There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

This paper draws on regulatory governance scholarship to argue that the analytic phenomenon currently known as ‘Big Data’ can be understood as a mode of ‘design-based’ regulation. Although Big Data decision-making technologies can take the form of automated decision-making systems, this paper focuses on algorithmic decision-guidance techniques. By highlighting correlations between data items that would not otherwise be observable, these techniques are being used to shape the informational choice context in which individual decision-making occurs, with the aim of channelling attention and decision-making in directions preferred by the ‘choice architect’. By relying upon the use of ‘nudge’ – a particular form of choice architecture that alters people’s behaviour in a predictable way without forbidding any options or significantly changing their economic incentives, these techniques constitute a ‘soft’ form of design-based control. But, unlike the static Nudges popularised by Thaler and Sunstein [(20...

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Hypernudge: Big Data as a mode of regulation by design

Surface composites by friction stir processing: A review

Particle-reinforced metal matrix nanocomposites fabricated by selective laser melting : a state of the art review

Particle-reinforced metal matrix nanocomposites (MMNCs) have been lauded for their potentially superior mechanical properties such as modulus, yield strength, and ultimate tensile strength. Though these materials have been synthesized using several modern solid- or liquid-phase processes, the relationships between material types, contents, processing conditions, and the resultant mechanical properties are not well understood. In this paper, we examine the yield strength of particle-reinforced MMNCs by considering individual strengthening mechanism candidates and yield strength prediction models. We first introduce several strengthening mechanisms that can account for increase in the yield strength in MMNC materials, and address the features of currently available yield strength superposition methods. We then apply these prediction models to the existing dataset of magnesium MMNCs. Through a series of quantitative analyses, it is demonstrated that grain refinement plays a significant role in determining the overall yield strength of most of the MMNCs developed to date. Also, it is found that the incorporation of the coefficient of thermal expansion mismatch and modulus mismatch strengthening mechanisms will considerably overestimate the experimental yield strength. Finally, it is shown that work-hardening during post-processing of MMNCs employed by many researchers is in part responsible for improvement to the yield strength of these materials.

Prediction models for the yield strength of particle-reinforced unimodal pure magnesium (Mg) metal matrix nanocomposites (MMNCs)

TiO2 additions up to 10 mass% behaved as a basic oxide and lowered the viscosity in the CaO–SiO2–17 mass% Al2O3–10 mass% MgO-based slags by depolymerizing the silicate network structure. Raman spectroscopy revealed the sum of NBO/Si 1 and 3 decreased while the sum of NBO/Si 2 and 4 increased with TiO2 content. Unlike the silicate structures, the aluminate structures seems to be relatively unaffected by TiO2 additions according to the FTIR results. 5 mass% TiO2 significantly decreased the viscosity compared to the TiO2 free slags, but beyond 5 mass% TiO2 meaningful changes in the viscosity was not observed. From the comparison of the viscosity at constant TiO2 of 5 and 10 mass% and varying CaO/SiO2 ratio, increasing the CaO/SiO2 ratio was more effective in decreasing the viscosity than TiO2 additions.

Effect of TiO2 on the Viscosity and Slag Structure in Blast Furnace Type Slags

This review focuses on elucidating the viscosity of ironmaking slags and the effect of various components in the multi-component calcium-silicate-based slag system on the viscosity. Using various widely used spectroscopic techniques including Fourier transform infrared (FTIR), Raman, X-ray photoelectron spectroscopy (XPS), and nuclear magnetic resonance (NMR), the viscous behavior and the slag structure of this system were correlated. In particular, the review begins with an introduction to the viscous behavior of binary silicate systems and an identification of the intricate details of the slag structure. Using the binary system as a basis, the compounded effect of typical ironmaking slag components such as Al2O3, MgO, alkali oxides, and TiO2 on the viscosity is introduced through expansion to a quinary slag system. Using a combination of the aforementioned spectroscopic methods, the meticulous details of the slag structure were ascertained, and the depolymerization or polymerization mechanisms of the silicate and alumino-silicate structures were subsequently identified. This review also compares well-known viscosity prediction models with experimentally measured values and identifies potential fields of research in the ironmaking industry associated with slag viscosities.

A Review of the Relationship between Viscosity and the Structure of Calcium‐Silicate‐Based Slags in Ironmaking

The viscosities of CaO-SiO2-20 wt%Al2O3-MgO slags (CaO/SiO2 = 1.0–1.2, wt%MgO = 5–13) were measured to estimate the effect of MgO on the viscous behaviour at elevated temperatures. The slag viscosity at 1773 K decreased with increasing MgO contents, which was typical of a basic oxide component at relatively low basicity (CaO/SiO2) of 1.0. The FT-IR spectroscopic analysis of the slag structure seems to verify this behaviour. However, an unexpected contradiction with the temperature dependence was observed above 10 wt%MgO and above CaO/SiO2 of 1.2. Although the apparent activation energy was expected to decrease with additions of the basic oxide component MgO, the apparent activation energy increased. This unexpected behaviour seems to be related to the change in the primary phase field correlating to the phase diagram corresponding to the slag composition. Therefore, in order to understand the viscosity at both high Al2O3 and MgO, not only should the typical depolymerization of the slag structure with high MgO content be considered but also the primary phases of which the molten slag originates.

The Effect of MgO on the Viscosity of the CaO-SiO2-20 wt%Al2O3-MgO Slag System

The effect of Al2O3 and CaO/SiO2 on the viscosity of the CaO-SiO2-10 mass pct MgO-Al2O3 slags was studied at fully liquid temperatures of 1773 K (1500 °C) and below. At fixed CaO/SiO2 between 0.8 and 1.3, higher Al2O3 content increased the slag viscosity due to the polymerization of the aluminate structures. At fixed Al2O3 of 15 and 20 mass pct, increasing the CaO/SiO2 from 0.8 to 1.3 resulted in lower viscosity due to the depolymerization of the aluminate structure.

Il Sohn

Papers

Prediction models for the yield strength of particle-reinforced unimodal pure magnesium (Mg) metal matrix nanocomposites (MMNCs)

Effect of TiO2 on the Viscosity and Slag Structure in Blast Furnace Type Slags

A Review of the Relationship between Viscosity and the Structure of Calcium‐Silicate‐Based Slags in Ironmaking

The Effect of MgO on the Viscosity of the CaO-SiO2-20 wt%Al2O3-MgO Slag System

Effect of Al2O3 and CaO/SiO2 on the Viscosity of Calcium-Silicate–Based Slags Containing 10 Mass Pct MgO