Competing Interactions among Supramolecular Structures on Surfaces

A simple model was constructed to describe the polar ordering of non-centrosymmetric supramolecular aggregates formed by self assembling triblock rodcoil polymers. The aggregates are modeled as dipoles in a lattice with an Ising-like penalty associated with reversing the orientation of nearest neighbor dipoles. The choice of the potentials is based on experimental results and structural features of the supramolecular objects. For films of finite thickness, we find a periodic structure along an arbitrary direction perpendicular to the substrate normal, where the repeat unit is composed of two equal width domains with dipole up and dipole down configuration. When a short range interaction between the surface and the dipoles is included the balance between the up and down dipole domains is broken. Our results suggest that due to surface effects, films of finite thickness have a none zero macroscopic polarization, and that the polarization per unit volume appears to be a function of film thickness.Comment: 3 pages, 3 eps figure

A machine learning approach to nonlinear modal analysis

Although linear modal analysis has proved itself to be the method of choice for the analysis of linear dynamic structures, its extension to nonlinear structures has proved to be a problem. A number of competing viewpoints on nonlinear modal analysis have emerged, each of which preserves a subset of the properties of the original linear theory. From the geometrical point of view, one can argue that the invariant manifold approach of Shaw and Pierre is the most natural generalisation. However, the Shaw–Pierre approach is rather demanding technically, depending as it does on the analytical construction of a mapping between spaces, which maps physical coordinates into invariant manifolds spanned by independent subsets of variables. The objective of the current paper is to demonstrate a data-based approach motivated by Shaw–Pierre method which exploits the idea of statistical independence to optimise a parametric form of the mapping. The approach can also be regarded as a generalisation of the Principal Orthogonal Decomposition (POD). A machine learning approach to inversion of the modal transformation is presented, based on the use of Gaussian processes, and this is equivalent to a nonlinear form of modal superposition. However, it is shown that issues can arise if the forward transformation is a polynomial and can thus have a multi-valued inverse. The overall approach is demonstrated using a number of case studies based on both simulated and experimental data

The Mg 2 h and k lines in a sample of dMe and dM stars

Both Mg II h and k line fluxes are presented for a sample of 4 dMe and 3 dM stars obtained with the IUE satellite in the long wavelength, low dispersion mode. The observed fluxes are converted to stellar surface flux units and the importance of chromospheric non radiative heating in this sample of M dwarf stars is intercompared. In addition, the net chromospheric radiative losses due to the Ca II H and K lines in those stars in the sample for which calibrated Ca II H and K line data exist are compared. Active region filling factors which likely give rise to the observed optical and ultraviolet chromospheric emission are estimated. The implications of the results for homogeneous, single component stellar model chromospheres analyses are discussed

An illustration of new methods in machine condition monitoring, Part I: Stochastic resonance

There have been many recent developments in the application of data-based methods to machine condition monitoring. A powerful methodology based on machine learning has emerged, where diagnostics are based on a two-step procedure: extraction of damage sensitive features, followed by unsupervised learning (novelty detection) or supervised learning (classification). The objective of the current pair of papers is simply to illustrate one state-of the-art procedure for each step, using synthetic data representative of reality in terms of size and complexity. The first paper in the pair will deal with feature extraction. Although some papers have appeared in the recent past considering stochastic resonance as a means of amplifying damage information in signals, they have largely relied on ad hoc specifications of the resonator used. In contrast, the current paper will adopt a principled optimisation-based approach to the resonator design. The paper will also show that a discrete dynamical system can provide all the benefits of a continuous system, but also provide a considerable speed-up in terms of simulation time in order to facilitate the optimisation approach

The effect of Duffing-type non-linearities and Coulomb damping on the response of an energy harvester to random excitations

