloidal gels, specifically the relative utility and validity of viscoplastic and viscoelastic rheological models under arbitrary tensorial loadings. The developed model is validated against experimental data, which points toward a novel method to estimate the shear and compressive yield strength of strongly flocculated colloidal gels from a series of equilibrium solids volume fraction profiles over various column widths.Comment: 37 pages, 12 figures, submitted to Journal of Rheolog
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