Fo and Ni Relations in Olivine Differentiate between Crystallization and Diffusion Trends

Nickel is a strongly compatible element in olivine, and thus fractional crystallization of olivine typically results in a concave-up trend on a Fo–Ni diagram. ‘Ni-enriched’ olivine compositions are considered those that fall above such a crystallization trend. To explain Ni-enriched olivine crystals, we develop a set of theoretical and computational models to describe how primitive olivine phenocrysts from a parent (high-Mg, high-Ni) basalt re-equilibrate with an evolved (low-Mg, low-Ni) melt through diffusion. These models describe the progressive loss of Fo and Ni in olivine cores during protracted diffusion for various crystal shapes and different relative diffusivities for Ni and Fe–Mg. In the case when the diffusivity of Ni is lower than that for Fe–Mg interdiffusion, then olivine phenocrysts affected by protracted diffusion form a concave-down trend that contrasts with the concave-up crystallization trend. Models for different simple geometries show that the concavity of the diffusion trend does not depend on the size of the crystals and only weakly depends on their shape. We also find that the effect of diffusion anisotropy on trend concavity is of the same magnitude as the effect of crystal shape. Thus, both diffusion anisotropy and crystal shape do not significantly change the concave-down diffusion trend. Three-dimensional numerical diffusion models using a range of more complex, realistic olivine morphologies with anisotropy corroborate this conclusion. Thus, the curvature of the concave-down diffusion trend is mainly determined by the ratio of Ni and Fe–Mg diffusion coefficients. The initial and final points of the diffusion trend are in turn determined by the compositional contrast between mafic and more evolved melts that have mixed to cause disequilibrium between olivine cores and surrounding melt. We present several examples of measurements on olivine from arc basalts from Kamchatka, and published olivine datasets from mafic magmas from non-subduction settings (lamproites and kimberlites) that are consistent with diffusion-controlled Fo–Ni behaviour. In each case the ratio of Ni and Fe–Mg diffusion coefficients is indicated to be <1. These examples show that crystallization and diffusion can be distinguished by concave-up and concave-down trends in Fo–Ni diagrams.

On the Assessment of Centrifugal Compressor Performance Parameters by Theoretical and Computational Models

1D performance prediction modeling and steady-state CFD are applied to assess a high-performance centrifugal compressor. Computed total pressure ratio is compared with experimental data obtained from a gas stand. The focus of the paper is to assess the validity range of the methodologies used. Another aim is to quantify the relative differences between experimental and predicted data, and distinguish differences in the conjectured loss budget. The RANS data manifest overall a higher degree of accuracy than the 1D model when compared with experiments. The 1D model considered shows comparable accuracy at design condition but larger discrepancies at higher speedlines towards surge and choke. Component-wise parametric losses are correlated to pinpoint flow regimes with larger differences between 1D and RANS data. The result exposes significant disparity in the, impeller, vaneless diffuser and the volute model, respectively, especially off-design. Improving these features in the 1D modeling would potentially be profitable for improved accuracy in the performance prognosis.

Understanding neural circuit development through theory and models

How are neural circuits organized and tuned to achieve stable function and produce robust behavior? The organization process begins early in development and involves a diversity of mechanisms unique to this period. We summarize recent progress in theoretical neuroscience that has substantially contributed to our understanding of development at the single neuron, synaptic and network level. We go beyond classical models of topographic map formation, and focus on the generation of complex spatiotemporal activity patterns, their role in refinements of particular circuit features, and the emergence of functional computations. Aided by the development of novel quantitative methods for data analysis, theoretical and computational models have enabled us to test the adequacy of specific assumptions, explain experimental data and propose testable hypotheses. With the accumulation of larger data sets, theory and models will likely play an even more important role in understanding the development of neural circuits.

Using Theoretical and Computational Models to Understand How Metals Function as Temperature Sensors

Electrical conductivity is a basic property of materials that determines how well the material conducts electricity. However, models are needed that help explain how conductors function as their size and temperature changes. This research demonstrates and explains how important atomic motion is in understanding electrical conductivity for conductors (and thus the ability of metals to function as temperature sensors). A derivation is performed (on an atomic level) that provides a theoretical relationship between electrical resistivity, temperature, and material thickness. Subsequently, computational models are used to determine the optimal parameters for the theoretical models as well as the conditions under which they are accurate. Comparisons are performed using experimental data showing that the models are valid and accurate.

Calibration and Validation of a Seismic Damage Propagation Model for Interdependent Infrastructure Systems

Barring a few exceptions, most theoretical and computational models of lifeline system fragility and interdependent response to extreme events still lack calibration and validation relative to real events. This paper expands on this area by evaluating and calibrating a recently proposed Interdependence Fragility Algorithm ( IFA) against field data observed after the 2010 Mw 8.8 offshore Maule, Chile, earthquake. This evaluation incorporates available and simulated properties of the Concepción and Talcahuano water and power networks to try to replicate their topology and seismic response, considering both direct damage and interdependent effects. The calibrated IFA predicts that the probabilities of exceeding the observed high connectivity losses of 0.70 (power) and 0.82 (water), if taken as limit states, are 97% and 72%, respectively. These predictions capture complex interdependent lifeline system responses reasonably well and reveal influential factors for IFA model accuracy and uncertainty reduction, enabling reliable planning, design, expansion, and maintenance of infrastructure systems in practice.

Effects of Information Source, Pedigree, and Reliability on Operators' Utilizaton of Diagnostic Advice

Studies have demonstrated that humans appear to apply norms of humanhuman interaction to their interaction with machines. Yet, there exist subtle differences in peoples' perceptions of automated aids compared to humans. We examined factors differentiating human-human and human-automation interaction, wherein participants (n = 180) performed a luggage-screening task with the assistance of human or automated advisers that differed in pedigree (expert vs. novice) and reliability (high vs. low). Dependence on advice was assessed. Participants agreed more with an automated 'novice' than a human 'novice' suggesting a bias toward automation. Automation biases broke down when automated aids portrayed as 'experts' generated errors, leading to a drop in compliance and reliance on automation relative to humans. The results have implications for the development of theoretical and computational models of optimal user dependence on decision aids.