Linear energy harvesters can only produce useful amounts of power when excited close to their natural frequency. Due to the uncertain nature of ambient vibrations, it has been hypothesised that such devices will perform poorly in real-world applications. To improve performance, it has been suggested that the introduction of non-linearities into such devices may extend the bandwidth over which they perform effectively. In this study, a magnetic levitation device with non-linearities similar to the Duffing oscillator is considered. The governing equations of the device are formed in which the effects of friction are considered. Analytical solutions are used to explore the effect that friction can have on the system when it is under harmonic excitations. Following this, a numerical model is formed. A differential evolution algorithm is used alongside experimental data to identify the relevant parameters of the device. The model is then validated using experimental data. Monte Carlo simulations are then used to analyse the effect of coulomb damping and Duffing-type non-linearities when the device is subjected to broadband white noise and coloured noise excitations. </jats:p

Dynamical response of the "GGG" rotor to test the Equivalence Principle: theory, simulation and experiment. Part I: the normal modes

Recent theoretical work suggests that violation of the Equivalence Principle might be revealed in a measurement of the fractional differential acceleration $\eta$ between two test bodies -of different composition, falling in the gravitational field of a source mass- if the measurement is made to the level of $\eta\simeq 10^{-13}$ or better. This being within the reach of ground based experiments, gives them a new impetus. However, while slowly rotating torsion balances in ground laboratories are close to reaching this level, only an experiment performed in low orbit around the Earth is likely to provide a much better accuracy. We report on the progress made with the "Galileo Galilei on the Ground" (GGG) experiment, which aims to compete with torsion balances using an instrument design also capable of being converted into a much higher sensitivity space test. In the present and following paper (Part I and Part II), we demonstrate that the dynamical response of the GGG differential accelerometer set into supercritical rotation -in particular its normal modes (Part I) and rejection of common mode effects (Part II)- can be predicted by means of a simple but effective model that embodies all the relevant physics. Analytical solutions are obtained under special limits, which provide the theoretical understanding. A simulation environment is set up, obtaining quantitative agreement with the available experimental data on the frequencies of the normal modes, and on the whirling behavior. This is a needed and reliable tool for controlling and separating perturbative effects from the expected signal, as well as for planning the optimization of the apparatus.Comment: Accepted for publication by "Review of Scientific Instruments" on Jan 16, 2006. 16 2-column pages, 9 figure

An Illustration of New Methods in Machine Condition Monitoring, Part II: Adaptive outlier detection

There have been many recent developments in the application of data-based methods to machine condition monitoring. A powerful methodology based on machine learning has emerged, where diagnostics are based on a two-step procedure: extraction of damagesensitive features, followed by unsupervised learning (novelty detection) or supervised learning (classification). The objective of the current pair of papers is simply to illustrate one state-ofthe-art procedure for each step, using synthetic data representative of reality in terms of size and complexity. The second paper in the pair will deal with novelty detection. Although there has been considerable progress in the use of outlier analysis for novelty detection, most of the papers produced so far have suffered from the fact that simple algorithms break down if multiple outliers are present or if damage is already present in a training set. The objective of the current paper is to illustrate the use of phase-space thresholding; an algorithm which has the ability to detect multiple outliers inclusively in a data set

Multiphase dolomitization of deeply buried Cambrian petroleum reservoirs, Tarim Basin, north-west China

Cambrian dolostone reservoirs in the Tarim Basin, China, have significant potential for future discoveries of petroleum, although exploration and production planning is hampered by limited understanding of the occurrence and distribution of dolomite in such ancient rocks buried to nearly 8 km. The study herein accessed new drill core samples which provide an opportunity to understand the dolomitization process in deep basins and its impact on Cambrian carbonate reservoirs. This study documents the origin of the dolostone reservoirs using a combination of petrology, fluid‐inclusion microthermometry, and stable and radiogenic‐isotopes of outcrop and core samples. An initial microbial dolomitization event occurred in restricted lagoon environments and is characterized by depleted δ13C values. Dolomicrite from lagoonal and sabkha facies, some fabric‐retentive dolomite and fabric‐obliterative dolomite in the peloidal shoal and reef facies show the highest δ18O values. These dolomites represent relatively early reflux dolomitization. The local occurrence of K‐feldspar in dolomicrite indicates that some radiogenic strontium was contributed via terrigenous input. Most fabric‐retentive dolomite may have precipitated from seawater at slightly elevated temperatures, suggested by petrological and isotopic data. Most fabric‐obliterative dolomite, and medium to coarse dolomite cement, formed between 90°C and 130°C from marine evaporitic brine. Saddle dolomite formed by hydrothermal dolomitization at temperatures up to 170°C, and involved the mixing of connate brines with Sr‐ enriched hydrothermal fluids. Intercrystalline, moldic, and breccia porosities are due to the early stages of dolomitization. Macroscopic, intergranular, vuggy, fracture and dissolution porosity are due to burial‐related dissolution and regional hydrothermal events. This work has shown that old (for example, Cambrian or even Precambrian) sucrosic dolomite with associated anhydrite, buried to as much as 8000 m, can still have a high potential for hosting substantial hydrocarbon resources and should be globally targeted for future exploration

Chromospheric Variability: Analysis of 36 years of Time Series from the National Solar Observatory/Sacramento Peak Ca II K-line Monitoring Program

Analysis of more than 36 years of time series of seven parameters measured in the NSO/AFRL/Sac Peak K-line monitoring program elucidates five elucidates five components of the variation: (1) the solar cycle (period approx. 11 years), (2) quasi-periodic variations (periods approx 100 days), (3) a broad band stochastic process (wide range of periods), (4) rotational modulation, and (5) random observational errors. Correlation and power spectrum analyses elucidate periodic and aperiodic variation of the chromospheric parameters. Time-frequency analysis illuminates periodic and quasi periodic signals, details of frequency modulation due to differential rotation, and in particular elucidates the rather complex harmonic structure (1) and (2) at time scales in the range approx 0.1 - 10 years. These results using only full-disk data further suggest that similar analyses will be useful at detecting and characterizing differential rotation in stars from stellar light-curves such as those being produced by NASA's Kepler observatory. Component (3) consists of variations over a range of timescales, in the manner of a 1/f random noise process. A timedependent Wilson-Bappu effect appears to be present in the solar cycle variations (1), but not in the stochastic process (3). Component (4) characterizes differential rotation of the active regions, and (5) is of course not characteristic of solar variability, but the fact that the observational errors are quite small greatly facilitates the analysis of the other components. The recent data suggest that the current cycle is starting late and may be relatively weak. The data analyzed in this paper can be found at the National Solar Observatory web site http://nsosp.nso.edu/cak_mon/, or by file transfer protocol at ftp://ftp.nso.edu/idl/cak.parameters

Characterization of Aura TES carbonyl sulfide retrievals over ocean

We present a description of the NASA Aura Tropospheric Emission Spectrometer (TES) carbonyl sulfide (OCS) retrieval algorithm for oceanic observations, along with evaluation of the biases and uncertainties using aircraft profiles from the HIPPO (HIAPER Pole-to-Pole Observations) campaign and data from the NOAA Mauna Loa site. In general, the OCS retrievals (1) have less than 1.0 degree of freedom for signals (DOFs), (2) are sensitive in the mid-troposphere with a peak sensitivity typically between 300 and 500 hPa, (3) but have much smaller systematic errors from temperature, CO₂ and H₂O calibrations relative to random errors from measurement noise. We estimate the monthly means from TES measurements averaged over multiple years so that random errors are reduced and useful information about OCS seasonal and latitudinal variability can be derived. With this averaging, TES OCS data are found to be consistent (within the calculated uncertainties) with NOAA ground observations and HIPPO aircraft measurements. TES OCS data also captures the seasonal and latitudinal variations observed by these in situ data